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Using Echo State Networks for Anomaly Detection in Underground Coal Mines


Oliver Obst, X. Rosalind Wang, and Mikhail Prokopenko. Using Echo State Networks for Anomaly Detection in Underground Coal Mines. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN 2008), pp. 219–229, IEEE Computer Society, April 2008.


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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 -- Bayes 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.


BiBTeX Entry


@InProceedings{   OWP08,
  author	= {Oliver Obst and X. Rosalind Wang and Mikhail Prokopenko},
  booktitle	= {Proceedings of the International Conference on
		   Information Processing in Sensor Networks (IPSN 2008)},
  isbn		= {978-0-7695-3157-1},
  keywords	= {echo state networks, recurrent neural networks, sensor
		   networks, bayesian networks, anomaly, novelty, coal mine},
  month 	= apr,
  pages 	= {219--229},
  publisher	= {IEEE Computer Society},
  title 	= {Using Echo State Networks for Anomaly Detection in
		   Underground Coal Mines},
  year		= {2008},
  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 only normal
		   data is available. Echo state networks utilize enabling a detection of
		   anomalies at any time during the sensor network operation. We evaluate this
		   approach against a benchmark -- Bayes 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. },
}