A diagnostic tool for online sensor health monitoring in air-conditioning systems

Healthy sensors are essential for the reliable monitoring and control of building automation systems (BAS). This paper presents a diagnostic tool to be used to assist building automation systems for online sensor heath monitoring and fault diagnosis of air-handling units. The tool employs a robust sensor fault detection and diagnosis (FDD) strategy based on the Principal Component Analysis (PCA) method. Two PCA models are built corresponding to the heat balance and pressure-flow balance of an air-handling process. Sensor faults are detected using the Q-statistic and diagnosed using an isolation-enhanced PCA method that combines the Q-contribution plot and knowledge-based analysis. The PCA models are updated using a condition-based adaptive scheme to follow the normal shifts in the process due to changing operating conditions. The sensor FDD strategy, the implementation of the diagnostic tool and experimental results in an existing building are presented in this paper.