Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 13; p. 4370
• Main Authors: Jin, Yongze, Xie, Guo, Li, Yankai, Zhang, Xiaohui, Han, Ning, Shangguan, Anqi, Chen, Wenbin
• Format: Journal Article
• Language: English
• Published: MDPI 25.06.2021; MDPI AG
• Subjects: expectation maximization (EM); fault diagnosis; high-speed train; information fusion; parameter identification; unscented Kalman filter (UKF)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21134370

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.