DocumentCode :
307035
Title :
A robust approach to dynamic statistical process control
Author :
McFarlane, Duncan C. ; Petersen, Ian R.
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
Volume :
3
fYear :
1996
fDate :
11-13 Dec 1996
Firstpage :
3113
Abstract :
Statistical process control (SPC) techniques are well established across a wide range of industries. In particular, the plotting of key steady state (product) variables with their statistical limits against time (Shewart charting) is a common approach for monitoring the normality of production. There has been increased interest in SPC applied to the monitoring of dynamic processes. This paper is concerned with extending Shewart charting techniques to the quality monitoring of variables driven by uncertain dynamic processes, which has particular application in the process industries where it is desirable, to monitor process variables online as well as the final product. The robust approach to dynamic statistical process control proposed in this paper is based on work on guaranteed cost filtering for linear systems and is intended to provide a basis for both a wider application of SPC monitoring and also motivate unstructured fault detection
Keywords :
Riccati equations; filtering theory; matrix algebra; monitoring; process control; statistical process control; uncertain systems; Shewart charting; dynamic statistical process control; guaranteed cost filtering; linear systems; quality monitoring; robust approach; uncertain dynamic processes; unstructured fault detection; Costs; Electrical equipment industry; Filtering; Industrial control; Monitoring; Nonlinear filters; Process control; Production; Robust control; Steady-state;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
ISSN :
0191-2216
Print_ISBN :
0-7803-3590-2
Type :
conf
DOI :
10.1109/CDC.1996.573605
Filename :
573605
Link To Document :
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