Abstract
In order to prevent coal mine gas disaster, a new methane anomaly detection method based on Grey Autoregressive algorithm (GMAR) was presented in this paper. A study of the trend of methane concentration real-time change is made. Once the predicted methane concentration value is deduced by the grey prediction model, an AR model can be established to discover the methane concentration change. So the residual ratio of the former reference methane concentration sequence and its predicted value is set up as the decision function to find whether there is danger information or not. Experiments show that this method can obviously reflect the anomaly characteristics of methane concentration data sequence.
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© 2011 Springer-Verlag Berlin Heidelberg
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Sun, K., Qing, R., Wang, N. (2011). Coal Face Gas Concentration Anomaly Detection Based on Grey Autoregressive Algorithm. In: Jiang, L. (eds) Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19–20, 2011, Melbourne, Australia. Advances in Intelligent and Soft Computing, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25194-8_35
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DOI: https://doi.org/10.1007/978-3-642-25194-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25193-1
Online ISBN: 978-3-642-25194-8
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