Machine diagnostics that consider both process and condition monitoring data yield superior results.
The past 20 years have seen continuous progress in the technological means available for monitoring the condition of Rotating Equipment. For years, machine protection relied almost exclusively on indirect state, or condition, variables such as vibration. Yet, more recent systems have been taking account of direct mechanical state variables such as piston rod position or thermodynamic variables like pressure volume (p-V) curves for e.g. reciprocating compressors. This is no longer being done exclusively for the purposes of machine protection, but in order to gain the most comprehensive information on the condition of a machine and its components.
Many machine operators are unsatisfied with basic protection mechanisms
Traditionally, the role of machine protection is to prevent damage to components that occur between scheduled maintenance inspections from causing catastrophic damage to a machine. Maintenance personnel will shut down a machine and disassemble it 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. Yet many machine operators are now unsatisfied with these kinds of basic safety mechanisms, where only a single variable (for example, 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 overtime (trending) is discernable. 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.
Various parameters are monitored to obtain the greatest degree of certainty
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. However, this is impossible to do in many cases, so instead various parameters are monitored 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.