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A multi-event combination maintenance model based on event correlation.

Chunhui Guo1,2,3, Chuan Lyu1,2,3, Jiayu Chen1,2,3

  • 1School of Reliability and Systems Engineering, Beihang University, Beijing, China.

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|November 27, 2018
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Summary
This summary is machine-generated.

This study introduces a novel multi-event combination maintenance model for complex production systems. It optimizes system availability and reduces maintenance costs by considering event correlations and shared resources.

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Area of Science:

  • Industrial Engineering
  • Operations Research
  • Systems Engineering

Background:

  • Complex production systems experience diverse, simultaneous, and dynamic maintenance events.
  • Current maintenance strategies often overlook the combined effects of different maintenance events.
  • Effective maintenance management is crucial for high system availability and cost savings.

Purpose of the Study:

  • To develop a multi-event combination maintenance model for complex production systems.
  • To address the gap in research concerning the combined maintenance of different event types.
  • To optimize system availability and minimize maintenance costs through correlated event management.

Main Methods:

  • Analysis of maintenance downtime and costs for various event types under different timings and degrees.
  • Development of shared maintenance downtime and cost models based on event correlations.
  • Construction of a multi-event combination maintenance model optimizing availability and cost rate.
  • Implementation of a particle swarm optimization algorithm for model solving.

Main Results:

  • The proposed model effectively integrates maintenance event correlations to reduce overall downtime and costs.
  • Shared resource utilization is optimized through the consideration of simultaneous maintenance events.
  • The model demonstrates improved system availability and cost-effectiveness compared to traditional methods.
  • Numerical examples validate the model's performance and applicability.

Conclusions:

  • A novel multi-event combination maintenance model based on event correlation is proposed.
  • The model successfully optimizes for the highest availability and lowest cost rate in complex systems.
  • The particle swarm optimization algorithm provides an efficient solution for the developed model.
  • This research offers a significant advancement in maintenance decision-making for complex industrial systems.