Intro Predictive Maintenance 2 Part 3 pdf

ago, few plants recognized the ability of predictive technology to detect and correct product-quality problems. Asset Protection. More than 60 percent (60.8%) of those interviewed included asset protection as the reason for implementation. Although asset management and protection is partially a maintenance issue, its inclusion as justification for a predictive maintenance program is a radical change from just a few years ago. ISO Certification. Almost 36 percent (35.8%) included ISO certification as a reason for implementing predictive maintenance. The primary focus of ISO 9000 is product quality. As a result, the certification process includes criteria that seek to ensure equipment reliability and consistent production of first-quality products. Predictive maintenance helps maintain consistent quality performance levels of critical plant production systems. Although ISO certification does not include specific requirements for predictive maintenance, its inclusion in the plant program will greatly improve the probability of certification and will ensure long-term compliance with ISO program requirements. Management Directive. Almost one-third (30.7 percent) of respondents stated that the primary reason for implementation was top management directives. More senior-level managers have recognized the absolute need for a tool to improve the overall reliability of critical plant systems. Many recognize the ability of predictive maintenance technologies as this critical management tool. Lower Insurance Rates. Insurance considerations were cited by 25 percent of those interviewed. Most plants have insurance policies that protect them against interruptions in production. These policies are primarily intended to protect the plant against losses caused by fire, flood, breakdowns, or other prolonged interruptions in the plant’s ability to operate. Over the past 10 years, insurance companies have begun to recognize the ability of predictive maintenance technology to reduce the frequency and severity of machine- and process-related production interruptions. As a result, the more progressive insurance companies now offer a substantially lower premium for production interruption insurance to plants that have a viable predictive maintenance program. Predictive Maintenance Costs The average maintenance budget of the plants interviewed was $12,053,000, but included those with budgets ranging from less than $100,000 to more than $100 million. The average plant invests 15.8 percent of its annual maintenance budget in predictive maintenance programs, but one-third (33%) of the plants interviewed in our May 2000 survey allocate less than 10 percent to predictive maintenance. According to the survey, the average cost of a predictive maintenance program is $1.9 million annually. This cost includes procuring instrumentation but consists primarily of the recurring labor cost required to sustain these programs. The burdened cost— 62 An Introduction to Predictive Maintenance including fringe benefits, overhead, taxes, and other nonpayroll costs—of labor varies depending on the location and type of plant. For example, the annual cost of an entry- level predictive analyst in a Chicago steel mill is about $70,000 per employee. The same analyst in a small food processing plant located in the South may be as low as $30,000. In the survey, the full range of predictive maintenance program costs varied from a low of $72,318 to a high of almost $4 million ($3.98 million) and included plants with total maintenance budgets from less than $100,000 to more than $100 million annually. This range of costs is to be expected because the survey included a variety of industries, ranging from food and kindred products that would tend to have fewer personnel assigned to predictive maintenance to large, integrated process plants that require substantially more personnel. The real message this measurement provides is that the recurring cost associated with data collection and analyses of a predictive maintenance program can be substantial and that the savings or improvements generated by the program must, at a minimum, offset these costs. Contract Predictive Maintenance Costs The survey indicates that most programs use a combination of in-house and contract personnel to sustain their predictive maintenance program. A series of questions designed to quantify the use of outside contractors was included in the survey and provided the following results. The average plant spends $386,500 each year for contract predictive maintenance services. Obviously, the actual expenditure varies with size and management commitment of each individual plant. According to the survey, annual expenditures ranged from nothing to more than $1 million. The types of contract services include the following: Vibration Monitoring. The results of our survey shows that 67.4 percent of the vibration monitoring programs are staffed with in-house personnel, and an additional 10.4 percent use a combination of plant personnel and outside contractors. The remaining 22.2 percent of these programs are outsourced to contract vibration-monitoring vendors. In part, the decision to outsource may be justified. In smaller plants the labor requirements for a full-plant predictive maintenance program may not be enough to warrant a full-time, in-house analyst. In this situation outsourcing is often a viable option. Other plants that can justify full-time, in-house personnel elect to use outside contractors in the belief that a cost saving is gained by this approach. Although the plant can eliminate the burden, such as retirement benefits, taxes, and overhead, associated with in-house labor, this approach is questionable. If the contractual agreement with the vendor guarantees the same quality, commitment, and continuity that is typical of Benefits of Predictive Maintenance 63 an in-house program, this approach can work; however, this is often not the case. Turnover and inconsistent