- Tytuł:
- An SFA-HMM performance evaluation method using state difference optimization for running gear systems in high-speed trains
- Autorzy:
-
Cheng, Chao
Wang, Meng
Wang, Jiuhe
Shao, Junjie
Chen, Hongtian - Powiązania:
- https://bibliotekanauki.pl/articles/2172116.pdf
- Data publikacji:
- 2022
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
slow feature analysis
SFA
performance evaluation
hidden Markov model
HMM
running gear system
analiza cech
ocena efektywności
ukryty model Markowa
układ biegowy - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 389--402
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł