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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(1): 66-72 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.10

Online Support Vector Machine Application for Model Based Fault Detection and Isolation of HVAC System

Davood Dehestani, Fahimeh Eftekhari, Ying Guo, Steven Ling, Steven Su, Hung Nguyen
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 realtime 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;

a. Faculty of Engineering and IT, University of Technology, Sydney (UTS), Sydney, Australia b. University of Mazandaran, Iran c. Autonomous Systems Lab, CSIRO ICT Center, Australia

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Cite:Davood Dehestani, Fahimeh Eftekhari, Ying Guo, Steven Ling, Steven Su, Hung Nguyen, "Online Support Vector Machine Application for Model Based Fault Detection and Isolation of HVAC System," International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 66-72, 2011.

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