Global condition monitoring services company Azima DLI on September 16 announced that its “Introduction to Vibration Analysis” book can now be downloaded for free from the company’s Web site (www.azimadli.com). The authoritative industry resource is also available for the first time in Spanish.

Authored by Glen White, the book serves as a reference text for maintenance engineers and technicians who are working with advanced machinery maintenance technology. Readers benefit from gaining a better understanding of the principles of vibration theory and analysis as well as the role of vibration analysis in a comprehensive machine condition monitoring program. Chapters cover machine diagnosis topics including calculating imbalance and misalignment, and the impact of these conditions on turbines, pumps and fans. Practitioners can reference the detailed vibration terminology glossary in addition to learning how to estimate vibration severity and apply related industry standards.

“This reference guide has become an industry standard for helping engineers learn the finer points of vibration analysis and providing practical information and advice that can be easily applied in the field,” said Jonathan Hakim, president, Azima DLI. “As Azima DLI’s machine condition monitoring services business grows around the world, we recognized the importance of expanding access to this important learning tool. By making it available for free on the Internet and developing a Spanish language version, we hope to be able to provide the guidance necessary to help international technicians improve the results of their vibration analysis programs.”

To access the English version, visit: http://www.azimadli.com/english/library/books.asp

To access the Spanish version, visit: http://www.maquinadevibracion.com/library/books.asp

About Azima DLI

Azima DLI is the only global condition monitoring services company exclusively focused on delivering accurate and timely equipment diagnostics to industrial plants, transportation organizations and the military. Azima DLI’s expert analysts, advanced software and flexible, Web-based delivery model provide customers with the information they need for insightful decision making that lowers maintenance costs and improves industrial reliability. Azima DLI’s WATCHMAN Reliability Services combine industry best practices with advanced technology to support sustainable, ROI-driven programs that improve equipment uptime and performance. Azima DLI is headquartered in Woburn, Mass., with offices across the U.S. and international representation in Asia-Pacific, Central America, Europe and South America.

The National Institute of Standards and Technology (NIST) has unveiled a method for calibrating entire waveforms – graphical shapes showing how electrical signals vary over time – rather than just parts of waveforms as is current practice. The new method improves accuracy in calibrations of oscilloscopes, common test instruments that measure voltage in communications and electronics devices, and potentially could boost performance and save money in other fields ranging from medical testing to structural analysis to remote sensing.

A waveform can take many different shapes, from staircase steps to irregular curves. A waveform typically is described by a single number – some key parameter of interest in a particular application. For example, engineers have described waveforms using terms such as pulse duration, or transition time between the levels representing ‘0’ and ‘1’ (the binary code used in digital electronics). But waveforms can be diverse and complex, especially in advanced high-speed devices, and a traditional analysis may not distinguish between similar shapes that differ in subtle ways. The result can be signal mistakes (a 1 mistaken for a 0, for instance) or misidentification of defects.

NIST’s new calibration method* defines waveforms completely, providing both signal reading and measurement uncertainty at regular intervals along the entire wave, and for the first time makes waveform calibrations traceable to fundamental physics. The mathematics-intensive method is laborious and currently is performed only at NIST (which has more than 750 oscilloscopes), but the developers plan to write a software program that will automate the technique and make it transferable to other users.

The new method offers NIST calibration customers, including major manufacturers and the military, more comprehensive characterization of a greater variety of waveforms, and helps to meet current and future demands for measurements at ever-higher frequencies, data rates, and bandwidths. The impact could be far reaching. The global market for oscilloscopes is $1.2 billion. Anecdotal data suggest that for one product alone, Ethernet optical fiber transceivers, industry could save tens or even hundreds of millions of dollars through manufacturing innovations (such as the new NIST method) that reduce component reject rates and/or boost yields.

Of particular interest to scientists and engineers, the NIST calibration method incorporates new techniques for quantifying errors in waveform measurements. This allows, for the first time, accurate transfer of measurement uncertainties between the time domain (results arranged by time) and the frequency domain (the same data arranged by frequency). Researchers in many fields have long used a technique called “Fourier transform,” which reveals patterns in a sequence of numbers, to transfer data from the time domain to the frequency domain. “The new NIST method is, in effect, a Fourier transform for uncertainty,” says NIST physicist Paul Hale.

Although the new method was developed for common lab test instruments, it also may have applications in measuring other types of waveforms, such as those generated in electrocardiograms for medical testing, ultrasound diagnostics of structural defects and failures, speech recognition, seismology and other remote sensing activities.

* P. Hale, A. Dienstfrey, J.C.M. Wang, D.F. Williams, A. Lewandowski, D.A. Keenan and T.S. Clement. Traceable waveform calibration with a covariance-based uncertainty analysis. IEEE Transactions on Instrumentation and Measurement. Vol. 58, No. 10. Oct.

Managing a multi-technology condition monitoring program at a large mining operation was a challenging task, especially when there has been a cultural shift required to integrate the ideas of condition monitoring into an already complex maintenance strategy. One of the major challenges included shifting the focus of an experienced maintenance crew from a run-to-failure mentality to a proactive mentality. Another major challenge was folding the new work practices into the current work flow model, one which didn’t have a lot of emphasis on backlogged maintenance items. Finally, coaching the supervision and management about why this is the right move proved to be one of the more difficult tasks of all.

But now less than a year later, dramatic results are being realized. As the condition monitoring statistics trend in the right direction, maintenance costs are shifting sharply as well. Overtime has dropped, and unplanned downtime has decreased significantly. The best part is the workforce is becoming more and more focused on fixing problems rather than fixing machines.

This paper will share some of the stories and many of the measures that made this shift possible. In addition, some of the software tools and work flow models that helped drive the measures and decision making processes will be demonstrated.

Introduction

The Four Corners Mine is a large, open-pit mining operation located 25 miles south of Lakeland, Fla. Four Corners is one of six mines belonging to The Mosaic Corporation, the world’s 13th-largest producer of phosphate fertilizer. The Four Corners Mine produces approximately 6.5 million tons of phosphate rock annually. The mine has three distinct areas:

• Dragline Operations: Electric draglines are used for mineral extraction. There are six draglines averaging 50 cubic yards each.
• Field Pumping System: 82 large 2,000-horsepower pumps are used to move the unprocessed phosphate rock (matrix) from the draglines to the float plant. This makes for nearly 70 miles of 20-inch and 22-inch pipe.
• Float Plant: The float plant includes numerous conveyors, vibrating screens, pumps and log washers that are all used to clean the phosphate rock and separate the debris and unwanted material.

All told there are 775 pieces of rotating equipment on the entire facility. There are 270 employees at the

Four Corners Mine, with 110 of them in the maintenance department.

The Four Corners Mine embarked on the implementation of a multi-technology condition monitoring program in March 2005 and has seen some very dramatic changes take place since then.

Understand the Challenges

There were and still remain several large challenges to getting the process completely ironed out. Like any process that involves more than one person, the relationships between the people play a vital role in continued success. Each of the individuals has to develop a level of trust in the others and as a group they have to develop a unified sense of purpose. Finding and retaining qualified personnel is another large challenge.

Condition monitoring personnel who want to work in a mining environment and have any experience at all are very difficult to come by. As everyone begins to work together on a daily basis attitudes and personalities start to play major roles in the group’s effectiveness and efficiency. Keeping all these technical people working and working together can prove to be a daunting task.

One of the key challenges is developing an understanding of the process between the maintenance group and condition monitoring group. The maintenance people have to understand the condition monitoring process, and the condition monitoring people have to understand the maintenance process.

Without each fully grasping the other’s process and where they themselves fit into the other process, any kind of forward momentum will be difficult to initiate. Finally, internal group politics can play a large role in extinguishing improvements in the overall process. If there are individuals who tend to not give credit where credit is due, hard feelings can develop. On the other hand, if the leaders of the change process are quick to hand out pats on the back and do it often, then everyone feels they are appreciated and tend to work even harder to advance the cause.

Desire to Succeed

Another key role in the success of any process is a strong desire to succeed. That desire is easily seen at the Four Corners Mine. The maintenance manager is the catalyst that keeps the program rolling on a daily basis. The manager’s attitude toward condition monitoring as a part of the overall maintenance strategy is what keeps the rest of the maintenance department focused. Above the maintenance manager, in the corporate office, the desire for the program to succeed is seen as well. The benefits of the condition monitoring program have been folded into the overall financial strategy of the mining operations at the Mosaic operations of central Florida. The condition monitoring program is expected to produce results in the safety, costs and availability areas of the overall plan.

The safety measures have been affected by means of reduced overtime and call-out work. When crafts personnel are not called out as much at night to work on equipment that has failed during a scheduled production shift, the likelihood for injury has been reduced. When the precise nature of a problem has been identified prior to work commencing and the craftsmen don’t have to spend unnecessary time and effort looking for the problem, the likelihood for injury has been reduced.

Being able to identify problems and get them corrected long before additional damage happens to other components of a machine reduces the overall cost of the repair. All of these add up to reduced unplanned downtime which is increased machinery availability.

Figure 1. Technology vs. Asset Ranking Graph.

Multi-technology Program

The Four Corners Mine decided early to employ as many condition monitoring technologies as they could to provide the highest likelihood of identifying the actual failure modes of the assets. Vibration analysis, contact ultrasonic emissions, oil analysis and mechanical infrared thermography were chosen to identify rotating machinery defects. Motor circuit analysis and electrical infrared thermography were chosen to identify the failure modes of the electric motors and the associated switchgear. See Figure 1 for the percentage of coverage by technology.

Best Technology Available

The Mosaic Company also decided early on that an investment in the best technology available would help ensure the best results. CSI 2130s were chosen as the vibration analysis platform. Mikron infrared cameras were chosen for the thermography effort. The UE Systems 10,000 was selected for contact and airborne ultrasonic analysis. And, the venerable PdMA MCE/Emax was selected as the motor circuit analyzer of choice.

