A New Fault Diagnosis Method Based on Attributes Weighted Neutrosophic Set

Fault diagnosis is an extensively applied issue to monitor condition and diagnose fault for safe and stable operation of the machine, which started to develop during the industrial revolution and contains various theories and technologies. Due to the growing complexity of contributing factors of a fault and the correlation of fault attributes which are often interrelated, traditional fault diagnosis methods fail to handle with this complex condition. To solve this problem, a new fault diagnosis method based on attributes weighted neutrosophic set is proposed in this paper. In the proposed approach, a attributes weighted model is developed to obtain the weights of attributes by the fault information. For a sample whose fault type is unknown, the neutrosophic set generated from the fault sample data are aggregated via the single valued neutrosophic power weighted averaging (SVNPWA) operator with the obtained attributes weights, then, the fault diagnosis results could be determined by the defuzzification method of fused neutrosophic set. This proposed method have capacity to differentiate the individual impact of attributes and handle the uncertain problems in the process of fault diagnosis. Finally, an illustrative example was provided to demonstrate the reasonableness and effectiveness of the proposed method.

Abstract licence: CC BY-NC-ND

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