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An integrated approach to estimate storage reliability with masked data from series system
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Anhui University of Technology, China
Faculty of Engineering and Sustainable Development, University of Gävle, Sweden
Submission date: 2023-07-27
Final revision date: 2023-08-21
Acceptance date: 2023-09-25
Online publication date: 2023-10-01
Publication date: 2023-10-01
Corresponding author
Yongjin Zhang   

Anhui University of Technology, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(4):172922
  • The masked data of components from the storage system is investigated.
  • The initial reliability of the storage products are introduced.
  • The improved EM-like algorithm is used to update the testing data.
  • An LS-based EM-like algorithm is proposed for estimating the storage reliability with masked data.
Storage reliability is of importance for the products that largely stay in storage in their total life-cycle such as warning systems for harmful radiation detection, etc. Usually, the field-testing data can be available, but the failure causes for a series system cannot be always known because of the masked information. In this paper, the storage reliability model with possibly initial failures is studied on the statistical analysis method when the masked data are considered. To optimize the use of the masked survival data from storage systems, a technique based on the least squares (LS) method with an EM-like algorithm, is proposed for the series system. The parametric estimation procedure based on the LS method is developed by applying the algorithm to update the testing data, and then the LS estimation for the initial reliability and failure rate of the components constituting the series system are investigated. The results should be useful for accurately evaluating the production reliability, identifying the production quality, and planning a storage environment.
This research is supported by the Natural Science Foundation of Anhui Province under grant number 1808085MG220, Anhui University Natural Science Fund under grant number KJ2021A0386, and the National Natural Science Foundation of China under grant number 32072785. The authors also wish to acknowled ge the constructive comments from reviewers and the editor.