results are too often the norm for contract predictive maintenance programs. There are good, well-qualified vendors, but there are also many contract predictive maintenance vendors who are totally unqualified to provide even minimum levels of performance. Lube Oil Analysis. The ratio is reversed for lubricating oils analysis. Sixty-eight percent of these programs are staffed with contractors, and only 15.1 percent use only in-plant personnel. An additional 17 percent of these programs use a combination of personnel. This statistic is a little surprising both in the number of users and approach taken. Until recently, lube oil analysis was limited to manual laboratory techniques that would normally preclude the use of in-house staff. As a result, most of the analysis required for this type of program was contracted to a material-testing laboratory. With this type of arrangement, we would have expected the survey to show a higher ratio of in-plant personnel involved in the program. Typically, in-house personnel are responsible for regular collections of lubricating oil samples, which are then sent to outside laboratories for analysis. This assumption is supported by the labor distribu- tion of the tribology programs included in this survey. The mix includes 36 percent in-house and 56 percent outside services. One would assume from these statistics that in-house personnel acquire samples and rely on the outside laboratories for wear particle, ferrographic, or spectrographic analyses. In the purely technical sense, lubricating oils analysis is not a predictive maintenance tool. Rather, it is a positive means of selecting and using lubricants in various plant applications. This technique evaluates the condition of the lubricants, not the condition of a machine or mechanical system. Although the sample may indicate that a defect or problem exists in a mechanical system, it does little to isolate the root-cause of the problem. One could conclude from the survey results that too many plants are using lubricating oils analysis incorrectly. Thermography. Thermography programs are almost equally divided between in- house and contract programs. In-house personnel staff 45.9 percent, outside contractors provide 42.5 percent, and a combination of personnel account for 11.6 percent. The higher-than-expected reliance on outside contractors may be caused by the high initial investment cost of state-of-the-art infrared scanning systems. A typical full- color system will cost about $60,000 and may be prohibitive in smaller plants. Derived Benefits Our survey attempted to quantify the benefits that have been derived from predictive maintenance programs. Almost 91 percent (90.9%) of participants reported measur- able savings as a result of their predictive maintenance program. On average, reductions in maintenance costs and downtime have recovered 113 percent of the total cost invested in these programs. Based on these statistics, the typical program will generate a net improvement of 13 percent. When compared to the average maintenance 64 An Introduction to Predictive Maintenance budget of survey participants ($12,053,000), the average annual savings are about $1.6 million. A successful predictive maintenance program, according to most publications, should generate a return on investment of between 10:1 and 12:1. In other words, the plant should save $10 to $12 for every dollar invested. The survey results clearly indicate that this is not the case. Based on the statistics, the average return on investment was only 1.13:1, slightly better than breakeven. If this statistic were true, few financial managers would authorize an investment in predictive maintenance. The statistics generated by the survey may be misleading. If you look carefully at the responses, you will see that 26.2 percent of respondents indicated that their programs recovered invested costs; 13 percent did not know; and 50.8 percent did not recover costs. From these statistics, one would have to question the worth of predictive technology; however, before you judge its worth, consider the remaining 10 percent. These plants not only recovered costs but also generated additional savings that increased bottom-line plant profitability. Almost half of these plants generated a profit five times greater than their total incurred cost, a return on investment of 5:1. Although this return is well below the reported norm of successful predictive maintenance programs, it does have a substantial, positive effect on profitability. The statistics also confirm our belief that few plants are taking full advantage of predictive maintenance capabilities. When fully utilized, these technologies can generate a return on investment well above 100:1 or $100 for every dollar invested. As we have stated many times, the technology is available, but it must be used properly to gain maximum benefits. The survey results clearly show that this is not yet occurring for many companies. Which Technology Is Most Beneficial Each of the participants was asked to rank each of the traditional predictive maintenance technologies based on its benefits to improved performance. Vibration analysis was selected as the most beneficial by 54.6 percent of respondents. This statistic is not surprising for two reasons. First, most of the equipment, machines, and systems that constitute a typical plant are mechanical and well suited for vibration monitoring. The second reason has two parts. First, vibration-monitoring technology and instruments have evolved much faster than some of the other technologies. In the past 10 years, data collection instrumentation and its associated software packages have evolved to a point that almost anyone can use this technology effectively. The same is not true of predictive technologies, which still require manual collection and analysis. The second part is that most users view vibration monitoring as being relatively easy. Simply follow the data collection route displayed on a portable