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A platform for research: civil engineering, architecture and urbanism
In order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from different angels, and the original high-dimensional and multi-domain feature set was constructed. Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class. Finally, a one-against-one approach was applied to train several relevance vector machine（RVM） binary classifiers, and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types. The experimental results of gear indicate that the proposed method is of high diagnostic accuracy.
In order to diagnose fault effectively by using sensitive features contained in the feature set, KFDA was improved in this paper and a fault diagnosis method based on improved KFDA individual feature selection was proposed. Firstly, the mixed feature of the fault vibration signal was extracted from different angels, and the original high-dimensional and multi-domain feature set was constructed. Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of class. Finally, a one-against-one approach was applied to train several relevance vector machine（RVM） binary classifiers, and sensitive feature was input into the multi-class fault diagnosis model for recognizing the fault types. The experimental results of gear indicate that the proposed method is of high diagnostic accuracy.
FAULT DIAGNOSIS BASED ON IMPROVED KFDA INDIVIDUAL FEATURE SELECTION
CHEN Rui (author)
2019
Article (Journal)
Electronic Resource
Unknown
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