Figure 2. Integrated Condition Status Report

Web-based Reporting

A Web-based reporting platform is used to communicate the results of all condition monitoring technologies to Mosaic personnel. A Web-based reporting tool was selected for numerous reasons:

• It requires no software to be installed on customer’s computers.
• It can be accessed from any computer with access to the World Wide Web.
• It is not limited to any particular hardware or software platform for the different technologies.
• It is easily expandable as other technologies are added or removed.

Mosaic wants all levels of supervision to be involved in the identification and elimination of machinery faults. Consequently everyone has access to the software. Figure 2 shows an example of a report available in the software that summarizes all of the problems identified in a given area of the plant.

Integrated Work Flow

A fully integrated work flow model would be defined as one where both the Mosaic personnel and the Allied Reliability personnel had equal ownership in getting the work identified and accomplished. Figure 3 shows the work flow model, which is color-coded to quickly identify who is responsible for which part of the process. It was decided that this integrated workflow would provide the following benefits:

• Get Mosaic and Allied interacting on a daily basis.
• Ensure Mosaic personnel took ownership of certain portions of the process.
• Technician can make clear recommendations not just alert the customer to the technical aspects of the problem.
• Interaction at the lowest level creates a learning process for both the technician and the craftsmen.
• The “Post Check” is the feedback loop for the maintenance repair process.

Relationship between the technician, supervisor and mechanic … crucial for success.

Figure 3. Four Corners – Condition Monitoring Work Flow Model

Workforce Education

It was agreed that workforce education was an important foundational element that would keep the process going. Four Corners continues to make a significant effort to educate all maintenance employees about P-F intervals (see Figure 4). In addition, technology familiarization was also a major topic. Making all of the front-line supervisors and craftsmen aware of the different condition monitoring technologies and how they worked was seen as a key component of gaining acceptance. The reliability engineers conduct monthly “lunch and learn” sessions where sandwiches are brought in and key program elements and/or technology demonstrations are discussed.

Figure 4. P-F Interval

Dedicated Focus

Without a dedicated focus, it is very easy to lose sight of the goal. To ensure this didn’t happen, Four Corners dedicated a good portion of the reliability engineer’s time to overseeing the process. Their goal is to maintain maintenance engagement and awareness while at the same time measuring and benchmarking the maintenance department’s results and success as well as Allied Reliability’s. Successes are purposely celebrated at weekly meetings and misses are clearly identified and root causes eliminated.

Figure 5. MTBF – Motor Failures (2005)

Constant Improvement

The entire process must be inundated with an attitude of constant improvement, and at Four Corners, it is. The maintenance manager is always pushing for more and better results. The reliability engineers are always pushing for more detailed explanations of problems and more focus on eliminating root causes. In an effort to help maintain this focus, Allied Reliability provides Four Corners with a monthly report on numerous measurements.

• Percent of Red, Yellow and Green Condition Entries, both overall and by technology
• Percent of Condition Entries Implemented
• Number of Work Orders Generated
• Route Compliance
• Average Time to Implementation

Real-world Measures

What follows are three graphs representing the effect that Four Corners’ attitude toward constant improvement and condition monitoring has had since March 2005.

Figure 6. Emergency Work Order (2005)

Figure 7. Field Pump Bearing Assembly Usage

Many experts working in the field of vibration analysis will agree that resonance is a very common cause of excessive machine vibration.

Resonance is the result of an external force vibrating at the same frequency as the natural frequency of a system. Natural frequency is a characteristic of every machine, structure and even animals. Often, resonance can be confused with the natural frequency or critical frequency. If equipment is operating in a state of resonance, the vibration levels will be amplified significantly, which can cause equipment failure and plant downtime. It is, therefore, important that the running speed of equipment be out of the resonance range.

How to identify a resonance frequency

Many techniques can be used to identify and/or confirm a high vibration level caused by a resonance frequency. It is very important to confirm a resonance phenomenon by at least two different types of tests before trying to correct it. We will look at a few techniques commonly used in the industry.

Techniques used to confirm a resonance

Impact test:One of the most commonly used methods for measuring a system’s natural frequency is to strike it with a mass and measure the response. This method is effective because the impact inputs a small amount of force in the equipment over a large frequency range. When performing this technique, it is important to try impacting different locations on the structure since all of a structure’s resonant frequencies will always be measurable by impacting at one location and measuring at the same location. Both drive point and transfer point measurements should be taken when attempting to identify machine resonances.

This type of test must be performed with the equipment off. This way you can easily identify the natural frequencies of the equipment (see Figure 1).

Figure 1. Impact Test, Equipment Off

Impact test using an instrumented hammer:This test is basically the same as a regular impact test, except that an instrumented hammer is used to excite the system. This hammer, equipped with an accelerometer at one end, is used in tandem with the sensor used to measure the vibration. A two-channel vibration analyzer is needed, in which one channel is connected to the instrumented hammer and the other to the vibration sensor.

Using this technique, you can effectively measure the force induced to the system by the instrumented hammer and the response at different frequencies. When the phase shifts by 90 degrees, the frequency at which it occurs is a natural frequency (Figure 2). The advantage in using this method is that it allows you to monitor phase shifts and coherence. With this information, you can create operating deflection shapes to visualize the vibrating body.

Figure 2. Impact Test with Force Hammer

Coast down peak hold:Another method used is to monitor the vibration level using a peak hold function, while shutting down the equipment, as performed normally. The vibration level should drop at a steady rate. If the vibration levels start rising at any time while the equipment is being shut down, the speed at which the amplitudes increase is a possible natural frequency (Figure 3).

Figure 3. Coast Down Peak Hold

Coast down peak phase:Like the coast down peak hold, this test is to be conducted while the equipment is being shut down. By installing a photo tack and a piece of reflective tape on the rotating shaft of the equipment, you can monitor the vibration and its phase. This will allow you to see the amplitude and phase shift at all running speeds of the equipment. If there is no resonance excited by the turning speed, the vibration levels should drop at a steady rate. If the vibration peaks at a certain speed and the phase shifts by 180 degrees, this indicates a natural frequency of the equipment or structure. The actual natural frequency is the frequency situated in the middle of the phase shift (90 degrees) (Figure 4).

Figure 4. Coast Down Peak Phase

Formula for natural frequency

The natural frequency is the frequency of free vibration of a system, in which a system vibrates to dissipate its energy. The natural frequency (ωn) of an equipment, expressed in radian per second, is a function of its stiffness (k) and its mass (m), as shown by the following equation:

If any of these two parameters are altered, the natural frequency will change.

How do you modify a natural frequency?

If we want to modify the natural frequency of a body, we have to either change the stiffness or the mass. Increasing the mass or lowering the stiffness will lower the natural frequency while reducing mass or increasing stiffness will increase natural frequency.

How can we operate critical equipment if we can’t change the natural frequency?

If we cannot change the stiffness or the mass of the equipment, two possible choices are offered to us. One easy solution is to change the operating speed of the equipment by 20 to 30 percent, but this is not usually an option. Another solution is to install a dynamic absorber on the equipment to significantly reduce the vibration levels of the equipment. The dynamic absorber is a spring-mass system that is installed in series with the resonant system to create an out-of-phase exciting force to effectively counteract the initial exciting force.

Conclusion

Resonance is probably one of the five common causes of excessive machine vibration. Identifying a resonance frequency effectively can be challenging. We need to positively identify the natural frequency by performing at least two different tests such as impact test, coast down peak hold, coast down peak phase or impact test using a force hammer.

Once the resonance is confirmed, either change the mass or the stiffness of the equipment to change its natural frequency. If it cannot be accomplished try to change the operating speed of the equipment. If that fails, consider installing a dynamic absorber to counteract the initial exciting force.

Reference

Fox, Randy. “Dynamic absorbers for solving resonance problems”.

About the author:
Alain Pellegrino is a predictive maintenance technician for Laurentide Controls Ltd. As the local business partner for Emerson Process Management, Laurentide Controls is the largest supplier of automation solutions in eastern Canada. For more information, visit www.laurentide.com.

This video highlights the overall reliability, risk reduction and sustainability services provided by Predictive Service. PS provides global reliability engineering and condition monitoring (CM) with predictive maintenance technologies. All CM services are bundled with ViewPoint Web-based asset managment software. This video is presented by Dale P. Smith, CMRP.

Access this 3-minute, 2-second video by clicking on the link below.

Azima DLI, a leader and premier provider of predictive machine condition monitoring and analysis services, on February 8 announced a series of simplified, pre-packaged programs to ensure comprehensive equipment reliability and uptime results. Based on its extensive industry experience, track record of customer success and established WATCHMAN Reliability Services, Azima DLI is able to strip away the complexity of traditional á la carte maintenance programs and introduce comprehensive, bundled solutions that are tailored to address specific requirements for lean and reliable plant operations.

Due to factors including an exodus of talent, streamlined operations and restricted budgets, Azima DLI has found that many plants are challenged to retain the expert analysis resources necessary to keep their condition monitoring programs running smoothly, or simply don’t know how to initiate a fresh start for a previously well-run program. WATCHMAN Reliability Service Plans were developed to help plants quickly address those challenges. There are three all-inclusive WATCHMAN Reliability Service Plans to choose from that feature an evaluation of the plant environment and risk profile along with a clearly-defined set of solutions and deliverables to meet specific uptime, compliance and cost-avoidance objectives.

