Research on Signal Denoising Method of Chatter Incubation Stage Based on IES

[1] (2017)

[2] YANG Kun, HUANG Lixin, Review of chatter issues in machining［J］, Chinese Journal of Applied Mechanics, 36, 6, pp. 1464-1470, (2019)

[3] Yao LI, LIU Qiang, Chatter identification in CNC milling based on wallet packet and Hilbert-Huang transform ［J］, Computer Integrated Manufacturing Systems, 1, pp. 204-216, (2015)

[4] ZAFAR T，, KAMAL K，, SHEIKH Z，, Et al., A neural network based approach for background noise reduction in airborne acoustic emission of a machining process［J］, Journal of Mechanical Science and Technology, 31, 7, pp. 3171-3182, (2017)

[5] CASTELLO D A., On the wavelet analysis of cutting forces for chatter identification in milling［J］, Advances in Manufacturing, 5, 2, pp. 130-142, (2017)

[6] YUE Caixu, GAO Haining, ZHANG Haitao, Et al., Dynamicning characteristics of hard milling process of splicing dies forautomobile cover panel［J］, Journal of Vibration，Measurement & Diagnosis, 37, 4, pp. 822-829, (2017)

[7] LIU Shudan, CHEN Zhixing, Nonlinear vibration signal de-noising based on singular value decomposition and EEMD［J］, Journal of Detection & Control, 41, 3, pp. 37-42, (2019)

[8] LIN T R, TAN J W., A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering［J］, Applied Acoustics, 121, pp. 33-45, (2017)

[9] YANG Z X, ZHONG J H., A hybrid EEMD-based SampEn and SVD for acoustic signal processing and fault diagnosis［J］, Entropy, 18, 4, (2016)

[10] ZHANG J，, JIANG R，, LI B，, Et al., An automatic recognition method of microseismic signals based on EEMD-SVD and ELM［J］, Computers and Geoscien - ces, 133, (2019)

[1] (2017)

[2] YANG Kun, HUANG Lixin, Review of chatter issues in machining［J］, Chinese Journal of Applied Mechanics, 36, 6, pp. 1464-1470, (2019)

[3] Yao LI, LIU Qiang, Chatter identification in CNC milling based on wallet packet and Hilbert-Huang transform ［J］, Computer Integrated Manufacturing Systems, 1, pp. 204-216, (2015)

[4] ZAFAR T，, KAMAL K，, SHEIKH Z，, Et al., A neural network based approach for background noise reduction in airborne acoustic emission of a machining process［J］, Journal of Mechanical Science and Technology, 31, 7, pp. 3171-3182, (2017)

[5] CASTELLO D A., On the wavelet analysis of cutting forces for chatter identification in milling［J］, Advances in Manufacturing, 5, 2, pp. 130-142, (2017)

[6] YUE Caixu, GAO Haining, ZHANG Haitao, Et al., Dynamicning characteristics of hard milling process of splicing dies forautomobile cover panel［J］, Journal of Vibration，Measurement & Diagnosis, 37, 4, pp. 822-829, (2017)

[7] LIU Shudan, CHEN Zhixing, Nonlinear vibration signal de-noising based on singular value decomposition and EEMD［J］, Journal of Detection & Control, 41, 3, pp. 37-42, (2019)

[8] LIN T R, TAN J W., A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering［J］, Applied Acoustics, 121, pp. 33-45, (2017)

[9] YANG Z X, ZHONG J H., A hybrid EEMD-based SampEn and SVD for acoustic signal processing and fault diagnosis［J］, Entropy, 18, 4, (2016)

[10] ZHANG J，, JIANG R，, LI B，, Et al., An automatic recognition method of microseismic signals based on EEMD-SVD and ELM［J］, Computers and Geoscien - ces, 133, (2019)