Fault diagnosis using spatial and temporal information with application to railway track circuits


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.


Downloads:
 * Online version of the paper
 * Corresponding technical report: pdf file (1.27 MB)
      Note: More information on the pdf file format mentioned above can be found here.


Bibtex entry:

@article{VerDeS:16-019,
        author={K. Verbert and B. {D}e Schutter and R. Babu{\v{s}}ka},
        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},
        doi={10.1016/j.engappai.2016.08.016}
        }



Go to the publications overview page.


This page is maintained by Bart De Schutter. Last update: June 27, 2018.