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AMAZON multi-meters discounts AMAZON oscilloscope discounts Training and Technical Support: Training and technical support are critical to the success of your predictive maintenance program. Regardless of the techniques or systems selected, your staff will have to be trained. This training will take two forms: system users' training and application knowledge for the specific techniques included in your program. Few, if any, of the existing staff will have the knowledge base required to implement the various predictive maintenance techniques discussed in the preceding sections. None will understand the operation of the systems that are purchased to support your program. Many of the predictive systems’ manufacturers are strictly hardware and software oriented. Therefore, they offer minimal training and no application training or technical support. Few plants can achieve minimum benefits from predictive maintenance without training and some degree of technical support. It’s therefore imperative that the selected system or system vendors provide a comprehensive support package that includes both training and technical support. System Cost: Cost should not be the primary deciding factor in system selection. The capabilities of the various systems vary greatly, and so does the cost. Care should be taken to ensure a fair comparison of the total system capability and price before selecting your system. For example, vibration-based systems are relatively competitive in price. The general spread is less than $1,000 for a complete system; however, the capabilities of these systems are not comparable. A system that provides minimum capability for vibration monitoring will be about the same price as one that provides full vibration monitoring capability and provides process parameter, visual inspection, and point of-use thermography. Operating Cost: The real cost of implementing and maintaining a predictive maintenance program is not the initial system cost. Rather, it’s the annual labor and overhead costs associated with acquiring, storing, trending, and analyzing the data required to determine the operating condition of plant equipment. This is the area where predictive maintenance systems have the greatest variance in capability. Systems that fully automate data acquisition, storing, and so on will provide the lowest operating costs. Manual systems and many of the low-end microprocessor-based systems require substantially more labor to accomplish the minimum objectives required by predictive maintenance. The list of users will again help you determine the long-term cost of the various systems. Most users will share their experience, including a general indication of labor cost. The Microprocessor: The data logger or microprocessor selected by your predictive maintenance program is critical to the program's success. A wide variety of systems are on the market, ranging from handheld overall value meters to advanced analyzers that can provide an almost unlimited amount of data. The key selection parameters for a data acquisition instrument should include the expertise required to operate, accuracy of data, type of data, and staffing required to meet the program demands. Expertise Required to Operate. One of the objectives for using microprocessor-based predictive maintenance systems is to reduce the expertise required to acquire error free, useful vibration and process data from a large population of machinery and systems within a plant. The system should not require user input to establish maximum amplitude, measurement bandwidths, filter settings, or allow free-form data input. All of these functions force the user to be a trained analyst and will increase the cost and time required to routinely acquire data from plant equipment. Many of the micro processors on the market provide easy, menu-driven measurement routes that lead the user through the process of acquiring accurate data. The ideal system should require a single key input to automatically acquire, analyze, alarm, and store all pertinent data from plant equipment. This type of system would enable an unskilled user to quickly and accurately acquire all of the data required for predictive maintenance. Accuracy of Data. The microprocessor should be able to automatically acquire accurate, repeatable data from equipment included in the program. The elimination of user input on filter settings, bandwidths, and other measurement parameters would greatly improve the accuracy of acquired data. The specific requirements that determine data accuracy will vary depending on the type of data. For example, a vibration instrument should be able to average data, reject spurious signals, auto-scale based on measured energy, and prevent aliasing. The basis of frequency-domain vibration analysis assumes that we monitor the rotational frequency components of a machine-train. If a single block of data is acquired, nonrepetitive or spurious data can be introduced into the database. The microprocessor should be able to acquire multiple blocks of data, average the total, and store the averaged value. Basically, this approach enables the data acquisition unit to automatically reject any spurious data and provide reliable data for trending and analysis. Systems that rely on a single block of data will severely limit the accuracy and repeatability of acquired data. They will also limit the benefits that can be derived from the program. The microprocessor should also have electronic circuitry that automatically checks each data set and block of data for accuracy and rejects any spurious data that may occur. Auto-rejection circuitry is available in several of the commercially