Fault Detection and Diagnosis (FDD) in Heating Ventilation Air Conditioning Systems (HVAC) Using an Interactive Multiple Model Augmented Unscented Kalman Filter (IMMAUKF)

Nowadays 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 is becoming 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. Among these control loops the discharge air temperature (DAT) loops, the static pressure loop (SP), and the variable air volume (VAV) terminal unit loop are the candidate loops requiring frequent re-tuning. Equipment failures and loss of control leading to less than acceptable indoor environment conditions is a common problem reported in these systems. In our paper we consider the degradation in the DAT loop performance caused by a gradual increase in backlash of the valve actuator and several disturbances that occur in the process. The main objective of this paper is to describe the application of an interactive multiple model (IMM) [9] based on an augmented unscented Kalman filter (UKF) estimation algorithm (IMMAUKF) [2] to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in DAT loop of the HVAC systems [4]. The proposed algorithm is an alternative to the interactive multiple model (IMM) developed in the literature based on the extended Kalman filter standard technique [2], [3]-[5], [7]-[11], the most popular estimation technique used in the last 40 years. The main advantage of the proposed algorithm is the less computation, consequently faster, high accuracy, robustness and eliminates completely the linearization of the system dynamics.