4.7 Article

Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings

Journal

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921719850576

Keywords

Generative adversarial networks; fault diagnostics; deep learning; health monitoring; ball bearings; vibration analysis

Funding

  1. Chilean National Fund for Scientific and Technological Development (Fondecyt) [1160494]

Ask authors/readers for more resources

With the availability of cheaper multisensor suites, one has access to massive and multidimensional datasets that can and should be used for fault diagnosis. However, from a time, resource, engineering, and computational perspective, it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data, that is, massive multidimensional data. Therefore, this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics. Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics. The results indicate that the proposed methodology is a promising approach for both unsupervised and semi-supervised fault diagnostics.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available