基于自适应滑窗递归稀疏主成分分析的工业过程故障监测

doi:10.3969/j.issn.0372-2112.2020.09.018

电子学报 ›› 2020, Vol. 48 ›› Issue (9): 1795-1803.DOI: 10.3969/j.issn.0372-2112.2020.09.018

基于自适应滑窗递归稀疏主成分分析的工业过程故障监测

刘金平¹, 王杰¹, 唐朝晖², 贺俊宾^1,4, 谢永芳², 马天雨^1,3

1. 湖南师范大学智能计算与语言信息处理湖南省重点实验室, 湖南长沙 410081;
2. 中南大学自动化学院, 湖南长沙 410083;
3. 湖南师范大学物理与电子科学学院, 湖南长沙 410081;
4. 湖南省计量检测研究院, 湖南长沙 410014

收稿日期:2019-07-25 修回日期:2019-10-24 出版日期:2020-09-25
- 通讯作者:
- 马天雨
- 作者简介:
- 刘金平 男,1983年生于湖南省洞口县.湖南师范大学信息科学与工程学院副教授.研究方向为智能信息处理.E-mail:ljp202518@163.com
  王杰男,1995年生于湖北随州.目前在湖南师范大学信息科学与工程学院攻读硕士学位,研究方向为智能信息处理、工业过程故障诊断.E-mail:wangjie_hunnu@163.com
  唐朝晖 男,1965年出生于湖南省长沙市.中南大学自动化学院教授.研究方向为复杂工业系统的建模与优化控制、复杂工业系统的建模与优化控制.E-mail:zhtang@csu.edu.cn
  贺俊宾 男,1989年生于湖南常德.目前在湖南师范大学信息科学与工程学院攻读硕士学位,研究方向为智能信息处理.E-mail:341078304@qq.com
  谢永芳 男,1972年出生于河南省郸城县.中南大学自动化学院教授.研究方向为复杂工业系统的建模与优化控制、分散鲁棒控制、工业过程故障诊断.E-mail:yfxie@csu.edu.cn
- 基金资助:
- 国家自然科学基金项目 (No.61971188，No.61771492，No.U1701261，No.61725306）; 湖南省自然科学基金项目 (No.2018JJ3349）; 湖南省知识产权战略推进专项项目 (No.2019F011K）; 湖南省研究生科研创新项目 (No.CX2018B31，No.CX20190415）

Industrial Process Fault Monitoring Based on Adaptive Sliding Window-Recursive Sparse Principal Component Analysis

LIU Jin-ping¹, WANG Jie¹, TANG Zhao-hui², HE Jun-bin^1,4, XIE Yong-fang², MA Tian-yu^1,3

1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan 410081, China;
2. School of Automation, Central South University, Changsha, Hunan 410083, China;
3. School of Physics and Electronics, Hunan Normal University, Changsha, Hunan 410081, China;
4. Hunan Institute of Metrology and Test, Changsha, Hunan 410014, China

Received:2019-07-25 Revised:2019-10-24 Online:2020-09-25 Published:2020-09-25
- Corresponding author:
- MA Tian-yu
- Supported by:
- National Natural Science Foundation of China (No.61971188, No.61771492, No.U1701261, No.61725306); Natural Science Foundation of Hunan Province, China (No.2018JJ3349); Hunan Province Intellectual Property Strategy Special Project (No.2019F011K); Postgraguate Technology Innovation Program of Hunan Province (No.CX2018B31, No.CX20190415)

摘要/Abstract

摘要： 本文提出一种自适应滑窗递归稀疏主成分分析方法，用于时变工业过程的在线故障监测.首先，通过滑窗提取正常过程数据空间的特征信息，并对当前窗口数据块矩阵进行稀疏主成分分析，构建稀疏主成分分析故障监测模型；然后，根据相邻窗口的相似度实时调整遗忘因子以自适应更新滑窗大小，使得所建立的稀疏主成分故障监测模型可以有效追踪复杂的时变过程；最后，通过递归更新滑窗稀疏载荷矩阵来动态更新故障监测模型.非线性数值仿真系统与田纳西-伊斯曼过程的故障监测结果表明，所提方法可以有效提高故障检测的准确率，适应于长流程时变工业过程在线故障监测.

关键词: 时变工业过程, 故障监测, 滑动窗口, 递归稀疏主成分分析

Abstract: This paper presents an adaptive sliding window recursive sparse principal component analysis method for the on-line fault monitoring of time-varying industrial processes. Firstly, feature information of normal process data space is extracted by the sliding window, and the sparse principal component analysis is applied to the current window block matrix to construct the sparse principal component analysis-based process fault monitoring model. Then, the forgetting factor is adjusted in real time according to the similarities of adjacent windows to update the sliding window size adaptively, so that the sparse principal component fault monitoring model can effectively track the time-varying process. Finally, the sparse load matrix of the sliding window is renewed recursively to update the fault monitoring model dynamically. Fault monitoring results of the nonlinear numerical simulation system and the Tennessee-Eastman process show that the proposed method can effectively improve the fault detection accuracy and adapt to the on-line fault monitoring of long process industries with time-varying processes.

Key words: time-varying industrial processes, fault monitoring, sliding window, recursive sparse principal component analysis(RSPCA)

中图分类号:

TP273

刘金平, 王杰, 唐朝晖, 等. 基于自适应滑窗递归稀疏主成分分析的工业过程故障监测[J]. 电子学报, 2020, 48(9): 1795-1803.

LIU Jin-ping, WANG Jie, TANG Zhao-hui, et al. Industrial Process Fault Monitoring Based on Adaptive Sliding Window-Recursive Sparse Principal Component Analysis[J]. Acta Electronica Sinica, 2020, 48(9): 1795-1803.

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基于自适应滑窗递归稀疏主成分分析的工业过程故障监测

Industrial Process Fault Monitoring Based on Adaptive Sliding Window-Recursive Sparse Principal Component Analysis

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