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Study of Punch Die Condition Discrimination Based on Wavelet Packet and Genetic Neural Network

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5264))

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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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References

  1. Irving, S., Liu, Y.: An effective method for improving IC package die failure during assembly punch processing. In: Proceedings of the 6th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Micro-Electronics and Micro-Systems - EuroSimE 2005, pp. 227–233 (2005)

    Google Scholar 

  2. Kaewkongka, T., Au, Y.H.J.: Application of acoustic emission to condition monitoring of rolling element bearings. Measurement and Contorl 34(8), 245–247 (2001)

    Google Scholar 

  3. Velayudham, A., Krishnamurthy, R., Soundarapandian, T.: Polymeric composite using wavelet packet transform. Materials Science and Engineering A 412(1-2), 141–145 (2005)

    Article  Google Scholar 

  4. Lin, S.T., Mcfadden, P.D.: Gear vibration analysis by b-spline wavelet-based linear transform. Mechanical Systems and Signal Processing 11(4), 603–609 (1997)

    Article  Google Scholar 

  5. Tian, B., Azimi-Sadjadi, M.R., Vonder-Haar, T.H., Reinke, D.L.: Temporal updating scheme for probabilistic nNeural networks with application to satellite cloud classification. IEEE Transactions on Neural Network 11(7), 903–920 (2000)

    Article  Google Scholar 

  6. Tao, Q., Fang, T., Qiao, H.: A novel continuous-time neural network for realizing associative memory. IEEE Transactions on Neural Networks 12(2), 418–423 (2001)

    Article  Google Scholar 

  7. Jin, J.L., Yang, X.B., Ding, J.: Real coding based acceleration genetic algorithm. Journal of Sichuan university (engineering science edition) 32(3), 20–24 (2000)

    Google Scholar 

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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

  • Print ISBN: 978-3-540-87733-2

  • Online ISBN: 978-3-540-87734-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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