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Fault Diagnosis of Induction Motor using C-Fuzzy Model Tree and SVM

In this paper, we proposed a diagnosis algorithm to detect the fault of induction motor using a local classifier based on c-fuzzy model tree and SVM. First, after reducing the input dimension of the sampled current values measured during one period using principal component analysis method, we extract feature vectors for each fault by linear discriminant analysis. Next, cluster centers are calculated by fuzzy clustering method using all the characteristic vectors. And then, each SVM based local classifier is constructed with the subset data splitted by fuzzy membership grades between centers and an input vector. To obtain various data set under normal and fault states, we set up an experimental unit consisting of induction motor drive and data acquisition module. For the experiments under various noise conditions, the proposed method showed better performance than conventional methods such as k-NN, RBF, MLP and multi-class SVM.