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Mixed shock model for multi-state weighted k-out-of-n: F systems with degraded resistance against shocks

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  • Wang, Xiaoyue
  • Zhao, Xian
  • Wu, Congshan
  • Wang, Siqi

Abstract

In previous studies of shock models, the probability of shocks causing damage to the system during operation is always constant. However, it is more practical for the system to become more vulnerable to shocks under a severe condition and the probabilities of subsequent shocks causing damage to the system increase. Furthermore, few studies have focused on the shock environment of weighted k-out-of-n: F systems. To fill these research gaps, this paper proposes a novel mixed shock model for a multi-state weighted k-out-of-n: F system with a consideration of its resistance against shocks. The components in the system operate in a two-stage process and different weights are taken for their different states. The system has multiple states determined by the total system weight/performance. System failure results from the competing failure criteria of insufficient system weight/performance and from reaching the critical number of failed components. A combination of finite Markov chain imbedding approach, universal generating function technique and phase-type distribution is adopted to analyze the probabilistic indices of the components and entire system. Numerical illustrations based on a power-generating plant are presented to demonstrate the applicability of new shock model and the effectiveness of proposed method.

Suggested Citation

  • Wang, Xiaoyue & Zhao, Xian & Wu, Congshan & Wang, Siqi, 2022. "Mixed shock model for multi-state weighted k-out-of-n: F systems with degraded resistance against shocks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  • Handle: RePEc:eee:reensy:v:217:y:2022:i:c:s0951832021005950
    DOI: 10.1016/j.ress.2021.108098
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    Cited by:

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    4. Wu, Congshan & Pan, Rong & Zhao, Xian & Wang, Xiaoyue, 2024. "Designing preventive maintenance for multi-state systems with performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Wang, Xiaoyue & Ning, Ru & Zhao, Xian & Zhou, Jian, 2022. "Reliability analyses of k-out-of-n: F capability-balanced systems in a multi-source shock environment," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    6. Che, Haiyang & Zeng, Shengkui & Li, Kehui & Guo, Jianbin, 2022. "Reliability analysis of load-sharing man-machine systems subject to machine degradation, human errors, and random shocks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Zhao, Xian & Li, Rong & Cao, Shuai & Qiu, Qingan, 2023. "Joint modeling of loading and mission abort policies for systems operating in dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Wang, Chaonan & Wang, Shuli & Xing, Liudong & Guan, Quanlong, 2023. "Efficient performability analysis of dynamic multi-state k-out-of-n: G systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Zhao, Xian & Li, Ziyue & Wang, Xiaoyue & Guo, Bin, 2023. "Reliability of performance-based system containing multiple load-sharing subsystems with protective devices considering protection randomness," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. Chadjiconstantinidis, Stathis & Eryilmaz, Serkan, 2023. "Reliability of a mixed δ-shock model with a random change point in shock magnitude distribution and an optimal replacement policy," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    11. Zhao, Xian & Qi, Xin & Wang, Xiaoyue, 2023. "Reliability assessment for coherent systems operating under a generalized mixed shock model with multiple change points of the environment," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
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    13. Yu, Xiaoyun & Hu, Linmin & Ma, Mengrao, 2023. "Reliability measures of discrete time k-out-of-n: G retrial systems based on Bernoulli shocks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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