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Multi-layer graph product attention fusion network for semi-supervised learning classification of bearing fault diagnosis

Published:2024-08-30 author:WEI Chunhu, CHENG Feng, ZENG Yuhai, et al. Browse: 519 Check PDF documents

Multilayer graph product attention fusion network for semisupervised

learning classification of bearing fault diagnosis

WEI Chunhu1, CHENG Feng1, ZENG Yuhai1, YANG Shifei2

(1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Nanjing Chaos

Data Technology Co., Ltd, Nanjing 210012, China)

Abstract: The smooth operation of graph convolution network leads to the problem that it can not capture deep information through deep network stacking. In order to solve this problem, a multi-layer graph convolution attention fusion network (MGCAN) suitable for semi-supervised learning classification of rolling bearing fault diagnosis was proposed. Firstly, the frequency domain graphing method was adopted to transform data into a graph model, the inherent structural information within the data was captured. The constructed graph data was inputted into the network, and feature information was extracted layer by layer, progressively deepening the network's understanding of data features from shallow to deep layers. Then, each layer of graph convolution information was orderly concatenated. Meanwhile, the graph attention mechanism was incorporated, the network was enabled to automatically focus on the important information for classification tasks. As a result, the performance and robustness of the network were enhanced. Finally, through iterative learning, the model parameters were continuously optimized, fault information was accurately identified by the networks, and multiple experiments on rolling bearings under different working conditions was conducted. This method was compared with traditional deep learning methods. The research results indicate that even with only 10% of labeled data, the network can still achieve an accuracy of over 88%, and it is applicable to various conditions such as uniform speed and variable speed. The results confirm that, after employing an appropriate method to retain useful information in multi-layer graph convolutions, deep graph convolutional networks can be a powerful tool for diagnosing faults in rolling bearings.

Key words: bearing fault diagnosis; multi-layer graph convolution attention fusion network(MGCAN); multi-layer graph convolution information; graph attention mechanism; k-nearest neighbor (KNN) graph; deep learning (DE); recognition accuracy

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