Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed fluctuation and extremely low labeled rates. (August 2022)

• Record Type: Journal Article
• Authors: Yan, Shen; Shao, Haidong; Xiao, Yiming; Zhou, Jian; Xu, Yuandong; Wan, Jiafu
• Is Part Of: Advanced engineering informatics. Volume 53(2022)
• Publication Date: 2022-08
• Subjects: Semi-supervised fault diagnosis -- LPS-DGAT -- Speed fluctuation -- Extremely low labeled rates -- Machinery; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101648
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Highlights: A new LPS is designed to further mine the limited label information. Construct the DGAT to enhance the discriminatory ability of neighbor nodes. Diagnosis cases focus on speed fluctuation and extremely low labeled rates. The proposed method performs better than other semi-supervised learning methods. Abstract: Recent research in semi-supervised fault diagnosis of machinery based on graph neural networks (GNNs) still has some problems, such as insufficient label information mining, static feature extraction of neighbor nodes, and relatively ideal diagnosis scenarios. In engineering practice, machinery often runs under speed fluctuation such as start-stop process, and labeling samples becomes increasingly expensive. To deal with the above challenges, a new semi-supervised fault diagnosis method called label propagation strategy and dynamic graph attention network (LPS-DGAT) is proposed in this paper. The designed LPS can take full advantage of the label co-dependency between samples, so as to realize the full utilization of the limited label information. The constructed DGAT by dynamic attention can effectively extract feature information of the different neighbor nodes under speed fluctuation. The proposed method is used to analyze the vibration signals of bearing and gear under speed fluctuation, and the comparison results show that even in the extreme situations where the labeled rates are no more than 1%, the proposed method can still accurately extract discriminative features and diagnose different fault modes, which is better than other GNNs.