Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
Transactions of the Institute of Measurement and Control
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Ling Wang
Right arrow Articles by Minrei Fei
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

A novel quantum ant colony optimization algorithm and its application to fault diagnosis

Ling Wang

Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China, wangling{at}shu.edu.cn

Qun Niu

Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China

Minrei Fei

Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China

Fault detection in chemical production processes is difficult because of the amount of data that needs to be analysed and the absence of fault data. This paper uses support vector machines (SVMs) to diagnose faults, as it fits the small sample problem. To eliminate system disturbances and noise from the high levels of data, it then combines SVMs with a novel quantum ant colony optimization (QACO) algorithm to select the fault features. The proposed method is then tested using the benchmark Tennessee Eastman Process and shown to be effective. In particular, it is demonstrated that QACO and SVMs can find the essential fault variables exactly, and will greatly improve the fault diagnosis performance of SVMs for a complex chemical process.

Key Words: ant colony optimization • fault diagnosis • quantum evolutionary algorithm.

Transactions of the Institute of Measurement and Control, Vol. 30, No. 3-4, 313-329 (2008)
DOI: 10.1177/0142331207088191
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?