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Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications

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  • Iamsumang, Chonlagarn
  • Mosleh, Ali
  • Modarres, Mohammad

Abstract

This paper presents a new modeling approach, computational algorithm, and an example application for health monitoring and learning in on-line System Health Management (SHM). A hybrid Dynamic Bayesian Network (DBN) is introduced to represent complex engineering systems with underlying physics of failure by modeling a theoretical or empirical degradation model with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small, localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using a pre-computation strategy and dynamic programming for on-line monitoring of system health. Proposed Monitoring and Anomaly Detection algorithm uses pattern recognition to improve failure detection and estimation of Remaining Useful Life (RUL). Pre-computation inference database enables efficient on-line learning and maintenance decision-making. The proposed methodology and algorithm are demonstrated with an Unmanned Aerial Vehicle (UAV) application.

Suggested Citation

  • Iamsumang, Chonlagarn & Mosleh, Ali & Modarres, Mohammad, 2018. "Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 118-129.
    • Handle: RePEc:eee:reensy:v:178:y:2018:i:c:p:118-129
    DOI: 10.1016/j.ress.2018.05.016
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    References listed on IDEAS

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    Cited by:

    1. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Guo, Kai & Ye, Zhisheng & Liu, Datong & Peng, Xiyuan, 2021. "UAV flight control sensing enhancement with a data-driven adaptive fusion model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Moradi, Ramin & Groth, Katrina M., 2020. "Modernizing risk assessment: A systematic integration of PRA and PHM techniques," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
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    5. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Jun Zhang & Haifeng Bian & Huanhuan Zhao & Xuexue Wang & Linlin Zhang & Yiping Bai, 2020. "Bayesian Network-Based Risk Assessment of Single-Phase Grounding Accidents of Power Transmission Lines," IJERPH, MDPI, vol. 17(6), pages 1-17, March.
    7. GAO, Guibing & ZHOU, Dengming & TANG, Hao & HU, Xin, 2021. "An Intelligent Health diagnosis and Maintenance Decision-making approach in Smart Manufacturing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. BahooToroody, Ahmad & De Carlo, Filippo & Paltrinieri, Nicola & Tucci, Mario & Van Gelder, P.H.A.J.M., 2020. "Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    9. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).
    10. Zhou, Jianxiong & Wei, Shanbi & Chai, Yi, 2021. "Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

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