Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

doi:10.1016/j.cjche.2014.09.022

›› 2014, Vol. 22 ›› Issue (11/12): 1260-1267.DOI: 10.1016/j.cjche.2014.09.022

• 过程系统工程与过程安全 • 上一篇下一篇

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Xiaogang Deng, Xuemin Tian

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China

收稿日期:2014-01-10 修回日期:2014-03-05 出版日期:2014-12-28 发布日期:2014-12-24
通讯作者: Xiaogang Deng
基金资助:
Supported by the National Natural Science Foundation of China (61273160, 61403418), the Natural Science Foundation of Shandong Province (ZR2011FM014), the Fundamental Research Funds for the Central Universities (10CX04046A) and the Doctoral Fund of Shandong Province (BS2012ZZ011).

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Xiaogang Deng, Xuemin Tian

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China

Received:2014-01-10 Revised:2014-03-05 Online:2014-12-28 Published:2014-12-24
Supported by:
Supported by the National Natural Science Foundation of China (61273160, 61403418), the Natural Science Foundation of Shandong Province (ZR2011FM014), the Fundamental Research Funds for the Central Universities (10CX04046A) and the Doctoral Fund of Shandong Province (BS2012ZZ011).

摘要/Abstract

摘要： Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process withmultiple operating modes. In order tomonitor themultimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis (LNSA). In the proposed method, prior process knowledge is not required and only themultimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis (PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank systemis used to demonstrate the effectiveness of the proposed method. The simulation results showthat LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

关键词: Multimode chemical process, Fault detection, Local neighborhood similarity analysis, Principal component analysis

Abstract: Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process withmultiple operating modes. In order tomonitor themultimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis (LNSA). In the proposed method, prior process knowledge is not required and only themultimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis (PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank systemis used to demonstrate the effectiveness of the proposed method. The simulation results showthat LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

Key words: Multimode chemical process, Fault detection, Local neighborhood similarity analysis, Principal component analysis

Xiaogang Deng, Xuemin Tian. Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis[J]. , 2014, 22(11/12): 1260-1267.

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Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

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