A semi-supervised fault diagnosis method for axial piston pump bearings based on DCGAN. (3rd September 2021)

• Record Type: Journal Article
• Authors: He, You; Tang, Hesheng; Ren, Yan; Kumar, Anil
• Is Part Of: Measurement science & technology. Volume 32:Number 12(2021)
• Publication Date: 2021-09-03
• Subjects: axial piston pump bearings -- continuous wavelet transform -- semi-supervised learning -- deep convolutional generative adversarial network -- 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/ac1fbe
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Recently, deep learning has developed rapidly in the fault diagnosis technology of axial piston pumps. However, when the training data is scarce and the label information is insufficient, many traditional intelligent fault diagnosis models are invalid. To solve these problems, an intelligent fault diagnosis method for axial piston pumps is proposed based on deep convolutional generative adversarial network (DCGAN). Firstly, the continuous wavelet transform (CWT) and DCGAN are designed to enhance the fault features and expand dataset, respectively. Secondly, according to the number of labeled samples, DCGAN and semi-supervised GAN (SGAN) are used to extract the deep features of the image domain. Finally, the clustering algorithm is used to classify the extracted features to realize the fault diagnosis of the axial piston pump bearing. To verify the feasibility of the proposed method, experimental investigation and public dataset are adopted. When the evaluation indicators of the clustering results are close to 1, the proposed method shows the advantages of high diagnostic accuracy, superior generalization ability and excellent anti-noise ability.