Reference
K. Verbert, B. De Schutter, and R. Babuška, "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.
Publisher page
Downloads
BibTeX
@article{VerDeS:16-019,
author = {Verbert, Kim and De Schutter, Bart and Babu{\v{s}}ka, Robert},
title = {Fault Diagnosis Using Spatial and Temporal Information with
Application to Railway Track Circuits},
journal = {Engineering Applications of Artificial Intelligence},
volume = {56},
pages = {200--211},
month = nov,
year = {2016}
}