An equipment multiple failure causes intelligent identification method based on integrated strategy for subway sliding plug door system under variable working condition. (1st December 2022)

• Record Type: Journal Article
• Authors: Guo, Jing; Chen, Gaige; Ma, Hongbo; Qi, Hui
• Is Part Of: Measurement science & technology. Volume 33:Number 12(2022)
• Publication Date: 2022-12-01
• Subjects: subway sliding plug door -- multiple failure causes -- integrated strategy -- multi-source monitoring data -- variable working condition; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac8a65
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The subway sliding plug door system has been opened and closed frequently for a long time under variable working conditions, and multiple failures are prone to occurring and resulting in train shutdowns and even major safety accidents. Due to the complex physical mechanism of the sliding plug door system, it is difficult for a single signal to accurately represent the failure states of the sliding plug door system. Thus, identifying the multiple failure causes of the subway sliding plug door system has become a challenging problem. Aiming at the problem, an equipment multiple failure causes intelligent identification method based on an integrated strategy is proposed for the subway sliding plug door system under variable working condition. In the proposed method, firstly, the sensitive features that can reflect the degradation state of equipment are obtained by using the random forest to measure the importance of fetatures and sort them. Secondly, feature dimensionality is reduced by using t-distributed stochastic neighbor mbedding (TSNE) to map the screened high-dimensional features to low-dimensional space. Finally, the parameters of the extreme learning machine (ELM) are optimized by using the particle swarm optimization (PSO) algorithm to build a multiple failure causes classification model. The proposed method is verified by the 1:1 benchmark test data of the subway sliding plug door system. The results show that the proposed method has higher classification accuracy, faster calculation speed, and stronger generalization ability. The proposed method is an effective integrated strategy to identify multiple failure causes in the subway sliding plug door system and guide the health management and operational maintenance of the subway sliding plug door system.