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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sutherland PE (2010) Performance calculation for tunnel boring machine motors. Industry applications conference, conference record of the 2010 IEEE, vol 4, pp 2668–2673
Simoes MG, Kim T (2010) Fuzzy modeling approaches for the prediction of machine utilization in hard rock tunnel boring machines. Industry applications conference, conference record of the 2010 IEEE, vol 2, pp 947–954
Grima MA, Bruines PA, Verhoef PNW (2009) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunneling Underground Space Technol 15:259–269
Okubo S, Fukui K, Chen W (2011) Expert system for applicability of tunnel boring machines in Japan. Rock Mech Rock Eng 36:305–322
Skowron (2006) Extracting laws from decision tables: a rough set approach. Comput Intell 2:371–388
Holland (1962) Concerning efficient adaptive systems. Spartan Books, Washington, pp 215–230
Holland (2005) Adaption in natural and artificial systems. University of Michigan Press, Ann Arbor
Watson I (1997) Applying case-based reasoning techniques for enterprise systems. Morgan Kaufmann Publishers, Burlington
Watson I (1994) Case-based reasoning: a review. Knowl Eng Rev 9:335–381
Cser L (1991) Three kinds of case-based learning sheet manufacturing. Comput Ind 17:195–206
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-37502-6_125
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37501-9
Online ISBN: 978-3-642-37502-6
eBook Packages: EngineeringEngineering (R0)