A semi-supervised fault diagnosis method for axial piston pump bearings based on DCGAN

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.

Automated summary

This research proposes an intelligent fault diagnosis method for axial piston pumps based on deep convolutional generative adversarial network (DCGAN). The method enhances fault features and expands dataset, extracts deep features using DCGAN and semi-supervised GAN (SGAN), and classifies the extracted features using a clustering algorithm to achieve fault diagnosis of the axial piston pump bearing. The experimental results show high diagnostic accuracy, superior generalization ability, and excellent anti-noise ability when evaluation indicators of the clustering results are close to 1.