Each solution includes primary technologies such as vibration analysis, lube oil analysis and infrared thermography as well as transparency of portal delivery, analysis support, and a range of optional services to ensure a complete condition monitoring solution. In contrast to competitors’ offerings that require heavy investments in hardware and software, Azima DLI’s flexible, scalable offerings don’t require up-front costs and are offered on a subscription basis:

WATCHMAN Professional – Built for plants or enterprises with 250 or more machine assets where unplanned outages cannot be tolerated. WATCHMAN Professional provides the procedures, processes, disciplines and qualified resources to achieve the highest standard in condition-based maintenance.

WATCHMAN Insight – Provides excellent coverage for moderate-sized plants of 75 to 250 machine assets, where unplanned outages are categorized as inconvenient and costly. WATCHMAN Insight provides a solid foundation for a quality condition-based maintenance program.

WATCHMAN Select – Applied to smaller plants with 15-150 machine assets, the program is intended to meet compliance obligations and maintenance planning budgets. WATCHMAN Select provides the essential elements of a growing condition-based maintenance program.

“In today’s market, á la carte choices vary widely from vendor to vendor. This causes uncertainty about how to communicate the value of condition monitoring programs to the C-suite, which has hampered progress and results for many of our competitors,” said Randy Johnson, vice president of sales and marketing, Azima DLI. “Azima DLI has taken important steps to develop easy-to-understand, easy-to-implement service packages that deliver immediate value by increasing plant availability and dramatically driving down unplanned downtime. By packaging our predictive services with a very low cost of entry, and delivering actionable results through our WATCHMAN Reliability Portal to those who need to make critical operational decisions, Azima DLI’s services are recognized by management as critical to plant profitability.”

Based on experience, Azima DLI knows that subscription-based predictive services are delivered best through plant personnel participating in vibration data and oil sample collection. Azima DLI provides the leading data acquisition tools necessary for a successful program, including the DCA-60 vibration data collector, to ensure maintenance professionals have the information they need to support equipment reliability and uptime goals. Additionally, the WATCHMAN Reliability Portal 2.0 provides secure Web-based access and automated alerts for the condition of all monitored equipment including history trends, statistics and reports; results can then be pushed to, or accessed remotely by, key stake holders.

“Azima DLI brings together the best analytics experts with proven technology to track, share and report on critical machinery health to drive better maintenance decisions. As a result, Azima DLI is highly qualified to deliver these new bundled solutions to ensure our customers’ success,” said Jonathan Hakim, president, Azima DLI. “Compared to small regional solutions providers, Azima DLI has an unparalleled bench of experts and broad industry experience associated with specialties such as vibration monitoring, thermography and lube oil analysis. And, unlike other global vendors in the market, Azima DLI is laser focused on machine condition monitoring so it can more effectively tailor programs to specifically meet industrial plants’ operational requirements.”

About Azima DLI
Azima DLI is a leader and premier provider of predictive machine condition monitoring and analysis services that align with customers’ high standards for reliability, availability and uptime. Azima DLI’s WATCHMAN Reliability Services utilize flexible deployment models, proven diagnostic software and unmatched analytical expertise to deliver sustainable, scalable and cost-effective condition-based maintenance programs. The company’s bundled solutions enable customers to choose comprehensive, proven programs that ensure asset availability and maximize productivity. Azima DLI is headquartered in Woburn, Mass., with offices across the U.S. and international representation in Asia-Pacific, Central America, Europe and South America. 

This article discusses the financial impact of motor condition on electric motor efficiency and reliability by reviewing a combination of motor circuit analysis (MCA) and vibration techniques. Cost impacts on energy, production and maintenance will be outlined. The topic will surround a utility study and U.S. Department of Energy market transformation success during 2000 and 2001. The primary areas of concern are phase balance, rotor bars, cleanliness and bearing issues.

Introduction

Electric motors are the prime mover of industry and our general comfort in commercial buildings. The motor systems consume 20 percent of all energy used in the United States and 59 percent of all electricity generated. Within each sector:

• 78% of electrical energy in industrial systems (greater than 90% in process industries)
• 43% of the electrical energy in commercial buildings
• 37% of the electrical energy in the home

There are well over 1.2 billion electric motors, of all types, used throughout the United States. However, electric motors are often out-of-sight, out-of-mind until production is down due to a burnout or catastrophic bearing failure.

It is important to understand that equipment usually fails over time, reliability decreases and losses increase (efficiency decreases) over time prior to most catastrophic failures. Although some equipment faults are instantaneous, the larger majority of catastrophic faults that impact production are the result of a failure in the implementation of a maintenance program. This failure is primarily due to management not fully understanding that maintenance is an investment in the business and not an expense of doing business. If you do not invest in materials, equipment and people, you do not have product to sell: If you do not invest in predictive maintenance practices (preventive maintenance, Total Productive Maintenance, Reliability-Centered Maintenance or any other program), you do not have product to sell or less of it at a higher overall production cost.

Proper implementation of a maintenance program has been shown to reduce energy consumption in plants by as much as 14% ^{1 2}, while also reducing unplanned production downtime. Average downtime costs are shown as follows:

Table 1: Estimations for Downtime Costs ³

In a recent utility energy and reliability project, a group of electric motors from five to 200 horsepower were reviewed in several industries, including: Petroleum and Chemical; Forest Products; Food Processing; Mining (Quarry); and Pulp & Paper. The plants varied from having no existing planned maintenance program to full implementation, including an existing energy program. Of these motors, randomly evaluated, 80% were found to have at least one deficiency with 60% of those (48% of the original) found to be cost effective to replace. The plants without programs had the greatest number of defective motors; the plants with existing maintenance and energy programs had the least number of defective motors. Eight percent of the motors were evaluated to determine the types of faults and the potential cost avoidance with corrective action (repair or replace) by using vibration analysis and motor circuit analysis (MCA). Several had a combination of electrical and mechanical problems:

Table 2: Utility Energy Project Findings

Several motors had combined vibration and electrical faults. A few had winding faults combined with insulation resistance faults. Several had shorted windings that were continuing to cause production problems, but were written off as nuisance trips (detected in the study by using MCA). “Findings of the advanced portion of the Motor PAT Tool demonstration project indicate that measuring for phase unbalance of resistance, inductance, impedance, phase angle and I/F (current/frequency response) provided more useful results.” ⁴ The combined incremental production cost avoidance of 20 of the defective motors, from five to 250 horsepower, was $297,100, rendering implementation costs insignificant.

The purpose of this article is to first provide information for determining cost avoidance through the application of a maintenance program on electric motors. This will be followed with a discussion of the implementation of motor circuit analysis (MCA) and vibration analysis.

Cost Avoidance through Maintenance

There are a number of ways to determine cost avoidance through the implementation of maintenance programs. In this discussion, the focus will be on the methods introduced through the U.S. Department of Energy’s Industrial Assessment Centers (IACs), which provide a very basic and conservative method. The PAT Tool Demonstration Project used a much more complex method ⁵, which is outside the scope of this article. However, some of the tools, such as MotorMaster Plus ⁶, will be used to provide cost information for motor repair costs.

Utility representatives have indicated that in a survey of facilities with no preventive maintenance programs, motor rewinds represented 85% of the total number of motor repairs (on average). After preventive maintenance programs were established, the number of rewinds were reduced to about 20% of the total. ⁷ This statement has been found to hold true through research projects including: Dreisilker’s Total Motor System Maintenance and Management Program (DTM 2), the PAT Tool Project, and others.

For the purpose of this discussion, we will consider a paperboard plant with 485 motors. There are two operating production lines that have a potential downtime cost of $6,575 each. An average of three motors were repaired per month, of which a majority (70%) required rewind replacement (normally caused by immersion, contamination or the motors became coated in material). The facility operated 8,000 hours per year, with the catastrophic failures normally causing one line to fail at a time. Additional costs not covered by this discussion included cleaning of the system prior to restarting the operation. No maintenance program in place.

Table 3: Breakdown of Motor Horsepower and Repair Costs

The first step is to calculate the unplanned production downtime costs:

Equation 1: Unplanned Production Downtime Cost

PC_Downtime = (MF/year) x (P_Lost/failure) x (P_Cost)

= (36 motors/year) x (4 hours/failure) x ($6,575/hour)

= $946,800/year

Where PC is the annual cost of unplanned downtime, MF is the number of motor failures, P represents production.

Step 2 is to calculate the average cost of rewinding equipment. In this case, we will concentrate on just 20 horsepower and larger.

Equation 2: Average Cost of Rewinding Motors

R_avg = ((N_n1 x RWC_n1) + … + (Nnn x RWC_nn))/N_T

= ((15₂₀ x $660₂₀) + (10₂₅ x $760₂₅) + … + (4₇₅₀ x $7735₇₅₀)) / 138 motors

= $1,650

Where R_avg is the average rewind cost, N_n is the number of motors for each horsepower, RWC_n is the rewind cost for each horsepower

The average cost for reconditioning the motors is calculated the same way, except the reconditioning cost is used instead of rewind costs. For this example, the average reconditioning cost would be $555.

Step 3 is to calculate the average repair cost per motor before and after maintenance implementation.

Equation 3: Average Repair Cost per Motor

R_avg = (% Recondition x $/Recondition) + (% Rewind x $/Rewind)

= (30% x $555) + (70% x $1,650)

= $1,322 / motor

Assuming that the number of motors rewound vs. reconditioned would be inverse with the application of the program, the number of rewound motors would be 30%, and the average cost of repair would be $884 per motor.

Once the program is implemented, the number of motors to be repaired, overall, will be reduced.

Step 4 uses the number of motors repaired per year and the difference between the reconditioned motors vs. rewound in order to come up with a conservative estimate of savings.

