online read us now
Paper details
Number 3 - September 2022
Volume 32 - 2022
An SFA-HMM performance evaluation method using state difference optimization for running gear systems in high-speed trains
Chao Cheng, Meng Wang, Jiuhe Wang, Junjie Shao, Hongtian Chen
Abstract
The evaluation of system performance plays an increasingly important role in the reliability analysis of cyber-physical systems. Factors of external instability affect the evaluation results in complex systems. Taking the running gear in high-speed trains as an example, its complex operating environment is the most critical factor affecting the performance evaluation
design. In order to optimize the evaluation while improving accuracy, this paper develops a performance evaluation method
based on slow feature analysis and a hidden Markov model (SFA-HMM). The utilization of SFA can screen out the slowest
features as HMM inputs, based on which a new HMM is established for performance evaluation of running gear systems. In
addition to directly classical performance evaluation for running gear systems of high-speed trains, the slow feature statistic
is proposed to detect the difference in the system state through test data, and then eliminate the error evaluation of the HMM
in the stable state. In addition, indicator planning and status classification of the data are performed through historical information and expert knowledge. Finally, a case study of the running gear system in high-speed trains is discussed. After
comparison, the result shows that the proposed method can enhance evaluation performance.
Keywords
slow feature analysis (SFA), performance evaluation, hidden Markov model (HMM), running gear systems