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Designs 2018, 2(4), 56; https://doi.org/10.3390/designs2040056

Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

1
Department of Engineering Sciences, University of Agder, 4877 Grimstad, Norway
2
NORCE Norwegian Research Centre AS, 4877 Grimstad, Norway
*
Author to whom correspondence should be addressed.
Received: 23 November 2018 / Revised: 13 December 2018 / Accepted: 16 December 2018 / Published: 19 December 2018
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Abstract

Detecting bearing faults is very important in preventing non-scheduled shutdowns, catastrophic failures, and production losses. Localized faults on bearings are normally detected based on characteristic frequencies associated with faults in time and frequency spectra. However, missing such characteristic frequency harmonics in a spectrum does not guarantee that a bearing is healthy, or noise might produce harmonics at characteristic frequencies in the healthy case. Further, some defects on roller bearings could not produce characteristic frequencies. To avoid misclassification, bearing defects can be detected via machine learning algorithms, namely convolutional neural network (CNN), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer learning are proposed. Transfer of knowledge is achieved by extracting features from a CNN pretrained on data from the imageNet database to classify faults in roller bearings. The effectiveness of the proposed method is investigated based on vibration and acoustic emission signal datasets from roller bearings with artificial damage. Finally, the accuracy and robustness of the fault classifiers are evaluated at different amounts of noise and training data. View Full-Text
Keywords: roller bearing; fault classification; transfer learning; convolutional neural network; support vector machine; autoencoder; vibration signals; acoustic emissions roller bearing; fault classification; transfer learning; convolutional neural network; support vector machine; autoencoder; vibration signals; acoustic emissions
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Hemmer, M.; Van Khang, H.; Robbersmyr, K.G.; Waag, T.I.; Meyer, T.J.J. Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network. Designs 2018, 2, 56.

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