Fault Verdict in Multi Phase Induction Machine using Intelligence Evolution Computation Algorithm Optimized Neural Network

Abstract

The least mean square filter (LMS) and a new hybrid neural network with mind evolution computation algorithm are used in this paper to present a new solution approach for identifying faults in a multiphase induction motor. The application of an artificial neural network (ANN) has stood out as a facilitating mechanism in solving problems in many areas. The entire process of teaching an artificial neural network (ANN) is often regarded as one of the most difficult processes in system learning, and it has recently attracted a large number of researchers. An efficient feature extractor based on LMS and a fault classifier based on a hybrid neural network are included in the proposed hybrid fault diagnosis approach. The performance and efficiency of the provided hybrid neural network classifier are determined by testing 600 samples modeled on the failure model. For various defect signals, the average correct classification with and without the mind evolution computation algorithm is around 98 percent and 96.17 percent, respectively. The simulation study results demonstrate the effectiveness of the proposed hybrid neural network for fault identification in multiphase induction motors.