Deep Residual Shrinkage Networks for Fault Diagnosis

By Minghang Zhao, Shisheng Zhong, Xu-yun Fu, Baoping Tang, Michael G. Pecht. IEEE Transactions on Industrial Informatics, 2020

1,184 citations recorded by the source corpus.

This article develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to eliminate unimportant features. Moreover, considering that it is generally challenging to set proper values for the thresholds, the developed deep residual shrinkage networks integrate a few specialized neural networks as trainable modules to automatically determine the thresholds, so that professional expertise on signal processing is not required. The efficacy of the developed methods is validated through experiments with various types of noise.

Subfields: Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b89b8ff76a1c9b2f71d968fe817cae29209329eb

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In Control and Systems Engineering

1. Min Xia
2. Haidong Shao

Citations in the corpus

• Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images (2020, background): “A large number of studies have claimed that the diagnosis results of different shallow learning models are largely affected by the effectiveness of extracted features [7]–[9].”