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 syste...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2010-03, Vol.18 (2), p.430-437
Main Authors: Hyun Cheol Cho, Knowles, J., Fadali, M.S., Kwon Soon Lee
Format: Article
Language:English
Subjects:
A.c. Machines
Applied sciences
Approximation
Bayesian analysis
Bayesian methods
Biomedical engineering
Computer science
control theory
systems
Condition monitoring
Control theory. Systems
Dynamic Bayesian model
Dynamical systems
Dynamics
Electrical engineering. Electrical power engineering
Electrical machines
Exact sciences and technology
Fault detection
fault detection/isolation
Fault diagnosis
Faults
Induction machines
Induction motors
Modelling and identification
Neural networks
Recurrent neural networks
stochastic approximation
Stochastic processes
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Summary: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.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2009.2020863