Measurement, Vol. 152, July 2020, DOI:10.1016/j.measurement.2019.107331

Deep Residual Shrinkage Networks for Fault Diagnosis

Minghang Zhao 1, Shisheng Zhong 1, Xuyun Fu 1, Baoping Tang 2, and Michael Pecht 3
1 School of Naval Architecture and Ocean Engineering, Harbin Institute of Technology, Weihai, China
2 State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
3 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA


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.

This article is available online here and to CALCE Consortium Members for personal review.

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