available systems. Coupled with multiple block averaging, this auto-rejection circuitry ensures maximum accuracy and repeatability of acquired data. A few of the microprocessor based systems require the user to input the maximum scale that is used to acquire data. This will severely limit the accuracy of data. Setting the scale too high will prevent acquisition of factual machine data, whereas too low a setting won’t capture any high-energy frequency components that may be generated by the machine-train. Therefore, the microprocessor should have auto scaling capability to ensure accurate data. Vibration data can be distorted by high frequency components that fold over into the lower frequencies of a machine's sig nature. Even though these aliased frequency components appear real, they don’t exist in the machine. Low-frequency components can also distort the midrange signature of a machine in the same manner as high frequency. The microprocessor selected for vibration should include a full range of anti-aliasing filters to prevent distortion of machine signatures. The features illustrated in the example also apply to nonvibration measurements. For example, pressure readings require the averaging capability to prevent spurious readings. Slight fluctuations in line or vessel pressure are normal in most plant systems. Without the averaging capability, the microprocessor cannot acquire an accurate reading of the true system pressure. Alert and Alarm Limits. The microprocessor should include the ability to automatically alert the user to changes in machine, equipment, or system condition. Most of the predictive maintenance techniques rely on a change in the operating condition of plant equipment to identify an incipient problem. Therefore, the system should be able to analyze data and report any change in the monitoring parameters that were established as part of the database development. Predictive maintenance systems use two methods to detect a change in the operating condition of plant equipment: static and dynamic. Static alert and alarm limits are pre selected thresholds that are downloaded into the microprocessor. If the measurement parameters exceed the preset limit, an alarm is displayed. This type of monitoring does not consider the rate of change or historical trends of a machine and therefore cannot anticipate when the alarm will be reached. The second method uses dynamic limits that monitor the rate of change in the measurement parameters. This type of monitoring can detect minor deviations in the rate that a machine or system is degrading and anticipate when an alarm will be reached. The use of dynamic limits will greatly enhance the automatic diagnostic capabilities of a predictive maintenance system and reduce the manual effort required to gain maximum benefits. Data Storage. The microprocessor must be able to acquire and store large amounts of data. The memory capacities of the various predictive maintenance systems vary. At a minimum, the system must be able to store a full eight hours of data before transferring it to the host computer. The actual memory requirements will depend on the type of data acquired. For example, a system used to acquire vibration data would need enough memory to store about 1,000 overall readings or 400 full signatures. Process monitoring would require a minimum of 1,000 readings to meet the minimum requirements. Data Transfer. The data acquisition unit won’t be used for long-term data storage. Therefore, it must be able to reliably transfer data into the host computer. The actual time required to transfer the microprocessor's data into the host computer is the only nonproductive time of the data acquisition unit. It cannot be used to acquire additional data during the data transfer operation. Therefore, the transfer time should be kept to a minimum. Most of the available systems use an RS 232 communication protocol that allows data transfer at rates of up to 19,200 baud. The time required to dump the full memory of a typical microprocessor can be 30 minutes or more. Some of the systems have incorporated an independent method of transferring data that eliminates the dead time altogether. These systems transfer stored data from the data logger into a battery-backed memory, bypassing the RS 232 link. Using this technique, data can be transferred at more than 350,000 baud and will reduce the non productive time to a few minutes. The microprocessor should also be able to support modem communication with remote computers. This feature will enable multiple plant operation and direct access to third-party diagnostic and analysis support. Data can be transferred anywhere in the world using this technique. Not all predictive maintenance systems use a true RS 232 communications protocol or support modem communications. These systems can severely limit the capabilities of your program. The various predictive maintenance techniques will add other specifications for an acceptable data acquisition unit. The Host Computer: The host computer provides all of the data management, storage, report generation, and analysis capabilities of the predictive maintenance program. Therefore, care should be exercised during the selection process. This is especially true if multiple technologies will be used within the predictive maintenance program. Each predictive maintenance system will have a unique host computer specification that will include hardware configuration, computer operating system, hard disk memory requirements, and many others. This can become a serious if not catastrophic problem. You may find that one system requires a special printer that is not compatible with other programs to provide hard copies of reports or graphic