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Integrated availability importance measure for multi-state complex systems analysis

Author

Listed:
  • Gu Dongwei
  • Zhong Yuhong
  • Hu Yanjuan
  • Chen Guang
  • Wang Zhixin
  • Li Nianhuan

Abstract

As an important tool to evaluate the key components of the multi-state system, the importance degree is essential in the system reliability design stage, to provide the basis for the system reliability improvement and maintenance. To accurately improve the reliability of the system, this paper provides an importance measure analysis method that comprehensively considers the state and maintenance effects. To measure the impact of components on the system more comprehensively, this paper proposes an Integrated Availability Importance Measure (IAIM) to evaluate the relative importance of components by combining component state probability, state transition rate, repair rate, and state repair transition rate and considering the impact of component reliability and maintainability on the performance of multi-state systems. Considering the randomness of system operation, a Monte Carlo simulation based IAIM analysis method for a multi-state system was developed. Taking the series system and the hybrid system as examples, the IAIM of the component is simulated and analyzed. Comparing IAIM with Integrated Importance Measure (IIM) and performance Utility Importance measure (UI), among them, UI considers the impact of the state on performance, while IIM considers state transition on the basis of UI, but does not consider the impact of maintenance. IAIM is more comprehensive than UI and IAIM. It can be seen that IAIM is different from importance measures based on reliability. This is because the IAIM fully examines the impact of component reliability and maintainability on multi-state systems. The IAIM improves the traditional shortcomings of only considering component reliability, and provides a more comprehensive way to evaluate the system.

Suggested Citation

  • Gu Dongwei & Zhong Yuhong & Hu Yanjuan & Chen Guang & Wang Zhixin & Li Nianhuan, 2024. "Integrated availability importance measure for multi-state complex systems analysis," Journal of Risk and Reliability, , vol. 238(3), pages 578-590, June.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:3:p:578-590
    DOI: 10.1177/1748006X231159823
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    References listed on IDEAS

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    1. Enrico Zio, 2013. "Advanced Monte Carlo Simulation Techniques for System Failure Probability Estimation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 109-156, Springer.
    2. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, July.
    3. Barlow, Richard E. & Proschan, Frank, 1975. "Importance of system components and fault tree events," Stochastic Processes and their Applications, Elsevier, vol. 3(2), pages 153-173, April.
    4. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    5. Vaisman, Radislav & Sun, Yuting, 2021. "Reliability and importance measure analysis of networks with shared risk link groups," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    6. Do, Phuc & Bérenguer, Christophe, 2020. "Conditional reliability-based importance measures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    7. Xu, Zhaoping & Ramirez-Marquez, Jose Emmanuel & Liu, Yu & Xiahou, Tangfan, 2020. "A new resilience-based component importance measure for multi-state networks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    8. Lyu, Dong & Si, Shubin, 2020. "Dynamic importance measure for the K-out-of-n: G system under repeated random load," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    9. Dui, Hongyan & Li, Shumin & Xing, Liudong & Liu, Hanlin, 2019. "System performance-based joint importance analysis guided maintenance for repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 162-175.
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