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基于DropOut降噪自编码的磨矿系统故障诊断
Alternative TitleDropOut denoising autoencoder-based fault diagnosis for grinding system
曲星宇1,2,3; 曾鹏1,2; 徐承成4; 付东东4
Department工业控制网络与系统研究室
Source Publication控制与决策
ISSN1001-0920
2018
Volume33Issue:9Pages:1662-1666
Indexed ByEI ; CSCD
EI Accession number20185206314451
CSCD IDCSCD:6334190
Contribution Rank1
Keyword故障诊断 自动编码器 Dropout 降噪自编码 Softmax分类器 深度学习
Abstract

磨矿系统的故障诊断主要依靠工人的经验,这为故障诊断增加了大量不确定性.此外,磨矿系统的数据较为复杂,不仅工人难以对故障的发生进行准确判断,而且传统机器学习算法也由于数据的线性不可分而表现不佳.为了解决线性不可分问题,使用神经网络进行故障分类;面对故障数据的高复杂度,为提高神经网络的表达能力,使用自动编码器增加网络深度;为减弱深层网络带来的过拟合现象,引入Drop Out降噪自编码.最后进行实验验证,实验结果表明,Drop Out降噪自编码网络对于故障的分类准确率可达到90.4%.

Other Abstract

The current fault diagnosis of the grinding system mainly relies on the experience of the workers, which adds a lot of uncertainty to the fault diagnosis. In addition, the grinding system data is too complex, which makes it hard for the workers to judge, and brings about poor performance for traditional machine learning algorithms due to the linear indivisiblity. In order to solve the problem of linear indivisibility, we use neural network for fault classification. In face of the high complexity of fault data, we use autoencoder to increase network depth in order to improve the expression ability of neural network. To reduce the over-fitting brought by deep network, we introduce DropOut and denoising autoencoder. Finally, the experimetal results show that the DropOut denoising autoencoder(DDA) can make the accuracy rate of the fault classification be 90.4% fault classification.

Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22412
Collection工业控制网络与系统研究室
Corresponding Author曲星宇
Affiliation1.中国科学院沈阳自动化研究所工业信息学重点实验室
2.中国科学院大学
3.北方重工集团有限公司
4.东北大学信息科学与工程学院
Recommended Citation
GB/T 7714
曲星宇,曾鹏,徐承成,等. 基于DropOut降噪自编码的磨矿系统故障诊断[J]. 控制与决策,2018,33(9):1662-1666.
APA 曲星宇,曾鹏,徐承成,&付东东.(2018).基于DropOut降噪自编码的磨矿系统故障诊断.控制与决策,33(9),1662-1666.
MLA 曲星宇,et al."基于DropOut降噪自编码的磨矿系统故障诊断".控制与决策 33.9(2018):1662-1666.
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