data. One program may be compatible with PC-DOS, whereas another requires a totally different operating program. Therefore, you should develop a complete computer specification sheet for each of the predictive maintenance systems that will be used. A comparison of the list will provide a compatible computer configuration to support each of the techniques. If this is not possible, you may have to reconsider your choice of techniques. Computers, like plant equipment, sometimes fail. Therefore, the use of a commercially available computer is recommended. The critical considerations include availability of repair parts and local vendor support. Most of the individual predictive maintenance techniques don’t require a dedicated computer. Therefore, there is usually sufficient storage and computing capacity to handle several, if not all, of the required techniques and still leave room for other support programs (e.g., word processing, database management). Use of commercially available PCs provides the user with the option of including these auxiliary programs in the host computer. The actual configuration of the host computer will depend on the specific requirements of the predictive maintenance techniques that will be used. Therefore, we won’t attempt to establish guidelines for selection. The Software: The software program provided with each predictive maintenance system is the heart of a successful program. It’s also the most difficult aspect to evaluate before purchase. The methodology used by vendors of predictive maintenance systems varies greatly. Many appear to have all of the capabilities required to meet the demands of a total-plant predictive maintenance program; however, on close inspection, usually after purchase, they are found to be lacking. Software is also the biggest potential limiting factor of a program. Even though all vendors use some form of formal computer language (e.g. , fortran, Cobol, Basic), their programs are normally not interchangeable with other programs. The apparently simple task of having one computer program communicate with another can often be impossible. This lack of compatibility among various computer programs prohibits transferring a predictive maintenance database from one vendor's system into a system manufactured by another vendor. The result is that once a predictive maintenance program is started, a plant cannot change to another system without losing the data already developed in the initial program. At a minimum, the software program should provide automatic database management, automatic trending, automatic report generation, and simplified diagnostics. As in the case of the microprocessor used to acquire data, the software must be user-friendly. User-Friendly Operation. The software program should be menu-driven with clear online user instructions. The program should protect the user from distorting or deleting stored data. Some of the predictive maintenance systems are written in DBASE software shells. Even though these programs provide a knowledgeable user with the ability to modify or customize the structure of the program (e.g., report formats), they also provide the means to distort or destroy stored data. A single key entry can destroy years of stored data. Protection should be built into the program to limit the user's ability to modify or delete data and to prevent accidental database damage. The program should have a clear, plain language user's manual that provides the logic and specific instructions required to set up and use the program. Automatic Trending. The software program should be capable of automatically storing all acquired data and updating the trends of all variables. This capability should include multiple parameters, not just a broadband or single variable. This will enable the user to display trends of all variables that affect plant operations. Automatic Report Generation. Report generation will be an important part of the predictive maintenance program. Maximum flexibility in format and detail is important to program success. The system should be able to automatically generate reports at multiple levels of detail. At a minimum, the system should be able to report: • A listing of machine-trains or other plant equipment that has exceeded or is projected to exceed one or more alarm limits-The report should also provide a projection to probable failure based on the historical data and last measurement. • A listing of missed measurement points, machines overdue for monitoring, and other program management information-These reports act as reminders to ensure that the program is maintained properly. • A listing of visual observations-Most of the microprocessor-based systems support visual observations as part of their approach to predictive maintenance. This report provides hard copies of the visual observations as well as maintaining the information in the computer's database. • Equipment history reports-These reports provide long-term data on the condition of plant equipment and are valuable for analysis. Simplified Diagnostics. Identification of specific failure modes of plant equipment requires manual analysis of data stored in the computer's memory. The software program should be able to display, modify, and compare stored data in a manner that simplifies the analysis of the actual operating condition of the equipment. At a minimum, the program should be able to directly compare data from similar machines, normalize data into compatible units, and display changes in machine parameters (e.g., vibration, process). Transducers: The final portion of a predictive maintenance system is the transducer that will be used to acquire data from plant equipment. Because we have assumed