Application of the Unscented Kalman Filter (UKF) Estimation Techniques for Fault Detection Diagnosis and Isolation (FDDI) in Attitude Control (AC) and Heating Ventilation Air Conditioning (HVAC) Systems

Abstract

Monitoring and controlling the modern and sophisticated Heating Ventilation Air Conditioning (HVAC) building systems under a wide variety of occupancy and load related operating conditions represents a difficult and challenging task. Their complexity drastically increases and the control becomes more difficult task due to the several control loops that interact between them. The main objective of this study is to compare the performance of the automated strategies for fault detection, diagnosis and isolation (FDDI) based on frequency and spectral analysis (FA) of the system response, and an interactive multiple model (IMM), based on the Unscented Kalman Filter (UKF) estimation technique to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in Discharge air temperature (DAT) loop of the HVAC systems. The both techniques are HVAC model-driven based and the simulations results reveal the superiority of the Interactive Multiple Model based on Unscented Kalman Filter estimation algorithm (IMM-UKF) concerning its accuracy and robustness to the changes in the system structure parameters. These algorithms are implemented in a simulation environment, and the fault diagnosis results are presented for a several fault scenarios in terms of mode probabilities and active fault index. From the preliminaries simulations, for different scenarios we found that the IMM-UKF algorithm is robust to the choice of the matrix probability and to the small changes in process and measurement noise level, result that is confirmed in the literature.