Online tracking and prediction of slip ring degradation using chaos theory based on LSTM neural network

Abstract

Abstract The online monitoring of the slip ring is important for ensuring normal operations of wind turbine equipment. A current-carrying friction experiment was conducted to simulate the degradation process of the slip ring. The chaotic parameter enclosing the radius and statistical parameter root mean square (RMS) were used to characterize the multi-sensor signals comprehensively. A new health indicator (HI) was proposed to evaluate the degradation state of slip rings based on long- and short-term memory neural networks. It was fused by the signals of friction vibration, friction torque, voltage and electric current. The HI presents a better prediction effect by the prediction model. At the severe stage of the slip ring, the evaluation criteria mean absolute error, root mean square error and mean percentage error of the HI were 0.0306, 0.0323 and 5.0225% respectively. These values are much better than the RMS of the vibration signal. The results verify that the method can effectively determine the real-time degradation state of the slip ring.