Turnout Fault Diagnosis Algorithms of Full-Electronic Interlocking System Based on BPAdaboost

• Published in: 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) pp. 906 - 910
• Main Authors: Chen, Guangwu, Yu, Yijian, Xing, Dongfeng, Yang, Juhau
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: BP_Adaboost; fault diagnosis; Railway Signaling System; turnout
• DOI: 10.1109/SAFEPROCESS45799.2019.9213424

With the rapid development of Chinese railways, railway station signal control system has developed rapidly with the help of the fourth generation of all-electronic interlocking system. According to the control circuit and switching state in switch module of electronic interlocking system and monitor switching current, analysis the monitoring machine of turnout active current, the characteristic input value of turnout is extracted and turnout fault model is established. Firstly, data training and test is classified by BP neural network, then strong classifier is constructed by optimized Adaboost, the matching classification between turnout characteristic quantity and turnout fault type is carried out. After simulation, when BP neural network algorithm is used alone, the fault diagnosis rate is 90.2%, while the strong classification effect of BP_Adaboost algorithm can improve accuracy of turnout fault diagnosis by 95.8%, and the accuracy of latter is 5% higher than that of the former. The method validity is verified, which provides important research significance for turnout fault diagnosis of all-electronic interlocking system.