<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

An SFA–HMM Performance Evaluation Method Using State Difference Optimization for Running Gear Systems in High–Speed Trains

Department of Chemical and Materials Engineering

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The evaluation of system performance plays an increasingly important role in the reliability analysis of cyber-physical systems. Factors of external...

An SFA–HMM Performance Evaluation Method Using State Difference Optimization for Running Gear Systems in High–Speed Trains

Pubblicato online: 08 ott 2022

Pagine: 389 - 402

Ricevuto: 04 lug 2021

Accettato: 24 mar 2022

DOI: https://doi.org/10.34768/amcs-2022-0028

Parole chiave
slow feature analysis (SFA), performance evaluation, hidden Markov model (HMM), running gear systems

© 2022 Chao Cheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An SFA–HMM Performance Evaluation Method Using State Difference Optimization for Running Gear Systems in High–Speed Trains

Pubblicato online: 08 ott 2022

Pagine: 389 - 402

Ricevuto: 04 lug 2021

Accettato: 24 mar 2022

DOI: https://doi.org/10.34768/amcs-2022-0028

Parole chiaveslow feature analysis (SFA), performance evaluation, hidden Markov model (HMM), running gear systems

© 2022 Chao Cheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
slow feature analysis (SFA), performance evaluation, hidden Markov model (HMM), running gear systems