Fault Diagnosis of Three Phase Induction Motor Using Neural Network Techniques

Fault diagnosis of induction motor is gaining importance in industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. Due to environmental stress and many others reasons different faults occur in induction motor. Many researchers proposed different techniques for fault detection and diagnosis. However, many techniques available presently require a good deal of expertise to apply them successfully. Simpler approaches are needed which allow relatively unskilled operators to make reliable decisions without a diagnosis specialist to examine data and diagnose problems. In this paper simple, reliable and economical Neural Network (NN) based fault classifier is proposed, in which stator current is used as input signal from motor. Thirteen statistical parameters are extracted from the stator current and PCA is used to select proper input. Data is generated from the experimentation on specially designed 2 Hp, 4 pole 50 Hz. three phase induction motor. For classification, NNs like MLP, SVM and statistical classifiers based on CART and Discriminant Analysis are verified. Robustness of classifier to noise is also verified on unseen data by introducing controlled Gaussian and Uniform noise in input and output.