Abstract
In order to ensure security and reliability of the equipment, so as to decrease the maintenance cost, combining with the characteristics of fault data, this paper adopts ε – support vector regression to establish a fault forecast model and evaluation system to prediction model effect which are proper to the electronic equipment. Selecting multi-electronic equipment and training on the ε – SVR with different kernel functions. It is demonstrated that the prediction effect is better and it is still of vital realistic significance for realizing condition-based maintenance of modern electronic equipment.
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Liu, L., Shen, J., Zhao, H. (2012). Fault Forecast of Electronic Equipment Based on ε –SVR. In: Wang, F.L., Lei, J., Gong, Z., Luo, X. (eds) Web Information Systems and Mining. WISM 2012. Lecture Notes in Computer Science, vol 7529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33469-6_64
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DOI: https://doi.org/10.1007/978-3-642-33469-6_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33468-9
Online ISBN: 978-3-642-33469-6
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