基于深度学习的故障诊断方法综述

文成林; 吕菲亚

doi:10.11999/JEIT190715

基于深度学习的故障诊断方法综述

doi: 10.11999/JEIT190715

文成林^1,,
吕菲亚^2, ,

1.
杭州电子科技大学自动化学院杭州 310018
2.
安阳师范学院软件学院安阳 455000

基金项目: 国家自然科学基金(U1509203, 61751304, 61573137, 61673160)，浙江省重点项目(LZ16F030002)

详细信息

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

¹⁾本文所讨论的故障实时诊断与预测技术均假定故障可被感知并能被分离，可被感知是指故障在一定程度上影响系统的状态和输出，能被分离是指依据现有信息可以指示故障发生部位和发生机理。²⁾机理分析方法指是通过对系统内部原因/机理的分析研究，从而找出其发展变化规律的一种科学研究方法，依赖于因果关系的提取与表征，适用于输入、输出及状态变量较少的系统^[6]，包括分析方法和统计方法。³⁾特征工程指的是把原始数据转变为模型的训练数据的过程，目的是获取更好的训练数据特征，包括特征构建、特征提取、特征选择3个部分。⁴⁾虽然神经网络可以以任意精度逼近非线性函数^[18]，但是面对复杂工业过程的高维、非高斯分布、非线性、时变、多模态等特性，传统的神经网络方法多是从逼近论的角度拟合监测数据并进行特征提取，受限于网络结构训练算法和计算复杂度的影响，通常只是设置2到3个隐层，降低了逼近的精度。
⁵⁾假设特征图长宽相同.
中图分类号: TP274
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-17
- 修回日期: 2019-12-02
- 网络出版日期: 2019-12-10
- 刊出日期: 2020-01-21

Review on Deep Learning Based Fault Diagnosis

Chenglin WEN^1
,,
Feiya LÜ^{2
, ,}

1.
Institute of Automation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
School of Software Engineering, Anyang Normal University, Anyang 455000, China

Funds: The National Natural Science Foundation of China (U1509203, 61751304, 61573137, 61673160), Zhejiang Provincial Foundation (LZ16F030002)

摘要

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。
- 故障诊断 /
- 数据驱动 /
- 深度学习 /
- 高阶相关性
Abstract:
The massive high-dimensional measurements accumulated by distributed control systems bring great computational and modeling complexity to the traditional fault diagnosis algorithms, which fail to take advantage of the higher-order information for online estimation. In view of its powerful ability of representation learning, deep learning based fault diagnosis is extensively studied, both in academia and in industry, making intelligent process control more automated and effective. In this paper, deep learning based fault diagnosis is reviewed and summarized as four parts, i.e., stacked auto-encoder based fault diagnosis, deep belief network based fault diagnosis, convolutional neural network based fault diagnosis, and recurrent neural network based fault diagnosis. Furthermore, some necessity and potential trends, "integrated innovation", "data + knowledge" and "information fusion", are discussed from the view of data preprocessing, network design and decision.
- Fault diagnosis /
- Data driven /
- Deep learning /
- Higher-order correlation
¹⁾本文所讨论的故障实时诊断与预测技术均假定故障可被感知并能被分离，可被感知是指故障在一定程度上影响系统的状态和输出，能被分离是指依据现有信息可以指示故障发生部位和发生机理。²⁾机理分析方法指是通过对系统内部原因/机理的分析研究，从而找出其发展变化规律的一种科学研究方法，依赖于因果关系的提取与表征，适用于输入、输出及状态变量较少的系统^[6]，包括分析方法和统计方法。³⁾特征工程指的是把原始数据转变为模型的训练数据的过程，目的是获取更好的训练数据特征，包括特征构建、特征提取、特征选择3个部分。⁴⁾虽然神经网络可以以任意精度逼近非线性函数^[18]，但是面对复杂工业过程的高维、非高斯分布、非线性、时变、多模态等特性，传统的神经网络方法多是从逼近论的角度拟合监测数据并进行特征提取，受限于网络结构训练算法和计算复杂度的影响，通常只是设置2到3个隐层，降低了逼近的精度。

⁵⁾假设特征图长宽相同.

HTML全文

图 1 数据驱动的故障诊断框架

下载: 全尺寸图片幻灯片

图 2 基于深度学习的故障诊断研究思路汇总

下载: 全尺寸图片幻灯片

图 3 基于深度学习的故障诊断方法分类

下载: 全尺寸图片幻灯片

图 4 栈式自编码网络的结构

下载: 全尺寸图片幻灯片

图 5 基于受限玻尔兹曼机的深度网络结构

下载: 全尺寸图片幻灯片

图 6 卷积神经网络的结构

下载: 全尺寸图片幻灯片

图 7 循环神经网络的结构

下载: 全尺寸图片幻灯片

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