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

Zhenfeng Huang,Rui Huang,Hanling Mao,Zhiwei Huang,Dahuan Wei,Pengwei Xu,Xinxin Li

doi:10.1109/access.2020.3031091

Zhenfeng Huang, Rui Huang + Show 5 more

Open Access

https://doi.org/10.1109/access.2020.3031091

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Abstract

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.

Highlights

Extracting dynamic features from complex engineering vibration signals is an effective way to study the state and inherent properties of structures
The time-frequency (TF) analysis (TFA) method is one of the most popular and effective methods to deal with vibration signals because the TFA method can show the frequency information of signals and reflect the law of frequency changing with time
We can see that the proposed method accurately describes the time-varying features of the signal (8) (see Fig. 3(a-b)), and obtains the high-resolution TF representation, which is almost equivalent to the ideal TFA (ITFA) (see Fig. 4(a-b))

Summary

Introduction

Extracting dynamic features from complex engineering vibration signals is an effective way to study the state and inherent properties of structures. These methods squeeze the TF coefficients into the ridge by applying multiple SST operations to improve the energy concentration of the TF result, they usually generate the IF trajectories that deviate from the true TF ridges when faced with the FM signals with noise, making it difficult to obtain a good TF representation.

Results

Conclusion