Predictive maintenance training

The only useful function of broadband analysis is longterm trending of the gross overall condition of machinery. Typically, sets of alert/alarm limits are established to monitor the overall condition of the machine-trains in a predictive maintenance program. However, this approach has limited value and, when used exclusively, severely limits the ability to achieve the full benefit of a comprehensive program. 

Reference or baseline data sets should be acquired for each machine-train or process system to be included in a predictive maintenance program when the machine is installed or after the first scheduled maintenance once the program is established. These data sets can be used as a reference or comparison for all future measurements. However, such data sets must be representative of the normal operating condition of each machine-train. Three criteria are critical to the proper use of baseline comparisons reset after maintenance, proper identification, and process envelope. 

Typically, a machine-train s vibration signature is made up of vibration components with each component associated with one or more of the true running speeds within the machine-train. Because most machinery problems show up at or near one or more of the running speeds, the narrowband capability is very beneficial in that high-resolution windows can be preset to monitor the running speeds. However, many of the microprocessor-based predictive maintenance systems available do not have narrowband capability. Therefore, care should be taken to ensure that the system utilized does have this capability. 

This limitation prohibits the use of most microprocessor-based vibration analyzers for complex machinery or machines with variable speeds. Single-channel data-acquisition technology assumes the vibration profile generated by a machine-train remains constant throughout the data-acquisition process. This is generally true in applications where machine speed remains relatively constant (i.e., within 5 to fO rpm). In this case, its use does not severely limit diagnostic accuracy and can be effectively used in a predictive-maintenance program. 

A process information sheet (PIS) should be developed for each machine-train and production process that is to be included in a predictive-maintenance program. These data sheets should include all process variables that affect the dynamics and vibration profiles of the monitored components. 

This change in vibration energy can be clearly observed in trend data acquired from machine-trains. A sawtooth trend is common to most predictive-maintenance programs, which can be directly attributed to variations in load. The only way to compensate for load variations is to track the actual load associated with each data set. 

A series of baseline or reference data sets should be taken for each machine-train included in a predictive-maintenance program. These data sets are necessary for future use as a reference point for trends, time traces, and FFT signatures that are collected over time. Such baseline data sets must be representative of the normal... 

Spectrographic analysis allows accurate, rapid measurements of many of the elements present in lubricating oil. These elements are generally classified as wear metals, contaminates, or additives. Some elements can be listed in more than one of these classifications. Standard lubricating oil analysis does not attempt to determine the specific failure modes of developing machine-train problems. Therefore, additional techniques must be used as part of a comprehensive predictive maintenance program. 

Accuracy Decisions on machine-train or plant system condition will be made based on the data acquired and reported by the predictive maintenance system. It must be accurate and repeatable. Errors can be input by the microprocessor and software as well as the operators. The accuracy of commercially available predictive maintenance system varies. While most will provide at least minimum acceptable accuracy, some are well below the acceptable level. 

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 is therefore imperative that the selected system or system vendors provide a comprehensive support package that includes both training and technical support. 

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 both the cost and time required to routinely acquire data from plant equipment. Many of the microprocessors 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. 

Successful rotating equipment Predictive Maintenance Programs require several elements. Typical elements include an effective lubrication program with oil analysis to detect residual metal particles, thermography, machine monitoring instrumentation, repair specifications, repair history records, advanced training of mechanics, and the use of data management computers. [10]... 

Implementation Routes and timing are established, people are trained, and the program is implemented. Readings are taken, analyses are performed, and recommendations for action are provided to maintenance, engineering, and operations. A strict condition-monitoring discipline will lead a plant to a successful predictive maintenance environment where people at all levels of a plant will be able to visualize the resultant equipment saves. ... 

Train plant management and workforce in the importance of preventive and predictive maintenance. 

Monitoring the trends of a machine-train or process system will provide the ability to prevent most catastrophic failures. The trend is similar to the bathtub curve used to schedule preventive maintenance. The difference between the preventive and predictive bathtub curve is that the latter is based on the actual condition of the equipment, not a statistical average. 

The system s vulnerability to errors in manufacture, maintenance and operation. These cause a substantial proportion of accidents. In order to reduce the likelihood of such accidents, it is necessary to make an analysis of the design to determine those features of it which are vulnerable to error and to try to eliminate these, or to ensure that instructions, checks, training, etc., can be relied upon to safeguard against the predictable errors (Lloyd and Tye, 1982, p. 111). 

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