HVAC Fault Diagnosis System Using Rough Set Theory and Support Vector Machine

Abstract

Preventive maintenance plays a very important role in the modern Heating, Ventilation and Air Conditioning (HVAC) systems for guaranteeing the thermal comfort, energy saving and reliability. The fault diagnosis on HVAC system is a difficult problem due to the complex structure of the HVAC and the presence of multi-excite sources. As the HVAC system fault information has inaccurate and uncertainty characteristic, A new kind of fault diagnosis system based on Rough Set Theory (RST) and Support Vector Machine (SVM) is presented in this paper. The hybrid model is integrated the advantages of RST effectively dealing with the uncertainty information and SVM’s greater generalization performance. The HVAC diagnosis experiment demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis, and verified the feasibility of engineering application. As a result, the presented hybrid fault diagnosis method can help to maintain the health of the HVAC systems, reduce energy consumption and maintenance cost.