An enhanced fault detection method for railway turnouts incorporating prior faulty information

Railway turnouts monitoring is critical for ensuring the safety of railway systems. Owing to the scarcity of faulty samples, many approaches focus on the fault detection of railway turnouts only using normal samples. However, although the faulty samples are insufficient, they can still provide useful information for fault detection. To improve the fault detection performance of railway turnouts, this paper proposes an enhanced fault detection method incorporating prior faulty information, which deals with the limitation of existing fault detection methods of turnouts that they fail to take insufficient faulty samples into account. In our method, a novel model which shares the architecture of deep autoencoders but has a different training objective is presented. By minimizing the difference between the averaged reconstruction error of normal samples and that of faulty samples, the proposed model enlarges the gap between the normal and faulty classes and making them more separable. The field data collected from a real high-speed railway in China is used to evaluate the proposed method, and a comparative study with several existing approaches is conducted. Experimental results show the effectiveness of the proposed method.