Intelligent Fault Detection and Isolation of HVAC System Based on Online Support Vector Machine

Abstract

Heating, Ventilation and Air Conditioning (HVAC) systems are often one of the largest energy consuming parts in modern buildings. Two focused issues of HVAC systems are energy saving and its safety. Regular checking and maintenance are usually the keys to tackle these problems. Due to the high cost of maintenance, preventive maintenance plays an important role. One cost-effective strategy is the development of analytic fault detection and isolation (FDI) modules by online monitoring of the key variables of HVAC systems. This chapter investigates real-time FDI for HVAC systems by using online support vector machine (SVM), by which we are able to train an FDI system with manageable complexity under real-time working conditions. It also proposes a new approach which allows us to detect unknown faults and update 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. This chapter also identifies the variables which are the indications of the particular faults we are interested in. Simulation studies are given to show the effectiveness of the proposed online FDI approach.