SEED-GRANT: Efficient Strategies for Condition Monitoring and Fault Detection of HVAC Systems for Energy Efficient Buildings
Large and complex HVAC systems are naturally difficult to monitor. Indeed, one of the outcomes of this research was the development of a top-down strategy for fault detection, which discovered several faults in the Science and Engineering building. The objective of this project is to develop methods for effective monitoring of system conditions and fault detection of large and complex HVAC systems. This project contributes to the development of intelligent control, monitoring, and fault detection technologies that are integral part of energy-efficient carbon-neutral buildings.
The key outcome of the project includes methods for spatial and temporal partition of large complex HVAC systems, development of pre-processing algorithms to discover new features in the monitored data set, selection of effective methods for HVAC monitoring and fault detection, and finally establishment of thresholds for various faults.