Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis

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Suh, Sungho
Lee, Haebom
Jo, Jun
Lukowicz, Paul
Lee, Yong Oh
Griffith University Author(s)
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2019
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http://creativecommons.org/licenses/by/4.0/
Abstract

In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals from a test bench are used as inputs after an image transformation procedure. Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies from 20:1 to 200:1). Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.

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Applied Sciences
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9
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4
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Subject
Neural networks
Mechanical engineering
Science & Technology
Physical Sciences
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Chemistry, Multidisciplinary
Engineering, Multidisciplinary
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Suh, S; Lee, H; Jo, J; Lukowicz, P; Lee, YO, Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosiss, Applied Sciences, 2019, 9 (4), pp. 746
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