Rotor fault diagnosis based on semi-supervised neighborhood adaptiveorthogonal discriminant projection

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (10) : 159-165.

CHANG Shuyuan,ZHAO Rongzhen,SHI Mingkuan

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Abstract

A rotor fault diagnosis method based on semi-supervised neighborhood adaptive orthogonal discriminant projection (SSNA-ODP) was proposed to solve the difficulty in fault identification due to the high dimension of the fault feature set.The features in time domain, frequency domain and time frequency domain of the original vibration signal were extracted to construct a feature set in mixed domain.The SSNA-ODP method was used to reduce the dimension of the feature set in mixed domain and extract the low-dimensional feature subset which was beneficial to classification and then the set was input into a support vector machine (SVM) for pattern recognition.The application verification by using typical fault data samples shows that the method can improve the generalization ability of the ODP method when there are fewer labeled samples and make use of the data manifold characteristics of global uniform neighborhood parameters, so as to effectively improve the accuracy of fault identification.

Key words

dimension reduction / semi-supervised / orthogonal discriminant projection(ODP) / fault diagnosis

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CHANG Shuyuan,ZHAO Rongzhen,SHI Mingkuan. Rotor fault diagnosis based on semi-supervised neighborhood adaptiveorthogonal discriminant projection[J]. Journal of Vibration and Shock, 2021, 40(10): 159-165

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