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Comprehensive reliability evaluation of multistate complex electromechanical systems based on similarity of cloud models
Author(s) -
Wang Rongxi,
Gao Xu,
Gao Zhiyong,
Li Shiqiang,
Gao Jianmin,
Xu Jinjin,
Deng Wei
Publication year - 2020
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.2614
Subject(s) - reliability (semiconductor) , ambiguity , similarity (geometry) , reliability engineering , cloud computing , computer science , data mining , complex system , fault (geology) , experimental data , engineering , artificial intelligence , mathematics , power (physics) , statistics , physics , quantum mechanics , seismology , image (mathematics) , programming language , geology , operating system
High reliability and security have become the hallmarks of complex electromechanical systems. Owing to the difficulties in fault data collection, ambiguity and uncertainty have been inevitably associated with complex electromechanical systems. Thus, the ability to perform reliability evaluation using scarce fault data is of immense significance to these machines and is the focus of this study. A similarity based cloud model is proposed to evaluate the running state of complex electromechanical systems. By combining objective and subjective factors, the reliability of complex electromechanical systems is evaluated by calculating the similarity between the cloud models of actual and standardised states. Next, the inverter of an offshore wind turbine is used to verify the accuracy and effectiveness of the proposed approach. The cloud model based framework for reliability evaluation inherits the preponderance of the uncertainty problem, overcomes the drawbacks of the current reliability approaches, and provides a theoretical basis, as well as a practical approach for the maintenance and repair of complex electromechanical systems with missing fault data. Additionally, it also provides a new methodology for solving the uncertainty problems caused by paucity of data.