Motor fault detection using Elman neural network with genetic algorithm-aided training

Abstract

Fault detection methods are crucial in acquiring safe and reliable operation in motor drive systems. Remarkable maintenance costs can also be saved by applying advanced detection techniques to find potential failures. However, conventional motor fault detection approaches often have to work with explicit motor models. In addition, most of them are deterministic or non-adaptive, and therefore cannot be used in time-varying cases. We propose an Elman neural network-based motor fault detection scheme to overcome these difficulties. The Elman neural network has the unique time series prediction capability because of its memory nodes as well as local recurrent connections. Motor faults are detected from changes in the expectation of the feature signal prediction error. A genetic algorithm (GA)-aided training strategy for the Elman neural network is further introduced to improve the approximation accuracy and achieve better detection performance. Computer simulations of a practical automobile transmission gear with an artificial fault are carried out to verify the effectiveness of our method. Encouraging fault detection results have been obtained without any prior information of the gear model.