Adaptive wavelet transform: Definition, parameter optimization algorithms, and application for concrete delamination detection from impact echo responses

Qu, Hongya; Li, Tiantian; Chen, Genda

doi:10.1177/1475921718776200

Cited by 11 publications

(1 citation statement)

References 43 publications

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“…Inspired by this idea of parameterized TFA, adaptive multiple second-order synchrosqueezing wavelet transform (AMWSST2) is brought up in this paper. Adaptive wavelet transform (AWT) algorithm based on CWT introduces the concept of short-time windows [25]. In each of them, the center frequency and scale factor controlling the WT can realize adaptive transformation of the signal by determining the window width at the local optimal TF resolution.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis

Yu¹,

Yi²,

Chen³

et al. 2021

Meas. Sci. Technol.

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Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing time-frequency (TF) analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated time-frequency representation (TFR), this paper proposes a new TFA method, namely adaptive multiple second-order synchrosqueezing wavelet transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical continuous wavelet transform, and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus adaptive wavelet transform is generated. Then, on the basis of the theoretical framework of synchrosqueezing transform and accurate instantaneous frequency estimation by the utilization of second-order local demodulation operator, adaptive second-order synchrosqueezing wavelet transform (AWSST2) is formed. Considering that the quality of actual TFA is greatly disturbed by noise components, through performing multiple synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

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Section: Introductionmentioning

confidence: 99%

Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis

Yu¹,

Yi²,

Chen³

et al. 2021

Meas. Sci. Technol.

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Wavelet Package Denoising of Acoustic Emission Signals for Lubrication Oil Monitoring in Engine Systems

Wei

Chen

et al. 2021

Proceedings of IncoME-V &Amp; CEPE Net-2020

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NDE Data Correlation Using Encoder–Decoder Networks with Wavelet Scalogram Images

Dargahi

Lattanzi²,

Azari³

2022

J Nondestruct Eval

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Adaptive wavelet transform: Definition, parameter optimization algorithms, and application for concrete delamination detection from impact echo responses

Cited by 11 publications

References 43 publications

Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis

Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis

Wavelet Package Denoising of Acoustic Emission Signals for Lubrication Oil Monitoring in Engine Systems

NDE Data Correlation Using Encoder–Decoder Networks with Wavelet Scalogram Images

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