MOBILE ROBOT MOTOR BEARING FAULT DETECTION AND CLASSIFICATION ON DISCRETE WAVELET TRANSFORM AND LSTM NETWORK

Abstract

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed.