RESEARCH PAPER
Research on maintenance decision-making approach based on dynamic opportunistic window
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1
Air force Engineering University, China
2
Baoji Titanium Industry Co., Ltd, China
Submission date: 2024-04-25
Final revision date: 2024-05-31
Acceptance date: 2024-07-23
Online publication date: 2024-08-01
Publication date: 2024-08-01
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(4):191515
HIGHLIGHTS
- We propose a dynamic maintenance approach for the opportunistic window that enables realtime updates of service age, reliability, preventive maintenance intervals, and duration of the opportunistic window.
- We propose an improved expectation maximization algorithm to solve transcendental equations in traditional algorithms when estimating parameters of mixed Weibull distribution.
- By employing a case study of serial system, We substantiates the significance of the proposed method in terms of parameter estimation accuracy, reliability of maintenance schedule setting, and rationality of maintenance plans
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ABSTRACT
We have developed a maintenance decision-making approach based on the dynamic opportunistic window, utilizing algorithms such as k-mean clustering, expected maximum, and parameter estimation to address the lack of a reasonable basis for the duration and divided number of opportunistic windows in current maintenance decision-making. Firstly, we have comprehensively summarized the multi-stage opportunistic maintenance decision-making approach, focusing on its current strengths and limitations. Secondly, the modeling concept of the dynamic opportunistic window is analyzed, and the underlying assumptions are established. Furthermore, it elaborates on the theoretical foundation of the proposed approach through a detailed modeling process. Finally, we validate the proposed model by conducting experiments on tandem components from the main combustion chamber in an aero engine. The experimental results demonstrate the significant value of the proposed approach in enhancing equipment reliability and optimizing maintenance support resources.