PCA-FDA-based fault diagnosis for sensors in VAV systems

Principal component analysis (PCA) and Fisher discriminant analysis (FDA) are presented in this paper to detect and diagnose the single sensor fault with fixed bias occurring in variable air volume systems. Based on the energy balance and the flow-pressure balance, both related to physical models of the systems, two PCA models are built to detect the occurrence of abnormalities in the systems. In addition, FDA, a linear dimensionality reduction technique, is developed to diagnose the fault source. Through the Fisher transformation, different faulty operation data classes can be optimally separated by maximizing the scatter between classes while minimizing the scatter within classes. Then the faulty sensor can be isolated through comparing Mahalanobis distances of the candidate sensors.