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System resilience distribution identification and analysis based on performance processes after disruptions.
Yeqing Song1, Ruiying Li1,2
1School of Reliability and Systems Engineering, Beihang University, Beijing, China.
Plos One
|November 3, 2022
Summary
This study introduces a resilience distribution identification and analysis (RDIA) method. The research found that system resilience typically follows a Weibull distribution, crucial for design and analysis.
Area of Science:
- Systems Engineering
- Reliability Engineering
- Probability and Statistics
Background:
- System resilience is critical for withstanding disruptions and enabling rapid recovery.
- Understanding resilience distribution is fundamental for system design, analysis, and reliability.
- Existing methods lack a systematic approach to identify and analyze resilience distributions.
Purpose of the Study:
- To propose a systematic Resilience Distribution Identification and Analysis (RDIA) method.
- To analyze the distribution characteristics of system resilience after disruptions.
- To provide a framework for resilience design and assessment.
Main Methods:
- Developed an RDIA method based on system performance degradation/recovery processes.
- Utilized Monte Carlo simulations to sample key performance parameters (max degradation, degradation/recovery durations).
- Employed distribution identification, parameter estimation, and goodness-of-fit tests to determine resilience distributions.
Main Results:
- The study found that system resilience, for typical performance processes, follows the Weibull distribution.
- The RDIA method was validated using orthogonal experiment and control variable methods.
- The proposed method can be applied to simulation or test data, as demonstrated with an end-to-end communication system.
Conclusions:
- The RDIA method provides a robust framework for understanding and quantifying system resilience.
- The Weibull distribution is a key finding for modeling resilience in various systems.
- This research offers practical implications for improving system reliability and design.
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