机械科学与技术2018，Vol.37Issue(3)：352-357,6.DOI:10.13433/j.cnki.1003-8728.2018.0304
稀疏自编码深度神经网络及其在滚动轴承故障诊断中的应用
Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis
摘要
Abstract
To overcome the problem of using supervised learning to extract fault features for most current rolling bearing fault diagnosis methods,a deep neural network algorithm is proposed,which is realized sparse auto-encoder,to achieve unsupervised feature learning by automatic extracting the inherent characteristics of the rolling bearing vibration signal for fault diagnosis of rolling bearing fault diagnosis.Firstly,the spectrum of the bearing vibration signal is used to train sparse auto-encoder in order to obtain parameters;secondly,the parameters from sparse autoencoder and spectrum of the rolling bearing vibration signal are used to train the deep neural network,and the backpropagation algorithm is used for fine-tuning the deep neural network with the purpose of improving classification accuracy.Finally,the deep neural network has been trained to identify faults of rolling bearings.The analysis results from vibration signals with roller normal condition of the rolling bearing,pitting fault of bearing outer ring,pitting fault of bearing inner ring and crack fault of bearing rolling element show that,compared with back propagation neural network,the proposed deep neural network can accurately identify fault type of rolling bearing faults.关键词
稀疏自编码/深度神经网络/滚动轴承/故障诊断Key words
sparse auto-encoder/deep neural network/rolling bearing/fault diagnosis分类
机械制造引用本文复制引用
汤芳,刘义伦,龙慧..稀疏自编码深度神经网络及其在滚动轴承故障诊断中的应用[J].机械科学与技术,2018,37(3):352-357,6.基金项目
国家自然科学基金项目(51375500,61402167)与湖南科技大学机械设备健康维护湖南省重点实验室开放基金项目(201605)资助 （51375500,61402167）
