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

Applied Sciences - Tập 9 Số 4 - Trang 746
Sungho Suh1,2, Haebom Lee2, Jun Jo2, Paul Lukowicz1, Yong Oh Lee2
1Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany
2Smart Convergence Group, Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH, 66123 Saarbrücken, Germany

Tóm tắt

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.

Từ khóa


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