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
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.