Journal
MEASUREMENT
Volume 152, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107331
Keywords
Deep learning; Deep residual learning; Fault diagnosis; Multiple wavelet regularization; Wavelet packet transform
Funding
- National Natural Science Foundation of China [51775065]
- Science and Technology Projects in Chongqing, China [cstc2019jcyj-zdxmX0026, cstc2018jszx-cyzdX0146]
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As an emerging deep learning method, deep residual networks are gradually becoming popular in the research field of machine fault diagnosis. A significant task in deep residual network-based fault diagnosis is to prevent overfitting, which is often a major reason for low diagnostic accuracy when there is insufficient training data. This paper develops a multiple wavelet regularized deep residual network (MWR-DRN) model that uses one wavelet basis function (WBF) as the primary WBF and other WBFs as the auxiliary WBFs. Regularized means that a constraint or restriction is applied to yield a high performance on the testing data. To be specific, the developed MWR-DRN model is trained not only by the 2D matrices from the primary WBF, but also by the 2D matrices from the auxiliary WBFs using a stochastic selection strategy. Experimental results validate the effectiveness of the developed MWR-DRN in improving diagnostic accuracy. (C) 2019 Elsevier Ltd. All rights reserved.
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