data collector; download acquired data to a PC; print an exception report; and repeat the process a few weeks or months later. Don’t laugh. This is exactly the way many vibration- monitoring programs are done. Will this approach reduce the number and frequency Benefits of Predictive Maintenance 65 of unscheduled delays? Yes, it will, but it will do little or nothing to reduce costs, improve availability, or increase bottom-line profits. The unfortunate part is that too many programs are judged solely on the number of measurement points acquired each month, how many points are in alarm, or the number of unscheduled delays. As a result, a program is viewed as being successful even though it is actually increasing costs. What Would You Change? Perhaps the most interesting results of the survey were the responses to questions per- taining to improvements or changes that should be made to these existing programs. The responses included the following: Do More Often. One of the favorite ploys used by upper management to reduce the perceived cost of predictive maintenance is to reduce the frequency of use. Instead of monitoring equipment on a frequency equal to its criticality, they elect to limit the frequency to quarterly, semi-annually, or even less. This approach will ensure failure or at best restrict the benefits of the program. To be effective, predictive maintenance technologies must be used. Limiting the evaluation cycle to abnormally long intervals destroys the program’s ability to detect minor changes in critical plant equipments’ operating condition. The proper monitoring frequency varies depending on the specific technology used and the criticality of the plant system. For example, plant systems that are essential for continued plant operation should be monitored continuously. Systems with lesser importance may require monthly or annual evaluation frequencies. When vibration monitoring is used, the maximum effective frequency is every 30 days. If the frequency is greater, the program effectiveness will be reduced in direct proportion to the analysis interval. In most cases, programs that use a monitoring frequency greater than 30 days for noncritical plant systems will never recover the recurring costs generated by the program. Thirty days is the maximum interval recommended for this program type. As the criticality of the plant system increases, so should the monitoring frequency. Some applications for thermography, such as roof surveys, should have an interval of 12 to 36 months. Nothing is gained by increasing the survey frequency in these types of applications; however, other applications, such as monitoring electrical equipment and other critical plant systems, should follow a much more frequent schedule. Similar to vibration monitoring, the monitoring frequency for thermographic programs should be based on the criticality of the system. Normal intervals range from weekly on essential systems to bimonthly on less critical equipment. Lubricating oil analysis, when used properly, does not require the same frequency as other predictive maintenance technologies. Because this technique is used solely to evaluate the operating condition of lubricants, a quarterly or semi-annual evaluation is often sufficient. Too many programs use a monthly sampling frequency in the mis- 66 An Introduction to Predictive Maintenance taken belief that lube oil analysis will detect machine problems. If it were the only technology used, this belief may have some validity; however, other techniques, such as vibration monitoring, will provide a much more cost-effective means of early detection. Lube oil analysis is not an effective machinery diagnostic tool. Although some failure mechanisms will release detectable contaminants, such as bearing Babbitt, into the lubricant, this analysis technique cannot isolate the root-cause of the problem. Nothing. Almost 13 percent of those interviewed stated that their predictive maintenance program did not require any change. This response is a little frightening. When one considers that only 10 percent of the surveyed programs generated a positive con- tribution to plant performance and more than 50 percent failed to recover the actual cost of their programs, it is difficult to believe that the programs do not need to be improved. This response probably partly results from an indication that too many plant personnel do not fully understand predictive maintenance technology. In one of my columns, I used the example of a program that was judged to be highly successful by plant personnel, including senior management. After 6 years of a total-plant vibration- monitoring program, unscheduled delays had been reduced by about 30 percent. Based exclusively on this statistic, the program was deemed successful, but when evaluated from a standpoint of the frequency of scheduled downtime and annual pro- curement of maintenance spares, another story emerged. Scheduled downtime for maintenance increased by almost 40 percent and annual cost of replacement parts by more than 80 percent. As an example, before implementing the predictive maintenance program, the plant purchased about $4.1 million of bearings each year. In the sixth year of the program, annual bearing replacement costs exceeded $14 million. Clearly the program was not successful in all respects. Don’t Know. Almost 9 percent of those interviewed could not answer this question. Coupled with the previous response, this can probably be attributed to a lack of viable program evaluation tools. How do you measure the success of a predictive maintenance program? Is it the number of points monitored? Or the change in the overall vibration level of monitored machinery? Both of these criteria are too often the only measurement of a program’s effectiveness. The true measure of success is capacity. An effective program will result in a positive increase in first-time-through capacity—this is the only true measure of success. The converse of the increase in capacity is