The subway sliding plug door has been opened and closed frequently for a long time under variable working conditions, and multiple failure are prone to occurring at the same time, resulting in train shutdowns and even major safety accidents. Due to the complex physical mechanism of the door system and the small difference in multi-source monitoring data collected between different states of the same part, it is difficult to identify the multiple failure causes of the door system components by using a single program. Therefore, identifying the multiple failure causes of the key components of the subway sliding plug door has become a challenging problem in the health management of the door system. Aiming at the problem, an equipment multiple failure causes intelligent identification method is proposed based on an integrated strategy by t-distributed stochastic neighbor embedding (TSNE) and particle swarm optimization extreme learning machine (PSO-ELM). 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 features and sort them; Secondly, feature dimensionality is reduced by using TSNE to map the screened high-dimensional features to low-dimensional space. Finally, the parameters of the ELM are optimized by using the PSO algorithm to build a multiple failure causes classification model. The proposed method is verified by the 1:1 benchmark test data of subway sliding plug doors. 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.

The subway sliding plug door system is crucial for ensuring normal operation. Due to the differences in the structure and motor control procedures of different sliding plug door systems, the rotational speed monitoring data curves show great differences. It is a challenging problem to recognize the intervals of complex data curves, which fundamentally affect the sensitivity of feature extraction and the prediction of an assessment model. Aiming at the problem, a subway sliding plug door system health state adaptive assessment method is proposed based on interval intelligent recognition of rotational speed operation data curve. In the proposed method, firstly, the rotational speed operation data curve is adaptively divided by a long short-term memory (LSTM) neural network into four intervals, according to the motion characteristics of the door system. Secondly, the sensitive features of the door system are screened out by the random forest (RF) algorithm. Finally, the health state of the door system is assessed using the adaptive boosting (AdaBoost) classifier. The proposed method is comprehensively verified by the benchmark experiment data set. The results show that the average diagnostic accuracy of the method on multiple bench doors can reach 98.15%. The wider application scope and the higher state classification accuracy indicate that the proposed method has important engineering value and theoretical significance for the health management of subway sliding plug door systems.

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It is a valuable and challenge problem to reveal the coupling mechanism between heat and electricity in lithium‐ion battery under variable load conditions. A temperature–voltage coupling equivalent electrical behavior model for lithium‐ion battery under variable load conditions is established to describe the electrical behavior of the battery by analyzing the effect of temperature on particle motion, the effect of particle motion on charge distribution, and the effect of charge distribution on the electrical behavior of the battery and to accurately portray the temperature and voltage coupling mechanism. In the model, heat is taken as the state variable of lithium‐ion battery, and the relationship between the external temperature and voltage is described. The model is verified by simulation and measured data of lithium‐ion battery used in electric unmanned aerial vehicles. The results indicate that the established temperature–voltage coupling model can accurately describe the electrical behavior of the battery and can accurately reveal the coupling relationship between temperature and voltage, and the effect of temperature on the electrical behavior of the battery reflected by the model is basically consistent (voltage and temperature mean absolute percentage error is 5.50% and 2.02%, respectively) with the measured data.