Relevance to PdM of Rotating Equipment
All the initiatives in industrial IoT serve two basic purposes:
- Process & System Usage Optimization
- Predictive Maintenance
Since this article is about Predictive & Prescriptive Maintenance (PdM), we will focus on what is useful to achieve that. After scouring through tonnes of resources, I feel following are the major tasks in Predictive Maintenance:
- Anomaly Detection
- Fault Characterization
- Remaining Time to Failure Determination
An anomaly can either be due to a process or system upset or due to an actual fault in rotating machinery. To accurately characterize an anomaly, whether it is due to a process upset or due to an actual fault or a combination of both, is probably the biggest challenge for the AI algorithms behind the plethora of IIoT software (sold by “reliability philosophers”).
A rise in vibration, sound, current or RPM (input characteristics) can be correlated to a rise in flow rate, pressure or temperature (output characteristics). If the correlation is really obvious, this is basically a process upset. However, just monitoring the output characteristics can also tell whether the anomaly is a process upset or a machine fault. This is a topic for my future articles. Thus, input characteristics are not really necessary for anomaly detection.
The fault characterization & Remaining Time to Failure prediction can only be achieved through analytics on output characteristics. Input characteristics being range-bound will not really show any trend (like output characteristic) that signals an increasing severity like below: