Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesian Modeling

Dynamic neural models provide an attractive means of fault detection and isolation in industrial process. One approach is to create a neural model to emulate normal system behavior and additional models to emulate various fault conditions. The neural models are then placed in parallel with the system to be monitored, and fault detection is achieved by comparing the outputs of the neural models with the real system outputs. Neural network training is achieved using simultaneous perturbation stochastic approximation (SPSA). Fault classification is based on a simple threshold test of the residuals formed by subtracting each neural model output from the corresponding output of the real system. We present a new approach based on this well known scheme where a Bayesian network is used to evaluate the residuals. The approach is applied to fault detection in a three-phase induction motor.