THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

WU DingHai; REN GuoQuan; WANG HuaiGuang; ZHANG YunQiang

doi:10.16579/j.issn.1001.9669.2020.05.002

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THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

更新时间：2022-09-22

- THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK
- Journal of Mechanical Strength Vol. 42, Issue 5, Pages: 1024-1032(2020)
- 作者机构：
  
  1. 陆军工程大学石家庄校区
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2020.05.002
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WU DingHai, REN GuoQuan, WANG HuaiGuang, et al. THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK. [J]. 42(5):1024-1032(2020)
DOI：

WU DingHai, REN GuoQuan, WANG HuaiGuang, et al. THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK. [J]. 42(5):1024-1032(2020) DOI： 10.16579/j.issn.1001.9669.2020.05.002.

摘要

深度学习善于从海量的数据中挖掘越来越抽象的特征并具有良好的泛化能力,受到了越来越多的研究人员的关注,而卷积神经网络(Convolutional Neural Network,CNN)则是其深度学习中一种经典而广泛应用的结构,在计算机视觉、目标检测、自然语言处理、语音识别等领域成效卓越。在详细分析机械系统故障诊断的现状和需求的基础上,首先介绍了CNN的典型结构,然后从数据输入类型、网络结构设计、迁移学习等方面对CNN在机械故障领域的应用进行了归纳总结,研究CNN应用的深层次特征提取与可视化等问题,最后,分析机械故障诊断中存在的困难,并展望了CNN在机械故障诊断领域应用中待研究解决的若干问题。

Abstract

Deep learning is good at abstract features from massive data and has good generalization ability,which has attracted more and more researchers’ attention. The Convolutional Neural Network（ CNN） is a classic structure of deep learing and which is being widely and successfully used in the fields of computer vision,target detection,natural language processing,and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis,this paper introduces the structure of CNN,and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type,network structure design and migration learning. The problems of deep feature extraction and visualization are also discussed,and finally,the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN are prospected.

关键词

深度学习卷积神经网络大数据状态监测故障诊断迁移学习特征提取

Keywords

Deep learningConvolutional neural networkBig dataCondition monitoringFault diagnosisTransfer learningFeature extraction

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