that a micro processor-based system will be used, we will limit this discussion to those sensors that can be used with this type of system. Acquiring accurate vibration and process data will require several types of transducers. Therefore, the system must be capable of accepting input from as many different types of transducers as possible. Any restriction of compatible transducers can become a serious limiting factor. This should eliminate systems that will accept inputs from a single type of transducer. Other systems are limited to a relatively small range of transducers that will also prohibit maximum utilization of the system. Selection of the specific transducers required to monitor the mechanical condition (e.g., vibration, flow, pressure) also deserves special consideration and will be discussed later. DATABASE DEVELOPMENT Each of the predictive maintenance technologies requires a logical method of acquiring, storing, evaluating, and trending massive amounts of data over an extended period. Therefore, a comprehensive database that is based on the actual requirements of critical plant systems must be developed for the predictive maintenance program. At a minimum, these databases should include the following capabilities: • Establishing data acquisition frequency • Setting up analysis parameters • Setting boundaries for signature analysis • Defining alert and alarm limits • Selecting transducers Establishing Data Acquisition Frequency During the implementation stage of a predictive maintenance program, all classes of machinery should be monitored to establish a valid baseline data set. Full vibration signatures should be acquired to verify the accuracy of the database setup and deter mine the initial operating condition of the machinery. Because a comprehensive program will include trending and projected time-to-failure, multiple readings are required on all machinery to provide sufficient data for the microprocessor to develop trend statistics. During this phase, measurements are usually acquired every two weeks. After the initial or baseline evaluation of the machinery, the frequency of data collection will vary depending on the classification of the machine-trains. Class I machines should be monitored on a two- to three-week cycle; Class II on a three- to four-week cycle; Class III on a four- to six-week cycle; and Class IV on a six- to ten week cycle. This frequency can, and should, be adjusted for the actual condition of specific machine-trains. If the rate of change of a specific machine indicates rapid degradation, you should either repair it or at least increase the monitoring frequency to prevent catastrophic failure. The recommended data acquisition frequencies are the maximum that will ensure prevention of most catastrophic failures. Less frequent monitoring will limit the ability of the program to detect and prevent unscheduled machine outages. To augment the vibration-based program, you should also schedule the nonvibration tasks. Bearing cap, point-of-use infrared measurements, visual inspections, and process parameters monitoring should be conducted in conjunction with the vibration data acquisition. Full infrared imaging or scanning on the equipment included in the vibration-monitoring program should be conducted on a quarterly basis. In addition, full thermal scanning of critical electrical equipment (e.g., switch gear, circuit breakers) and all heat transfer systems (e.g., heat exchangers, condensers, process piping) that are not in the vibration program should be conducted quarterly. Lubricating oil samples from all equipment included in the program should be taken on a monthly basis. At a minimum, a full spectrographic analysis should be conducted on these samples. Wear particle or other analysis techniques should be used on an as-needed basis. Setting Up Analysis Parameters The next step in establishing the program's database is to set up the analysis parameters that will be used to routinely monitor plant equipment. Each of these parameters will be based on the specific machine-train requirements that we have just developed. For non-mechanical equipment, the analysis parameter set usually consists of the calculated values derived from measuring the thermal profile or process parameters. Each classification of equipment or system will have its own unique analysis parameter set. Setting Boundaries for Signature Analysis All vibration-monitoring systems have finite limits on the resolution or ability to graphically display the unique frequency components that make up a machine's vibration signature. The upper limit (Fmax) for signature analysis should be set high enough to capture and display enough data so that the analyst can determine the operating condition of the machine-train, but no higher. Most vibration-based predictive maintenance systems are capable of resolutions up to 12,000 lines; the tendency is to acquire high-resolution signatures as part of the routine monitoring sequence. Although this approach is technically viable, the use of high-resolution signatures (i.e., 1,000 lines or higher) dramatically increases the memory required to store acquired data. Because most of the data collectors have limited memory, this will limit the number of signatures that can be stored without uploading them to the host computer. The time lost because of the combined use of high-resolution signatures and the limited data collector memory will severely hamper the program's effectiveness. Effective programs limit routine monitoring to a maximum of 800 lines of resolution. This resolution will provide enough definition to detect incipient problems without the negatives associated with higher resolutions. To determine the impact