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Peer-reviewedCC BY 4.0Mathematics

Doubly Stochastic Hidden Semi-Markov Model Optimized by Swarm Intelligence for Railway Electronic Equipment Health Management

  • Yanzhi Pang1
  • Renwei Kang2
  • Jianqiu Chen1
  • Lei Liu2
  • Jidong Lv3
  1. 1Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation, Nanning University, Nanning 530000, China
  2. 2Signal & Communication Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing 100081, China
  3. 3School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
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Abstract

Electronic equipment is essential to railway transport, as its performance directly impacts the safety of train operations. However, current operation and maintenance practices face several problems, including unrecognized degradation of equipment performance, difficulty in diagnosing faults after they occur, and coarse-grained maintenance strategies, These problems may lead to an excessively broad scope of fault impact, the continued operation of defective equipment, and other serious consequences. In this context, the paper presents an intermittent fault (IF)-based health management approach for railway electronic equipment. Building on fault mechanism analysis for electronic equipment, a mathematical parameterized characterization of intermittent faults is achieved by integrating the fault frequency and intensity vectors into the state transition probability matrix of a hidden Semi-Markov model (HSMM). To solve the non-convex parameter estimation problem, a global optimization algorithm based on swarm intelligence is constructed to maximize the log-likelihood objective function, so as to evaluate the current operational state of the equipment. The paper then uses the equipment observation sequences predicted by the long short-term memory network (LSTM) to obtain the remaining useful life (RUL) values at any timepoint. The estimation serves as the basis for proactively adjusting maintenance decisions. In experiments based on real-world power module faults, compared with the conventional Baum–Welch trained HSMM method, the proposed method in this paper improves state recognition accuracy by 2.15% and reduces the RMSE of RUL prediction by 16.67%, verifying its effectiveness.

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Cite

BibTeXRIS
BibTeX
@article{pang2026doubly,
  title = {{Doubly Stochastic Hidden Semi-Markov Model Optimized by Swarm Intelligence for Railway Electronic Equipment Health Management}},
  author = {Yanzhi Pang and Renwei Kang and Jianqiu Chen and Lei Liu and Jidong Lv},
  journal = {Mathematics},
  year = {2026},
  month = oct,
  doi = {10.3390/math14213869},
  url = {https://www.mdpi.com/2227-7390/14/21/3869},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Doubly Stochastic Hidden Semi-Markov Model Optimized by Swarm Intelligence for Railway Electronic Equipment Health Management
AU  - Yanzhi Pang
AU  - Renwei Kang
AU  - Jianqiu Chen
AU  - Lei Liu
AU  - Jidong Lv
PY  - 2026
DA  - 2026/10/23
JO  - Mathematics
DO  - 10.3390/math14213869
UR  - https://www.mdpi.com/2227-7390/14/21/3869
AB  - Electronic equipment is essential to railway transport, as its performance directly impacts the safety of train operations. However, current operation and maintenance practices face several problems, including unrecognized degradation of equipment performance, difficulty in diagnosing faults after they occur, and coarse-grained maintenance strategies, These problems may lead to an excessively broad scope of fault impact, the continued operation of defective equipment, and other serious consequences. In this context, the paper presents an intermittent fault (IF)-based health management approach for railway electronic equipment. Building on fault mechanism analysis for electronic equipment, a mathematical parameterized characterization of intermittent faults is achieved by integrating the fault frequency and intensity vectors into the state transition probability matrix of a hidden Semi-Markov model (HSMM). To solve the non-convex parameter estimation problem, a global optimization algorithm based on swarm intelligence is constructed to maximize the log-likelihood objective function, so as to evaluate the current operational state of the equipment. The paper then uses the equipment observation sequences predicted by the long short-term memory network (LSTM) to obtain the remaining useful life (RUL) values at any timepoint. The estimation serves as the basis for proactively adjusting maintenance decisions. In experiments based on real-world power module faults, compared with the conventional Baum–Welch trained HSMM method, the proposed method in this paper improves state recognition accuracy by 2.15% and reduces the RMSE of RUL prediction by 16.67%, verifying its effectiveness.
SN  - 2227-7390
ER  -