70
L. Wugang et al.
of the impact damage point, and the different dispersion abilities of the AE signal
frequency components have changed with the increase of propagation distances. The
signal wave form and its frequency components have more common features near the
impact source than those far from it. By comparison to the analysis of the AE signal
waves, the correlation coefficients for frequency components have more abilities to
represent the resemblances of AE signals.
6.7 Conclusions
In this paper, we examine the cross-correlations among the AE signals obtained
from a hypervelocity impact simulation. The correlation coefficients for the impact
signals were computed and the results show great differences of the coefficients from
different propagation distances. The correlation coefficients represent the fluctuation
trend and attenuate irregularly with the increase of propagation distance. There is no
evident relationship between correlation coefficients and AE signal wave features, but
the correlation coefficients for frequency components have more abilities to represent
the resemblances of AE signals.
Reference
1. Y.H. Chen, S.F. Liu, R.S. Geng, et al., Spectral and correlation analysis of acoustic emission
signals. Nondestruct Test 24(9), 395–399 (2002)
2. Z. Tan, C. Min, X. Zhan, et al., The application research of acoustic emission technique on hightemperature strength-thermal coupling experiment for composite structure. Struct. Environ.
Eng. 42(5), 59–64 (2015)
3. S. Ying, Signal analysis and the use in engineering test. J. Chongqing Petroleum College 4(5),
22–24 (2002)
4. W.J.Z. Jianli, The application of wavelet transform and autocorrelation analysis to fault location
on power cable. Electr. Eng. 2, 46–49 (2009)
5. B. Chen, Y. Zhang, J. Li, Study on acoustic emission features and correlations of sandstone
fracture. Mining Res. Develop. 34(3), 74–77 (2014)
6. T. Shi, J. Gao, The choice of the modeling mechanism based on source signal distribution
characteristics and correlation. China Rural Water Hydropower 29(119), 69–72 (2005)
7. W. Han, J. Wu, Application of wavelet transform based signal correlation technique in fault
location of power cable. Power Syst. Technol. 29(119), 69–72 (2005)
8. Z.M.L.L.L. Xiujuan, The research on locating method of acoustic emission source in metal
plate based on correlation analysis. J. China Three Gorges University (Natural Sciences) 31(5),
60–62 (2009)
9. W. Liu, B.J. Pang, Z.Y. Han, et al., Damage identification of single aluminum plate produced
by hypervelocity impact based acoustic emission. J. Astronautics 32(3), 671–675 (2011)
10. Z. Jin, M.S. Jiang, Q.M. Sui, et al., Acoustic emission localization technique based on generalized cross-correlation time difference estimation algorithm. Chinese J. Sens. Actuat. 26(11),
1513–1518 (2013)
L. Wugang et al.
of the impact damage point, and the different dispersion abilities of the AE signal
frequency components have changed with the increase of propagation distances. The
signal wave form and its frequency components have more common features near the
impact source than those far from it. By comparison to the analysis of the AE signal
waves, the correlation coefficients for frequency components have more abilities to
represent the resemblances of AE signals.
6.7 Conclusions
In this paper, we examine the cross-correlations among the AE signals obtained
from a hypervelocity impact simulation. The correlation coefficients for the impact
signals were computed and the results show great differences of the coefficients from
different propagation distances. The correlation coefficients represent the fluctuation
trend and attenuate irregularly with the increase of propagation distance. There is no
evident relationship between correlation coefficients and AE signal wave features, but
the correlation coefficients for frequency components have more abilities to represent
the resemblances of AE signals.
Reference
1. Y.H. Chen, S.F. Liu, R.S. Geng, et al., Spectral and correlation analysis of acoustic emission
signals. Nondestruct Test 24(9), 395–399 (2002)
2. Z. Tan, C. Min, X. Zhan, et al., The application research of acoustic emission technique on hightemperature strength-thermal coupling experiment for composite structure. Struct. Environ.
Eng. 42(5), 59–64 (2015)
3. S. Ying, Signal analysis and the use in engineering test. J. Chongqing Petroleum College 4(5),
22–24 (2002)
4. W.J.Z. Jianli, The application of wavelet transform and autocorrelation analysis to fault location
on power cable. Electr. Eng. 2, 46–49 (2009)
5. B. Chen, Y. Zhang, J. Li, Study on acoustic emission features and correlations of sandstone
fracture. Mining Res. Develop. 34(3), 74–77 (2014)
6. T. Shi, J. Gao, The choice of the modeling mechanism based on source signal distribution
characteristics and correlation. China Rural Water Hydropower 29(119), 69–72 (2005)
7. W. Han, J. Wu, Application of wavelet transform based signal correlation technique in fault
location of power cable. Power Syst. Technol. 29(119), 69–72 (2005)
8. Z.M.L.L.L. Xiujuan, The research on locating method of acoustic emission source in metal
plate based on correlation analysis. J. China Three Gorges University (Natural Sciences) 31(5),
60–62 (2009)
9. W. Liu, B.J. Pang, Z.Y. Han, et al., Damage identification of single aluminum plate produced
by hypervelocity impact based acoustic emission. J. Astronautics 32(3), 671–675 (2011)
10. Z. Jin, M.S. Jiang, Q.M. Sui, et al., Acoustic emission localization technique based on generalized cross-correlation time difference estimation algorithm. Chinese J. Sens. Actuat. 26(11),
1513–1518 (2013)