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.
|Number of pages||11|
|Journal||Building Services Engineering Research and Technology|
|Publication status||Published - 6 Nov 2003|
ASJC Scopus subject areas
- Building and Construction