A hidden feature label propagation method based on deep convolution variational autoencoder for fault diagnosis. (1st May 2022)

• Record Type: Journal Article
• Authors: She, Bo; Wang, Xuan
• Is Part Of: Measurement science & technology. Volume 33:Number 5(2022)
• Publication Date: 2022-05-01
• Subjects: convolution -- variational autoencoder -- label propagation -- new emerge fault class -- fault diagnosis; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac4ffa
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Vibration signal of mechanical component usually exhibits non-linear and non-stationary characteristics, the key step of fault diagnosis is to extract discriminant features hidden in the vibration signal, in order to improve diagnostic performance and identify new fault class, a novel fault diagnosis method of hidden feature label propagation based on deep convolution variational autoencoder (HFLPDCVA) is proposed. Firstly, the raw vibration signal is transformed into frequency spectrum data by using fast Fourier transform as the input of the model. Secondly, the variational autoencoder is used to construct the convolutional neural network, and the non-fixed dropout parameter is fused to change the network to improve the identification of network hidden layer features. Finally, the label propagation algorithm is applied to process the hidden features of the full connection layer, and the local common features between the unknown label samples and the known label samples are extracted to predict the class of unknown label samples, including the new emerge fault class. The effectiveness of the proposed method is verified on bearing fault data sets under variable working conditions and damage data sets of self-priming centrifugal pump under a single working condition. Experimental results show that the proposed HFLPDCVA method can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers.