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Data-driven Techniques on Alarm System Analysis and Improvement Open Access

Descriptions

Other title
Subject/Keyword
Principal component pursuit
Alarm management
Sequence matching
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Cheng, Yue
Supervisor and department
Chen, Tonwen (Electrical and Computer Engineering)
Examining committee member and department
Shah, Sirish L. (Chemical and Materials Engineering)
Miller, Daniel (Electrical and Computer Engineering, University of Waterloo)
Lynch, Alan (Electrical and Computer Engineering)
Dubljevic, Stevan (Chemical and Materials Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Control Systems
Date accepted
2013-08-28T14:29:30Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
To meet the demands of safety, quality and efficiency, process monitoring is of great importance. However, a serious problem exists in the industry: too many alarms are raised for operators to handle. Consequently, techniques need to be developed in order to reduce nuisance and false alarms to an acceptable level. Motivated by this, my thesis focuses on alarm systems improvement, specifically the development of data-driven techniques for the analysis and design of alarm systems. Developed methods are based on either process data or alarm data, the two types of data mainly used in alarm systems. Three problems are considered. First, a univariate alarm signal filtering technique is discussed. The design of an optimal alarm filter for the best alarm accuracy, namely, minimizing a weighted sum of false and missed alarm rates (probabilities), is presented. Moreover, a sufficient condition for moving average filters being optimal linear alarm filters is also provided. Second, alarm flood pattern analysis based on multivariate alarm data is addressed. A modified Smith-Waterman algorithm considering time stamp information is proposed for alarm flood pattern matching. Third, the application of a new multivariate statistical analysis technique, the principal component pursuit (PCP) method, to process monitoring is thoroughly discussed. An optimal scaling method is proposed as the preprocessing step. A coordinate descent algorithm is provided to search for the optimal scaling vector, whose global convergence is proved. After multivariate process modeling, a PCP-based fault detection and diagnosis approach is introduced. An industrial project on a major extraction process in Alberta to improve its alarm system is described. Based on the historical data, a predicted reduction on the alarm rate by applying a variety of alarm system rationalization techniques is estimated. A significant improvement on the alarm system is expected.
Language
English
DOI
doi:10.7939/R38S4K095
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Y. Cheng, I. Izadi, and T. Chen. On optimal alarm filter design. In Proceedings of International Symposium on Advanced Control of Industrial Processes, pages 139--145, Hangzhou, China, May 2011.Y. Cheng, I. Izadi, and T. Chen. Optimal alarm signal processing: Filter design and performance analysis. IEEE Transactions on Automation Science and Engineering, 10(2):446--451, 2013.Y. Cheng, I. Izadi, and T. Chen. Pattern matching of alarm flood sequences by a modified Smith-Waterman algorithm. Chemical Engineering Research and Design, 91:1085--1094, 2013.Y. Cheng and T. Chen. Application of principal component pursuit to process fault detection and diagnosis. In Proceedings of American Control Conference 2013, Washington D.C., USA, June 2013.

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