Application of Inductive Monitoring System for equipment condition monitoring

In most industries model based reasoning techniques are used as the monitoring and diagnosis scheme for process faults. But even though model-based reasoning is an excellent tool for performing system monitoring, it is often difficult and time consuming to build the right model for the system. Inductive Monitoring System (IMS) is a technique that automatically produces health monitoring knowledge bases for systems that are either difficult to model or which require models that are too complex to use for real time monitoring. The technique automatically defines groups of consistent system parameter data by examining nominal system data and generates a normal operation baseline. It uses a clustering algorithm to form groups of nominal values for sets of related parameters. The nominal values generate constraints on those parameter values that should hold during nominal operation. During abnormal operation, the constraints set on the parameter values are broken and IMS automatically identifies the anomalous behavior of the system. IMS can also provide a statistically weighted measure of the deviation of current system behavior from the normal operation baseline. The advantage of using IMS is that it is fast and simple yet very effective. Each of the clusters generated by IMS represents a different mode of operation of the plant. Abnormal behavior can also be grouped into clusters and identified as specific failure mode. This paper explains the application of IMS for monitoring industrial processes with specific case studies concerned with monitoring of rotating equipment.