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Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

delete2021-01-01
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OA
AI
Z
Zhenfeng Huang
D
Dahuan Wei
Z
Zhiwei Huang
毛
毛汉领 (Hanling Mao) *
X
Xinxin Li
R
Rui Huang
P
Pengwei Xu
DOI:10.1109/ACCESS.2020.3031091delete
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摘要

摘要

En 中文
The time-frequency (TF) analysis (TFA) method is an effective tool for analyzing the time-variant features of non-stationary signals. Synchrosqueezing transform (SST) is a promising TFA method that has recently shown its usefulness in a wide range of engineering signal processing applications. On the other hand, the SST method suffers from some drawbacks, one of which is that when processing the frequency-modulated (FM) signal, the TF representation will smear heavily, which hinders its application in engineering vibration signals. In this paper, we propose a new TFA method named parameterized local maximum synchrosqueezing transform (PLMSST) to study engineering vibration signals with FM characteristics. First, the limitation of SST in signal processing is discussed. Next, we demodulate the signal by parameterizing the short-time Fourier transform (STFT) to correct the deviation of instantaneous frequency (IF) estimation. Further, we detect the local maximum of the spectrogram in the frequency direction to get the accurate IF estimate, and then obtain the energy-concentrated TF representation. Finally, we introduce the reconstruction function of this method. The performance of the proposed method is validated by both the numerical and experimental signals including vibration signals of the rolling bearing and the bridge. The results show that the proposed method is more effective in processing engineering vibration signals than other TFA methods.
Keyword:
Frequency modulation
Vibrations
Time-frequency analysis
Estimation
Signal resolution
Fourier transforms
Engineering vibration signal
frequency modulation
instantaneous frequency
synchrosqueezing transform
time-frequency analysis
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期刊

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

G
guangxi university
学者数:
3.4W
论文数: 1.8W
被引数: 25
引用论文

引用论文

Demodulated High-Order Synchrosqueezing Transform With Application to Machine Fault Diagnosis
err2019-04-01
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PREAI
errTu, Xiaotong; Hu, Yue; Li, Fucai; Abbas, Saqlain; Liu, Zhen; Bao, Wenjie
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