program cost. This criterion should include all incremental cost caused by the program, not just the labor required to maintain the program. For example, the frequency of scheduled or planned repairs may increase as a result of the program. This increase will generate additional or incremental charges that must be added to the program cost. The problem that most programs face is that existing performance tracking programs do not provide an accurate means of evaluation. Plant data are too often fragmented, distorted, or conflicting and are not usable as a measurement of program success. This Benefits of Predictive Maintenance 67 problem is not limited to effective measurement of predictive maintenance programs, but severely restricts the ability to manage all plant functions. The ability to effectively use predictive maintenance technologies strictly depends on your ability to measure change. Therefore, it is essential that the plant implements and maintains an effective plant performance evaluation program. Universal use of a viable set of measurement criteria is essential. More Management Involvement. Only 1 percent of the survey participants stated that more management involvement was needed. Of all the survey responses, this is the greatest surprise. Lack of management commitment and involvement is the primary reason that most predictive maintenance programs fail. Based on the other responses, this view may be a result of the respondents’ failure to recognize the real reason for ineffective programs. Most of the responses, including increasing the monitoring frequency, have their roots in a lack of management involvement. Why else would the frequency be too great? When you consider that 30.7 percent of these programs were implemented because of management directives, one would conclude that management commitment is automatic. Unfortunately, this is too often not the case. Like most of those interviewed, plant management does not have a complete understanding of predictive maintenance. They do not understand the absolute necessity of regular, timely monitoring cycles; the labor required to gain maximum benefits; or the need to fully use the information generated by the program. As a result, too many programs are only partially implemented. Staffing, training, and universal use of data are restricted in a misguided attempt to minimize cost. Conclusions The survey revealed many positive changes in the application and use of predictive maintenance technology. More participants are beginning to understand that this tool offers more than just the ability to prevent catastrophic failure of plant machinery. In addition, more plants are adopting these technologies and either have or plan to imple- ment them in their plants. Apparently, few question the merit of these technologies as a tool to improve product quality, increase capacity, and reduce costs. These are all positive indications that predictive maintenance has gained credibility and will continue to be used by a growing number of plants. The bad news is that too many plants are not fully utilizing predictive maintenance. Many of you have heard about or read my adamant opinion that predictive maintenance is not working. The survey results confirm this viewpoint. When fewer than 10 percent of the programs generate a positive return on investment, it would be difficult to disagree with this point. Is this a failure of the technology or are we doing some- thing wrong? In my opinion, the latter is the sole reason that predictive maintenance has failed to consistently achieve its full potential. The technology is real, and the evolution of 68 An Introduction to Predictive Maintenance microprocessor-based instrumentation and dedicated software programs has simpli- fied the use of these technologies to a point that almost anyone can effectively use them. The failure is not because of technology limitations. We simply are not using the tools effectively. In most cases, the reason for failure is a lack of planning and preparation before implementing the program. Many predictive maintenance system vendors suggest that implementing a predictive maintenance program is easy and requires little effort to set up. Nothing could be further from the truth. There are no easy solutions to the high costs of maintenance. The amount of time and effort required to select predictive methods that will provide the most cost-effective means to (1) evaluate the operating condition of critical plant systems; (2) establish a program plan; (3) create a viable database; and (4) establish a baseline value is substantial. The actual time and labor required will vary depending on plant size and the complexity of process systems. For a small company, the time required to develop a viable program will be about three person-months. For large, integrated process plants, this initial effort may be as much as 15 person-years. Are the benefits worth this level of effort? In almost every instance, the answer is an absolute yes. 4.1.2 As a Plant Optimization Tool Predictive maintenance technologies can provide even more benefit when used as a plant optimization tool. For example, these technologies can be used to establish the best production procedures and practices for all critical production systems within a plant. Few of today’s plants are operating within the original design limits of their production systems. Over time, the products that these lines produce have changed. Competitive and market pressure have demanded increasingly higher production rates. As a result, the operating procedures that were appropriate for the as-designed systems are no longer valid. Predictive technologies can be used to map the actual operating conditions of these critical systems and to provide the data needed to establish valid procedures that will meet the demand for higher production rates without a corre- sponding increase in maintenance cost and reduced useful life. Simply stated, these technologies permit plant personnel to quantify the cause-and-effect relationship of various modes of operation. This ability to actually measure the effect of different operating modes on the reliability and resultant maintenance costs should provide the means to make sound business decisions. 