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

Sungho Suh; Haebom Lee; Jun Jo; Paul Lukowicz; Yong Oh Lee
In: Applied Sciences, Vol. 9, No. 4 (746), Pages 1-16, MDPI, 1/2019.


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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