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An inspection-based replacement planning in consideration of state-driven imperfect inspections

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  • Wang, Jiantai
  • Zhou, Shihan
  • Peng, Rui
  • Qiu, Qingan
  • Yang, Li

Abstract

Inspection, as a fundamental component of asset management, is crucial to reveal underlying defects and report health evolution. Due to technical restrictions or human errors, inspections are often imperfect to miss the actual system state, posing challenges to the optimality of decision-makings. This study investigates an inspection-based replacement management strategy for two-phase continuous deteriorating systems subject to inspection errors. The non-steady evolution trajectory is captured by piecewise stochastic process with random state transition time. Inspections are equally spaced to reveal the underlying system state (both discrete and continuous), with a certain probability to miss the hidden defective state. Upon inspection, two levels of control limits are jointly scheduled to adjust execution frequencies of preventive replacement. The cost model is formulated and optimized, and the impact of imperfect inspection on system performance is assessed. The applicability of the proposed optimization model is validated through a case study from high-speed train bearings, which scales well in operational cost control, reducing about 39 and 4% cost compared with two conventional policies.

Suggested Citation

  • Wang, Jiantai & Zhou, Shihan & Peng, Rui & Qiu, Qingan & Yang, Li, 2023. "An inspection-based replacement planning in consideration of state-driven imperfect inspections," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006792
    DOI: 10.1016/j.ress.2022.109064
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    References listed on IDEAS

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    6. Dinh, Duc-Hanh & Do, Phuc & Hoang, Van-Thanh & Vo, Nhu-Thanh & Bang, Tao Quang, 2024. "A predictive maintenance policy for manufacturing systems considering degradation of health monitoring device," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    7. Chengye Ma & Yongjun Du & Lijun Shang & Li Yang & Kaiye Gao, 2023. "Random Maintenance Strategy Modeling of Warranted Products with Reliability Heterogeneity," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    8. Gao, Hongda & Tu, Tengfei & Qiu, Qingan, 2024. "Reliability analysis for a generalized sparse connection multi-state consecutive-k-out-of-n linear system," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    9. Ji, Ziguang & Chen, Yi & Ma, Xiaobing & Cai, Yikun & Yang, Li, 2024. "Hierarchical condition-based maintenance planning for corrosion process considering natural environmental impact," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    10. Yuhan Ma & Fanping Wei & Xiaobing Ma & Qingan Qiu & Li Yang, 2024. "Adaptive Mission Abort Planning Integrating Bayesian Parameter Learning," Mathematics, MDPI, vol. 12(16), pages 1-19, August.
    11. Zhao, Xian & Han, He & Jiao, Chunhui & Qiu, Qingan, 2024. "Reliability modeling of k-out-of-n: F balanced systems with common bus performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    12. Wang, Jiantai & Ma, Xiaobing & Yang, Li & Qiu, Qingan & Shang, Lijun & Wang, Jingjing, 2024. "A hybrid inspection-replacement policy for multi-stage degradation considering imperfect inspection with variable probabilities," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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