Equation 4: Repair Cost Reduction Estimate (RRC_est)

RRC_est = (motors repaired/year x initial repair costs) (motors repaired/year x new repair costs)

= (36 motors/year x $1,322/motor) (36 motors/year x $884/motor)

= $15,768 per year

Step 5 is to determine potential energy savings. For the purposes of conservative estimation, a 2% improvement in efficiency will be assumed. Maintenance components include (and the type of test system, vibration and MCA only, for this article, used to evaluate):

• Improved lubrication (vibration)
• Proper alignment and balancing (vibration)
• Correction of circuit unbalances (MCA)
• Reduced motor temperatures (MCA, vibration)
• Reduced efficiency losses caused by rewinds (U.S. Department of Energy estimates one percentage
• point efficiency reduction per rewind)
• Improved drive system performance

Equation 5: Energy Cost Savings

Energy Savings = (total hp of motors considered) x (load factor) x (operating hours) x (%savings) x (.746 kW/hp) x (Electrical usage costs)

= 14,930 horsepower x 75% load x 8,000 hrs x 2% savings x 0.746 kW/hp x $0.06/kWh

= $80,192 per year

Step 6 is to determine the in-house labor costs to implement the program. Assume one man-hour per motor per year. Estimated costs for this example will be based upon $25 per hour.

Equation 6: In-House Labor Costs

Labor = (1 hour/month/motor) x (# of motors) x (12 months/year) x ($/man-hour)

= 1hour/month/motor x 138 motors x 12 months/year x ($25/man-hour)

= $41,400 per year

Step 7 is the purchase price for the MCA and vibration analysis equipment. For the purposes of this article, the same equipment selected for the utility PAT Project will be used. The estimated combined costs for the ALL-TEST IV PRO 2000 MCA instrument and the Pruftechnik vibration analysis equipment is $22,000.

Step 8 is the training costs for implementing the system. Assuming equipment training costs of $4,500 per person and maintenance training costs of $6,000 per person, the cost should be approximately $10,500 per person.

The final step is to determine the simple payback for the implementation of the program. In the case of this example, assume a 50% reduction in unplanned downtime for the first year:

Table 4: Costs and Savings for Maintenance Implementation

Equation 7: Simple Maintenance Payback

Payback = (Total Costs per Year) / (Total Savings per Year)

= $73,900 / $569,360

= 0.13 years or 1.6 months

The smaller size of this particular plant would allow for complete implementation of a maintenance program. Larger manufacturing plants will often have thousands of electric motors and may require a breakdown of departments or areas for successful implementation.

Application of Vibration Analysis

Vibration analysis is used by maintenance professionals as a means to detect mechanical and some limited electrical faults in rotating equipment. By performing regularly scheduled testing, the operating reliability of an electric motor can be determined through trending.

Based upon bearing failure, greasing, belt tension, misalignment or other unbalances, increases in energy losses can occur. These losses show as vibration, noise and heat. Improper belt tension and greasing will increase the friction and windage losses of the motor. This can be calculated as:

Equation 8: Bearing Losses

Watts Loss = (load,lbs x JournalDiameter,inches x rpm x f) / 169

f is dependent upon oil used and temperature;  0.005 is typical

Vibration analysis for troubleshooting will detect bearing (41% of failures) faults, balance and alignment (12% of failures) faults, primarily. It will also detect rotor faults (10% of failures) and some electrical faults (37% of failures), to some extent. However, electrical and rotor faults tend to fall in frequency ranges that can be related to other equipment, and are directly load related. Vibration analysis requires the electric motor to be operating at a load that is constant during each test that would be trended.

Application of Motor Circuit Analysis

There are many tools available to perform quality preventive maintenance of individual motors. Of these, motor circuit analysis (MCA) systems hold great promise for identifying motor problems before expensive failure and for improving the general efficiency of motor systems in general. ⁸

Motor circuit analysis allows the analyst to detect winding faults and rotor faults in the electric motor. One power of this type of test method is that it requires the equipment to be de-energized, which allows for initial incoming testing of the electric motors and troubleshooting when equipment fails. Primary energy losses that can be detected include phase unbalance and I²R losses, while faults include shorted windings, loose connections, ground faults and rotor faults.

A resistive fault gives of heat, as a loss. For instance, a 0.5-ohm loose connection on a 100-horsepower electric motor operating at 95 amps:

Equation 9: Resistive Losses

Kilo-Watts Loss = (I²R)/1000

= (95² x 0.5)/1000

= 4.5 kW (demand loss)

Equation 10: Energy Usage Loss

$/year = kW x hours/year x $/kWh

= 4.5 kW x 8000 hours/year x $0.06/kWh

= $2,160 per year

Electric motor phase unbalances (inductance and impedance) affect the current unbalances, cause motors to run hotter and reduce the motor’s ability to produce torque. The percentage unbalance of impedance can be evaluated to determine efficiency reduction and additional heating of the electric motor. A general rule is that, for every 10 degrees Celsius increase in operating temperature, the life of the equipment is reduced by half.

Figure 1. Efficiency Reduction Due to Impedance Unbalance

For instance, the paperboard company has a 100-horsepower electric motor, that would normally be 95% efficient, that has a 3.5% impedance unbalance. The efficiency would be reduced by 4 points of efficiency, or to 91%.

Equation 10: Energy Cost Due to Phase Unbalance Losses

$/yr savings = hp x 0.746 x %load x $/kWh x hours of operation ((100/Le) (100/He))

= 100 hp x 0.756 x .75 load x $0.06/kWh x 8000 hours ((100/91) (100/95))

= $1,240 / year

Figure 2. Increase in Temperature Rise Due to Phase Unbalance

The impedance unbalance will also cause an increase in operating temperature based upon an increase in I²R losses. In the case of the 100-horsepower electric motor, this means a temperature rise of about 30 degrees Celsius, or a reduction in motor insulation life to 13% of its original.

Motor circuit analysis is also used to evaluate the windings for contamination. “Frequent cleaning of a motor’s intake (if any) and cooling fins is especially important in dirty environments. … Tests confirm that even severe duty, generously rated, and oversized motors can quickly fail in such conditions if they become thickly coated or if lightly coated and with their airflow reduced by half. Their insulation life can then fall to 13 to 25% of normal.” ⁹ The same phenomenon occurs if the windings become coated in contaminants.

The MCA rotor test requires inductance and impedance readings through 360 degrees of rotation of the rotor. The readings are graphed and viewed for symmetry. Rotor test results provide a definitive condition of the rotor and is often performed following identification of a possible rotor fault by vibration, as part of an acceptance program, during repair or when the motor is identified as having torque problems.

Conclusion

The implementation of an electric motor maintenance program will have a significant impact on a company’s bottom line. Whether the company has a few hundred motors or many thousands, the simple payback from the investment into vibration and MCA is usually termed in months. Payback is impacted from savings from production availability, reduced equipment repair costs and improved energy costs, all with a minimum investment in manpower, training and equipment.

The application of these two technologies complement one another while also evaluating the progress of the maintenance program and improving upon equipment availability. Vibration analysis evaluates the mechanical condition of equipment while MCA evaluates the electrical condition of equipment. Combined, the analyst has the ability to view the complete condition of the electric motor.

About the author:

Howard W. Penrose, Ph.D., provided this article on behalf of ALL-TEST Pro, LLC. For more information, visit www.alltestpro.com, call 860-399-4222 or e-mail info@alltestpro.com.

References
¹ Industrial Productivity Training Manual, 1996 Annual IAC Directors Meeting, Rutgers University, U.S. Department of Energy Office of Industrial Technologies, 1996.

²Electric Motors Performance Analysis Testing Tool Demonstration Project, Pacific Gas & Electric, 2001.

³Industrial Productivity Training Manual, 1996 Annual IAC Directors Meeting, Rutgers University, U.S. Department of Energy Office of Industrial Technologies, 1996.

⁴Electric Motors Performance Analysis Testing Tool Demonstration Project, Pacific Gas & Electric, 2001.

⁵Electric Motors Performance Analysis Testing Tool Demonstration Project, Pacific Gas & Electric, 2001.

⁶MotorMaster Plus is a free motor energy and management software available through the U.S. Department of Energy; www.oit.doe.gov/bestpractices/.

⁷Industrial Productivity Training Manual, 1996 Annual IAC Directors Meeting, Rutgers University, U.S. Department of Energy Office of Industrial Technologies, 1996.

⁸DrivePower, Chapter 12, 1993

⁹DrivePower, Chapter 12, 1993

When learning vibration analysis, we are taught that determining bearing defect frequencies is as simple as a mathematical formula. Simply input some numbers and presto! While this is the fundamental technique needed to determine these frequencies, there are some other important considerations that need to be made. If you find that you have a frequency that you believe is a defective bearing but you just cannot get the frequencies to line up with your fault frequency overlays, here are some things you need to consider.

1. Check the true running speed of the machine. You may have the machine identified as an 1,800 rpm machine that is actually running at 1,780 rpm.
2. Ensure the bearing characteristics match those in your vibration database. Most vibration software comes with a database of bearings. You may find your bearing number in that database; however, the actual bearing may be slightly different. The bearing may have one more or one less ball than the selected bearing.
3. Determine if axial loading is occurring. High axial loading will change the roller path and thus effect the frequency calculations. As the axial load increases, the rollers have less distance to travel because they are being forced on the raceway shoulder.
4. Determine if wear is an issue. As a bearing wears over time, the distance the rollers have to travel will increase as a result. The rollers will actually decrease in diameter as wear occurs.
5. Determine if you have identified the right bearing. Occasionally when equipment is rebuilt, the original bearings are replaced with a similar bearing of a different brand. This can lead to differences in bearing defect frequencies.
6. Ensure you have enough resolution to separate defect frequencies from running speed harmonics.

If you follow these simple guidelines for determining the proper bearing defect frequencies, you will find that your fault overlay will match your suspected bearing defect frequencies a much higher percentage of the time.

