Abstract
The acoustic emission (AE) detecting is an effective online tank bottom corrosion monitoring method. It is easy to discover early corrosion. Aiming at random unstable features of AE signals from tank bottom corrosion, genetic-matching pursuit arithmetic was presented to extract features of tank bottom corrosion AE signal. The main characteristics of the signals are given by the acoustic emission detector. The characteristics include count, energy, amplitude, and average frequency. Matching Pursuit (MP) arithmetic is used to extract characteristic parameters. The AE signals can be well reconfigured. The genetic algorithm (GA) was used to optimize MP algorithm. The projection on the atom of the signal or its residue in MP arithmetic was served as the GA fitness function, and the best matching atomic parameter was confirmed. The experimental results show that the best pursuit atomic parameters are extracted and the amount of calculation is reduced substantially by this method. In the reconstructed signal process, signal noise can be effectively removed by this method, so as to achieve a certain de-noising effect. It has much practical value and theoretical application value.
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Acknowledgment
This work is supported by the Creative Team Project Foundation of the Education Department of Liaoning Province, China (Grant No. LT2010082).
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Yu, Y., Zhang, N., Yang, P., Liu, B., Fu, Y. (2015). Feature Extraction of Corrosion Acoustic Emission Signals Based on Genetic-Matching Pursuit Algorithm. In: Shen, G., Wu, Z., Zhang, J. (eds) Advances in Acoustic Emission Technology. Springer Proceedings in Physics, vol 158. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1239-1_17
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DOI: https://doi.org/10.1007/978-1-4939-1239-1_17
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