of resolution, calculate the display capabilities of your system. For example, a vibration signature with a maximum frequency (Fmax) of 1,000Hz taken with an instrument capable of 400 lines of resolution would result in a display in which each line will be equal to 2.5Hz or 150 rotations per minute (rpm). Any frequencies that fall between 2.5 and 5.0 (i.e., the next displayed line) would be lost. Defining Alert and Alarm Limits The methods of establishing and using alert and alarm limits vary depending on the particular vibration-monitoring system that you select. These systems usually use either static or dynamic limits to monitor, trend, and alarm measured vibration. We won’t attempt to define the different dynamic methods of monitoring vibration severity in this book. We will, however, provide a guideline for the maximum limits that should be considered acceptable for most plant mechanical equipment. The systems that use dynamic alert and alarm limits base their logic (correctly in my opinion) on the concept that the rate of change of vibration amplitude is more important than the actual level. Any change in the vibration amplitude is a direct indication that a corresponding change in the machine's mechanical condition has occurred; however, there should be a maximum acceptable limit (i.e., absolute fault). The accepted severity limit for casing vibration is 0.628 inches per second, ips-Peak (velocity). This unfiltered broadband value normally represents a bandwidth between 10 and 10,000Hz. This value can be used to establish the absolute fault or maximum vibration amplitude for broadband measurement on most plant machinery. The exception would be machines with running speeds below 1,200 rpm or above 3,600 rpm. Narrowband limits (i.e., discrete bandwidth within the broadband) can be established using the following guideline: Normally, 60 to 70 percent of the total vibration energy will occur at the true running speed of the machine. Therefore, the absolute fault limit for a narrowband established to monitor the true running speed would be 0.42 ips-Peak. This value can also be used for any narrowbands established to monitor frequencies below the true running speed. Absolute fault limits for narrowbands established to monitor frequencies above running speed could be ratioed using the 0.42 ips-Peak limit established for the true running speed. For example, the absolute fault limit for a narrowband created to monitor the blade-passing frequency of a fan with 10 blades would be set at 0.042 or 0.42 divided by 10. Narrowband designed to monitor high-speed components (i.e., above 1,000Hz) should have an absolute fault of 3.0 inches per second, g's-Peak (acceleration). Rolling-element bearings, based on factor recommendations, have an absolute fault limit of 0.01 ips-Peak. Sleeve or fluid-film bearings should be watched closely. If the fractional components that identify oil whip or whirl are present at any level, the bearing is subject to damage and the problem should be corrected. Nonmechanical equipment and systems will normally have an absolute fault limit that specifies the maximum recommended level for continued operation. Equipment or systems vendors can usually provide this information. Selecting Transducers The type of transducers and data acquisition techniques that you will use for the program is the final critical factor that can determine the success or failure of your program. Their accuracy, proper application, and mounting will determine whether valid data will be collected. The optimum predictive maintenance program developed in earlier sections is predicated on vibration analysis as the principle technique for the program. It’s also the most sensitive to problems created by using the wrong transducer or mounting technique. Three basic types of vibration transducers can be used to monitor the mechanical condition of plant machinery: displacement probe, velocity transducer, and accelerometers. Each has specific applications and limitations within the plant. Displacement Probes: Displacement, or eddy-current, probes are designed to measure the actual movement (i.e., displacement) of a machine's shaft relative to the probe. Therefore, the displacement probe must be rigidly mounted to a stationary structure to gain accurate, repeatable data. Permanently mounted displacement probes will provide the most accurate data on machines with a low-relative to the casing and support structure-rotor weight. Turbines, large process compressors, and other plant equipment should have displacement transducers permanently mounted at key measurement locations to acquire data for the program. The useful frequency range for displacement probes is from 10 to 1,000Hz or 600 to 60,000 rpm. Frequency components below or above this range will be distorted and therefore unreliable for determining machine condition. The major limitation with displacement or proximity probes is cost. The typical cost for installing a single probe, including a power supply, signal conditioning, and so on, will average $1,000. If each machine in your program requires 10 measurements, the cost per machine will be about $10,000. Using displacement transducers for all plant machinery will dramatically increase the initial cost of the program. Displacement data are normally recorded in terms of mils or .001 inch, peak-to-peak. This valve expresses the maximum deflection or displacement off the true centerline of a machine's shaft. Velocity Transducers: Velocity transducers are electromechanical sensors designed to monitor casing or relative vibration. Unlike the displacement probe, velocity transducers measure the rate of displacement, not actual movement. Velocity data are normally