About Ludeca Inc.
Ludeca is the exclusive distributor and factory-authorized service and training center for PRUFTECHNIK alignment systems and condition monitoring products in the United States, the Caribbean and Venezuela. It is also the manufacturer and distributor of DotLine Laser and SheaveMaster Pulley alignment tools. For more information, visit www.ludeca.com or call 305-591-8935.

One of the most dangerous events that can occur in flight is a phenomena called "flutter". Flutter is an aerodynamically induced vibration of a wing, tail, or control surface that can result in total structural failure in a matter of seconds. The prediction of flutter is not a precise science and requires flight verification that flutter will not occur within the normal flight envelope.

The aerodynamic surfaces of an airplane are constructed so that they can carry the loads that are produced in flight. For example the wing must be capable of supporting the weight of the airplane as well as the additional lift produced during turning flight. The resulting wing structure can be viewed as a blade or spring extending from the fuselage. If we "tap" the spring with a hammer, it will vibrate at a frequency which relates to the stiffness of the spring. A stiff spring will vibrate at a higher frequency than a more limber spring. This frequency is known as the "natural frequency" of the spring.

Flutter will usually occur at or near the natural frequency of the structure, that is, some small aerodynamic force will cause the structure to vibrate at its natural frequency. If this small force persists at the same frequency as the natural frequency of the structure, a condition called "resonance" occurs. Under a resonant condition, the amplitude of the vibration will increase dramatically in a very short time and can cause catastrophic failure in the structure.

Access this 7-minute, 40-second video by clicking on the link below.
 

This video lecture provides an explanation on vibration analysis and Rayleigh's Method. It is led by Professor Amitabha Ghosh from the Department of Mechanical Engineering at IIT Kanpur.

Access this 53-minute, 16-second video by clicking on the link below.

The video demonstrates the features and benefits of the easy-to-use and powerful VIBXPERT vibration analyzer from Ludeca.

Access this 2-minute, 47-second video by clicking on the link below.

MISTRAS Group Inc. on February 23 announced the appointment of Mark Carlos to chairman of the American Society for Testing and Materials (ASTM), E07 Committee on Non-Destructive Testing (NDT).

ASTM International is a worldwide volunteer organization, providing a forum for producers, users, ultimate consumers, and those having an interest to create consensus standards for materials, products, systems and services.

The E07 Committee on NDT currently has jurisdiction over 175 NDT standards, published in Volume 03.03 Annual Book of ASTM Standards. E07 has 12 technical subcommittees that maintain jurisdiction over these standards. These standards have, and continue to play, a pre-eminent role in all aspects relating to traditional and emerging NDT methodologies (including thermal and visual), Radiology (X, Gamma and Neutron), Liquid Penetrant, Magnetic Particle, Acoustic Emission, Ultrasonics, Electromagnetics, Leak Testing and Reference Radiological Images.

As an active and participating ASTM member for over 20 years, Mark has progressed from chairman of the E07.04, Acoustic Emission Subcommittee (which he still presides over), to secretary of the committee, vice chairman and now chairman of the committee for NDT. "Standards play a large role in the Non-Destructive Testing business," said Carlos. "I am happy to be a part of ASTM and contribute to the professional growth of the NDT industry."

Mark is the group executive vice president for the Products & Systems Division of MISTRAS Group, responsible for research and development, engineering and manufacturing.

MISTRAS Group Inc. provides non-destructive testing products and services under industry-recognized brand names including CONAM Inspection and Engineering Services Inc., Physical Acoustics Corporation and Vibra-Metrics as well as regional or product specific brand names. MISTRAS Group offers asset inspection and mechanical integrity solutions to the oil & gas, power generation, aerospace, infrastructure and manufacturing sectors as well as strategic on-line instrumentation and systems that facilitates plant asset management. In addition, MISTRAS provides enterprise solution software that aids in the safe and profitable operation of industrial facilities worldwide.

Vibration analysis can be used to determine if a soft foot condition exists on a machine that is running. If you cannot shut the machine down to check for soft foot, using the simple procedure outlined in this article will help determine if soft foot is your problem:

1. Mount your accelerometer on the suspected soft foot.
    
2. Use the live monitoring mode of your vibration analyzer to monitor the 1x rotation speed vibration. Use enough resolution to distinguish the desired peak, but not so much that it requires long collection time between averages.
    
3. While monitoring the 1x vibration, loosen the mounting bolt to approximately hand-tight.
    
4. If the vibration at 1x rotational speed reduces significantly in amplitude, it is very likely that you have a soft foot condition.
    
5. Retighten the mounting bolt and schedule the appropriate corrections.

When corrections are not made to eliminate a soft foot condition, the foot will deflect when tightened. This will affect the alignment and the motor air gap, and can cause significant vibration at rotational speed. By loosening the foot while running, the force deflecting the foot is removed.

SKF offers the Multilog On-line System DMx, a multi-featured vibration monitor for both conventional and hazardous environments. Awarded ATEX, IECEx, and cULus certifications, the DMx monitoring system can be directly installed within a hazardous area, removing or reducing cabling, cabinets and isolation barriers, and ultimately saving time and money during installation.

The SKF Multilog DMx system combines both protection (to help avoid catastrophic failures) and condition monitoring functionality in a single device, making it ideal for monitoring critical machinery such as gas turbines, generator sets, motors, pumps and compressors in the hydrocarbon processing Industry (HPI), power generation industry and other applications.

“Until now, field-installed intrinsically safe vibration devices have been confined to simple transmitters that provide only basic machine health information,” said Chris James, business area director, SKF Reliability Systems. “The complexity of the vibration measurement has been in conflict with the power restrictions imposed by intrinsic safety. With the DMx, we've been able to resolve this conflict and offer a vibration monitoring solution that – for the first time – allows the requirements of critical machinery monitoring, from transducer to dynamic data processing, to be fulfilled by an intrinsically safe device located on the machine skid.”

The SKF Multilog DMx system’s modular design, distributed architecture and small footprint have cost and installation advantages over traditional 19-inch rack systems, including energy savings due to the low power consumption of this intrinsically safe device.

Features include four dynamic input channels for vibration, axial thrust and dynamic pressure, with two channels for speed/phase measurements. Multiple LEDs provide local indications of module, channel and alarm status and diagnostic data. The device can be easily connected to plant control and information networks by common protocols.

The Multilog DMx functions as an independent monitor or as a complete system connecting to plant-wide fieldbus infrastructure and SKF’s asset management tool – @ptitude Monitoring Suite. The DMx has been adopted in many HPI sites around the world for critical plant applications.

For more information about the SKF Mulitlog On-line System DMx, visit www.skf.com/cm.

Broken equipment is a cement producer’s worst enemy, causing lost time, productivity and money. While many of these problems can be avoided through an effective predictive maintenance (PdM) program, operators are often hesitant to implement one due to the up-front and ongoing investment required.

According to Ken Rone, vice president of manufacturing services at Ash Grove Cement, a PdM investment is well worth the costs, paying for itself every time equipment failure and downtime is avoided.

Building the foundation
Based in Overland Park, Kan., Ash Grove Cement has nearly 9 million tons of cement capacity and is considered a leader in the U.S. cement industry. The company was founded more than 125 years ago.

Ash Grove first started its corporate Maintenance Excellence Process (MEP) in 2000, establishing standardized PdM equipment and procedures at each of its 10 U.S. plants. What varied was the staffing and consistency of data reporting. Some locations relied on onsite technical personnel while others used third-party consultants for various levels of vibration analysis, infrared thermography and oil analysis.

“We had a high-level, sophisticated program, but we identified vibration analysis as a weak link where we lacked consistency,” said Rone. “We wanted to bring in an independent third party with expertise in this area and began evaluating potential partners in August 2008. We were ready to go with Timken in early 2009.”

Timken is a technical solutions provider focused on improving uptime in industrial equipment. By applying its extensive knowledge of metallurgy, tribology, engineering and data analysis, Timken engineers help to maximize reliability and energy efficiency in machines critical to plant operation.

“We’re at our best when we can bring all our knowledge together to solve a problem,” said Rick Brooks, project manager for Timken’s services group. “When we combine optimal bearing, sealing and lubrication technology with things like proper maintenance, condition monitoring and diagnostic analysis, we really start seeing better results in how our customers’ equipment performs.”

The Ohio-based company also offers in-depth training and consulting services, working with customers to develop a program that suits their needs.

“Timken was the right choice for us because of their consulting experience,” said Rone. “Their evaluation devices also use the same technology platform as our existing PdM equipment, so we were already compatible.”

Establishing a new baseline
One challenge Ash Grove had was consistently collecting baseline readings for vibration analysis levels.

“It’s critical to establish baselines for trending purposes,” said Tod Cotter, project manager for Timken’s services group. “Once the baselines are in place, you have something to compare future readings to – if the amplitude of the readings trend upward, that’s when a problem might be occurring.”

Knowing this, Ash Grove asked Timken’s reliability experts to establish baseline vibration analysis readings and provide consistent, verifiable data for Criticality 1 and Criticality 2 equipment at each plant.

Cotter collected the data and performed analysis on all machines of 1,000 horsepower or more. He then made recommendations for changes in maintenance scheduling and procedures.

“Based on our findings, we provided an audit of what was good and bad at each location,” said Cotter. “Each plant had different equipment and operating parameters, but the process helped us identify existing best practices that could be shared among all the facilities.”

For example, the Ash Grove plants in Leamington, Utah, and Seattle stood out as having solid vibration analysis programs managed by a combination of in-house technicians and outside contractors. Their effective processes were highlighted in Timken’s recommendations and later incorporated into operations at other sites.

Expanding the collaboration
Impressed with Timken’s technical expertise and suggestions, Rone asked the company if it would be willing to help manage predictive maintenance activities at four Ash Grove plants having issues with their existing programs.

