Predicting the remaining useful life of rails based on improved deep spiking residual neural network

the remaining useful life (RUL) of rails is important to ensure safe and reliable operation of railway transportation lines. However, the severity of deterioration of rails from different types of damage varies. Multichannel vibrational data collected by sensors can be utilized to identify the relevant complex correlations and temporal relationships; however, existing methods struggle to predict rail damage accurately. Thus, this paper proposes a RUL prediction method for rails based on an improved deep spiking residual neural network. First, multichannel data collected by sensors are used directly as input to the predictive network without requiring a separate feature extraction step. Additionally, an encoding module is designed to adaptively convert the temporal sequences of data into spiking signals to reduce the information loss during the encoding process. Second, a spiking residual convolutional neural network (CNN) is incorporated into the proposed method to extract optimal features. The separable, spiking CNN can model the relationships among multichannel data accurately, and an attention mechanism is implemented to recalibrate the spiking feature maps such that the predictive network can distinguish between items of information effectively. Third, the spiking residual connections are altered to mitigate network degradation caused by the incompatibility between the conventional residual connections and the spiking neural network. Finally, the obtained spiking features are input to a fully connected layer to predict the rail RUL. Experimental results demonstrated that the proposed method can predict the rail RUL accurately, and validation results obtained on a rolling bearing dataset demonstrated the high generalizability of the proposed method.