Deep Residual Shrinkage Networks for Fault Diagnosis
Deep Residual Shrinkage Networks for Fault Diagnosis
复制标题
用于故障诊断的深层残余收缩网络
DOI：
10.1109/tii.2019.2943898
复制
发表时间：
2020-07-01
影响因子：
12.3
通讯作者：
Pecht, Michael
中科院分区：
文献类型：
--
作者：
Zhao, Minghang;Zhong, Shisheng;Pecht, Michael
本文开发了新的深度学习方法，即深度残差收缩网络，以提高对高噪声振动信号的特征学习能力，并实现高故障诊断精度。软阈值作为非线性变换层插入到深层架构中以消除不重要的特征。此外，考虑到为阈值设置合适的值通常具有挑战性，所开发的深度残差收缩网络集成了一些专用神经网络作为可训练模块来自动确定阈值，因此不需要信号处理方面的专业知识。所开发的方法的有效性进行了验证，通过实验与各种类型的噪声。
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.