A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytical problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II, case study experiments with a d.c. motor, centrifugal pump, a heat exchanger and an industrial robot show practical results of the model based fault diagnosis.
Fault diagnosis of transformer using association rule mining and knowledge base
