Plant monitoring and fault detection: Synergy between data reconciliation and principal component analysis

• Published in: Computers & chemical engineering Vol. 25; no. 4; pp. 501 - 507
• Main Authors: Amand, Th, Heyen, G, Kalitventzeff, B
• Format: Journal Article Web Resource
• Language: English
• Published: Elsevier Ltd 01-05-2001; Pergamon Press - An Imprint of Elsevier Science
• Subjects: Chemical engineering; Computer science; Engineering, computing & technology; Fault detection; Ingénierie chimique; Ingénierie, informatique & technologie; Plant monitoring; Sciences informatiques; Techniques; Fault detection; Techniques; Plant monitoring
• ISSN: 0098-1354; 1873-4375; 1873-4375
• DOI: 10.1016/S0098-1354(01)00630-5

Data reconciliation and principal component analysis are two recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). Principal component analysis can then be applied to raw process data for monitoring purpose. The combined use of these techniques aims at a better efficiency in fault detection. It relies mainly in a lower number of components to monitor. The method is applied to a modelled ammonia synthesis loop.