An intelligent diagnosis for railway turnout fault
Ke Ting, Ge Xuechun, Zhang Lidong, Hui Ping Lv · DOAJ (DOAJ: Directory of Open Access Journals) · 2020
The traditional turnout fault detection method not only leads to consume a lot of manpower, material resources and financial resources, but also relies on manual experience. With the rapid development of artificial intelligence, designing an intelligent diagnostic system to diagnose the turnout is a key problem. In this paper, an intelligent detection system is proposed, which contains data preprocessing, feature extraction, switch intelligent classifier and more suitable evaluation criterion design. It is simulated by MATLAB, the experimental results on Guangzhou village station switch current data of model W1902# and model W1904# shows that the current intelligent detection method not only has the ability of self-learning, but also can be detected efficiently in the complex changes of the environment, and the recognition time is only 0.04 s, which meets the real-time requirement of railway.