Fault detection and diagnosis for variable-air-volume systems using combined residual, qualitative and quantitative techniques

Due to its complex structure and frequent operation, the variable-air-volume (VAV) system are prone to failure. Since faults in VAV systems can reduce its efficiency and increase its energy consumption dramatically, various fault detection and diagnosis (FDD) strategies have been developed. However, due to the diversity of buildings and system structural complexity, most of these efforts are limited to local scale and fault detection at system level has rarely been considered. In this study, the complexity of VAV system modeling is dispersed with a hierarchical modeling framework, and a system level to component scale FDD strategy for VAV systems using combined residual, qualitative and quantitative techniques is developed. The three layers of the modelling framework, namely system layer, unit layer and component layer, adopt different modeling and fault diagnosis methods to improve the accuracy and reliability of FDD. On the other hand, detecting progressive faults is not easy for VAV systems because their symptoms are non-obvious. This study proposes a control quantity based residual statistics in the unit layer, which detects progressive failures with the residual between fault-free-model-predicted and measured control quantities of the zone thermal system. The proposed method is easy to implement and integrate within existing VAV control systems. The results of experiments and a simulation based on the Skyspark platform data from an office building verify the effectiveness of the approach. (c) 2021 Elsevier B.V. All rights reserved.