Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods

Abstract

Early detection and diagnosis of incipient faults are desired for online condition monitoring and improved operational efficiency of induction motors. In this study, an artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm is developed to detect broken rotor bar and broken connector faults in induction motors. The proposed algorithm uses only one phase stator current as input without the need for any other signals. The new feature signal called envelop is obtained by using Hilbert transform. This signal is examined in a phase space that is constructed by nonlinear time series analysis method. The artificial immune algorithm called negative selection is used to detect faults. The cluster centers of healthy motor phase space are obtained by fuzzy clustering method and they are taken as self patterns. Pie detectors of negative selection are generated by genetic algorithm. Self patterns generated by fuzzy clustering speed up the training stage of our algorithm and only small numbers of detectors are sufficient to detect any faults of induction motor. Results have demonstrated that the proposed system is able to detect faults in a three phase induction motor, successfully.