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Shock and Vibration
Volume 2017 (2017), Article ID 5067651, 17 pages
Research Article

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

1Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
2Computer Science Department, University of Chile, Santiago, Chile
3Mechanical Engineering Department, University of Chile, Santiago, Chile

Correspondence should be addressed to David Verstraete

Received 13 May 2017; Accepted 14 August 2017; Published 9 October 2017

Academic Editor: Matthew J. Whelan

Copyright © 2017 David Verstraete et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.