“Our approach is to establish a collaboration, not just come in and dictate what a company should do,” said Brooks. “We’re glad to work with Ash Grove’s people – both internal and external – to come up with a custom plan that helps them keep their critical assets running.”

The plan is working. In the first 10 months alone, Timken and Ash Grove discovered 10 instances in which equipment critical to plant operations was starting to fail. These discoveries enabled maintenance personnel to make repairs and replace damaged components before a problem could cause unscheduled downtime.

Avoiding breakdowns in major equipment such as kilns, crushers and fans has the potential to save Ash Grove millions of dollars. One “save” on a raw mill motor at the Leamington plant helped the company avoid approximately $350,000 in downtime costs.

In another case, Timken helped Ash Grove’s plant in Foreman, Ark., avoid an estimated 12 hours of downtime by analyzing data that indicated a fault in a ball mill’s fan gearbox. Removal of the gearbox bearing, which showed severe pitting damage, confirmed a problem. Ash Grove’s maintenance personnel replaced the gearbox before it led to a total system shutdown.

Ash Grove also has made the switch to Timken’s machine evaluator for data collection. The machine evaluator is a handheld instrument that combines sophisticated vibration analysis with shock-pulse monitoring to detect bearing damage and inadequate lubrication, as well as many other problems with mechanical and electrical equipment.

“Shock-pulse works particularly well for monitoring conditions in slow-speed cement equipment,” said Cotter. “The machine evaluator has advanced diagnosis capabilities for detecting machine faults such as imbalance, misalignment and structural weaknesses.”

Ash Grove maintenance personnel and onsite technicians also are receiving training on how to use the machine evaluator and integrate it into a comprehensive PdM program. Additional training related to maintenance and reliability is on the horizon.

Rone said training was a differentiating factor that helped Ash Grove select Timken, and Timken’s willingness to share its technical knowledge has translated into real business value. He also believes that the most successful PdM programs are consistent and ongoing. For example, part of Ash Grove’s MEP strategy is to reconfirm its vibration analysis baselines annually, with the help of Timken.

“Timken has definitely helped us strengthen our PdM practices,” he said. “With their onsite support and accurate data reporting, our own people have more confidence in their decision making. Right from the start, Timken demonstrated a level of technical know-how that is helping us achieve best-in-class results.”

For more information on this and other topics, visit the Timken Web site at www.timken.com.

Check out these titles from the Noria Corporation Bookstore:

Azima DLI, the leader and premier provider of predictive machine condition monitoring and analysis services, on April 1 announced the results of a survey aimed at gaining insight into the state of predictive maintenance and condition monitoring programs among U.S. plants. Overall findings show that while a majority (76 percent) of respondents is satisfied with current programs, more than half of those surveyed agree that it’s difficult to know exactly what solutions and tools are needed to maintain a successful program. Combined with insufficient staff, limited in-house expertise and poor training as the top factors negatively impacting results, plants must re-examine existing programs now in order to ensure sustainability and the ability to meet long-term productivity and reliability goals.

The findings of Azima DLI’s inaugural “State of the Condition Monitoring Industry” report are based on a survey conducted among engineers and plant managers. Of those surveyed, 65 percent have a machine condition monitoring/predictive maintenance program in place. The top three components of those programs are vibration analysis/monitoring, lube oil analysis and thermography. Of those who don’t currently have a program in place, 16 percent plan to start one this year.

Reaching the C-Suite
While the majority of respondents agree that predictive maintenance programs directly impact the bottom line, when queried about barriers to success, difficulty demonstrating ROI was one of the top factors. This could be directly related to other issues such as insufficient staff and limited budget support, but nonetheless, ROI remains an important measure of program traction that resonates with the C-suite. Therefore, plants must adopt the right technology and partner services to enable managers to better capture and report the benefits of condition monitoring programs, focusing on metrics such as decreased downtime, improved productivity, and cost savings related to improved equipment health and reliability.

Outsourcing Predicted to Rise
For those with a condition monitoring/predictive maintenance program in place, just over half of the respondents use a combination of in-house and outsourced solutions. While only 8 percent currently outsourcing data collection and analysis, based on this survey, outsourcing may be on the rise. Of those handling programs in-house, 53 percent responded that they believe there are benefits to outsourcing the program.

In considering a partner for third-party support, the following factors were ranked, in order of importance, as most influential in making that decision:

• Analytical software and services
• Ability to speak with a customer service representative 24/7
• Advanced reporting capabilities
• Web-based or on-demand access to data analysis

“While the benefits of an effective condition monitoring and predictive maintenance program are clear to plant personnel and management, many programs have been left on auto-pilot during tight economic times,” said Burt Hurlock, CEO, Azima DLI. “We believe one of the keys to long-term success is greater visibility among the C-suite regarding the quantifiable impact these programs can have on productivity and plants’ ability to comply with important industry standards for reliability. For example, by investing in cost-effective data collection analysis capabilities, plants can make informed maintenance decisions and generate results in terms of cost-avoidance related to unscheduled downtime and unnecessary repairs.”

About Azima DLI
Azima DLI is the leader and premier provider of predictive machine condition monitoring and analysis services that align with customers’ high standards for reliability, availability and uptime. Azima DLI’s WATCHMAN Reliability Services utilize flexible deployment models, proven diagnostic software and unmatched analytical expertise to deliver sustainable, scalable and cost-effective condition-based maintenance programs. The company’s bundled solutions enable customers to choose comprehensive, proven programs that ensure asset availability and maximize productivity. Azima DLI is headquartered in Woburn, Mass., with offices across the U.S. and international representation in Asia-Pacific, Central America, Europe and South America. 

The life and reliability of greased machinery depend on proper bearing lubrication. Unfortunately, inadequate grease lubrication is a very common failure mode. With greased bearings, it is difficult to determine lubricant state along with knowing when to grease, how to grease and when to stop greasing. Fortunately, predictive technologies and work processes are being successfully applied to monitor and optimize the lubrication condition.

Improper lubrication creates energy that manifests itself as mechanical stress energy and thermal energy (heat). By analyzing this energy using predictive maintenance (PdM) technologies, much can be learned about the bearing lubrication condition. This article discusses and compares the use of ultrasonic analysis, conventional vibration analysis and advanced vibration signal processing techniques as means toward determining lubrication state and achieving optimum lubrication.

Specifically, this article will look at:

1. the characteristics and progression of lubrication stress,
2. an overview and comparision of
   1. ultrasonic analysis
   2. conventional vibration analysis and
   3. advanced vibration analysis
3. the strengths and weakness of each monitoring method,
4. the characteristics of various lubricant states obtained in a controlled test environment, and
5. equipment lubrication case studies.

Traditional greasing programs use time-based preventive maintenance (PM). This method can result in over- or under-greasing, depending on the recommended periodicity of greasing, operating conditions and run time of the machinery. One plant surveyed stated that 90 percent of all motor failures were grease related – both over- and under-greasing. This particular plant was using motor manufacturer recommendations as the foundation of their greasing program.

An alternative greasing program uses PdM processes and technologies. Using PdM, greasing activities are based on the condition of the grease. Several PdM technologies for grease monitoring are available. Vibration tends to be the most common tool used. However, most commonly used vibration techniques do not detect early lubrication faults. Infrared is also an option, although it is not preferred since initial lubrication starvation has little impact on temperature. Some sources maintain grease sampling and analysis is the best method. However, in most circumstances, this is not practical. Many bearings do not have an access port to the grease and those that do present their own problems. The grease samples taken most likely do not represent the true condition of the grease inside the bearing and may also contain particulate and contamination picked up during the sampling. Ultrasonic analysis and advanced vibration signal processing are viable methods of monitoring grease condition since they measure the stress energy caused by friction between the rolling and sliding elements in the bearings.

Section 1: The Characteristics and Progression of Lubrication Stress
In rolling element bearings, lubrication sound is created by friction-induced stress waves from the interaction of the roller to the race and the roller to the cage. Good roller-to-race interaction is rolling with elastohydrodynamic lubrication. Good roller-to-cage interaction is sliding with hydrodynamic lubrication. Assuming a RMS surface roughness of 0.3 micron, Figure 1 shows that the minimum lubrication film thickness for the roller-to-race is 1 micron, or approximately 1/75 the thickness of a human hair. The minimum lubrication film thickness for the roller-to-cage is 10 microns. As lubrication starvation occurs, the film thickness will decrease, resulting in a greater coefficient of friction. The greater coefficient of friction creates additional energy in the form of heat and sound.

The lube sound characteristics for under-lubricated bearings are:

1. high frequency energy,
2. broadband energy, and
3. primarily random and non-periodic noise.

Figure 2 shows the vibration frequency domain of an under-lubricated bearing. Note the high-frequency energy hump in the 20 to 32 kilohertz (KHz) range.

Figure 3 shows the vibration frequency and time domain for a gearbox running with an incorrectly installed oil pump. Note the high frequency in the 10 KHz to 20 KHz range. This energy probably continues to higher frequency range but shows a drop-off due to the accelerometer range and mounting conditions. The time domain shows a raised noise floor with random high spikes.

As the lubrication in a bearing degrades, the high-frequency sound characteristics change. A properly lubricated bearing creates random noise with a low noise flow. As the lubricant degrades, the noise floor begins to elevate (Figure 4, Chart 1). Listening to a heterodyned 30 KHz signal, the sound is like a waterfall or rushing river. As the lube degrades, spiking begins as the noise floor elevates which sounds like a mild popping sound (Figure 4, Chart 2). Severe lubrication faults cause more frequent high-amplitude spikes, which sound like bacon sizzling. In Chart 2, as grease was added, the noise floor immediately lowered and the spiking disappeared.

