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基于深度学习的故障诊断方法综述

文成林 吕菲亚

引用本文: 文成林, 吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报, 2020, 42(1): 234-248. doi: 10.11999/JEIT190715 shu
Citation:  Chenglin WEN, Feiya LÜ. Review on Deep Learning Based Fault Diagnosis[J]. Journal of Electronics and Information Technology, 2020, 42(1): 234-248. doi: 10.11999/JEIT190715 shu

基于深度学习的故障诊断方法综述

    作者简介: 文成林: 男,1963年生,教授,主要研究方向为故障诊断,多目标跟踪,信息融合等;
    吕菲亚: 女,1991年生,博士,讲师,主要研究方向为故障诊断,机器学习,信息融合等
    通讯作者: 吕菲亚,lvfeiya0215@126.com
  • 基金项目: 国家自然科学基金(U1509203, 61751304, 61573137, 61673160),浙江省重点项目(LZ16F030002)

摘要: 海量高维度的过程测量信息给传统的故障诊断算法带来极大的计算复杂度和建模复杂度,且传统诊断算法存在难以利用高阶量进行在线估计的不足。鉴于深度学习技术强大的数据表示学习和分析能力,基于深度学习的故障诊断引起了工业界和学术界的广泛关注,并促使智能过程控制更加自动化和有效。该文从方法上将基于深度学习的故障诊断技术分为:基于栈式自编码的故障诊断方法、基于深度置信网络的故障诊断方法、基于卷积神经网络的故障诊断方法及基于循环神经网络的故障诊断方法4类,分别进行了回顾和总结,最后从数据预处理、深度网络设计和决策3个层面对这一领域进行展望,提出了“集成创新”、“数据+知识”和“多技术融合”等故障诊断思想,阐明基于深度学习技术进行复杂系统的故障诊断仍具有巨大潜力。

English

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  • 图 1  数据驱动的故障诊断框架

    图 2  基于深度学习的故障诊断研究思路汇总

    图 3  基于深度学习的故障诊断方法分类

    图 4  栈式自编码网络的结构

    图 5  基于受限玻尔兹曼机的深度网络结构

    图 6  卷积神经网络的结构

    图 7  循环神经网络的结构

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  • 通讯作者:  吕菲亚, lvfeiya0215@126.com
  • 收稿日期:  2019-09-17
  • 录用日期:  2019-12-02
  • 网络出版日期:  2019-12-10
  • 刊出日期:  2020-01-01
通讯作者: 陈斌, bchen63@163.com
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