Reference:
K. Verbert,
R. Babuska, and
B. De Schutter,
"Combining knowledge and historical data for system-level fault
diagnosis of HVAC systems," Engineering Applications of Artificial
Intelligence, vol. 59, pp. 260-273, Mar. 2017.
Abstract:
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.