Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes

By Minghang Zhao, Myeongsu Kang, Baoping Tang, Michael G. Pecht. IEEE transactions on industrial electronics (1982. Print), 2018

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Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “The proposed method This paper proposes a novel intelligent method based on an improved DRNN for fault diagnosis of rolling bearings.”
Deep Residual Shrinkage Networks for Fault Diagnosis (2020, methodology): “The size of the mini batch is set to 128 in order to be consistent with [20].”
A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “ed in the field of fault diagnosis. Thus in this subsection, we will review these publications according to different 26 network variants. Applications of ResNet to fault classification Zhao et al. [140] employed a series of wavelet packet coefficients as the model input and proposed a deep residua”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “Other examples include a mixture of CNN and DBN [162], deep residual network (DRN) [163], [164], deep stack network (DSN) [165], deep generative network [78], RNN based autoencoder [166], sparse ﬁltering [170], a new deep learning architecture capsule network [168] proposed by Hinton [167].”