Abstract
In a heating, ventilation and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be employed to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based Pattern Matching (WPM) procedure and a feature based principal component analysis PCA (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally-occurred faults were collected through the Building Automation System to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a high detection rate and a low false alarm rate.
Research on Fault Detection Method of Infrared Thermal Imaging for Power Equipment based on Deep Learning
