[1]CUI Tiejun,LI Shasha.System fault pattern recognition based on the connection number and attribute polygon[J].CAAI Transactions on Intelligent Systems,2022,17(3):568-575.[doi:10.11992/tis.202011019]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 3
Page number:
568-575
Column:
学术论文—智能系统
Public date:
2022-05-05
- Title:
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System fault pattern recognition based on the connection number and attribute polygon
- Author(s):
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CUI Tiejun1; LI Shasha2
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1. College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China;
2. School of Business Administration, Liaoning Technical University, Huludao 125105, China
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- Keywords:
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safety system engineering; set pair analysis; connection number; attribute polygon; multi factor influence; system fault; maximum membership; pattern recognition
- CLC:
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TP18; X913
- DOI:
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10.11992/tis.202011019
- Abstract:
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To identify the system fault sample patterns under the influence of multiple factors, an attribute polygon is defined, and the system fault sample pattern recognition method is proposed based on the connection number and the attribute polygon. First, the fault pattern recognition system is established, the connection degree of a single factor is determined by the characteristic function, and the structure of the attribute polygon is determined. Second, the area with an identical difference, contrary to the attribute polygon, is used to determine the fault pattern connection degree affected by multiple factors. Finally, the fault sample pattern is identified according to the maximum membership principle of the fault standard pattern. Taking a simple electrical system as an example, the recognition method is implemented, thereby obtaining the recognition results of each fault sample pattern synergistically influenced by multiple factors. The results show that via the two-stage connection degree calculation, the fault pattern characteristics can be comprehensively considered under the influence of a single factor and multiple factors, thereby identifying the system fault sample pattern.