The Application of Self-Adaptive Wavelet Neural Network in Multi-Layered Structure Ultrasonic Testing of the Solid Rocket-Motor

Xi Peng Yin; You Li; Wei Cheng; Chang Chen

doi:10.4028/www.scientific.net/AMM.421.474

Paper Titles

Online Measurement for the Thermal Geometric Parameters of Hot Large Forgings
p.453

Effects of Roughness Strip and Acoustic Sensor Height on Subsonic Boundary Layer by Experiment
p.459

A Numerical Investigation into the Tensile Split Hopkinson Pressure Bars Test for Sheet Metals
p.464

Research of Method for Multi-Component Gas Monitoring Based on Non-Dispersive Infrared Technology
p.468

The Application of Self-Adaptive Wavelet Neural Network in Multi-Layered Structure Ultrasonic Testing of the Solid Rocket-Motor
p.474

Design Fabrication and Experimental Studies of ACFM Testing Instrument
p.479

Lines of Curvature and Umbilical Points: Computation Method and Applications
p.489

Levy Flight Algorithm for Optimization Problems - A Literature Review
p.496

Cuckoo Search Algorithm for Optimization Problems - A Literature Review
p.502

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 421The Application of Self-Adaptive Wavelet Neural...

The Application of Self-Adaptive Wavelet Neural Network in Multi-Layered Structure Ultrasonic Testing of the Solid Rocket-Motor

Abstract:

t is important to remove the noise signal effectively in non-destructive ultrasonic testing. Use the wavelet and neural network algorithm in multi-layered structure ultrasonic testing system of the solid rocket-motor, and construct self-adaptive wavelet neural network in the ultrasonic testing in order to restrain the noise. Better fitting signal is achieved by choosing Orthogonal Daubechies wavelet group and optimize the scale parameter by a searching algorithm. The simulation result shows that the wavelet neural network can make the testing system less distortion and better noise cancellation, and the method can be widely applied to ultrasonic detecting.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 421)

Pages:

474-478

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.421.474

Citation:

Cite this paper

Online since:

September 2013

Authors:

Xi Peng Yin, You Li, Wei Cheng, Chang Chen

Keywords:

Neural Network (NN), Self-Adaptive, Ultrasonic, Wavelet Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Abdulhamit Subasi, Ahmet Alkan, Etem Koklukaya, M. Kemal Kiymik, Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing, Neural Networks, vol. 18, pp.985-997, (2005).

DOI: 10.1016/j.neunet.2005.01.006

Google Scholar

[2] Baruah, P. and Chinnam, R.B., HMMs for diagnostics and prognostics in machining processes, International Journal of Production Research, vol. 43, no. 6, pp.1275-1293, March (2005).

DOI: 10.1080/00207540412331327727

Google Scholar

[3] Chinnam, R.B. and Mohan, P., On-Line Reliability Estimation of Physical Systems Using Neural Networks and Wavelets, International Journal of Smart Engineering System Design, vol. 4, pp.253-264, (2002).

DOI: 10.1080/10255810215021

Google Scholar

[4] Y. C. Huang, Condition assessment of power transformers using genetic-based neural networks, Proc. Inst. Elect. Eng., Sci. Meas. Technol., vol. 150, no. 1, p.19–24, (2003).

DOI: 10.1049/ip-smt:20020638

Google Scholar

[5] L. N. de Castro and F. J. Von Zuben, Learning and optimization using the clonal selection principle, IEEE Trans. Evol. Comput, vol. 6, NO. 3, p.239–251, Jun. (2002).

DOI: 10.1109/tevc.2002.1011539

Google Scholar

[6] He, S.C. and He, Z.Y., Blind equalization of nonlinear communication channels using recurrent wavelet neural networks, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp.3305-3308, April (1997).

DOI: 10.1109/icassp.1997.595500

Google Scholar