Journal
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
Volume 19, Issue 2, Pages 390-411Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921719850576
Keywords
Generative adversarial networks; fault diagnostics; deep learning; health monitoring; ball bearings; vibration analysis
Funding
- Chilean National Fund for Scientific and Technological Development (Fondecyt) [1160494]
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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.
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