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).
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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.
