Fault detection and diagnosis of valve actuators in HVAC systems

In modern buildings heating, ventilating and air conditioning (HVAC) systems are becoming more and more sophisticated with increasingly complex system configurations. Equipment failures and loss of control leading to less than acceptable indoor environment conditions is a common problem reported in many buildings. For this reason, monitoring and controlling these systems under a wide variety of occupancy and load related operating conditions is becoming a difficult and challenging task. In our paper, we consider a discharge air temperature (DAT) system which is one of the most important subsystems of a central HVAC system. The degradation in the DAT system performance caused by a gradual increase in backlash of the valve actuator creates significant fluctuations in supply air temperatures leading to thermal discomfort and higher energy consumption. To this end, the main objective of this paper is to describe the application of an interactive Kalman filter estimation algorithm, similiar to the interactive multi model algorithm developed in the literature (IMM) to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in DAT systems. The algorithm is based on the assumption that the system mode sequence is first-order Markov chain and an initial matrix transition probabilities is given. The main interest is focused on dealing with the unanticipated valve actuator failures and statistic properties of the process noise and measurements such as the mean and covariance matrices, in the most general formulation based on the Kalman filter estimation theory. The proposed algorithm gives a systematic procedure for proper fault accommodation under unanticipated failures, and it is more accurate and robust compared to the algorithms based on the spectral analysis developed in literature