New fault detection method based on reduced kernel principal component analysis (RKPCA). Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Taouali, Okba; Jaffel, Ines; Lahdhiri, Hajer; Harkat, Mohamed; Messaoud, Hassani
• Is Part Of: International journal of advanced manufacturing technology. Volume 85:Issue 5/8(2016)
• Page Start: 1547
• Page End: 1552
• Publication Date: 2016-07
• Subjects: KPCA -- RKPCA -- PCs -- SPE -- Fault detection; Manufacturing processes -- Periodicals; Production engineering -- Periodicals; 670.427
• DOI: 10.1007/s00170-015-8059-1
• Languages: English
• ISSNs: 0268-3768

Abstract

Abstract This paper proposes a new method for fault detection using a reduced kernel principal component analysis (RKPCA). The proposed RKPCA method consists on approximating the retained principal components given by the KPCA method by a set of observation vectors which point to the directions of the largest variances with the retained principal components. The proposed method has been tested on a chemical reactor and the results were satisfactory.