|Table of Contents|

Research on fault diagnosis of industrial process based onimproved PCA method and data reconstruction(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年01期
Page:
72-
Research Field:
Publishing date:

Info

Title:
Research on fault diagnosis of industrial process based onimproved PCA method and data reconstruction
Author(s):
Du Hailian1Miao Shiyu2Du Wenxia1Lv Feng1
1.College of Career Technology,Hebei Normal University,Shijiazhuang 050024,China; 2.School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China
Keywords:
principal component analysis fault analysis fault reconstruction principal-component-related variable residual common variable residual production safety
PACS:
TP206
DOI:
10.14177/j.cnki.32-1397n.2019.43.01.010
Abstract:
Not only in order to determine the fault more accurately in the industrial system,but also in order to make the production system operation more stable,the improved principal component analysis method and data reconstruction method is used in the industrial process. The data of the normal and fault state of industrial system are collected,the SPE statistics of the traditional principal component analysis is divided into principal-component-related variable residual(PVR)and common variable residua(CVR),which are used to diagnose the system. In order to minimize the impact of the failure data on the system after detecting the failure,the data reconstruction method is further applied. The failure data are reconstituted into normal data,and the validity index is used to verify. When the fault happenes,the fault is repaired and excluded,and the failure impact on the production system is minimized. In order to verify the diagnosis method,the method is applied to the data of the Tennessee-Eastman system,the detection result of the fault is more precise,and the normal production system is ensured to work.

References:

[1] Wang Yifei,Ma Xiandong,Joyce M J. Reducing sensor complexity for monitoring wind turbine performance using principal component analysis[J]. Renewable Energy,2016,97:444-456.
[2]Gao Xin,Hou Jian. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process[J]. Neurocomputing,2016,174(22):906-911.
[3]Xu Xianzhen,Xie Lei,Wang Shuqing. Multimode process monitoring with PCA mixture model[J]. Computers & Electrical Engineering,2014,40(7):2101-2112.
[4]Zhou Bo,Ye Hao,Zhang Haifeng,et al. Process monitoring of iron-making process in a blast furnace with PCA-based methods[J]. Control Engineering Practice,2016:47:1-14.
[5]Pan Yijun,Yang Chunjie,An Ruqiao,et al. Fault detection with improved principal component pursuit method[J]. Chemometrics and Intelligent Laboratory Systems,2016:157:111-119.
[6]周东华,李钢,李元. 数据驱动的工业过程故障诊断技术[M]. 北京:科学出版社,2011:23-27,58-59.
[7]肖应旺,徐保国. 改进PCA在发酵过程监测与故障诊断中的应用[J]. 控制与决策,2005,20(5):571-574.
Xiao Yingwang,Xu Baoguo. Application of improved PCA to fermentation process monitoring and fault diagnosis[J]. Control and Decision,2005,20(5):571-574.
[8]张新荣,熊伟丽,徐保国. 基于Q统计量分量的故障检测算法研究[J]. 计算机与应用化学,2008.
Zhang Xinrong,Xiong Weili,Xu Baoguo. A study of approach on fault detection based on Q statistic separation[J]. Computers and Applied Chemistry,2008,25(12):1537-1542.
[9]王海清,宋执环,李平. 改进PCA及其在过程监测与故障诊断中的应用[J]. 化工学报,2001,52(6):471-475.
Wang Haiqing,Song Zhihuan,Li Ping. Improved PCA with application to process monitoring and fault diagnosis[J]. Journal of Chemical Industry and Engineering,2001,52(6):471-475.

[10]赵忠盖,刘飞,徐保国. 基于改进混合概率主元分析模型的过程监控[J]. 控制与决策,2006,21(7):745-749.
Zhao Zhonggai,Liu Fei,Xu Baoguo. Process monitoring based on improved mixture probabilistic principal component analysis model[J]. Control and Decision,2006,21(7):745-749.
[11]徐涛,王祁. PCA在火箭发动机试车台传感器故障诊断中的应用[J]. 南京理工大学学报,2006,30(6):669-672.
Xu Tao,Wang Qi. Application of PCA in sensor fault diagnosis of rocket engine ground testing bed[J]. Journal of Nanjing University of Science and Technology,2006,30(6):669-672.
[12]成成,黄道. IPCA在故障检测与分离中的应用[J]. 华东理工大学学报,2000:502-506.
Cheng Cheng,Huang Dao. Fault detection and diagnosis by integrated principal component analysis[J]. Journal of East China University of Science and Technology,2000:502-506.

Memo

Memo:
-
Last Update: 2019-02-28