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Research on health state assessment and prediction for complex equipment based on the improved FMECA and GM (1,1)

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  • Mengyao Gu

    (China Jiliang University)

  • Jiangqin Ge

    (China Jiliang University)

Abstract

Health state assessment and prediction is the foundation of maintenance decision-making and resource-managing for complex equipment. Aiming at complex equipment faults’ multiplicity, coupling, and fuzziness, a novel health state assessment and prediction method is proposed. This proposed method is based on the improved failure mode effects and criticality analysis (FMECA) and grey model of the first-order differential equation with one variable (GM (1,1)). First, the improved FMECA is raised to measure the failure risk degree (FRD) of complex equipment and determine its health state in accordance with its FRD. Then, according to the state assessment results, the development trend of its health state is predicted by the improved GM (1,1). Finally, the effectiveness and superiority of the proposed method are verified through the health state assessment and prediction of the circulating water pump in company A. The implementing results reveal that the proposed method has significant advantages and is suitable for state assessment during a certain time period and state prediction in the short-range scenarios. Furthermore, reasonable values of parameters $$\varphi$$ φ and $$\theta$$ θ can effectively improve its prediction accuracy.

Suggested Citation

  • Mengyao Gu & Jiangqin Ge, 2023. "Research on health state assessment and prediction for complex equipment based on the improved FMECA and GM (1,1)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 523-538, March.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-023-01884-6
    DOI: 10.1007/s13198-023-01884-6
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    References listed on IDEAS

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    1. Eryilmaz, Serkan, 2016. "A reliability model for a three-state degraded system having random degradation rates," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 59-63.
    2. Hossein Safari & Zahra Faraji & Setareh Majidian, 2016. "Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 475-486, April.
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Rabiei, Masoud & Modarres, Mohammad, 2013. "A recursive Bayesian framework for structural health management using online monitoring and periodic inspections," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 154-164.
    5. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
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