Multiple wavelet regularized deep residual networks for fault diagnosis

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.