A Condition Monitoring Method for Train Air conditioning Systems Based on CNN LSTM Attention Model with Adaptive Status Index and Statistical Threshold

Abstract

With the rapid development of rail transit systems, daily operation and maintenance tasks for trains are becoming increasingly demanding. The reliable operation of the air-conditioning system is directly related to running safety and passenger comfort. However, existing monitoring methods mostly rely on traditional threshold-based approaches for anomaly warning, which suffer from delayed detection of early anomalies and a high false alarm rate. To address these issues, this paper proposes a condition monitoring and anomaly early warning method that integrates a convolutional neural network (CNN), a long short-term memory network (LSTM), and an attention mechanism. The method uses the CNN to extract local temporal features from multi-sensor data, employs the LSTM to model long-term dependencies, and introduces the attention mechanism to dynamically focus on critical time steps, thereby improving overall prediction accuracy. Furthermore, an adaptive recursive status index (ARSI) combined with statistical process control (SPC) is used to determine a statistical control threshold, enabling real-time system status monitoring and anomaly early warning. Validation is performed using TCMS data from a specific model of high-speed train. Experimental results show that the proposed model achieves significantly higher prediction accuracy on healthy samples than baseline models such as LSTM, MSET, and TCN. In anomaly detection tasks, the model attains an accuracy of 98.63% with a false alarm rate of only 1.42%, and issues a warning 31 minutes before the TCMS-reported fault alarm of a compressor pressure fault. Moreover, on a cooling performance degradation fault caused by fan performance deterioration, the model achieves an accuracy of 98.65% with a FAR of only 1.04%, and issues a warning approximately 73 minutes before the fault alarm, confirming its potential applicability across different fault modes. Ablation studies further confirm the necessity of the synergistic collaboration of CNN, LSTM, and the attention mechanism.