4.1.3 As a Reliability Improvement Tool As a reliability improvement tool, predictive maintenance technologies cannot be beat. The ability to measure even slight deviations from normal operating parameters permits appropriate plant personnel (e.g., reliability engineers, maintenance planners) to plan and schedule minor adjustments to prevent degradation of the machine or system, thereby eliminating the need for major rebuilds and the associated downtime. Predictive maintenance technologies are not limited to simple electromechanical machines. These technologies can be used effectively on almost every critical system Benefits of Predictive Maintenance 69 or component within a typical plant. For example, time-domain vibration can be used to quantify the response characteristics of valves, cylinders, linear-motion machines, and complex systems, such as oscillators on continuous casters. In effect, this type of predictive maintenance can be used on any machine where timing is critical. The same is true for thermography. In addition to its traditional use as a tool to survey roofs and building structures for leaks or heat loss, this tool can be used for a variety of reliability-related applications. It is ideal for any system where surface temperature indicates the system’s operating condition. The applications are almost endless, but few plants even attempt to use infrared as a reliability tool. 4.1.4 The Difference Other than the mission or intent of how predictive maintenance is used in your plant, the real difference between the limited benefits of a traditional predictive maintenance program and the maximum benefits that these technologies could provide is the diagnostic logic that is used. In traditional predictive maintenance applications, analysts typically receive between 5 and 15 days of formal instruction. This training is always limited to the particular technique (e.g., vibration, thermography) and excludes all other knowledge that might help them understand the true operating condition of the machine, equipment, or system they are attempting to analyze. The obvious fallacy in this is that none of the predictive technologies can be used as stand-alone tools to accurately evaluate the operating condition of critical production systems. Therefore, analysts must use a variety of technologies to achieve anything more than simple prevention of catastrophic failures. At a minimum, analysts should have a practical knowledge of machine design, operating dynamics, and the use of at least the three major predictive technologies (i.e., vibration, thermography, and tribology). Without this minimum knowledge, they cannot be expected to provide accurate evaluations or cost-effective corrective actions. In summary, there are two fundamental requirements of a truly successful predictive maintenance program: (1) a mission that focuses the program on total-plant optimization and (2) proper training for technicians and analysts. The mission or scope of the program must be driven by life-cycle cost, maximum reliability, and best practices from all functional organizations within the plant. If the program is properly structured, the second requirement is to give the personnel responsible for the program the tools and skills required for proper execution. 4.1.5 Benefits of a Total-Plant Predictive Program A survey of 500 plants that have implemented predictive maintenance methods indicates substantial improvements in reliability, availability, and operating costs. The successful programs included in the survey include a cross-section of industries and provide an overview of the types of improvements that can be expected. Based 70 An Introduction to Predictive Maintenance on the survey results, major improvements can be achieved in maintenance costs, unscheduled machine failures, repair downtime, spare parts inventory, and both direct and indirect overtime premiums. In addition, the survey indicated a dramatic improvement in machine life, production, operator safety, product quality, and overall profitability. Based on the survey, the actual costs normally associated with the maintenance operation were reduced by more than 50 percent. The comparison of maintenance costs included the actual labor and overhead of the maintenance department. It also included the actual materials cost of repair parts, tools, and other equipment required to maintain plant equipment. The analysis did not include lost production time, variances in direct labor, or other costs that should be directly attributed to inefficient maintenance practices. The addition of regular monitoring of the actual condition of process machinery and systems reduced the number of catastrophic, unexpected machine failures by an average of 55 percent. The comparison used the frequency of unexpected machine failures before implementing the predictive maintenance program to the failure rate during the two-year period following the addition of condition monitoring to the program. Projections of the survey results indicate that reductions of 90 percent can be achieved using regular monitoring of the actual machine condition. Predictive maintenance was shown to reduce the actual time required to repair or rebuild plant equipment. The average improvement in mean-time-to-repair (MTTR) was a reduction of 60 percent. To determine the average improvement, actual repair times before the predictive maintenance program were compared to the actual time to repair after one year of operation using predictive maintenance management techniques. The regular monitoring and analysis of machine condition identified the specific failed component(s) in each machine and enabled the maintenance staff to plan each repair. The ability to predetermine the