Extraction method for signal effective component based on extreme-point symmetric mode decomposition and Kullback–Leibler divergence
- 8 Downloads
Data processing is widely used to extract effective component from original signal, which is essential in mechanical condition monitoring and fault diagnosis. In order to solve the invalid component and non-stationary feature in the measured signal, the extraction method for effective signal component is proposed based on extreme-point symmetric mode decomposition (ESMD) and Kullback–Leibler (K–L) divergence. This method fully integrates the characteristics of ESMD in self-adaptive decomposition and the advantages of K–L divergence in measuring the distance between different signals. The effective and invalid components of non-stationary signal are automatically separated by ESMD, and the effective components are further identified through K–L divergence calculation. Some analyses of simulated data and experimental data were investigated. And the effect of the proposed method in effective component extraction was emphatically explored. Research results indicate that the proposed method can adaptively acquire effective signal components with higher accuracy. Moreover, compared with the classic method, it is more efficient in the extraction of effective components from complex signal. In addition, this research solves the interference problem of invalid signals and accurately reconstructs the desired useful signal.
KeywordsMechanical signal Effective component extraction Extreme-point symmetric mode decomposition Kullback–Leibler divergence
This work is supported by National Natural Science Foundation of China (51805214, 51875498, 51609106) and Natural Science Foundation of Hebei Province (E2018203339). The authors would also like to thank the reviewers for their valuable suggestions and comments.
- 2.Long Y, Xie QM, Zhong MS, Lu L, Li XH (2012) Research on trend removing methods in preprocessing analysis of blasting vibration monitoring signals. Eng Mech 29(10):63–68Google Scholar
- 4.Liu TY, Jiang Q, Li Y, Xu ZD (2018) A review of rotating machinery fault signal processing and diagnosis methods. China Energy Environ Prot 40(1):163–166Google Scholar
- 7.Zhang F, Fu J, Fan YL, Zhou XJ (2017) Main shaft run-out research of hydraulic generator unit in load rejection process based on empirical mode decomposition. J Drainage Irrig Mach Eng 35(10):863–868Google Scholar
- 10.Liang B, Wang TQ (2013) Method of vibration signal trend extraction based on HHT. Electronic Meas Technol 36(2):119–122Google Scholar
- 11.Jia R, Ma FQ, Wu H, Luo X, Ma X (2018) Coupling fault feature extraction method based on bivariate empirical mode decomposition and full spectrum for rotating machinery. Math Probl Eng 2:1–10Google Scholar
- 20.Han ZH, Zhu XX, Li WH (2012) A false component identification method of EMD based on Kullback–Leibler divergence. Proc CSEE 32(11):112–117Google Scholar
- 32.Zhu Y, Jiang WL, Kong XD, Zheng Z, Hu HS (2015) An accurate integral method for vibration signal based on feature information extraction. Shock Vib 2015:962793Google Scholar
- 33.Xue ZH, Cao X, Wang TZ (2018) Vibration test and analysis on the centrifugal pump. J Drainage Irrig Mach Eng 36(6):472–477Google Scholar
- 34.Ren Y, Zhang K (2018) Integrated condition monitoring and fault diagnosis technology for wind turbine drive-train. J Drainage Irrig Mach Eng 36(7):613–616Google Scholar
- 35.He NC, Tan MG, Liu HL, Huang X, Wu XF (2018) Test and analysis on pressure pulsation and hydraulic performance of saddle zone in axial flow pump. J Drainage Irrig Mach Eng 36(2):118–123Google Scholar
- 36.Zhong WY, Zhu RS, Wang XL, Lu YG, Liu Y, Kang JJ (2018) Mechanical properties of nuclear reactor coolant pump impeller based on bidirectional fluid structure interaction. J Drainage Irrig Mach Eng 36(6):485–493Google Scholar