Abstract
Real-time monitoring of heating, ventilation, and air conditioning (HVAC) systems is crucial to maintaining optimal performance such as providing thermal comfort and acceptable indoor air quality, guaranteeing energy saving, and assuring system reliability. In a realistic situation, HVAC systems can degrade in performance or even fail due to a variety of operational problems, such as stuck open or closed air dampers and water valves, supply or exhaust air fan faults, hot or chilled water pump faults, and inefficiencies in the way HVAC systems or pieces of equipemnt are controlled. This paper presents automatic fault detection techniques, as well as a key sensor sets selection approach that can help to maintain the performance of HVAC systems, and optimise fault detection results. One key step to make sure the approach succeeds is the sensor feature selection process. This paper implements the ensemble rapid centroid estimation (ERCE) as the data-driven sensor and feature selection algorithm, which is the core method to assure the automatic fault detection can function correctly. Instead of choosing sensors manually, ERCE method can automatically select representative features that are unique and relevant to the faults in a HVAC system. The methodology presented is implemented in real-world commercial buildings with experimental results showing that different types of faults are detected successfully.
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Guo, Y., Wall, J., Li, J., West, S. (2017). Real-Time HVAC Sensor Monitoring and Automatic Fault Detection System. In: Mukhopadhyay, S., Postolache, O., Jayasundera, K., Swain, A. (eds) Sensors for Everyday Life. Smart Sensors, Measurement and Instrumentation, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-47322-2_3
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