| Title |
Real-time Fault Diagnosis of Induction Motor Using Clustering and Radial Basis Function |
| Authors |
Jang-Hwan Park ; Dae-Jong Lee ; Myung-Geun Chun |
| Keywords |
Induction motor ; Fault diagnosis ; Kernel PCA ; LDA ; RBF network |
| Abstract |
For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load. |