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High-order synchrosqueezing wavelet transform and application to planetary gearbox fault diagnosis

机译:高阶同步压缩小波变换及其在行星齿轮箱故障诊断中的应用

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The synchrosqueezing transform (SST) is a powerful tool for time-frequency analysis of signals with slowly varying instantaneous frequency (IF). However, the SST and its extensions provide poor time-frequency resolution for signals with wide frequency range and fast varying IF. In this paper, a new SST method called high-order synchrosqueezing wavelet transform is proposed to achieve a highly energy-concentrated time-frequency representation (TFR) for nonstationary signals with wide frequency range and fast varying IF. This method uses high-order group delay and chirp rate operators to obtain the accurate estimation of instantaneous frequency. The proposed method can effectively improve the energy concentration of the TFR and remain invertible simultaneously. The numerical simulations investigate the performance and noise robustness of the proposed method when analyzing a typical amplitude-modulated and frequency-modulated (AM-FM) multicomponent signal. Finally, the application of planetary gearbox fault diagnosis in the variable operating condition verifies the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.
机译:同步压缩变换(SST)是一种功能强大的工具,可对瞬时频率(IF)缓慢变化的信号进行时频分析。但是,SST及其扩展对频率范围宽且IF变化快的信号提供了较差的时频分辨率。本文提出了一种新的SST方法,称为高阶同步压缩小波变换,以实现宽频率范围和IF快速变化的非平稳信号的高能量集中时频表示(TFR)。该方法使用高阶群时延和线性调频率算子来获得瞬时频率的准确估计。所提出的方法可以有效地提高TFR的能量集中并且同时保持可逆。数值仿真研究了该方法在分析典型的幅度调制和频率调制(AM-FM)多分量信号时的性能和噪声鲁棒性。最后,在变速工况下行星齿轮箱故障诊断的应用验证了该方法的有效性。 (C)2019 Elsevier Ltd.保留所有权利。

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