Do not expect bearing to display the same sound characteristics. The amplitudes, sound patterns and frequencies of the stress energy are influenced by factors such as:

1. Speed
2. Bearing geometry
3. Roller design and quality
4. Load
5. Cage design
6. Type of lubricant

The following summarizes key points related to the characteristics of lubrication sound/stress.

1. As lubrication condition degrades, stress energy increases.
2. Lubrication stress energy is broadband, high frequency (about 8 KHz and higher) and random.
3. Other sources of high-frequency stress energy exist, such as:
   1. Bearing/gear defects, electrical – periodic
   2. Rubs, fluid turbulence – random

• To evaluate lube condition, high-frequency diagnostic tools that can measure and differentiate random from periodic noise are required.

Section 2: Overview and Comparison of Ultrasonic Analysis, Conventional Vibration Analysis, and Advanced Vibration Analysis
When monitoring for lubrication faults, sensor selection is critical. Sensors are not the same, so the characteristics of a sensor must be understood. Figure 5 shows the sensor performance of several different diagnostic technologies. The red broad-band sensor would be a poor choice because the high-frequency sounds would be drowned out by the low-frequency noise. The blue narrow-band sensor in the bearing fault area would completely miss any lubricant stress noise. The black broad-band sensor represents a typical accelerometer. It may have a response in the lubrication range, but mounting techniques are critical (see Figure 6). The blue narrow-band sensor in the lubrication area has good sensitivity and completely isolates itself from lower-frequency noise.

Figure 6 shows that when using an accelerometer to monitor lubrication stress, either a flat magnet on a clean machined surface or a permanently mounted accelerometer must be used to get the required frequency response.

Five technologies to identify and monitor lubrication faults are:

1. Infrared thermography
2. Listening using an accelerometer
3. Ultrasonic
4. Conventional vibration analysis
5. Vibration analysis with advanced signal processing using PeakVue and Autocorrelation

The next few paragraphs will briefly introduce each of these technologies. This will be followed by side-by-side comparison test results for the latter three.

Infrared thermography is good for detecting possible late-stage lubrication problems. It is not good for catching lubrication problems early. Also, additional technologies will be required to confirm the source of the abnormal temperature signature.

Some vibration analyzers have a headphone output to listen to the accelerometer output (Figure 7). This can be used to listen to the noise within the bearing. A high-pass filter on the sound output is very useful to filter out the low-frequency noise so that the lubrication noise can be heard. This is a quick qualitative method that provides good signal pattern information. It can also indicate a drop in volume, while greasing can indicate that the grease has reached the rollers.

Ultrasonic monitoring provides both listening capability and quantitative measurements (Figure 8). The ultrasonic sound is heterodyned into our listening range, and specific sound parameters are measured. The ultrasonic device should isolate the higher frequencies and filter out all lower-frequency noise. If the ultrasonic device uses a narrow-band sensor or resonant sensor, mounting is much easier than with an accelerometer. Generally, just placing the probe on a clean surface provides a good signal. Paint and other obstructions will lower the signal amplitude.

Conventional vibration analysis analyzes macro vibration – the motion of a body. It focuses on the lower-frequency events. For an 1,800 rpm motor, a typical maximum frequency would be 60 times running speed, or 1,800 hertz (Hz). Figure 9 shows a typical frequency domain plot.

The CSI PeakVue measurement is an advanced signal-processing method. It performs micro vibration analysis, which measures the microscopic movement (stress waves) within a body. It analyzes the energy above the conventional analysis range. PeakVue is not an averaging process, so it captures the true peak amplitude values from the high-frequency information. To create the PeakVue time waveform, the accelerometer signal is routed through a high-pass filter to cut out all low-frequency information. The signal is sampled at more than 100,000 times per second. This digital data stream is broken into data blocks which are a function of the desired Fmax. The only value saved for each data block is the true peak “G” amplitude value. These true peak G levels create the PeakVue time waveform. This PeakVue time waveform is then processed to create a PeakVue frequency domain spectrum (Figure 10).

PeakVue provides a window into the area where lubrication data is located. The nature of lubrication faults make the PeakVue time domain data noisy and difficult to interpret. To assist with this, another advanced signal-processing technique, CSI Autocorrelation, can be applied. Autocorrelation is an averaging process to remove random activity from the time domain. The resultant correlation factor (Figure 11) is proportional to the amount of periodic energy. If at any point on the correlated waveform the correlation factor is small, then almost all of the energy at that point is random.

Figure 12 shows a test motor. To compare ultrasonic, conventional vibration analysis and advanced vibration analysis, the top inboard bearing was monitoring both with and without lubricant. The next few paragraphs will compare the results from the three technologies. For the vibration measurements, a mounting pad was glued to a machine flat on the bearing housing. A flat magnet with accelerometer was attached to the pad. A CSI SonicScan was the ultrasonic test device. It measured both lower-frequency energy (4 KHz) and ultrasonic energy (30 KHz).

Figure 13 compares the time domain data for the three technologies. The conventional vibration data shows almost no difference between lubrication states. PeakVue clearly measures the stress energy caused by the lubrication fault. The SonicScan data was consistent. The lower-frequency energy measurement showed some but little change. The ultrasonic data showed significant change in both the average reading and peak amplitude reading.

Figure 14 shows a comparison of the frequency domains. With conventional vibration, a slight change in periodic activity is noticed. This is likely because the existing bearing mechanical defects are being amplified a little without lubricant. The PeakVue spectrum shows a dramatic increase in stress energy.

The PeakVue time domain for the no-lubricant condition is messy and hard to interpret. When the Autocorrelation is performed (Figure 15), it is immediately apparent that almost all of the stress energy is random, which is a primary characteristic of lubrication faults.

Table 1 provides a summary comparison of the discussed technologies. The left side of the table shows technology features that make testing accurate and easy. Accelerometer listening and conventional vibration analysis will not provide good results. Headphones should be used when performing advanced vibration analysis to add a very important qualitative attribute. Advanced vibration analysis provides the best information for determining lubrication condition but requires additional vibration analysis training and special sensor mounting. Ultrasonics provides a good indication of lubrication condition (without access to detailed signature analysis tools) and are easy to use but also require some specialized training.

Section 3: Characteristics of Various Lubricant States Obtained in a Controlled Test Environment
To better understand and verify theoretical assumption, a lubrication test stand was built for controlled lubrication testing. Figure 16 shows the test stand that was used to collect the controlled grease lubrication testing data.

A motor drives a control, idler and test bearing. Grease can be introduced into each bearing through its outer race using a zerk fitting on the bearing housing. The idler bearing can be variably loaded to 1,000 pounds. The bearings tested were sealed SKF single-row ball bearings with nine balls. The bearings have an ABEC 1 rating and were driven at 1,780 rpm. For each lubrication condition, peak and average decibel (dB) levels were measured at three frequency ranges. The ranges were 30 kHz (lubrication range), 4 kHz (impact range) and 40 kHz (traditional ultrasonic range). The peak dB level records the highest signal amplitude, such as in a spike, during a data collection period. The average dB level records the average of all the signal peaks during a data collection period and is least affected by spikes. The sound parameters were collected using a Model 7100 SonicScan analyzer using a magnet mount sensor. A magnet mount sensor allowed for hands-free operation and greater repeatability. The temperature was monitored on the bearing housing using a CSI Model 515 spot radiometer with laser pointer.

First, to evaluate the effects on sound of over-lubrication, a new bearing running at steady state under load was slowly greased until grease squeezed through the seals (see Figure 17).

Figure 18 shows no significant increase in sound up to 12 minutes after over-greasing. Over-greasing did create a 7 percent rise in the temperature on the outside of the bearing housing. It should be noted that the rate of grease oxidation and deterioration will increase with increasing temperature.

Second, to evaluate the effects of under-lubrication, a new bearing with seals removed was brought to steady-state temperature under full load. The bearing was stopped long enough to wipe grease off of one side of the bearing and then retested. The bearing was then washed in solvent to remove all grease (see Figure 19) and then retested. When the bearing with no grease was approaching total failure, it was progressively regreased and monitored for 30 minutes until it reached steady state.

Figure 20 shows results from lubricant starvation and then re-lubrication. The dB levels increase with deteriorating lube conditions and they decrease with improving lubrication conditions. The temperature remains constant except when lubricant is absent. While temperature is a good indicator of total lube starvation, it was not a good indicator of partial lube starvation.

Seen from Figure 20, the initial re-lubrication made a significant improvement in sound level and temperature even though only one pump, or 0.04 ounces of grease, was introduced. Figure 21 shows the sound waveform (heterodyned 30 kHz audio output from SonicScan) immediately prior to and after the first pump of grease. A very distinct drop in level occurs when the grease hits the rollers. Additional greasing resulted in a minor sound level decrease and a negligible effect on temperature. Permanent damage likely occurred to the bearing since the sound levels did not return to their original levels.

The results of the sound monitoring in a controlled environment as they apply to lubrication monitoring can be summarized as follows:

1. Sound analysis is a poor technology for monitoring over-lubrication since over-lubrication does not significantly affect the sound parameters,
2. Temperature analysis is a good technology for monitoring over-lubrication since it does significantly increase with over-greasing; however, in the field, be aware of process and environmental effects on the temperature,
3. Sound analysis is a good technology for monitoring normal and under-lubricated bearings since the sound levels increase with deteriorating conditions and decrease with improving conditions,
4. Lubrication problems that are not corrected in early stages will result in permanent mechanical damage, and
5. Listening during greasing for a decrease in the sound level is a good indicator of grease entering the bearing.

