Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

doi:10.1016/j.cjche.2014.09.022

Abstract

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

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

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

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