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Modeling and Solving of Uncertain Process Abnormity Diagnosis Problem

Author

Listed:
  • Shiwang Hou

    (School of Business, Huaihua University, Huaihua, Hunan 418000, China
    Department of Mathematics, Brunel University London, London, Uxbridge UB8 3PH, UK)

  • Haijun Wen

    (School of Mechanical Engineering, North University of China, Taiyuan, Shanxi 030051, China)

Abstract

There are many uncertain factors that contribute to process faults and this make it is hard to locate the assignable causes when a process fault occurs. The fuzzy relational equation (FRE) is effective to represent the uncertain relationship between the causes and effects, but the solving difficulties greatly limit its practical utilization. In this paper, the relation between the occurrence degree of abnormal patterns and assignable causes was modeled by FRE. Considering an objective function of least distance between the occurrence degree of abnormal patterns and its assignable cause’s contribution degree determined by FRE, the FRE solution can be obtained by solving an optimization problem with a genetic algorithm (GA). Taking the previous optimization solution as the initial solution of the following run, the GA was run repeatedly. As a result, an optimal interval FRE solution was achieved. Finally, the proposed approach was validated by an application case and some simulation cases. The results show that the model and its solving method are both feasible and effective.

Suggested Citation

  • Shiwang Hou & Haijun Wen, 2019. "Modeling and Solving of Uncertain Process Abnormity Diagnosis Problem," Energies, MDPI, vol. 12(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1580-:d:225971
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