Commissioning of AHU sensors using principal component analysis method

Principal component analysis (PCA) models are implemented for monitoring and fault detection and diagnosis (FDD) of air-handling units (AHUs), which is modified to solve the problems caused by the nonlinearity and variations in the environment. Independent heat balance and pressure-flow balance models are developed to reduce the effects of the system nonlinearity and to make the PCA method valid in different control modes. Sensor faults are detected and partly isolated using Q-statistic (square prediction error) and Q-contribution plot. Simulation tests are conducted to demonstrate the use of the PCA method for automatic commissioning of AMD monitoring instrumentations and to validate the PCA method in detecting and diagnosing the AHU sensor faults under various typical operating conditions.