A Neutrosophic Set Based Fault Diagnosis Method Based on Multi-Stage Fault Template Data

作者：Jiang, Wen^*; Zhong, Yu; Deng, Xinyang

来源：Symmetry-Basel, 2018, 10(8): 346.

DOI：10.3390/sym10080346

• neutrosophic set
• fault diagnosis
• normal distribution
• defuzzification
• simplified neutrosophic weighted averaging operator

Fault diagnosis is an important issue in various fields and aims to detect and identify the faults of systems, products, and processes. The cause of a fault is complicated due to the uncertainty of the actual environment. Nevertheless, it is difficult to consider uncertain factors adequately with many traditional methods. In addition, the same fault may show multiple features and the same feature might be caused by different faults. In this paper, a neutrosophic set based fault diagnosis method based on multi-stage fault template data is proposed to solve this problem. For an unknown fault sample whose fault type is unknown and needs to be diagnosed, the neutrosophic set based on multi-stage fault template data is generated, and then the generated neutrosophic set is fused via the simplified neutrosophic weighted averaging (SNWA) operator. Afterwards, the fault diagnosis results can be determined by the application of defuzzification method for a defuzzying neutrosophic set. Most kinds of uncertain problems in the process of fault diagnosis, including uncertain information and inconsistent information, could be handled well with the integration of multi-stage fault template data and the neutrosophic set. Finally, the practicality and effectiveness of the proposed method are demonstrated via an illustrative example.

• 出版日期2018-8
• 单位西北工业大学