Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study

Model-free fault detection and isolation of a benchmark process control system based on multiple... This paper presents a combined data-driven framework for fault detection and isolation (FDI) based on the ensemble of diverse classification schemes. The proposed FDI scheme is configured in series and parallel forms in the sense that in series form the decision on the occurrence of fault is made in FD module, and subsequently, the FI module coupled to the FD module will be activated for fault indication purposes. On the other hand, in parallel form a single module is employed for FDI purposes, simultaneously. In other words, two separate multiple-classifiers schemes are presented by using fourteen various statistical and non-statistical classification schemes. Furthermore, in this study, a novel ensemble classification scheme namely blended learning (BL) is proposed for the first time where single and boosted classifiers are blended as the local classifiers in order to enrich the classification performance. Single-classifier schemes are also exploited in FDI modules along with the ensemble-classifier methods for comparison purposes. In order to show the performance of proposed FDI method, it was also tested and validated on DAMADICS actuator system benchmark. Besides, comparative study with the related works done on this benchmark is provided to show the pros and cons of the proposed FDI method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Control Engineering Practice Elsevier

Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study

Loading next page...
 
/lp/elsevier/model-free-fault-detection-and-isolation-of-a-benchmark-process-Dt829sg0fF
Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0967-0661
D.O.I.
10.1016/j.conengprac.2018.01.007
Publisher site
See Article on Publisher Site

Abstract

This paper presents a combined data-driven framework for fault detection and isolation (FDI) based on the ensemble of diverse classification schemes. The proposed FDI scheme is configured in series and parallel forms in the sense that in series form the decision on the occurrence of fault is made in FD module, and subsequently, the FI module coupled to the FD module will be activated for fault indication purposes. On the other hand, in parallel form a single module is employed for FDI purposes, simultaneously. In other words, two separate multiple-classifiers schemes are presented by using fourteen various statistical and non-statistical classification schemes. Furthermore, in this study, a novel ensemble classification scheme namely blended learning (BL) is proposed for the first time where single and boosted classifiers are blended as the local classifiers in order to enrich the classification performance. Single-classifier schemes are also exploited in FDI modules along with the ensemble-classifier methods for comparison purposes. In order to show the performance of proposed FDI method, it was also tested and validated on DAMADICS actuator system benchmark. Besides, comparative study with the related works done on this benchmark is provided to show the pros and cons of the proposed FDI method.

Journal

Control Engineering PracticeElsevier

Published: Apr 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from Google Scholar, PubMed
Create lists to organize your research
Export lists, citations
Access to DeepDyve database
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off