Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings. (March 2020)

• Record Type: Journal Article
• Authors: Verstraete, David Benjamin; Droguett, Enrique López; Meruane, Viviana; Modarres, Mohammad; Ferrada, Andrés
• Is Part Of: Structural health monitoring. Volume 19:Number 2(2020)
• Page Start: 390
• Page End: 411
• Publication Date: 2020-03
• Subjects: Generative adversarial networks -- fault diagnostics -- deep learning -- health monitoring -- ball bearings -- vibration analysis; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921719850576
• Languages: English
• ISSNs: 1475-9217

Abstract

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.