Online support vector machine application for model based fault detection and isolation of HVAC system

Publisher:
IACSIT Press
Publication Type:
Journal Article
Citation:
International Journal of Machine Learning and Computing, 2011, 1 (1), pp. 66 - 72 (7)
Issue Date:
2011-04-01
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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
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