Reference
K. Verbert, R. Babuška, 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.
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BibTeX
@article{VerDeS:16-029,
author = {Verbert, Kim and Babu{\v{s}}ka, Robert and De Schutter, Bart},
title = {Combining Knowledge and Historical Data for System-Level Fault
Diagnosis of {HVAC} systems},
journal = {Engineering Applications of Artificial Intelligence},
volume = {59},
pages = {260--273},
month = mar,
year = {2017}
}