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清华大学学报(自然科学版)  2020, Vol. 60 Issue (8): 693-700    DOI: 10.16511/j.cnki.qhdxxb.2020.25.026
  专题:过程系统工程 本期目录 | 过刊浏览 | 高级检索 |
基于正交局部慢性特征的故障检测方法
张展博1, 王振雷1, 王昕2
1. 华东理工大学 化工过程先进控制和优化技术教育部重点实验室, 上海 200237;
2. 上海交通大学 电工与电子技术中心, 上海 200240
Fault detection based on orthogonal local slow features
ZHANG Zhanbo1, WANG Zhenlei1, WANG Xin2
1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University ofScience and Technology, Shanghai 200237, China;
2. Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China
全文: PDF(1270 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 为提高化工行业中数据驱动故障检测的效果,该文针对实际工业系统中闭环控制导致的过程动态特性和数据流形中蕴含的局部信息,提出了一种基于局部时空正则慢特征提取(local time-space regularized slow feature extraction,LTSS)的方法进行故障检测。首先,构造基于局部时空正则的目标函数得到投影矩阵,进而得到预提取特征S,则S张成的空间中包含了静态信息,而S的一阶差分张成的空间中包含了动态信息。其次,基于独立成分分析(independent components analysis,ICA)方法,分别为2个空间构建对应的S2和SPE统计量进行监控,用于实时故障检测。在TE(Tennessee Eastman)过程上的案例研究可以证明所提方法的有效性。
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张展博
王振雷
王昕
关键词 故障检测动态特性局部相似性慢性先验独立成分分析(ICA)TE过程    
Abstract:A local time-space regularized slow feature extraction method was developed to improve data-driven fault detection in the chemical industry based on the process dynamics of closed-loop control systems and the local information contained in the data manifold. An objective function was defined based on the local time-space term to obtain a projection matrix and the pre-extraction feature, S. The span of S contains the static information, while the first derivative of the span of S contains the dynamic information. An independent component analysis was used to obtain statistics for S2 and SPE for both spaces for real-time fault detection. A case study on the Tennessee Eastman process shows the validity of this method.
Key wordsfault detection    process dynamics    local similarity    slow feature    independent component analysis (ICA)    Tennessee Eastman (TE) process
收稿日期: 2019-12-08      出版日期: 2020-06-17
基金资助:王昕,副教授,E-mail:wangxin26@sjtu.edu.cn
引用本文:   
张展博, 王振雷, 王昕. 基于正交局部慢性特征的故障检测方法[J]. 清华大学学报(自然科学版), 2020, 60(8): 693-700.
ZHANG Zhanbo, WANG Zhenlei, WANG Xin. Fault detection based on orthogonal local slow features. Journal of Tsinghua University(Science and Technology), 2020, 60(8): 693-700.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.25.026  或          http://jst.tsinghuajournals.com/CN/Y2020/V60/I8/693
  
  
  
  
  
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