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A Reliability Test of a Complex System Based on Empirical Likelihood.

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This study introduces an empirical likelihood method to assess complex system reliability when subsystem distributions are unknown. The approach provides a test statistic and confidence intervals for improved statistical inference in reliability engineering.

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Area of Science:

  • Reliability Engineering
  • Statistical Inference
  • System Analysis

Background:

  • Assessing the reliability of complex systems is crucial for safety and performance.
  • Traditional methods often require known subsystem distributions, limiting their applicability.
  • Minimal path sets are frequently used to define complex system structures.

Purpose of the Study:

  • To develop a novel method for analyzing complex system reliability.
  • To address the challenge of unknown subsystem distributions in reliability testing.
  • To provide a statistically sound framework for reliability inference.

Main Methods:

  • An empirical likelihood method is proposed for reliability analysis.
  • A novel reliability test statistic for the complex system is derived.
  • The asymptotic distribution of the test statistic is determined.

Main Results:

  • The empirical likelihood method effectively handles unknown subsystem distributions.
  • A confidence interval for system reliability can be obtained.
  • Simulation studies validate the theoretical findings.

Conclusions:

  • The proposed method offers a robust approach to complex system reliability assessment.
  • This work enhances statistical inference capabilities in reliability engineering.
  • The findings are applicable to systems where subsystem data is limited or unknown.