Hierarchical Bayesian Fuzzy Inference Nets for Internal Fault Diagnosis of Three Phase Squirrel Cage Induction Motor

A generalized Bayesian inference nets model (GBINM) and a new approach for internal fault detection and diagnosis of three-phase induction motor is proposed. The input data set for designing and testing the fault diagnostic system are acquired through on-site experiment. The preprocessed input data and experts' rich diagnostic experience/knowledge are used to define the membership functions and fault fuzzy sets. With GBINM and the defined fault fuzzy sets, the hierarchical Bayesian fuzzy inference nets are constructed to carry out the complex motor fault diagnostic procedure. The propagation of probability is used to address the uncertainties involved in detecting and diagnosing the motor incipient faults. The immense difficulties of defining and assigning statistical parameters, required for calculating the propagation of probability are effectively solved. The validity and effectiveness of the proposed approach is witnessed clearly from the testing results obtained.