Autor: |
Yahui Wang, Rong Dong, Xinchao Wang, Xunying Zhang |
Jazyk: |
angličtina |
Rok vydání: |
2023 |
Předmět: |
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Zdroj: |
Shock and Vibration, Vol 2023 (2023) |
Druh dokumentu: |
article |
ISSN: |
1875-9203 |
DOI: |
10.1155/2023/5600690 |
Popis: |
A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the bearing in four states: normal, rolling element fault, inner ring fault, and outer ring fault. The Volterra time-domain kernel is used as the feature vector for kernel principal component analysis to classify and identify the faults. The feasibility of the fault diagnosis method of the Volterra level and kernel principal component analysis is verified by the experimental results. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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