expressed in terms of inches per second, peak (ips-peak) and are perhaps the best method of expressing the energy created by machine vibration. Velocity transducers, like displacement probes, have an effective frequency range of about 10 to 1,000Hz. They should not be used to monitor frequencies below or above this range. The major limitation of velocity transducers is their sensitivity to mechanical and thermal damage. Normal plant use can cause a loss of calibration, and therefore a strict recalibration program must be used to prevent distortion of data. Velocity transducers should be recalibrated at least every six months. Even with periodic recalibration, programs using velocity transducers are prone to bad or distorted data that results from loss of calibration. Accelerometers: Accelerometers use a piezoelectric crystal to convert mechanical energy into electrical signals. Data acquired with this type of transducer are relative vibration, not actual displacement, and are expressed in terms of g's or inches per second. Acceleration is perhaps the best method of determining the force created by machine vibration. Accelerometers are susceptible to thermal damage. If sufficient heat is allowed to radiate into the crystal, it can be damaged or destroyed; however, because the data acquisition time using temporary mounting techniques is relatively short (less than 30 seconds), thermal damage is rare. Accelerometers don’t require a recalibration program to ensure accuracy. The effective range of general-purpose accelerometers is from about 1 to 10,000Hz. Ultrasonic accelerometers are available for frequencies up to 1MHz. Machine data above 1,000Hz or 60,000 rpm should be taken and analyzed in acceleration or g's. Mounting Techniques: Predictive maintenance programs using vibration analysis must have accurate, repeat able data to determine the operating condition of plant machinery. In addition to the transducer, three factors will affect data quality: measurement point, orientation, and compressive load. Key measurement point locations and orientation to the machine's shaft were selected as part of the database setup to provide the best possible detection of incipient machine-train problems. Deviation from the exact point or orientation will affect the accuracy of acquired data. Therefore, it’s important that every measurement through out the life of the program be acquired at exactly the same point and orientation. In addition, the compressive load or downward force applied to the transducer should be the same for each measurement. For accuracy of data, a direct mechanical link to the machine's casing or bearing cap is necessary. Slight deviations in this load will induce errors in the amplitude of vibration and may create false frequency components that have nothing to do with the machine. The best method of ensuring that these three factors are the same each time is to hard mount vibration transducers to the selected measurement points. This technique will guarantee accuracy and repeatability of acquired data, but it will also increase the initial cost of the program. The average cost of installing a general-purpose accelerometer will be about $300 per measurement point or $3,000 for a typical machine-train. To eliminate the capital cost associated with permanently mounting transducers, a well-designed quick-disconnect mounting can be used. This mounting technique permanently mounts a quick-disconnect stud, with an average cost of less than $5, at each measurement point location. A mating sleeve, built into a general-purpose accelerometer, is then used to acquire accurate, repeatable data. A well-designed quick disconnect mounting technique provides the same accuracy and repeatability as the permanent mounting technique but at a much lower cost. The third mounting technique that can be used is a magnetic mount. For general purpose use, below 1,000Hz, a transducer can be used in conjunction with a magnetic base. Even though the transducer/magnet assembly will have a resonant frequency that may provide some distortion to acquired data, this technique can be used with marginal success. Because the magnet can be placed anywhere on the machine, it won’t guarantee that the exact location and orientation is maintained on each measurement. The final method used by some plants to acquire vibration data is handheld transducers. This approach is not recommended if any other method can be used. Handheld transducers won’t provide the accuracy and repeatability required to gain maximum benefit from a predictive maintenance program. If this technique must be used, extreme care should be exercised to ensure that the exact point, orientation, and compressive load is used for every measurement point. GETTING STARTED The steps we have defined provide guidelines for establishing a predictive maintenance database. The only steps remaining to get the program started are to establish measurement routes and take the initial or baseline measurements. Remember, the predictive maintenance system will need multiple data sets to develop trends on each machine. With this database, you will be able to monitor the critical machinery in your plant for degradation and begin to achieve the benefits that predictive maintenance can provide. The actual steps required to implement a database will depend on the specific predictive maintenance system selected for your program. The system vendor should provide the training and technical support required to properly develop the database with the information discussed in the preceding sections. Training One of the key issues that has severely limited both equipment reliability and predictive maintenance programs is the lack of proper training