Title :
The application of kernel density estimates to condition monitoring for process industries
Author :
Chen, Q. ; Goulding, P. ; Sandoz, D. ; Wynne, R.
Author_Institution :
Sheffield Univ., UK
Abstract :
Discusses the application of kernel extraction for estimating the non-parametric density function of a multivariate process system for condition monitoring purposes. In particular, the paper concentrates on a real industrial case study to demonstrate the differences and practical capability of three different estimators. It is shown that the kernel density estimate has the potential to be an important technique of obtaining real nonparametric empirical density function of the process population as an aid to more effective intelligent condition monitoring
Keywords :
estimation theory; monitoring; statistical analysis; statistical process control; condition monitoring; kernel density estimates; multivariate process system; nonparametric density function; process industries; process population; Bandwidth; Condition monitoring; Data mining; Density functional theory; Kernel; Least squares methods; Principal component analysis; Probability distribution; Process control; Smoothing methods;
Conference_Titel :
American Control Conference, 1998. Proceedings of the 1998
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-4530-4
DOI :
10.1109/ACC.1998.703187
