Abstract
According to the characteristics of the acoustic emission signal which was induced by punch die when It fails, the characteristic parameters of failure signal is determined. The energy eigenvector of signal failure die is extracted by wavelet packet analysis technology, and the comparison between the energy in different frequency bands and total energy is taken as the characteristic parameters. Then a BP neural network is established in which the time factor is considered based on genetic algorithm. The characteristic parameters are used as input specimen, learning and training the network to complete the pattern recognition of model working state. Experiments show that the method can quickly and reliably discriminate the conditions of the punch die and has strong practicability.
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© 2008 Springer-Verlag Berlin Heidelberg
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Luo, Z., Wang, X., Li, J., Fan, B., Guo, X. (2008). Study of Punch Die Condition Discrimination Based on Wavelet Packet and Genetic Neural Network. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_55
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DOI: https://doi.org/10.1007/978-3-540-87734-9_55
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-87734-9
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