Abstract
This paper represented the main state parameters of Tunnel Boring Machine (TBM) system, analyzed the variation tendency of time series which were TBM characteristic parameters, predicted the development tendency for characteristic parameters of TBM equipment status combing the grey and neural network prediction, and then built the prediction model for characteristic parameters of TBM based on the grey theory and neural network. Through calculating the projects, the improvement measure of prediction model was given. The modified prediction model could ensure the running condition for 10 h when prediction accuracy reaches first class. Finally, this paper introduced the part of parameters prediction for the TBM fault diagnosis system developed by the author, so prediction results would be presented before the workers more directly.
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© 2013 Springer-Verlag Berlin Heidelberg
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Zhang, T., Dai, Y., Lu, C., Zhao, H., Yu, T. (2013). Analysis of TBM Monitoring Data Based on Grey Theory and Neural Network. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_125
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DOI: https://doi.org/10.1007/978-3-642-37502-6_125
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