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A new reliability health status assessment model for complex systems based on belief rule base

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  • Liu, Mingyuan
  • He, Wei
  • Ma, Ning
  • Zhu, Hailong
  • Zhou, Guohui

Abstract

In complex systems, health status assessment identifies system conditions and potential issues. However, large uncertainties and variations make efficient model construction challenging. The belief rule base (BRB), which addresses uncertainty through data-driven and knowledge-driven methods, is widely used for health status assessment of complex systems. Current BRB modeling methods focus primarily on accuracy, leaving a gap in research on reliability. Therefore, a reliable BRB (RE-BRB), which enables effective modeling for complex system health assessment under high reliability requirements, is proposed in this paper. First, a systematic reliability analysis of the BRB is performed, and the reliability criteria are defined. Second, the model parameters of the RE-BRB are optimized via the nondominated sorting whale optimization algorithm with reliability constraints (NSWOA), and the reliability of the model is ensured. In addition, a perturbation analysis of the RE-BRB model is conducted to identify the perturbation thresholds. The perturbation thresholds acceptable to the model provide guidance for managers in making decisions. Last, using the WD615 diesel engine and flywheel bearing as examples, this method achieves reliable system health status assessment by accurately assessing system status, incorporating the ability to address external perturbations and providing an easily interpretable output process.

Suggested Citation

  • Liu, Mingyuan & He, Wei & Ma, Ning & Zhu, Hailong & Zhou, Guohui, 2025. "A new reliability health status assessment model for complex systems based on belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    • Handle: RePEc:eee:reensy:v:254:y:2025:i:pa:s0951832024006859
    DOI: 10.1016/j.ress.2024.110614
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    1. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Lian, Zheng & Zhou, Zhi-Jie & Hu, Chang-Hua & Wang, Jie & Zhang, Chun-Chao & Zhang, Chao-Li, 2024. "A health assessment method with attribute importance modeling for complex systems using belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    4. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Li, Baode & Lu, Jing & Li, Jing & Zhu, Xuebin & Huang, Chuan & Su, Wan, 2022. "Scenario evolutionary analysis for maritime emergencies using an ensemble belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Woldesellasse, Haile & Tesfamariam, Solomon, 2023. "Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    8. Farahmand, Hamed & Liu, Xueming & Dong, Shangjia & Mostafavi, Ali & Gao, Jianxi, 2022. "A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
    10. Möller, Niklas & Hansson, Sven Ove, 2008. "Principles of engineering safety: Risk and uncertainty reduction," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 798-805.
    11. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2021. "An Integrated Data and Knowledge Model Addressing Aleatory and Epistemic Uncertainty for Oil Condition Monitoring," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    12. Feng, Zhichao & Zhou, Zhijie & Hu, Changhua & Ban, Xiaojun & Hu, Guanyu, 2020. "A safety assessment model based on belief rule base with new optimization method," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    13. Flavio Vismari, Lucio & Camargo Junior, João Batista, 2011. "A safety assessment methodology applied to CNS/ATM-based air traffic control system," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 727-738.
    14. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Ouyang, Tiancheng & Pan, Mingming & Tan, Xianlin & Li, Lulu & Huang, Youbin & Mo, Chunlan, 2024. "Power prediction and packed bed heat storage control for marine diesel engine waste heat recovery," Applied Energy, Elsevier, vol. 357(C).
    16. Zhou, Liang & Wang, Huawei, 2024. "An adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for industrial equipment health status assessment and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    17. Men, Tianli & Li, Yan-Fu & Ji, Yujun & Zhang, Xinliang & Liu, Pengfei, 2022. "Health assessment of high-speed train wheels based on group-profile data," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
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