of technicians, analysts, and engineers. Most programs have limited training to a few days or a few weeks of training that is typically provided by the system vendor. For the most part, these training programs are limited to use of the vendor's system and perhaps a cursory under standing of data acquisition and analysis techniques. Even the few plants that invest in vibration, thermography, or tribology training tend to limit the duration and depth of training provided to their predictive teams. Contrary to popular opinion, the skills required to interpret the data provided by these predictive maintenance technologies cannot be acquired in a few three- to five-day courses. I have used these technologies for more than 30 years and still learn some thing new almost every day. In addition to the limitations imposed by companies that won’t authorize sufficient training for their predictive maintenance teams, there is also a severe lack of viable predictive training courses. If we exclude the overview courses offered by the system vendors, only one or two companies offer any training in predictive maintenance technologies. With few exceptions, these courses are less than adequate and don’t provide the level of training required for a new analyst/engineer to master the use of these technologies. Generally, these courses are either pure theory and have little practical use in the field or are basic introductions to one or more techniques, such as vibration or infrared interpretation. Few, if any, of these courses are designed to address the unique requirements of your plant. For example, vibration courses are limited to general machinery, such as compressors, pumps, and fans, and exclude the process systems that are unique to your industry or plant. Although these common machines are important, your predictive maintenance team must be taught to analyze the critical processes, such as paper machines, rolling mills, and presses, that you rely on to produce your products and revenue. Over the past 30 years, we have trained several thousand predictive maintenance analysts and reliability engineers. We have found that a minimum of 13 to 26 weeks of formal training, along with a similar period of supervised practical application, is required before a new predictive maintenance engineer or analyst can become proficient in the use of the three basic technologies used in most predictive maintenance programs. A significant difference exists between the 5 to 15 days of training that most predictive analysts receive and the minimum level required to use basic predictive maintenance tools. How can you close the gap without an excessive investment? Unfortunately, the answer is that you cannot. With the training courses that are avail able in today's market, you have only two options: (1) you either restrict training to the limited number of short courses that are available, or (2) you hire a consulting/training company to provide a long-term, plant-specific training program for your predictive maintenance staff. The former option costs less, but will severely limit your benefits. The latter option is expensive and will require a long-term investment, but will provide absolute assurance that your predictive maintenance program will generate maximum improvements in equipment reliability and profitability. An ideal third option would be to use interactive training programs that would permit new analysts to learn predictive maintenance skills at their own pace and without the expense of formal instructor training. From our viewpoint, there is a real need for an interactive training program that can provide comprehensive, industry-specific predictive maintenance training. The computer technology exists to support this approach, but someone must develop the courses that are needed to provide this type of comprehensive training program. Successful completion of this critical phase of creating a total-plant predictive maintenance program will require a firm grasp of the operating dynamics of plant machinery, systems, and equipment. Normally, some if not all of this knowledge exists within the plant staff; however, the knowledge may not be within the staff selected to implement and maintain the predictive maintenance program. In addition, a good working knowledge of the predictive maintenance techniques and systems that will be included in the program is necessary. This knowledge probably does not exist within current plant staff. Therefore, training-before attempting to establish a program-is strongly recommended. The minimum recommended level of training includes user training for each predictive maintenance system that will be used, a course on machine dynamics, and a basic theory course on each of the techniques that will be used. In some cases, the systems vendors can provide all of these courses. If not, several companies and professional organizations offer courses on most nondestructive testing techniques. Technical Support The labor and knowledge required to properly establish a predictive maintenance program is often too much for plant staff members to handle. To overcome this problem, the initial responsibility for creating a viable, total-plant program can be contracted to a company that specializes in this area. A few companies provide full consulting and engineering services directed specifically toward predictive maintenance. These companies have the knowledge required and years of experience. They can provide all of the labor required to implement a full-plant program and normally can reduce total time required to get the program up and running. Caution should be used in selecting a contractor to provide this startup service. Check references very carefully. |