RII Track-4: Adaptive Fault Detection and Diagnosis Based on Growing Gaussian Mixture Regressions for High-Performance HVAC Systems

点击翻译按钮获取中文摘要

Environmental impacts, as well as resource consumption, of building operations are significant throughout the entire life cycle of buildings. Heating ventilation and air conditioning (HVAC) systems consume about two-thirds of the total energy used in commercial buildings. Despite national efforts toward improving performance and sustainability, many existing HVAC systems in buildings do not run efficiently, due to equipment degradation, sensors being out of calibration, or improper control operations. Such problems can result in high maintenance costs, occupant discomfort, and wasted energy. Fault detection and diagnosis (FDD) for HVAC systems in buildings detect and identify operational faults based on the analysis of measured system behaviors. FDD technology is critical to improving building energy efficiency, and reducing or eliminating wasted energy in buildings caused by operational faults. The major challenge in current FDD technology is that the training data available to create diagnostic algorithms do not include all possible operating conditions that the testing systems experience throughout the life cycle. Given that the training data for FDDs does not cover all operating conditions, FDD algorithms for building HVAC systems must evolve along with the changes in building systems and components. The goal of this project is to enhance the robustness and efficiency of FDD technology for high-performance HVAC systems. The proposed research will lead to several broader impacts including research participation of underrepresented undergraduates, K-12 outreach activities, and sharing the experimental data and the FDD method for high-performance HVAC systems with other researchers. The knowledge gained from this research has the potential to significantly enhance building energy efficiency. The overall research goal is to advance robustness and efficiency of Fault detection and diagnosis (FDD) technology through an adaptive machine learning-based approach for high-performance Heating ventilation and air conditioning (HVAC) systems. This research closes critical knowledge gaps in the FDDs for high-performance HVAC systems. First, the experimental study on common faults in high-performance HVAC systems at the Center for High Performance Buildings, Purdue University will result in a thorough understanding of fault features, including system behaviors as well as impacts on energy consumption and environmental conditions. While extensive research has been conducted on the FDD for conventional HVAC systems, the FDD for high-performance HVAC systems has rarely been studied. The experimental data pertaining to common faults in high-performance HVAC systems that will be obtained as a part of this project will, thus, be an invaluable asset to the FDD research community. Second, this research will yield an adaptive FDD method based on growing Gaussian mixture regressions for high-performance HVAC systems in commercial buildings. Traditional FDD methods learn from training data tested under limited operating conditions, after which the learning stops. This new FDD method adapts to the changes in HVAC operating environments, evolves with the changes in building systems and components, and learns to diagnose new faulty conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.