Title :
Analysis of extended partial least squares for monitoring large-scale processes
Author :
Chen, Qian ; Kruger, Uwe
Author_Institution :
Coll. of Aerosp. Eng., Nanjing Univ. of Aeronaut. & Astronaut., China
Abstract :
This brief analyzes the recently proposed extended partial least squares (EPLS) algorithm and reveals that it does not: 1) allow the generalized score variables to be geometrically interpreted, 2) reconstruct the recorded process variables, and 3) produce statistically independent variables for process monitoring. To overcome these deficiencies, an improved EPLS algorithm is introduced, which utilizes generalized scores to identify statistical monitoring models. The brief finally presents an industrial application study of a chemical reaction process to show that improved EPLS offers enhanced diagnosis of abnormal process behavior.
Keywords :
chemical industry; large-scale systems; least mean squares methods; process monitoring; statistical analysis; chemical reaction process; extended partial least squares; large-scale processes; process monitoring; statistical monitoring models; Algorithm design and analysis; Chemical industry; Chemical processes; Fault diagnosis; Large-scale systems; Least squares methods; Monitoring; Page description languages; Process control; Statistics; Data compression; fault diagnosis; process control; process monitoring; statistics;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2005.852113