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Research on multiple‐state industrial robot system with epistemic uncertainty reliability allocation method
Author(s) -
Bai Bin,
Li Ze,
Zhang Junyi,
Zhang Dequan,
Fei Chengwei
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2753
Subject(s) - reliability (semiconductor) , reliability engineering , computer science , uncertainty quantification , uncertainty theory , mathematical optimization , operations research , mathematics , engineering , machine learning , power (physics) , physics , quantum mechanics
Abstract Reliability allocation of industrial robot (IR) system is one of the important means to improve its whole life cycle, reduce maintenance cost, and characterize weak subsystems. The IR system is not only very complex but also has strong customization; meanwhile, its sample data are small, resulting in unclear degeneration and failure. Based on the above two epistemic uncertainties, a new methodology called multiple‐state IR system reliability allocation method with epistemic uncertainty (MIRS‐RAM‐EU) is proposed. First, the Dempster‐Shafer (D‐S) evidence theory is used to quantify the epistemic uncertainty. Then, the Kolmogorov differential equations of MIR's subsystems are calculated. The reliability index of MIRS is allocated based on Birnbaum importance degree theory, and the reliability allocation coefficient of each IR subsystem is clearly expressed by this method. Finally, compared with traditional importance allocation method, the MIRS‐RAM‐EU is more efficient and accurate. This method is usefully directive for reliability evaluation of IR.