Section 4: Equipment Lubrication Case Studies
Lubrication ultrasonic analysis was performed on the following operating industrial equipment: food processing conveyor bearings, 75-horsepower General Electric motors, and 450 HP Baldor motor. The conveyor testing occurred during a single day, while the motor testing occurred over a seven-month period. The testing confirmed and validated the characteristics of lubrication sound and the findings from the controlled environment testing. The instrument used to analyze the sound was a CSI Model 7100 SonicScan with a multi-frequency contact probe (see Figure 22) instead of a magnet mount sensor. Measurements were taken on the top of the zerk fitting. In general, contact probe measurements are not as repeatable as magnet mount sensor because of variable contact angles and pressures. The advantage of the contact probe is that it can easily fit into tight places and can be used on zerk fittings.

Food Processing Conveyor Bearings
The survey included 13 bearings from a conveyor in a food processing plant. Most of the bearings were pressure-washed frequently with water and greased daily. Bearing No. 11 was elevated above floor level, in a difficult-to-reach position, and apparently not greased regularly.

Each bearing was tested three times before greasing and three times after greasing. Of all the bearings that were greased daily, the additional greasing had little or no effect on the bearing sound. Greasing significantly affected bearing No. 11. Figure 23 shows a summary of the readings taken at both the 4 kHz and 30 kHz frequency ranges. It also shows the peak (PH) and average (AV) readings at each frequency.

For each set of columns, the first column shows the mean dB reading of all 13 bearings. The second column shows the dB reading of bearing No. 11 before greasing. The third column shows the dB reading of bearing No. 11 after greasing. In both the 4 kHz peak and 4 kHz average readings, bearing No. 11 was slighter higher than mean before greasing and slightly lower than mean after greasing. The 30 kHz peak reading shows a significantly elevated dB before greasing and a significant dB drop after greasing. The greatest change occurred with the 30 kHz average reading with the average reading dropping from the upper 20s to almost zero. In this test, the 30 kHz average reading was the most sensitive parameter for measuring lubrication condition. It should also be noted that permanent mechanical damage was unlikely since the 4 kHz peak (mechanical impact) reading dropped to below mean and the 30 kHz average (lubrication) reading dropped to almost zero.

Headphones were used with the SonicScan to provide additional qualitative information. Unfortunately, in this environment, a lot of noise existed in all of the measurements. Metal cans were clattering on the conveyor, belts were sliding and the background was loud. For these or other reasons, the headphones did not provide useful information in this study.

75 HP General Electric Motors
Several 75 HP General Electric motors were monitored. The motors run at 1,780 rpm and drive blowers. Each bearing has a single shield toward the rotor. The grease cavity has a zerk fitting at 2 o’clock and an unused grease vent plug at 10 o’clock. The motors are greased every 3,500 hours with 12 grease gun pumps. Currently, the maintenance group has no idea of the lubricant condition before or after greasing.

Figure 24 plots the lubrication sound levels according to the number of hours on the motor since greasing. Notice that the inboard bearing dB level dropped to almost zero after greasing and remained constant through 766 hours. The outboard bearing performed similarly except that the dB level began to increase after 358 hours. This could be caused by a lot of factors, such as possibly only a small amount of the grease that entered the cavity actually made to it the bearing.

Figure 25 shows a similar plot for a second 75 HP General Electric motor. The 30 kHz sound level performed as expected. The 4 kHz dB level seemed to not be affected by greasing. The 4 kHz dB levels very likely can be affected by noise from other machinery and processes since sonic sound travels through material much better and attenuates much slower than ultrasonic signals. The 40 kHz signal did not perform as expected. In general, the 40 kHz signal is less reliable since the amount of lubrication energy created at 40 kHz is less than what is created at 30 kHz.

450 HP Baldor Motor
Several 450 HP Baldor Super E motors were monitored. The motors run at 1,780 rpm and drive large blowers. Each bearing is neither shielded nor sealed. The grease cavity has a zerk fitting at 12 o’clock and an unused grease vent plug at 6 o’clock. Access to the grease vents is restricted because of the design of the motor mount. The motors are greased every 3,500 hours with 24 grease gun pumps. Currently, the maintenance group has no idea of the lubricant condition before or after greasing.

Figure 26 plots the lubrication sound levels according to the number of hours on the motor since greasing. At 3,411 hours, the bearing was greased – first four pumps and then an additional four pumps. With the 30 kHz readings, the sound levels dropped significantly, while they increased slightly with additional grease. Because of this increase, greasing was stopped. Only after 552 hours, the sound level returned to its previous level, that is most likely a state of adequate lubrication. Like with the GE motors, the 4 kHz sound was not affected by the greasing.

Figure 27 charts another Baldor motor bearing. At 30 kHz, this bearing has a steady sound level until 3,278 hours. After greasing, the sound level dropped back close to the previous level.

The results of the sound monitoring in an industrial environment as they apply to lubrication monitoring can be summarized as follows:

1. In all of the GE and Baldor bearings, the 30 kHz average reading provided the most predictable readings. The 40 kHz readings generally followed the 30 kHz readings but were less consistent.
2. Greasing did not affect the 4 kHz readings. The 4 kHz reading can be very useful in analyzing the bearing’s mechanical condition or as a confirmation technique for conventional vibration analysis.
3. Trying to determine lubrication condition by listening alone is ineffective since the lubrication sound is primarily white noise. Quantifiable and repeatable analysis is required to accurately assess lubrication condition. The qualitative information from listening is very useful for general listening and checking for other problems. It can also be useful during lubrication to determine when grease enters the bearings.
4. For all of the industrial bearings tested, optimum or baseline lubrication levels (30 kHz frequency and average sound parameter) were around 10 dB or lower. The normal operatinglubrication levels were around 10 to 20 dB. For the test stands bearing and conveyor bearing, the critical levelbefore permanent damage occurred was around 30 dB. This would imply that the greasing levelshould occur between 20 and 30 dB. These estimates are very general. More exact levels should be determined by type of bearing and application. This could be accomplished through testing and long-term trending.
5. All the bearing testing did not exhibit a standard lubrication cycle except that increasing hours eventually resulted in increasing sound levels and that re-lubrication resulted in decreasing sound levels except for the case of already over-lubricated bearings. Specific lubrication cycles for specific bearings could be understood through long-term trending. This would provide much information on the real-time lubrication condition and projected performance.
6. By trending the sound levels along with the lubrication intervals and grease amounts, the grease interval and volume can be optimized. The most favorable temperatures will be obtained when the bearing is supplied with the minimum quantity of lubricant to provide proper lubrication. Therefore, as a general rule, less grease is better than more grease. The trending will reveal the cause and effect of lubrication quantity on lubrication condition and interval. Lubrication monitoring with vibration was performed on a pump, scrubber fan and exhaust blower.3

Figure 28 shows the conventional vibration data for a pump. This data shows no indication of a lubrication fault. The PeakVue data for the same pump shows high G levels and random patterns which could indicate a lubrication fault (Figure 29). The autocorrelation of the PeakVue waveform confirms that the energy is almost all random (Figure 30). The pump locked up one month later due to lack of lubricant.

The next vibration case history is for a scrubber fan. The PeakVue data on April 19 (Figure 31) was below the alert level. On May 29, the PeakVue data (Figure 32) was in alarm with a 500 percent increase in G amplitude. The autocorrelation of the May 29 data (Figure 33) revealed no periodicity. This random high-frequency energy is a strong indicator of a lubrication fault. Figure 34 shows a comparison of the PeakVue time waveforms. Figure 35 shows a comparison of the conventional time waveform data. Note that no increase occurred with the conventional data (Fmax of 3 KHz). The source of the lubrication fault was a faulty oiler bowl. As soon as the oiler bowl was fixed, the PeakVue data returned to normal levels (Figure 36).

The third vibration equipment case study is for an exhaust blower. A new type of grease with limestone and graphite additives was used. Figure 37 shows the frequency domain before and after the new grease was added. The new grease caused significant high-frequency energy – 8 KHz and higher. Figure 38 shows the PeakVue time domain comparison. Notice the large increase in friction energy because of the different lubricant.

These three equipment case studies show that advanced vibration analysis using PeakVue and autocorrelation are effective techniques to identify and monitor lubrication condition. They also show that conventional vibration analysis is not as effective.

Conclusion
To achieve optimum lubrication, it is important to be able to determine:

1. the lubrication condition at any time;
2. the conditions when re-lubrication is necessary;
3. the volume of grease required for re-greasing.

As seen from this article, multiple predictive technologies can provide immediate information about the lubrication condition while the equipment is operating. Each of these technologies has strengths and limitations. For effective lubrication monitoring, each of the technology’s strengths and weaknesses must be understood along with the physical characteristics of lubrication energy.

Lubrication analysis using advanced vibration analysis should be performed on all critical machines, especially if the equipment is already being monitored for bearing defects. If equipment is critical enough to warrant vibration analysis, then the lubrication can be monitored as a part of that process. Many companies do not monitor lubrication because of the cost to prepare a proper mounting surface for the accelerometer. This cost is insignificant when compared to the cost of a lubrication fault in terms of lost production, lost equipment life and higher maintenance costs.

When lubrication analysis is combined with good trending and monitoring, greasing intervals and amounts can be optimized. All of this results in knowing the equipment better and being better able to maintain it, extend its life, and predict its performance.

For more information on this subject, visit www.emersonprocess.com.

References
1 Chart based on information from “Improving the Reliability of Machines by Understanding the Failure of Their Moving Parts,” Master Series Course taught at CSI by M. Neale and D. Summers-Smith, October 1997.
2 “Machinery Surveillance Employing Sonic/Ultrasonic Sensors” by J. C. Robinson, J. B. Van Voorhis, K. R. Piety, and W. King, Reliability Week 1999.
3 “Monitoring Lubrication Using a Multi-Frequency Sonic/Ultrasonic Sensor” by Ray Garvey, Computational Systems Inc.
3 Three case studies from “Advanced Vibration and Autocorrelation Analysis for Improved Diagnostics”; James Robinson, Bob Cook, James Crow; Emerson Global Users Exchange, 2006