Skip to Main Content
Buildings worldwide account for nearly 40% of global energy consumption. The biggest energy consumer in buildings is the Heating, Ventilation and Air Conditioning (HVAC) systems. HVAC also ranks top in terms of number of complaints by tenants. Maintaining HVAC systems in good conditions through early fault detection is thus a critical problem. The problem, however, is difficult since HVAC systems are large in scale, consisting of many coupling subsystems, building and equipment dependent, and working under time-varying conditions. In this paper, a model-based and data-driven method is presented for robust system-level fault detection with potential for large-scale implementation. It is a synergistic integration of: ) Statistical Process Control (SPC) for measuring and analyzing variations; 2) Kalman filtering based on gray-box models to provide predictions and to determine SPC control limits; and (3) system analysis for analyzing propagation of faults' effects across subsystems. In the method, two new SPC rules are developed for detecting sudden and gradual faults. The method has been tested against a simulation model of the HVAC system for a 420-meter-high building. It detects both sudden faults and gradual degradation, and both device and sensor faults. Furthermore, the method is simple and generic, and has potential replicability and scalability.