DocumentCode
3438536
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
Volume
6
fYear
1998
fDate
21-26 Jun 1998
Firstpage
3312
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;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1998. Proceedings of the 1998
Conference_Location
Philadelphia, PA
ISSN
0743-1619
Print_ISBN
0-7803-4530-4
Type
conf
DOI
10.1109/ACC.1998.703187
Filename
703187
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