Bayesian network based FDD strategy for variable air volume terminals

a r t i c l e i n f o This paper presents a diagnostic Bayesian network (DBN) for fault detection and diagnosis (FDD) of variable air volume (VAV) terminals. The structure of the DBN illustrates qualitatively the casual relationships between faults and symptoms. The parameters of the DBN describe quantitatively the probabilistic dependences between faults and evidence. The inputs of the DBN are the evidences which can be obtained from measurements in building management systems (BMSs) and manual tests. The outputs are the probabilities of faults concerned. Two rules are adopted to isolate the fault on the basis of the fault probabilities to improve the robustness of the method. Compared with conventional rule-based FDD methods, the proposed method can work well with uncertain and incomplete information, because the faults are reported with probabilities rather than in the Boolean format. Evaluations are made on a dynamic simulator of a VAV airconditioning system serving an office space using TRNSYS. The results show that it can correctly diagnose ten typical VAV terminal faults. Variable air volume (VAV) air conditioning systems are widely used in offices and commercial buildings nowadays. Building professionals usually consider that VAV systems have better performance in terms of thermal comfort and energy saving than fan coil unit systems and constant air volume systems. However, VAV terminals easily suffer from various faults which cause the performance of VAV systems to hardly meet the high expectations. Qin and Wang found that 20.9% of 1251 VAV terminals were ineffective in a site survey conducted in a commercial building in Hong Kong [1]. Preventive maintenance of VAV terminals is a difficult task since a large number of VAV terminals are installed above ceilings. Fault detection and diagnosis (FDD) tools for VAV terminals are essential for reliable indoor environment control, saving maintenance efforts, and eliminating the associated energy waste. There was little research conducted on FDD of VAV terminals in the last decades. Yoshida proposed an automatic regressive exogenous (RARX) model and an extended Kalman filter model to detect faults in a VAV unit and an air handling unit (AHU) cooling coil system [3,4]. Seem et al. described a set of indexes to assess the performance of control loops and to detect faults in VAV terminals and AHUs [5,6]. The performance indexes were embedded in commercial VAV terminal controllers to quickly identify terminals that were not operating correctly. Schein proposed VAV box …