doi: 10.7763/IJMLC.2011.V1.10
Online Support Vector Machine Application for Model Based Fault Detection and Isolation of 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
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
Keywords
- Intelligent
- method;
- Unsupervised
- Fault
- detection;
- Online
- SVM;
- HVAC
- system;
How to Cite
Davood Dehestani, Fahimeh Eftekhari, Ying Guo, Steven Ling, Steven Su, and Hung Nguyen, "Online Support Vector Machine Application for Model Based Fault Detection and Isolation of HVAC System," International Journal of Machine Learning, vol. 1, no. 1, pp. 66-72, 2011. https://doi.org/10.7763/IJMLC.2011.V1.10
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).