The application of principal component analysis and kernel density estimation to enhance process monitoring

This paper discusses the application of kernel density estimation (KDE) and principal component analysis (PCA) to provide enhanced monitoring of multivariate processes. Different KDE algorithms are studied and assessed in depth in the context of practical applications so that one bandwidth selection algorithm is recommended for process monitoring. The results of the case studies clearly demonstrate the power and advantages of the KDE approach over parametric density estimation which is still widely used. Statistical summary charts are suggested to raise early warning of faults and locate the physical variables which are the prime indicators of the faults. (C) 2000 Elsevier Science Ltd. All rights reserved.