M²BIST-SPNet: RUL prediction for railway signaling electromechanical devices

Railway signaling electromechanical devices (RSEDs) play a pivotal role in the railway industry. Normal wear and tear of these devices occur during day-and-night operation and even progressively develop into failures. Hence, it is imperative to predict the remaining useful life (RUL) for reliable services. However, there are three existing challenges in addressing the issue. To overcome these challenges, we introduce M \(^2\) ${}^2$ BIST-SPNet for RSEDs RUL prediction. The model incorporates advanced spatiotemporal feature extraction mechanisms, including a spatial-temporal attention-based convolution networks (STACN), multiple branches with multi-scale multifrequency module (MBM), and communication module (CM). Extensive experiments conducted on three typical object datasets (i.e., railway safety relay, EMU contactor, switch circuit controller) shows that the proposed approach has demonstrated state-of-the-art performance in long-term multiparameter prediction and RUL prediction.