A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings

作     者：Ding Yunhao Jia Minping 丁云浩;贾民平

作者机构：School of Mechanical EngineeringSoutheast UniversityNanjing 211189China 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2019年第35卷第4期

页      面：417-423页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The National Natural Science Foundation of China(No.51675098) 

主　　题：fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction 

摘      要：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 *** 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 *** on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed *** results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,*** addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional *** final results show that the proposed model has a better recognition effect for rolling bearing fault data.