Fault Detection of Discriminant Improved Local Tangent Space Alignment

Sihang Li, Ping Qian, Haoyu Gu
{"title":"Fault Detection of Discriminant Improved Local Tangent Space Alignment","authors":"Sihang Li, Ping Qian, Haoyu Gu","doi":"10.1109/ICIIBMS46890.2019.8991493","DOIUrl":null,"url":null,"abstract":"With the increasing complexity of automation systems in chemical processes, online monitoring and fault diagnosis are effective ways to ensure product quality and reduce equipment failure. Aiming at the characteristics of nonlinear, multi-noise, Gaussian and non-Gaussian mixing of real industrial process data, this paper proposes a chemical process fault detection method based on Discriminant Improved Local Tangent Space Alignment (DILTSA). Firstly, the low-dimensional eigenvectors of high-dimensional matrices are extracted by using the discriminative improvement of DILTSA with high robustness and less adjustment parameters. The dimension reduction is performed to obtain the corresponding optimal mapping matrix. Then, the support vector data description (SVDD) is introduced. The SVDD model is built from the feature space. Finally, the method proposed in this paper is applied to the TE platform. The classical detection method is compared and the experimental simulation results are analyzed. It shows that the method can achieve better fault detection results.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"2003 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIIBMS46890.2019.8991493","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

With the increasing complexity of automation systems in chemical processes, online monitoring and fault diagnosis are effective ways to ensure product quality and reduce equipment failure. Aiming at the characteristics of nonlinear, multi-noise, Gaussian and non-Gaussian mixing of real industrial process data, this paper proposes a chemical process fault detection method based on Discriminant Improved Local Tangent Space Alignment (DILTSA). Firstly, the low-dimensional eigenvectors of high-dimensional matrices are extracted by using the discriminative improvement of DILTSA with high robustness and less adjustment parameters. The dimension reduction is performed to obtain the corresponding optimal mapping matrix. Then, the support vector data description (SVDD) is introduced. The SVDD model is built from the feature space. Finally, the method proposed in this paper is applied to the TE platform. The classical detection method is compared and the experimental simulation results are analyzed. It shows that the method can achieve better fault detection results.
判别改进局部切线空间对准的故障检测
随着化工过程自动化系统的日益复杂,在线监测和故障诊断是保证产品质量和减少设备故障的有效途径。针对实际工业过程数据的非线性、多噪声、高斯和非高斯混合的特点,提出了一种基于判别改进局部切线空间对准(DILTSA)的化工过程故障检测方法。首先,采用鲁棒性强、调整参数少的DILTSA判别改进方法提取高维矩阵的低维特征向量;通过降维得到相应的最优映射矩阵。然后,引入了支持向量数据描述(SVDD)。SVDD模型是由特征空间构建的。最后,将本文提出的方法应用于TE平台。对经典检测方法进行了比较,并对实验仿真结果进行了分析。结果表明,该方法能取得较好的故障检测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信