specific repair parts, tools, and labor skills required provided the dramatic reduction in both repair time and costs. The ability to predict machine-train and equipment failures and the specific failure mode provided the means to reduce spare parts inventories by more than 30 percent. Rather than carry repair parts in inventory, the surveyed plants had sufficient lead time to order repair or replacement parts as needed. The comparison included the actual cost of spare parts and the inventory carrying costs for each plant. Prevention of catastrophic failures and early detection of incipient machine and systems problems increased the useful operating life of plant machinery by an average of 30 percent. The increase in machine life was a projection based on five years of operation after implementation of a predictive maintenance program. The calculation included frequency of repairs, severity of machine damage, and actual condition of machinery after repair. A condition-based predictive maintenance program prevents serious damage to machinery and other plant systems. This reduction in damage severity increases the operating life of plant equipment. Benefits of Predictive Maintenance 71 [...]... 72 An Introduction to Predictive Maintenance A side benefit of predictive maintenance is the automatic ability to monitor the meantime-between-failures (MTBF) These data provide the means to determine the most cost-effective time to replace machinery rather than continue to absorb high maintenance costs The MTBF of plant equipment is reduced each time a major repair or rebuild occurs Predictive maintenance. .. Shaft Speed Number of Links in Chain For example: Chain Speed = 25 teeth ¥ 100 rpm 25 00 = = 10 cpm = 10 rpm 25 0 links 25 0 5 .2. 2 Couplings Couplings cannot be monitored directly, but they generate forcing functions that affect the vibration profile of both the driver and driven machine-train component Each 80 An Introduction to Predictive Maintenance coupling should be evaluated to determine the specific... analysis, a key predictive maintenance tool, can be used to determine whether the repairs corrected existing problems and/or created additional abnormal Benefits of Predictive Maintenance 73 behavior before the system is restarted This ability eliminates the need for the second outage that is often required to correct improper or incomplete repairs Data acquired as part of a predictive maintenance program... the ball mill was three weeks, and the repair cost averaged $30 0,000 The addition of predictive maintenance techniques as an outage-scheduling tool reduced the outage to five days and resulted in a total savings of $20 0,000 The predictive maintenance data eliminated the need for many of the repairs that would normally have been included in the maintenance outage Based on the ball mill’s actual condition,... length where the sheaves have different diameters: Drive PC + Driven PC + (2 ¥ Center Distance) 2 PITCH DIAMETER Belt Length = Center Distance Belt Length = Pitch Circumference + (2 ¥ Center Distance) Figure 5–4 Pitch diameter and center-to-center distance between belt sheaves 86 An Introduction to Predictive Maintenance 5 .3 DRIVEN COMPONENTS This module cannot effectively discuss all possible combinations... correct major problems or schedule preventive maintenance rebuilds during annual maintenance outages Predictive data can provide the information required to plan the specific repairs and other activities during the outage One example of this benefit is a maintenance outage scheduled to rebuild a ball mill in an aluminum foundry The normal outage, before predictive maintenance techniques were implemented in... companies are offering premium reductions to plants that have a condition-based predictive maintenance program in effect Several other benefits can be derived from a viable predictive maintenance management program: verification of new equipment condition, verification of repairs and rebuild work, and product quality improvement Predictive maintenance techniques can be used during site acceptance testing to determine... Typically, this deflection results in a vibration frequency at the second (2X) or third (3X) harmonic of shaft speed A narrowband window should be established to monitor the fundamental (1X), second (2X), and third (3X) harmonic of shaft speed With these windows, the energy associated with shaft deflection, or mode shape, can be monitored 5 .3. 3 Generators As with electric-motor rotors, generator rotors always... the second harmonic (2X) of turning speed In extreme cases, the jackshaft deflects further and operates in the third mode When this happens, it generates distinct frequencies at the fundamental (1X), second harmonic (2X), and third harmonic (3X) of turning speed As a rule, narrowband windows should be established to monitor at least these three distinct frequencies (i.e., 1X, 2X, and 3X) In addition, narrowbands... each time the crankshaft completes one full revolution, the total energy of all pistons is displayed at the fundamental (1X) and second harmonic (2X) locations In a four-cycle machine, two 90 An Introduction to Predictive Maintenance complete revolutions ( 720 degrees) are required for all cylinders to complete a full cycle Piston orientations Crankshafts on positive-displacement reciprocating compressors . its annual maintenance budget in predictive maintenance programs, but one-third (33 %) of the plants interviewed in our May 20 00 survey allocate less than 10 percent to predictive maintenance. According. maintenance 64 An Introduction to Predictive Maintenance budget of survey participants ($ 12, 0 53, 000), the average annual savings are about $1.6 million. A successful predictive maintenance program,. insurance to plants that have a viable predictive maintenance program. Predictive Maintenance Costs The average maintenance budget of the plants interviewed was $ 12, 0 53, 000, but included those with budgets

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