东南大学学报（英文版）2019，Vol.35Issue(4)：417-423,7.DOI:10.3969/j.issn.1003-7985.2019.04.003
一种多尺度卷积自编码网络及其在滚动轴承故障诊断中的应用
A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings
摘要
Abstract
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75％,99.3％ and 100％diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with waditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data.关键词
故障诊断/深度学习/卷积自编码网络/多尺度卷积核/特征提取Key words
fault diagnosis/deep learning/convolutional auto-encoder/multi-scale convolutional kernel/feature extraction分类
机械制造引用本文复制引用
丁云浩,贾民平..一种多尺度卷积自编码网络及其在滚动轴承故障诊断中的应用[J].东南大学学报（英文版）,2019,35(4):417-423,7.基金项目
The National Natural Science Foundation of China (No.51675098). （No.51675098）
