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Conversion and fusion method of multi-source and different populations maintainability prior data.

Cheng Zhou1, Da Xu1, Zhaoyang Wang1

  • 1Department of Arms and Control, Army Academy of Armored Forces, Beijing, 100072, China.

Heliyon
|November 13, 2023
PubMed
Summary

This study introduces a novel data conversion and fusion method to improve the reliability of equipment maintainability verification. The approach ensures accurate comprehensive prior distributions for better maintenance assessments.

Keywords:
Conversion factorData conversionDifferent populations dataMaintainability verificationMulti-source data fusion

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Area of Science:

  • Engineering
  • Quality Management

Background:

  • Maintainability is a critical quality characteristic for weapon and equipment lifecycle management.
  • Accurate verification of maintainability requirements is essential for equipment acceptance.
  • Existing methods suffer from low reliability due to inaccurate prior data fusion.

Purpose of the Study:

  • To propose a data conversion and fusion method for accurate equipment maintainability verification.
  • To address the challenge of fusing multi-source and different population prior data.
  • To enhance the reliability of maintainability verification results.

Main Methods:

  • Developed a data conversion model using the mean value ratio of failure mode maintenance data.
  • Employed Bayes bootstrap and improved sample-resampling methods for distribution fitting.
  • Constructed a multi-source data fusion model utilizing improved KL divergence.

Main Results:

  • The proposed method effectively converts prior data from different populations to a common population.
  • Accurate and comprehensive prior distributions were obtained through weighted fusion.
  • The method lays the foundation for applying Bayes test methods to equipment maintainability.

Conclusions:

  • The data conversion and fusion method significantly improves the accuracy of prior distributions for maintainability verification.
  • This approach enhances the reliability of equipment acceptance and identification processes.
  • The study provides a robust framework for quantitative maintainability index verification.