The past twenty years have seen continuous progress in the technological means available for monitoring the condition of reciprocating compressors. For years, machine protection relied almost exclusively on indirect state, or condition, variables such as vibration, but more recent systems have been taking account of direct mechanical state variables such as piston rod position or thermodynamic variables like PV curves. This is no longer only being done for purposes of machine protection, but in order to gain the most comprehensive information on the condition of a machine and its components.
Traditionally, the role of machine protection is to prevent damage to components occurring between scheduled maintenance inspections from causing catastrophic damage to a machine. Maintenance personnel will shut down a machine and disassemble it in order to get a comprehensive idea of the state of its components. In such cases, machine protection parameters are usually only determined based on a single threshold value, so that the only available information is either the “OK“ or the “ALARM” status. The operator often lacks any information whatsoever on how the parameter used for machine protection purposes is trending. For example, he or she may not know whether the machine is operating at levels close to or well below an alarm threshold.
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Yet many machine operators are no longer satisfied with these kinds of basic protection mechanisms where only a single variable, e.g. frame vibration, is analyzed. At the very least, machine protection parameters in the 21st century should be recorded in such a way that their evolution over time (trending) is discernable. In order to be able to derive added value from ongoing analysis, a measured variable’s curve has to be evaluated, and machines usually have to be equipped with a finer net of sensors to obtain information on the condition of as many components as feasible.
Ideally, the state of any component can be judged based on a single parameter. The state of a bearing, for example, could be determined solely and unequivocally based on its temperature. This, however, is impossible to do in many cases, so instead various parameters are monitored in order to obtain the greatest degree of certainty on a machine’s state. The more complex a machine, and the more components need monitoring, the more parameters are drawn into the picture. However, measurement variables and analyses in ever greater numbers also make the interpretation of parameter developments more difficult. Often, an expert needs to be brought in to reliably interpret the plethora of monitoring parameters and assess a machine’s state. Proper instrumentation, often referred to as an “expert system,” is likewise required.
This is where automated diagnosis systems come into play: They provide the expert with a kind of “artificial intelligence” that can automatically diagnose a machine based on the data monitored. How detailed this diagnosis is, for example in terms of which component is faulty and possible remedies, will depend on the number of parameters being monitored and on the quality of the diagnostic method. In recent years, PROGNOST Systems, in collaboration with operators, has acquired a great deal of experience in the area of implementing diagnostic systems, with certain diagnostic methods proving to be more effective than others
What is automated diagnosis?
In today’s world, the term diagnosis is widely used in various different disciplines. Originally the term diagnostics (from dia: through, throughout, separated; and gnosis: knowledge) referred to the process of acquiring knowledge for the purpose of distinguishing between objects. The term is often extended to mean not only the process of identifying features but also the adoption of measures. Machine diagnostics can also be understood in this way. The purpose of technical diagnosis is to detect faults early enough to be able to infer suitable corrective measures, thereby increasing a system’s safety, availability, lifespan and reliability, while also minimizing maintenance and operating costs. A fault refers to a deviation from a normal state, either on account of operational conditions or failures, outages, or defects.
A clear distinction needs to be drawn between machine diagnosis and machine monitoring or analysis. In machine diagnosis, data collected by machine monitoring systems is evaluated in terms of what the typical features associated with (incipient) faults are. The quality of this diagnosis depends on two factors:
- the measurement data collected (type, number, location)
- the method used for feature acquisition
At the feature acquisition stage, features relevant to fault detection are extracted from the plenty of measurement data collected. The fundamental assumption here is that a fault will also be reflected by changed features, i.e. that a defect will indeed cause a change in at least one of the parameters.
