Fault Prediction of Railway Turnout Systems Based on Improved Sparse Auto Encoder and Gated Recurrent Unit Network

Railway turnout systems (RTS), as one of the most critical ground infrastructures of the high-speed rail, which directly impact the railway operation safety, are also susceptible to frequent faults. Hence, efficient fault prediction and intelligent maintenance decision support of RTS are significant to avoid unexpected outages and guarantee the operation safety of trains. A key challenge for fault prediction of RTS is to extract and select effective feature combinations that describe the degradation status of the system and then generate health indicators (HI) with high monotonicity, correlation, and robustness. In this paper, we focus on the jam fault caused by insufficient lubrication, which is one of the most frequent faults of RTS, and we present a novel unsupervised adaptive latent feature extraction method based on the improved sparse auto-encoder (ISAE). The locally weighted regression (LOESS) is performed to smooth the extracted latent features to overcome the influence of the noise. Improved auto-associative kernel regression (IAAKR) is utilized to fuse the feature combination and generate HI, which can intuitively describe the degradation status of RTS. Finally, the gated recurrent unit (GRU) network is introduced to enhance the correlation data process and construct the fault prediction models. The parameters of GRU are optimized by the improved particle swarm optimization (IPSO) algorithm. Extensive experiments are conducted on a real-world RTS degradation dataset collected from the Changsha Railway Station in China, which is related to the jam fault caused by the lubrication reduction, and the results show the proposed method achieves better prediction performance compared to state-of-the-art methods in terms of different evaluation metrics.