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Research on interval triangular fuzzy multi-attribute fault diagnosis methods based on the grey relation grade

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  • Yin Liu
  • Zhaoqin Lu

Abstract

Considering the fuzzy multi-attribute problems of components' failure information, a fault diagnosis model of a Bayesian network based on the interval triangular fuzzy multi-attribute decision method was built. First, the fault multi-attribute hierarchies were represented as a Bayesian network structure by Bayesian network causal dependencies, which reduced the complexity of the multi-attribute hierarchy. Second, in the condition that weight information is completely unknown, grey correlation optimal models were constructed based on the grey correlation analysis method. Furthermore, faulty components were sorted and the component with the highest value was optimised, and the calculation method of interval triangle fuzzy multi-attribute decision based on the grey correlation was given, which realised the fault diagnosis of the system fuzzy multiple-attribute problem. Finally, the method was applied in an analysis of a case of fault diagnosis on a CNC machine tool servo system with a low voltage alarm, which verified the effectiveness of the method.

Suggested Citation

  • Yin Liu & Zhaoqin Lu, 2018. "Research on interval triangular fuzzy multi-attribute fault diagnosis methods based on the grey relation grade," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 28(3), pages 331-345.
    • Handle: RePEc:ids:ijisen:v:28:y:2018:i:3:p:331-345
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