A Multicomponent Signal Decomposition Method: Time-Frequency Filtering Decomposition

ZHANG Kang; LIU Peng-fei; CAO Zhen-hua; CHEN Xiang-min; TIAN Ze-yu

doi:10.12263/DZXB.20230758

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A Multicomponent Signal Decomposition Method: Time-Frequency Filtering Decomposition

PAPERS | 更新时间：2025-12-08

- A Multicomponent Signal Decomposition Method: Time-Frequency Filtering Decomposition
- ACTA ELECTRONICA SINICA Vol. 52, Issue 8, Pages: 2618-2627(2024)
- 作者机构：
  
  1.长沙理工大学能源与动力工程学院，湖南长沙 410114
  2.华能国际电力股份有限公司湖南清洁能源分公司，湖南长沙 410015
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Hunan Province(2018JJ3541);Excellent Youth Project of Hunan Provincial Education Department(21B0347)
- DOI：10.12263/DZXB.20230758
  CLC： TN911.7;
- Received：04 August 2023，
  
  Revised：2023-11-29，
  
  Published：25 August 2024
- 稿件说明：
移动端阅览
张亢, 刘鹏飞, 曹振华, 等. 一种多分量信号分解方法：时频滤波分解[J]. 电子学报, 2024, 52(08): 2618-2627.

ZHANG Kang, LIU Peng-fei, CAO Zhen-hua, et al. A Multicomponent Signal Decomposition Method: Time-Frequency Filtering Decomposition[J]. Acta Electronica Sinica, 2024, 52(08): 2618-2627.
张亢, 刘鹏飞, 曹振华, 等. 一种多分量信号分解方法：时频滤波分解[J]. 电子学报, 2024, 52(08): 2618-2627. DOI：10.12263/DZXB.20230758

ZHANG Kang, LIU Peng-fei, CAO Zhen-hua, et al. A Multicomponent Signal Decomposition Method: Time-Frequency Filtering Decomposition[J]. Acta Electronica Sinica, 2024, 52(08): 2618-2627. DOI：10.12263/DZXB.20230758

摘要

为解决目前信号分解分量时频域紧邻、重叠和间歇性复杂时频特征的多分量非平稳、非线性信号时，分解困难和效率低的问题，本文提出了一种基于信号时频分布的多分量非平稳信号分解方法——时频滤波分解（Time-Frequency Filtering Decomposition，TFFD）.TFFD通过拟合所选反映信号中各分量时频瞬时特征与规律的时频域基准数据点，得到与分量实际瞬时频率（Instantaneous Frequency，IF）一致的拟合IF曲线，并以拟合IF曲线的时频坐标为基础，设置距离阈值条件确定分量在时频图上的分布区域，构造以拟合IF曲线时频坐标为中心频率、分布区域频宽为通带宽度的时频滤波器组，实现对多分量信号的时频滤波分解.通过对具有代表性时频特征的仿真和实际信号分析，并与经典信号分解方法对比，证明了TFFD方法优良的分解能力和分解效率.

Abstract

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.

关键词

Keywords

references

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

IATSENKO D , MCCLINTOCK P V E , STEFANOVSKA A . Nonlinear mode decomposition: A noise-robust, adaptive decomposition method [J ] . Physical Review E, Statistical, Nonlinear, and Soft Matter Physics , 2015 , 92 ( 3 ): 032916 .

TIWARI P , UPADHYAY S H . Novel self-adaptive vibration signal analysis: Concealed component decomposition and its application in bearing fault diagnosis [J ] . Journal of Sound Vibration , 2021 , 502 : 116079 .

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 [J ] . Proceedings of the Royal Society of London Series A , 1998 , 454 ( 1971 ): 903 - 998 .

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

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

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

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

陈是扦 , 彭志科 , 周鹏 . 信号分解及其在机械故障诊断中的应用研究综述 [J ] . 机械工程学报 , 2020 , 56 ( 17 ): 91 - 107 .

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

STANKOVIĆ L , BRAJOVIĆ M , DAKOVIĆ M , et al . On the decomposition of multichannel nonstationary multicomponent signals [J ] . Signal Processing , 2020 , 167 : 107261 .

刘秋红 , 李天昀 , 王彬 , 等 . 基于离散小波分解和频率脊线分析的CPM信号符号速率盲估计 [J ] . 电子学报 , 2020 , 48 ( 3 ): 470 - 477 .

LIU Q H , LI T Y , WANG B , et al . Symbol rate estimation for CPM signal based on discrete wavelet decomposition and frequency ridge analysis [J ] . Acta Electronica Sinica , 2020 , 48 ( 3 ): 470 - 477 . (in Chinese)

SAULIG N , LERGA J , MILANOVIĆ Ž , et al . Extraction of useful information content from noisy signals based on structural affinity of clustered TFDs’ coefficients [J ] . IEEE Transactions on Signal Processing , 2019 , 67 ( 12 ): 3154 - 3167 .

CICONE A , PELLEGRINO E . Multivariate fast iterative filtering for the decomposition of nonstationary signals [J ] . IEEE Transactions on Signal Processing , 2022 , 70 : 1521 - 1531 .

CHEN S Q , PENG Z K , YANG Y , et al . Intrinsic chirp component decomposition by using Fourier Series representation [J ] . Signal Processing , 2017 , 137 : 319 - 327 .

BRAJOVIĆ M , STANKOVIĆ L , DAKOVIĆ M . Decomposition of multichannel multicomponent nonstationary signals by combining the eigenvectors of autocorrelation matrix using genetic algorithm [J ] . Digital Signal Processing , 2020 , 102 : 102738 .

BOASHASH B . Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals [J ] . IEEE , 1992 , 80 ( 4 ): 520 - 538 .

张亢 , 田泽宇 , 陈向民 , 等 . 多通道多分量分解方法在变转速工况齿轮故障特征提取中的应用 [J ] . 中国机械工程 , 2022 , 33 ( 20 ): 2483 - 2491 .

ZHANG K , TIAN Z Y , CHEN X M , et al . Application of MMD in gear fault feature extraction under variable rotating speed working conditions [J ] . China Mechanical Engineering , 2022 , 33 ( 20 ): 2483 - 2491 . (in Chinese)

ZHANG R , WANG Z M , TAN Y , et al . Local maximum frequency-chirp-rate synchrosqueezed chirplet transform [J ] . Digital Signal Processing , 2022 , 130 : 103710 .

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