Fault Diagnosis for Induction Machines Using Kernel Principal Component Analysis

Abstract

For the fault diagnosis of three-phase induction motors, we set up an experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of induction motor drive and data acquisition module to obtain the fault signals. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the input data, three-phase currents are transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel PCA. Finally, we used the linear classifier based on two types of distance measures. To show the effectiveness, the proposed fault diagnostic system has been intensively tested with the various data acquired under the different electrical and mechanical faults with varying load.