Entropy-graph attention network for semi-supervised fault diagnosis of rotating machinery under extremely low label rate and variable rotating speed conditions#br#

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (19) : 242-248.

XIE Junwen, TONG Jinyu, ZHENG Jinde, PAN Haiyang, BAO Jiahan

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Abstract

In the case of very low labeling rates, existing Graph Neural Networks (GNN) have problems such as insufficient mining of correlation information between nodes when constructing graphs. In industrial production, rotating machinery often operates at variable speeds, and labelling fault samples is getting expensive. Aiming at the above two problems, a novel semi-supervised fault diagnosis method based on Jenson's Shannon relative entropy (JS) and dynamic graph attention network (DGAT) for rotating machinery under variable speed conditions is proposed in this paper. Firstly, a graph construction method based on JS is designed, which can fully mine the relevant information between samples. Secondly, a semi-supervised learning model based on entropy-dynamic graph attention networks (E-DGAT) is constructed to further mine the the sensitive features of faults in samples through dynamic attention mechanism. The proposed method is validated on a dataset of bearings and gearbox under variable speed conditions, and the results show that the proposed method is able to accurately diagnose different fault types of rotating machinery with a very low labelling rate of no more than 1%, and the proposed method is better than other commonly used GNN.

Key words

rotating machinery / fault diagnosis / graph neural networks / relative entropy / variable speed / low labelling rate

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XIE Junwen, TONG Jinyu, ZHENG Jinde, PAN Haiyang, BAO Jiahan. Entropy-graph attention network for semi-supervised fault diagnosis of rotating machinery under extremely low label rate and variable rotating speed conditions#br#[J]. Journal of Vibration and Shock, 2024, 43(19): 242-248

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