A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis

Author

Vedam, Hiranmayee ; Venkatasubramanian, Venkat

Author_Institution

Sch. of Chem. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

1

fYear

1997

fDate

4-6 Jun 1997

Firstpage

309

Abstract

We discuss the development of a wavelet theory-based adaptive system for trend analysis (W-ASTRA). W-ASTRA performs process-monitoring and diagnosis. The main contributions of this paper are two fold. A wavelet theory based nonlinear adaptive algorithm has been developed for identification of trends from sensor data. In order to perform diagnosis using the identified trends, a knowledge base is required. Our second contribution is the development of an automated framework for knowledge base development. W-ASTRA uses the adaptive algorithm for identification of sensor trends and the knowledge base generated by the automated framework for diagnosing fault origins from the identified trends. The application of W-ASTRA is demonstrated on the Amoco Model IV FCCU

Keywords

computerised monitoring; diagnostic expert systems; fault diagnosis; identification; petroleum industry; process control; wavelet transforms; Amoco Model IV FCCU; adaptive trend analysis; fault diagnosis; identification; knowledge base; process monitoring; wavelet theory; Adaptive algorithm; Adaptive systems; Chemical analysis; Chemical engineering; Chemical sensors; Fault diagnosis; Intelligent systems; Laboratories; Monitoring; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.611807

Filename

611807