TY - JOUR
T1 - Combining knowledge and historical data for system-level fault diagnosis of HVAC systems
AU - Verbert, K.
AU - Babuška, R.
AU - De Schutter, B.
N1 - Accepted Author Manuscript
PY - 2017
Y1 - 2017
N2 - Interdependencies among system components and the existence of multiple operating modes present a challenge for fault diagnosis of Heating, Ventilation, and Air Conditioning (HVAC) systems. Reliable and timely diagnosis can only be ensured when it is performed in all operating modes, and at the system level, rather than at the level of the individual components. Nevertheless, almost no HVAC fault diagnosis methods that satisfy these requirements are described in literature. In this paper, we propose a multiple-model approach to system-level HVAC fault diagnosis that takes component interdependencies and multiple operating modes into account. For each operating mode, a distinct Bayesian network (diagnostic model) is defined at the system level. The models are constructed based on knowledge regarding component interdependencies and conservation laws, and based on historical data through the use of virtual sensors. We show that component interdependencies provide useful features for fault diagnosis. Incorporating these features results in better diagnosis results, especially when only a few monitoring signals are available. Simulations demonstrate the performance of the proposed method: faults are timely and correctly diagnosed, provided that the faults result in observable behavior.
AB - Interdependencies among system components and the existence of multiple operating modes present a challenge for fault diagnosis of Heating, Ventilation, and Air Conditioning (HVAC) systems. Reliable and timely diagnosis can only be ensured when it is performed in all operating modes, and at the system level, rather than at the level of the individual components. Nevertheless, almost no HVAC fault diagnosis methods that satisfy these requirements are described in literature. In this paper, we propose a multiple-model approach to system-level HVAC fault diagnosis that takes component interdependencies and multiple operating modes into account. For each operating mode, a distinct Bayesian network (diagnostic model) is defined at the system level. The models are constructed based on knowledge regarding component interdependencies and conservation laws, and based on historical data through the use of virtual sensors. We show that component interdependencies provide useful features for fault diagnosis. Incorporating these features results in better diagnosis results, especially when only a few monitoring signals are available. Simulations demonstrate the performance of the proposed method: faults are timely and correctly diagnosed, provided that the faults result in observable behavior.
KW - Bayesian networks
KW - Fault diagnosis
KW - HVAC systems
KW - Virtual sensors
UR - http://resolver.tudelft.nl/uuid:cfbfdd1d-a9c5-4a64-946f-ede2df9ce820
U2 - 10.1016/j.engappai.2016.12.021
DO - 10.1016/j.engappai.2016.12.021
M3 - Article
AN - SCOPUS:85009343101
SN - 0952-1976
VL - 59
SP - 260
EP - 273
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
ER -