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- Title
Multi-information Fusion Fault Diagnosis Based on KNN and Improved Evidence Theory.
- Authors
Liu, Yuwei; Cheng, Yuqiang; Zhang, Zhenzhen; Wu, Jianjun
- Abstract
Purpose: This paper proposes a new multi-information fusion fault diagnosis method, which combines the K-Nearest Neighbor and the improved Dempster–Shafer (D–S) evidence theory to consider the uncertainty comprehensively. Method: First, KNN is used to perform a local diagnosis of each fault to obtain the prior probability. Then the D–S evidence theory is improved by combining the Jousselme distance, and basic probability assignments of the evidence body are weighed and revised. Finally, the Dempster combination rule is used to fuse the information at the decision-making level. Results: Using the test data of the rotor test bench in the laboratory, the effectiveness of the proposed KNN–DS method is proved and compared with the improved evidence theory method proposed by Murphy. Conclusion: The results show that the proposed method obtains a better diagnosis result.
- Subjects
K-nearest neighbor classification; PROBABILITY theory; DIAGNOSIS methods; FAULT diagnosis
- Publication
Journal of Vibration Engineering & Technologies, 2022, Vol 10, Issue 3, p841
- ISSN
2523-3920
- Publication type
Article
- DOI
10.1007/s42417-021-00413-8