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Applying Wavelets to Autocorrelated Process Monitoring(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2010年04期
Page:
31-34
Research Field:
Publishing date:

Info

Title:
Applying Wavelets to Autocorrelated Process Monitoring
Author(s):
Shi Rongzhen1; Shi Guosheng2
1.Department of Electromechanical Engineering,Nanjing Normal University Taizhou College,Taizhou 225300,China;2.School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210042,China
Keywords:
mu lt-i scale wave lets analysis;  autocorre lated process;  averag e run leng th;  contro l cha
PACS:
TB114.2
DOI:
-
Abstract:
T raditiona l contro l charts a re based on the statistical assum ption tha tm easurements are independent and identica lly distributed. In industry applications, how ever, observa tions a re au to corre la ted due to the inherent cause o f the process. Thus trad itiona lm ethods w ill be inappropr ia te for au to co rre la ted process m onitor ing. In this paper, mu lt-i scale w avelets analysis is introduced to autocorre la ted processes. Process mon ito ring is reached by integrating Shewhart contro l charts w ith mu lt-i scale wave lets analysis. Fina lly, take ARMA ( 1, 1) pro cess as an examp le. M onte-Car lo simu lations about step-type, trend- type, cyc les-type, and a lternating-type disturbances in autocorrelated processes are perform ed to expla in the av erage run leng th ( ARL) property o f the contro l charts. The sim ulation resu lts show that the m ethod is e-f fective.

References:

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Last Update: 2013-04-02