Resource type
Date created
2018-02-02
Authors/Contributors
Author: Nozari, Hasan Abbasi
Author: Nazeri, Sina
Author: Dehghan Banadaki, Hamed
Author: Castaldi, Paolo
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.
Document
Published as
Hasan Abbasi Nozari, Sina Nazeri, Hamed Dehghan Banadaki, Paolo Castaldi, MODEL-FREE FAULT DETECTION AND ISOLATION OF A BENCHMARK PROCESS CONTROL SYSTEM BASED ON MULTIPLE CLASSIFIERS TECHNIQUES-A COMPARATIVE STUDY. Elsevier Editorial System(tm) for Control Engineering Practice, Volume 73, April 2018, Pages 134–148. doi: 10.1016/j.conengprac.2018.01.007
Publication details
Publication title
Elsevier Editorial System(tm) for Control Engineering Practice
Document title
Model-Free Fault Detection and Isolation of a Benchmark Process Control System Based on Multiple Classifiers Techniques—A Comparative Study
Date
2018
Volume
73
First page
134
Last page
148
Publisher DOI
10.1016/j.conengprac.2018.01.007
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection
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CONENGPRAC-Nozari.pdf | 3.85 MB |