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

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 th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.7732-7742
Main Authors: Huang, Zhenfeng, Wei, Dahuan, Huang, Zhiwei, Mao, Hanling, Li, Xinxin, Huang, Rui, Xu, Pengwei
Format: Article
Language:English
Subjects:
Engineering
Engineering vibration signal
Estimation
Fourier transforms
Frequency modulation
instantaneous frequency
Parameterization
Representations
Roller bearings
Signal processing
Signal resolution
synchrosqueezing transform
Time-frequency analysis
Vibration analysis
Vibrations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3031091