Anomaly detection in monitoring sensor data for preventive maintenance. Rabatel, J., Bringay, S., & Poncelet, P. Expert Systems with Applications, 38(6):7003–7015, June, 2011. ![Anomaly detection in monitoring sensor data for preventive maintenance [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly.
@article{rabatel_anomaly_2011,
title = {Anomaly detection in monitoring sensor data for preventive maintenance},
volume = {38},
issn = {0957-4174},
url = {https://www.sciencedirect.com/science/article/pii/S0957417410013771},
doi = {10.1016/j.eswa.2010.12.014},
abstract = {Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly.},
language = {en},
number = {6},
urldate = {2022-01-13},
journal = {Expert Systems with Applications},
author = {Rabatel, Julien and Bringay, Sandra and Poncelet, Pascal},
month = jun,
year = {2011},
keywords = {Anomaly detection, Behavior characterization, Preventive maintenance, Sequential patterns},
pages = {7003--7015},
}
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