A Signal Decomposition Method Based on Multi-Layer Iteration Structured Singular Spectrum Analysis

doi:10.16182/j.issn1004731x.joss.20-0267

Abstract

Abstract: To process the complex signals with concentrated frequency distribution, an adaptive decomposition method based on singular spectrum analysis with multi-layer iteration structure is researched. The traditional singular spectrum analysis is improved by frequency band subdivision and iterative filtering approach. A high-precision decomposition algorithm base on recursive structure is therefore designed, solving the problems such as insufficient adaptive capability and unsatisfied decomposition. Simulation results show that adaptive decomposition capability of the proposed method is effectively enhanced. For the multi-mode vibration signal with 0.2% ratio of spitting frequency to center frequency, the components are all accurately extracted, and consistent well with the theoretical value.

Key words: singular spectrum analysis, multi-layer iteration, adaptive decomposition, multi-mode vibration

CLC Number:

TM743
TP391

Wei Yumiao, Zhang Zhili, Li Hongguang, Li Shuqing. A Signal Decomposition Method Based on Multi-Layer Iteration Structured Singular Spectrum Analysis[J]. Journal of System Simulation, 2021, 33(8): 1818-1824.

References

[1] Feng Z, Yu X, Zhang D, et al.Generalized Adaptive Mode Decomposition for Nonstationary Signal Analysis of Rotating Machinery: Principle and Applications[J]. Mechanical Systems and Signal Processing (S0888-3270), 2020, 136: 106530.
[2] Feng Z, Zhang D, Zuo M, et al.Adaptive Mode Decomposition Methods and Their Applications in Signal Analysis for Machinery Fault Diagnosis: A Review with Examples[J]. IEEE Access (S2169-3536), 2017, 5: 24301-24331.
[3] 魏玉淼, 张志利, 常振军, 等. 一种从受迫振动响应提取动力学参数方法的仿真分析[J]. 系统仿真学报, 2019, 31(4): 618-623.
Wei Yumiao, Zhang Zhili, Chang Zhenjun, et al.Simulation Analyses of a Method for Extracting Dynamic Parameters from Forced Vibration Responses[J]. Journal of System Simulation, 2019, 31(4): 618-623.
[4] 许杏花, 潘庭龙, 吴定会. 基于变分模态分解的风电场风速降噪研究[J]. 系统仿真学报, 2019, 31(1): 87-93.
Xu Xinghua, Pan Tinglong, Wu Dinghui.Wind Speed Noise Reduction in Wind Farm Based on Variational Mode Decomposition[J]. Journal of System Simulation, 2019, 31(1): 87-93.
[5] 贾亚飞, 朱永利, 王刘旺. 基于VMD和Wigner-Ville分布的局放信号时频分析[J]. 系统仿真学报, 2018, 30(2): 569-578.
Jia Yafei, Zhu Yongli, Wang Liuwang.Time-frequency Analysis of Partial Discharge Signal Based on VMD and Wigner-Ville Distribution[J]. Journal of System Simulation, 2018, 30(2): 569-578.
[6] Kim H, Yuan F.Adaptive Signal Decomposition and Dispersion Removal Based on the Matching Pursuit Algorithm Using Dispersion-based Dictionary for Enhancing Damage Imaging[J]. Ultrasonics (S0041-624X), 2020, 103: 106087.
[7] Lian J, Liu Z, Wang H, et al.Adaptive Variational Mode Decomposition Method for Signal Processing Based on Mode Characteristic[J]. Mechanical Systems and Signal Processing (S0888-3270), 2018, 107: 53-77.
[8] 何振鹏, 朱志琪, 谢海超, 等. 基于最小二乘法线性拟合抑制EMD端点效应[J]. 系统仿真学报, 2018, 30(9): 3377-3385.
He Zhenpeng, Zhu Zhiqi, Xie Haichao, et al.Restrain Boundary Effect of EMD Based on Least Square Fitting[J]. Journal of System Simulation, 2018, 30(9): 3377-3385.
[9] Wei Y, Dong Y, Huang X, et al.Nonlinearity Measurement for Low-pressure Encapsulated MEMS Gyroscopes by Transient Response[J]. Mechanical Systems and Signal Processing (S0888-3270), 2018, 100(2): 534-549.
[10] Wei Y, Dong Y, Huang X, et al.A Stepped Frequency Sweeping Method for Nonlinearity Measurement of Microresonators[J]. Sensors (S1424-8220), 2016, 16(10): 1700-1715.
[11] 魏玉淼, 董永贵. 从共振衰减响应中提取微机械陀螺的骨架曲线[J]. 中国惯性技术学报, 2016, 24(5): 660-666.
Wei Yumiao, Dong Yonggui.Backbone Curves Extraction of Micromechanical Gyroscopes from Resonance Decay Responses[J]. Journal of Chinese Inertial Technology, 2016, 24(5): 660-666.
[12] 赵迪, 徐志胜. 基于多分辨率奇异熵的混凝土缺陷检测[J]. 系统仿真学报, 2017, 29(4): 761-766.
Zhao Di, Xu Zhisheng.Forestry Fire Spatial Diffusion Model Based on Integration of Multi-Agent Algorithm with Cellular Automata[J]. Journal of System Simulation, 2017, 29(4): 761-766.
[13] Yang B, Dong Y G, Yu C Y, et al.Singular Spectrum Analysis Window Length Selection in Processing Capacitive Captured Biopotential Signals[J]. IEEE Sensors Journal (S1558-1748), 2016, 16(19): 7183-7193.
[14] Golyandina N Z A. Singular Spectrum Analysis for Time Series[M]. Heidelberg: Springer Science & Business Media, 2013: 11-70.
[15] Kim H, Yoo S, Lee J.Recursive Approach of EEG-segment-based Principal Component Analysis Substantially Reduces Cryogenic Pump Artifacts in Simultaneous EEG-fMRI data[J]. Neuroimage (S1053-8119), 2015, 104: 437-451.