Next Article in Journal
Performance Evaluation of Low-Cost Seismic Sensors for Dense Earthquake Early Warning: 2018–2019 Field Testing in Southwest China
Next Article in Special Issue
A Novel Health Indicator Based on Cointegration for Rolling Bearings’ Run-To-Failure Process
Previous Article in Journal
Wirelessly Powered Light and Temperature Sensors Facilitated by Electrically Small Omnidirectional and Huygens Dipole Antennas
Previous Article in Special Issue
Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit
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; https://doi.org/10.3390/s19092000
Received: 28 March 2019 / Revised: 24 April 2019 / Accepted: 25 April 2019 / Published: 29 April 2019
  |  
PDF [2399 KB, uploaded 29 April 2019]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top