Development of high-fidelity air handling unit fault models for FDD innovation: lessons learned and recommendations

Abstract

Interest in automated building analytics, including fault detection and diagnostics has been increasing; however, developers of these solutions have lacked access to ground-truth-validated data across a wide range of weather conditions for algorithm development and performance assessment. This study presents the development, and validation of faulted and fault-free models for air handling units (AHUs) – a common HVAC system design. Detailed models for the single-duct AHU (Modelica) and dual-duct AHU (HVACSIM+) were used to conduct annual simulations, for common sensor, mechanical, and control sequence faults. We report lessons learned during the efforts, including challenges and insights regarding how these simulation models, typically used for design applications, can be purposed to accurately reflect real-world system operational behaviours. Finally, we highlight considerations for researchers and FDD developers who may wish to leverage this dataset to assess the performance of their algorithms, and evolving performance of FDD solutions over time.