Reference:
K. Verbert,
B. De Schutter, and
R. Babuska,
"Fault diagnosis using spatial and temporal information with
application to railway track circuits," Engineering Applications
of Artificial Intelligence, vol. 56, pp. 200-211, Nov. 2016.
Abstract:
Adequate fault diagnosis requires actual system data to discriminate
between healthy behavior and various types of faulty behavior.
Especially in large networks, it is often impracticable to monitor a
large number of variables for each subsystem. This results in a need
for fault diagnosis methods that can work with a limited set of
monitoring signals. In this paper, we propose such an approach for
fault diagnosis in networks. This approach is knowledge based and uses
the temporal, spatial, and spatio-temporal network dependencies as
diagnostic features. These features can be derived from the existing
monitoring signals; so no additional sensors are required. Besides
that the proposed approach requires only a few monitoring devices, it
is, thanks to the use of the spatial dependencies, robust with respect
to environmental disturbances. For a railway track circuit example, we
show that, without the temporal, spatial, and spatio-temporal
features, it is not possible to identify the cause of a detected
fault. Including the additional features allows potential causes to be
identified. For the track circuit case, based on one signal, we can
distinguish between six fault classes.