Joint UKF estimator for fault detection and isolation in Heating Ventilation and Air Conditioning systems

In this paper, we propose to use as an alternative to Extended Kalman Filter Estimator (EKFE), the joint version of Unscented Kalman Filter (UKF) Estimator (UKFE) to solve simultaneously the state and parameter estimation problems, suitable integrated in our proposed Fault Detection and Diagnosis (FDDI) strategy. This strategy is useful to monitor and to control the recent generation of high complexity Heating Ventilation Air Conditioning (HVAC) building systems under a wide variety of occupancy and load related operating conditions that represents one of the most difficult and challenging task. The main objective of this study is to develop a new approach of the fault detection, diagnosis and isolation (FDDI) automated techniques applied to the valve actuator failures in HVAC systems. Our approach is based on the Joint Unscented Kalman Filter (JUKF) as a suitable alternative to the Extended Kalman Filter Estimator (EKFE) due to its superiority compared to that. In UKF joint approach the state space dynamics is augmented by the dynamics of the faulty parameters to detect and isolate the faulty valve (stuck opened and stuck closed), and also to determine the fault severity. The simulations results reveal similar performance compared to EKFE, namely its accuracy and robustness to the changes in the system structure. The superiority of this approach is its capability to deal with the nonlinear dynamics of the system, compared to EKFE that is based on the linearization of the nonlinear dynamics computing Jacobean matrices. This algorithm is implemented in a simulation environment, and the fault diagnosis results could be evaluated for a several fault scenarios in terms of the injection fault, detection time, and its severity.