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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.

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Summary
This summary is machine-generated.

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.

Keywords:
Load-share parametersMaximum likelihood estimatorParallel systemSystem dependencelifetime prediction

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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.