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Open AccessArticle

Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input

Research Center for High-Speed Railway Network Management of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2000;
Received: 28 March 2019 / Revised: 24 April 2019 / Accepted: 25 April 2019 / Published: 29 April 2019
The bearing is a component of the support shaft that guides the rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint. In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods. Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained. Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input. Experimental results showed that the proposed method has a desirable 99.93% classification accuracy in the case of less labeled data from the public data set of West Reserve University, which is better than the state-of-the-art methods. View Full-Text
Keywords: bearing; fault diagnosis; GAN; semi-supervised bearing; fault diagnosis; GAN; semi-supervised
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Zhao, D.; Liu, F.; Meng, H. Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input. Sensors 2019, 19, 2000.

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