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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2013 Vol.5(3): 472-477 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2013.V5.732

HVAC Model Based Fault Detection by Incremental Online Support Vector Machine

Davood Dehestani
Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates real-time FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC systems

Index Terms—Intelligent method, unsupervised fault detection, online SVM, HVAC system.

Davood Dehestani is with the University of Technology Sydney (UTS), Building1, UTS building, Broadway, Sydney Australia (e-mail: Davood.dehestani@student.uts.edu.au).


Cite:Davood Dehestani, "HVAC Model Based Fault Detection by Incremental Online Support Vector Machine," International Journal of Computer Theory and Engineering vol. 5, no. 3, pp. 472-477, 2013.

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