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Research on Signal Analysis Method of Acoustic Emission of Material 2.25Cr-1Mo Based on Wavelet Filter and Clustering

  • Feifei Long
  • Haifeng Xu
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 126)

Abstract

Extracting acoustic emission (AE) signals of cracks and micro-cracks after material yield phase from noise is of great significance for the study of AE character of material 2.25Cr-1Mo. In this paper wavelet was used to find out the main frequency bands of the burst cracking AE signals from the tensile test of material 2.25Cr-1Mo. Burst cracking waveform was successfully extracted from mixed waveform by reconstructing wavelet coefficients with the main frequency bands. Then the new descriptors of the waveform were determined by using of a 30% floating threshold. The DBSCAN clustering was applied to separate noise signals of electric and vibration successfully. At last the k-mean clustering was used to separate burst cracking signal data from data set effectively and accurately. According to the Analysis of the material of 2.25Cr-1Mo tensile test, the cumulative energy of burst AE signals could reflect the yield point and the degree of material damage.

Keywords

Acoustic Emission Continuous Wavelet Transform Original Parameter Acoustic Emission Data Acoustic Emission Waveform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Northeast Petroleum UniversityDaqingChina

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