A Multicomponent Signal Decomposition Method: Time-Frequency Filtering Decomposition

被引：0

作者：

Zhang, Kang ^{[1
]}

Liu, Peng-Fei ^{[1
]}

Cao, Zhen-Hua ^{[1
]}

Chen, Xiang-Min ^{[1
]}

Tian, Ze-Yu ^{[2
]}

机构：

[1] School of Energy and Power Engineering, Changsha University of Science and Technology, Hunan, Changsha

[2] Hunan Clean Energy Branch, Huaneng Power International Co., Ltd., Hunan, Changsha

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 08期

关键词：

instantaneous frequency; multicomponent signal; signal decomposition; time-frequency distribution; time-frequency filtering;

D O I：

10.12263/DZXB.20230758

中图分类号：

学科分类号：

摘要：

The problems of difficulty and low efficiency in decomposing multicomponent nonstationary nonlinear signal with complex time-frequency characteristics such as contiguity, overlap and intermittency in time-frequency domain are solved. Based on the time-frequency distribution of signal, a multicomponent nonstationary signal decomposition method called time-frequency filtering decomposition (TFFD) is proposed. TFFD gets the fitting IF curve which is consistent with the instantaneous frequency (IF) of the components by fitting the time-frequency datum points which can reflect the instantaneous characteristics and laws of the components in the signal. Based on the time-frequency coordinates of fitting IF curve, the distribution area of component is determined by setting the distance threshold condition. Thus, a time-frequency filter bank is constructed, which is based on fitting IF curve time-frequency coordinates as the central frequency and the bandwidth of distribution area as the passband width, to achieve time-frequency filtering decomposition for multicomponent signal. Through the analysis of the simulation and the actual signal with the representative time-frequency characteristics, and the comparison with the classical signal decomposition methods, it is proved that the TFFD method has good decomposition ability and efficiency. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：2618 / 2627

页数：9

共 18 条

[1]

CHEN S Q, DONG X J, PENG Z K, Et al., Nonlinear chirp mode decomposition: A variational method, IEEE Transactions on Signal Processing, 65, 22, pp. 6024-6037, (2017)

[2]

IATSENKO D, MCCLINTOCK P V E, STEFANOVSKA A., Nonlinear mode decomposition: A noise-robust, adaptive decomposition method, Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 92, 3, (2015)

[3]

TIWARI P, UPADHYAY S H., Novel self-adaptive vibration signal analysis: Concealed component decomposition and its application in bearing fault diagnosis, Journal of Sound Vibration, 502, (2021)

[4]

HUANG N E, SHEN Z, LONG S R, Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London Series A, 454, 1971, pp. 903-998, (1998)

[5]

SMITH J S., The local mean decomposition and its application to EEG perception data, Journal of the Royal Society, Interface, 2, 5, pp. 443-454, (2005)

[6]

FREI M G, OSORIO I., Intrinsic time-scale decomposition: Time-frequency-energy analysis and real-time filtering of non-stationary signals, Proceedings of the Royal Society of London Series A, 463, 2078, pp. 321-342, (2007)

[7]

GILLES J., Empirical wavelet transform, IEEE Transactions on Ssignal Processing, 61, 16, pp. 3999-4010, (2013)

[8]

REHMAN N U, AFTAB H., Multivariate variational mode decomposition, IEEE Transactions on Signal Processing, 67, 23, pp. 6039-6052, (2019)

[9]

CHEN S Q, PENG Z K, ZHOU P., Review of signal decomposition theory and its applications in machine fault diagnosis, Journal of Mechanical Engineering, 56, 17, pp. 91-107, (2020)

[10]

STANKOVIC L, BRAJOVIC M, DAKOVIC M, Et al., On the decomposition of multichannel nonstationary multicomponent signals, Signal Processing, 167, (2020)

← 1 2 →