RUL Prediction of Wind Turbine Gearbox Bearings Based on Self-Calibration Temporal Convolutional Network

Abstract

The prediction of the remaining useful life (RUL) of wind turbine gearbox bearings is critical to avoid catastrophic accidents and minimize downtime. Temporal convolutional network (TCN), as a potential method of RUL prediction, utilizes dilated causal convolution to extract historic information in the time series, by which it can avoid the disadvantage of long-term dependence faced by classical recurrent neural networks (RNNs). However, a large amount of local information is lost after dilated causal convolution, restricting further improvement of accuracy in RUL prediction or even making TCN invalid when the time series data are not sufficient. To address this issue, an improved TCN denoted as self-calibration temporal convolutional network (SCTCN) is proposed for RUL prediction of wind turbine gearbox bearings, in which the dilated causal convolution of TCN is inherited to extract the long-term historic information, and the self-calibration module is used to focus on the local information in the time series. As a result, SCTCN can learn more complete historic information to improve the accuracy of RUL prediction. Bearing RUL prediction experiments on both test bench and wind turbine gearbox are performed to verify the effectiveness of the proposed method, and the experimental results show that SCTCN has higher prediction accuracy compared with other state-of-the-art methods.