Using echo state networks for anomaly detection in underground coal mines

Oliver Obst*, X. Rosalind Wang, Mikhail Prokopenko

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

15 Citations (Scopus)

Abstract

We investigate the problem of identifying anomalies in monitoring critical gas concentrations using a sensor network in an underground coal mine. In this domain, one of the main problems is a provision of mine specific anomaly detection, with cyclical (moving) instead of flatline (static) alarm threshold levels. An additional practical difficulty in modelling a specific mine is the lack of fully labelled data of normal and abnormal situations. We present an approach addressing these difficulties based on echo state networks learning mine specific anomalies when only normal data is available. Echo state networks utilize incremental updates driven by new sensor readings, thus enabling a detection of anomalies at any time during the sensor network operation. We evaluate this approach against a benchmark - Bayesian network based anomaly detection, and observe that the quality of the overall predictions is comparable to the benchmark. However, the echo state networks maintain the same level of predictive accuracy for data from multiple sources. Therefore, the ability of echo state networks to model dynamical systems make this approach more suitable for anomaly detection and predictions in sensor networks.

Original languageEnglish
Title of host publicationProceedings - 2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
Place of PublicationLos Aalmitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages219-229
Number of pages11
ISBN (Print)9780769531571
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 International Conference on Information Processing in Sensor Networks, IPSN 2008 - St. Louis, MO, United States
Duration: 22 Apr 200824 Apr 2008

Other

Other2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
CountryUnited States
CitySt. Louis, MO
Period22/04/0824/04/08

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