Adaptable, scalable, probabilistic fault detection and diagnostic methods for the HVAC secondary system
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As the popularity of building automation system (BAS) increases, there is an increasing need to understand/analyze the HVAC system behavior with the monitoring data. However, the current constraints prevent FDD technology from being widely accepted, which include: 1)Difficult to understand the diagnostic results; 2)FDD methods have strong system dependency and low adaptability; 3)The performance of FDD methods is still not satisfactory; 4)Lack of information. This thesis aims at removing the constraints, with a specific focus on air handling unit (AHU), which is one of the most common HVAC components in commercial buildings. To achieve the target, following work has been done in the thesis. On understanding the diagnostic results, a standard information structure including probability, criticality and risk is proposed. On improving method's adaptability, a low system dependency FDD method: rule augmented CUSUM method is developed and tested, another highly adaptable method: principal component analysis (PCA) method is implemented and tested. On improving the overall FDD performance (detection sensitivity and diagnostic accuracy), a hypothesis that using integrated approach to combine different FDD methods could improve the FDD performance is proposed, both deterministic and probabilistic integration approaches are implemented to verify this hypothesis. On understanding the value of information, the FDD results for a testing system under different information availability scenarios are compared. The results show that rule augmented CUSUM method is able to detect the abrupt faults and most incipient faults, therefore is a reliable method to use. The results also show that overall improvement of FDD method is possible using Bayesian integration approach, given accurate parameters (sensitivity and specificity), but not guaranteed with deterministic integration approach, although which is simpler to use. The study of information availability reveals that most of the faults can be detected in low and medium information availability scenario, moving further to high information availability scenario only slightly improves the diagnostic performance. The key message from this thesis to the community is that: using Bayesian approach to integrate high adaptable FDD methods and delivering the results in a probability context is an optimal solution to remove the current constraints and push FDD technology to a new position.