期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 8, 页码 5450-5460出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3034189
关键词
Convolution deep belief network (CDBN); electromechanical system; fault diagnosis; graph neural network (GNN); semisupervised learning
类别
资金
- National Natural Science Foundation of China [52075095]
- Postgraduate Research and Practice Innovation Program of Jiangsu Province, China [SJKY19_0064, KYCX19_0063]
This article proposes an intelligent fault diagnosis method for electromechanical systems based on a new semisupervised graph convolution deep belief network algorithm, which can achieve high diagnostic accuracy with a small amount of labeled data.
The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semisupervised graph convolution deep belief network algorithm is proposed in this article. Specifically, the labeled and unlabeled samples are first employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss. Finally, the local and discriminative objective functions are reconstructed under the semisupervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66% accuracy with only 10% of training labeled data, which indicates that it is a promising semisupervised intelligent fault diagnosis method.
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