A novel process monitoring and fault detection approach based on statistics locality preserving projections. (January 2016)

• Record Type: Journal Article
• Authors: He, Fei; Xu, Jinwu
• Is Part Of: Journal of process control. Volume 37(2016:Jan.)
• Page Start: 46
• Page End: 57
• Publication Date: 2016-01
• Subjects: Statistics pattern analysis -- Locality preserving projections -- Process monitoring -- Parallel analysis -- Fault detection; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.11.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Statistics locality preserving projections (SLPP) is proposed for process monitoring and fault detection. SPA is applied to grasp the non-Gaussian statistical property. LPP method is used to discover local manifold structure of the process statistics. Parallel analysis is introduced to retain the number of latent variables for LPP and SLPP. Abstract: Data-driven fault detection technique has exhibited its wide applications in industrial process monitoring. However, how to extract the local and non-Gaussian features effectively is still an open problem. In this paper, statistics locality preserving projections (SLPP) is proposed to extract the local and non-Gaussian features. Firstly, statistics pattern analysis (SPA) is applied to construct process statistics and grasp the non-Gaussian statistical property using high order statistics. Then, locality preserving projections (LPP) method is used to discover local manifold structure of the statistics. In essence, LPP tries to map the close points in the original space to close in the low-dimensional space. Lastly, T 2 and squared prediction error (SPE) charts of SLPP model are used to detect process faults. One simple simulated system and the Tennessee Eastman process show that the proposed SLPP method is more effective than principal component analysis, LPP and statistics principal component analysis in fault detection performance.