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Reliability analysis of load-sharing systems with memory
Dewei Wang1, Chendi Jiang2, Chanseok Park3
1Department of Statistics, University of South Carolina, Columbia, SC, 29208, USA. deweiwang@stat.sc.edu.
Lifetime Data Analysis
|February 23, 2018
Summary
Load-sharing models in parallel systems assume workload redistribution affects component performance. This study introduces a work-history-aware framework, revealing memoryless models can lead to inaccurate reliability predictions.
Area of Science:
- Reliability Engineering
- Statistical Modeling
- Stochastic Processes
Background:
- Load-sharing models analyze component dependence in parallel systems, crucial for engineering applications like fiber composites and software reliability.
- Existing memoryless models assume component hazard rates change instantly upon workload redistribution, ignoring prior operational time.
Purpose of the Study:
- To develop a generalized framework for load-sharing models that incorporates the work history of surviving components.
- To investigate the impact of memoryless assumptions on reliability inference in dependent systems.
Main Methods:
- Proposed a novel framework for load-sharing models that accounts for component work history.
- Conducted simulation studies to compare memoryless models with the proposed history-aware approach.
- Applied the new methodology to analyze real-world data from plasma display devices.
Main Results:
- Simulation results demonstrate that ignoring component work history can lead to significant inaccuracies in inferring the effects of workload redistribution.
- The proposed framework provides a more realistic analysis of system reliability under changing load conditions.
Conclusions:
- The memoryless assumption in load-sharing models is a potential limitation that can compromise reliability predictions.
- Incorporating work history into load-sharing models is essential for accurate performance and reliability assessments in various engineering fields.
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