Work

Prediction of the remaining useful life of D cables for high-speed trains based on SA-LSTM

Year: 2026

Type: article

Abstract: The D cable is a key piece of on-board equipment for high-speed railway trains, and accurately predicting its remaining useful life (RUL) is of great significance for ensuring the safe and efficient operation of trains. This paper proposes a RUL prediction method for the D cable based on SA-LSTM. Firstly, this paper analyses the internal structure, main failure modes and failure mechanisms of the D cable, and builds a finite element model of the D cable based on ANSYS. Secondly, based on the thermal breakdown failure mechanism, a full-lifecycle accelerated degradation dataset of the D cable under different thermal stress conditions is constructed. Then, through the random forest algorithm, key degradation features are actively screened, and the self-attention (SA) mechanism is introduced to integrate statistical features and combine the long short-term memory (LSTM) network to achieve accurate RUL prediction. Finally, the effectiveness of the proposed method is verified by the D cable degradation simulation dataset. Experimental results show that the proposed SA-LSTM model significantly improves prediction accuracy. Compared with traditional methods such as LSTM, GRU, SVR and FNN, the proposed model achieves an average reduction of 52.4% in mean absolute error and 49.7% in root mean square error across six typical thermal stress conditions, proving its accuracy and effectiveness.

Source: Transportation Safety and Environment

DOI: 10.1093/tse/tdag013