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Enhanced Transfer Learning Method for Rolling Bearing Fault Diagnosis Based on Linear Superposition Network by Chunran Huo; Quansheng Jiang; Yehu Shen; Qixin Zhu; Qingkui Zhang is a Computer Science article available to read on EtoBox.

What is Enhanced Transfer Learning Method for Rolling Bearing Fault Diagnosis Based on Linear Superposition Network about?

Deep transfer learning is used to solve the problem of unsupervised intelligent fault diagnosis of rolling bearings. However, when the data distribution between two domains is different, the existing deep transfer learning models which only rely on the domain-invariant features are not enough to complete the target domain data learning. To solve this problem, an enhanced transfer learning method based on the linear superposition network is proposed for rolling bearing fault diagnosis. This method improves the structure of the one-dimensional convolutional neural network (1D-CNN) by constructing linear superposition convolution blocks. At the same time, the loss function of transfer learning is constructed by using the pseudo-label of the target domain from the network, which enhances the ability of rolling bearing fault feature extraction. Compared with the traditional feature-based transfer learning methods, the proposed enhanced transfer learning method based on the linear superposition network can make the network place more stress on the feature learning of the target domain. Experimental results on the Paderborn University (PU) dataset show that, compared with the improved dee

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Author

Chunran Huo; Quansheng Jiang; Yehu Shen; Qixin Zhu; Qingkui Zhang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)