Journal
MEASUREMENT SCIENCE AND TECHNOLOGY
Volume 32, Issue 12, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1361-6501/ac1fbe
Keywords
axial piston pump bearings; continuous wavelet transform; semi-supervised learning; deep convolutional generative adversarial network; fault diagnosis
Funding
- National Natural Science Foundation of China [51805376]
- ZheJiang Provincial Natural Science Foundation of China [LD21E050001]
- Wenzhou Major Science and Technology Innovation Project of China [ZG2020051]
- Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems [GZKF-201719]
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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.
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.
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