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• W2365222312 abstract "Considering the disadvantages existing in conventional fault diagnosis methods for rotating machinery, such as necessity of manual intervention,low accuracy and difficulty to obtain fault samples,a fault diagnosis method was proposed based on manifold learning and K-nearest neighbor classifier(KNNC).Multi-domain information entropy of vibration signal was extracted to reflect fully the working status and construct high-dimensional characteristic sets.Then the second feature extraction property of the nonlinear manifold learning algorithm,orthogonal neighborhood preserving embedding(ONPE),was used for dimensionality reduction and to make the features get better clustering property. Finally,improved KNNC was used for pattern classification.The method is more suitable for small sample classification. A diagnostic case of a bearing proves the effectiveness of the model." @default.
• W2365222312 created "2016-06-24" @default.
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• W2365222312 date "2013-01-01" @default.
• W2365222312 modified "2023-09-27" @default.
• W2365222312 title "Fault diagnosis method for rotating machinery based on manifold learning and K-nearest neighbor classifier" @default.
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