仅为学习，翻译进行中...
【题目】Multiple Wavelet Regularized Deep Residual Networks for Fault Diagnosis
【翻译】基于多组小波正则化深度残差网络的故障诊断
Abstract (摘要)
Abstract: 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.
【翻译】作为一种新兴的深度学习方法，深度残差网络在故障诊断领域逐渐流行起来。在基于深度残差网络的故障诊断中，一个重要的任务是避免过拟合。其中，过拟合是样本量不足时故障诊断准确率低的一个主要原因。本文提出了一种多组小波正则化的深度残差网络（Multiple Wavelet Regularized Deep Residual Network，MWR-DRN），使用一个小波基函数（wavelet basis function，WBF）作为主WBF，将其他WBF作为辅助WBF。具体而言，所提出的MWR-DRN模型不仅被主WBF的二维矩阵所训练，而且被随机选择策略下的辅助WBF的二维矩阵所训练。实验结果验证了所提出MWR-DRN在提高诊断准确率时的有效性。
【关键词】Deep learning, deep residual learning, fault diagnosis, multiple wavelet regularization, wavelet packet transform.
【翻译】深度学习，深度残差学习，故障诊断，多组小波阈值化，小波包变换
Reference
M. Zhao, B. Tang, L. Deng, and M. Pecht, "Multiple Wavelet Regularized Deep Residual Networks for Fault Diagnosis," Measurement, 2019.
https://www.sciencedirect.com/science/article/pii/S0263224119311959
