180
S. Wu et al.
16.4 Conclusion
The CEEMD algorithm solves the problem of residual noise in the reconstructed
signal by adding a pair of positive and negative relative noise and retains the useful
information of the original signal well. Therefore, the method is referenced to the
MAE signal processing. In the static load state, since the large amount of dislocations
inside the material, the MAE signal strength continuously decreases as the tensile
force of the static load increases. Moreover, when the curve obtained by the CEEMD
algorithm is near the yield strength of 235 Mpa, the whole pattern trend becomes
smooth and stable. According to the comparison of SNR, the proposed method
effectively improves the SNR of the MAE signal. In summary, the CEEMD algorithm
has a unique advantage in the extraction of MAE signal characteristic parameters
under static load state.
References
1. J.-r. Yeh, J.-s. Shieh, N., Complementary ensemble empirical mode decomposition: a novel
enhanced data analysis method. Adv. Adapt. Data Anal. 2(2), 135–156 (2010)
2. P.A. Delgado-Arredondo, D. Morinigo-Sotelo, R.A. Osornio-Rios et al., Methodology for fault
detection in induction motors via sound and vibration signals. Mech. Syst. Signal Process. 83,
568–589 (2016)
3. M. Manataki, A. Sarris, A. Vafidis, Combining CEEMD and predictive deconvolution for
the suppression of multiple reflections and coherent noise in GPR signals, inInternational
Workshop on Advanced Ground Penetrating Radar, Florence, Italy. IEEE Press, pp. 1–4 (2015)
4. J. Li, C. Liu, Z. Zeng et al., GPR signal denoising and target extraction with the CEEMD
method. IEEE Geosci. Remote Sens. Lett. 12(8), 1615–1619 (2015)
5. C. Franco, J. Fontecave-Jallon, N. Vuillerme, et al.: Towards a suitable time-scale representation
of cardio-respiratory signals through empirical mode decomposition algorithms: a simulation
and validation tool, inConference Proceedings IEEE Engineering in Medicine and Biology
Society, Boston, MA, USA. IEEE Press, p. 802 (2011)
6. W. Zhao, Z. Wang, J. Ma et al., Fault diagnosis of a hydraulic pump based on the CEEMDSTFT time-frequency entropy method and multiclass SVM classifier. Shock Vibr. 1, 8 (2016),
26 Sept. 2016
7. U. Satija, B. Ramkumar, M.S. Manikandan, Automated ECG noise detection and classification
system for unsupervised healthcare monitoring. IEEE J. Biomed. Health Inform. 99, 1–1 (2017)
8. M. Li, H. Wang, G. Tang et al., An improved method based on CEEMD for fault diagnosis
of rolling bearing, in Advances in Mechanical Engineering, Hindawi Publishing Corporation,
pp. 1–10 (2014)
9. L. Zhao, W. Yu, R. Yan, Rolling bearing fault diagnosis based on CEEMD and time series
modeling. Math. Probl. Eng. (2014-7-7), 1, 1–13 (2014)
10. N. Huang, Z. Shen, R. Long Steven et al., The empirical mode decomposition and the
Hilbertspectrum for nonlinear and non-stationary time series analysis. R. Soc. 454, 903–995
(1998)
11. Z.-h. Wu, N. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis
method. Adv. Adapt. Data Anal. 1(1), 1–41 (2008)
12. J.R. Yeh, J.S. Shieh, N.E. Huang, Complementary ensemble empirical mode decomposition:
a novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2(2), 135–156 (2010)
S. Wu et al.
16.4 Conclusion
The CEEMD algorithm solves the problem of residual noise in the reconstructed
signal by adding a pair of positive and negative relative noise and retains the useful
information of the original signal well. Therefore, the method is referenced to the
MAE signal processing. In the static load state, since the large amount of dislocations
inside the material, the MAE signal strength continuously decreases as the tensile
force of the static load increases. Moreover, when the curve obtained by the CEEMD
algorithm is near the yield strength of 235 Mpa, the whole pattern trend becomes
smooth and stable. According to the comparison of SNR, the proposed method
effectively improves the SNR of the MAE signal. In summary, the CEEMD algorithm
has a unique advantage in the extraction of MAE signal characteristic parameters
under static load state.
References
1. J.-r. Yeh, J.-s. Shieh, N., Complementary ensemble empirical mode decomposition: a novel
enhanced data analysis method. Adv. Adapt. Data Anal. 2(2), 135–156 (2010)
2. P.A. Delgado-Arredondo, D. Morinigo-Sotelo, R.A. Osornio-Rios et al., Methodology for fault
detection in induction motors via sound and vibration signals. Mech. Syst. Signal Process. 83,
568–589 (2016)
3. M. Manataki, A. Sarris, A. Vafidis, Combining CEEMD and predictive deconvolution for
the suppression of multiple reflections and coherent noise in GPR signals, inInternational
Workshop on Advanced Ground Penetrating Radar, Florence, Italy. IEEE Press, pp. 1–4 (2015)
4. J. Li, C. Liu, Z. Zeng et al., GPR signal denoising and target extraction with the CEEMD
method. IEEE Geosci. Remote Sens. Lett. 12(8), 1615–1619 (2015)
5. C. Franco, J. Fontecave-Jallon, N. Vuillerme, et al.: Towards a suitable time-scale representation
of cardio-respiratory signals through empirical mode decomposition algorithms: a simulation
and validation tool, inConference Proceedings IEEE Engineering in Medicine and Biology
Society, Boston, MA, USA. IEEE Press, p. 802 (2011)
6. W. Zhao, Z. Wang, J. Ma et al., Fault diagnosis of a hydraulic pump based on the CEEMDSTFT time-frequency entropy method and multiclass SVM classifier. Shock Vibr. 1, 8 (2016),
26 Sept. 2016
7. U. Satija, B. Ramkumar, M.S. Manikandan, Automated ECG noise detection and classification
system for unsupervised healthcare monitoring. IEEE J. Biomed. Health Inform. 99, 1–1 (2017)
8. M. Li, H. Wang, G. Tang et al., An improved method based on CEEMD for fault diagnosis
of rolling bearing, in Advances in Mechanical Engineering, Hindawi Publishing Corporation,
pp. 1–10 (2014)
9. L. Zhao, W. Yu, R. Yan, Rolling bearing fault diagnosis based on CEEMD and time series
modeling. Math. Probl. Eng. (2014-7-7), 1, 1–13 (2014)
10. N. Huang, Z. Shen, R. Long Steven et al., The empirical mode decomposition and the
Hilbertspectrum for nonlinear and non-stationary time series analysis. R. Soc. 454, 903–995
(1998)
11. Z.-h. Wu, N. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis
method. Adv. Adapt. Data Anal. 1(1), 1–41 (2008)
12. J.R. Yeh, J.S. Shieh, N.E. Huang, Complementary ensemble empirical mode decomposition:
a novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2(2), 135–156 (2010)