An aero–engine remaining useful life prediction model based on clustering analysis and the improved GRU–TCN

Abstract Accurately predicting the remaining useful life (RUL) of engines is paramount for implementing effective preventive maintenance strategies, preventing injuries and fatalities caused by equipment failures, and significantly reducing routine repair and replacement costs. However, existing deep learning models often ignore the variable operating conditions in real engineering applications and do not sufficiently consider the interaction between time series and degradation laws, which directly leads to the inability to effectively extract to degradation feature extraction. To address this problem, this study developed a novel combined network model named CA–DRGRU–TTCN, aimed at accurately predicting the RUL of engines. Firstly, a density–based spatial clustering of applications with noise algorithm is used to identify multiple operating conditions, and incorporate the recognition results into the model as additional new features. The first degradation time point is determined by Jensen–Shannon divergence. Secondly, the deep connectivity of the residual gated recurrent unit (GRU) module is designed to extract deeper degradation features, and an improved time–based mean square error loss function based on the first degradation time point is applied to temporal convolutional networks (TCN) to better capture the dependency between the time series and the real degradation degree of the engine. Finally, experiment results on the C–MAPSS dataset show that the proposed method achieves better performance compared to existing state–of–the–art methods.