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Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing

机译:时间分配同步压缩变换:该算法及其在机械信号处理中的应用

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Synchrosqueezing transform (SST) is an effective post-processing time-frequency analysis (TFA) method in mechanical signal processing. It improves the concentration of the time-frequency (TF) representation of non-stationary signals composed of multiple components with slow varying instantaneous frequency (IF). However, for components whose TF ridge curves are fast varying, or even nearly parallel with frequency axis, the SST still suffers from TF blurs. In this paper, we introduce a TFA method called time-reassigned synchrosqueezing transform (TSST) that achieves highly concentrated TFR for impulsive-like signal whose TF ridge curves is nearly parallel with frequency axis. Moreover, the TSST enables signal reconstruction, compared with the standard TF reassignment methods, such as reassigned short-time Fourier transform and reassigned wavelet transform. In the algorithm of TSST, the group delay estimator is calculated rather than the IF estimator. Furthermore, the TF coefficients are reassigned in the time direction rather than in frequency direction as the SST did. Then we compare the concentration between SST and TSST at different length of Gaussian window and chirp-rate, which is followed by the respective application scope of SST and TSST. Furthermore, we describe an efficient numerical algorithm for practical implementation of TSST. It is found that the SST is suitable for characterizing signal with small chirp-rate while TSST performs better for a large chirp rate condition. Thus, the TSST is more capable of extracting transient features of impulsive-like signal. Finally, the effectiveness of the TSST and its inverse transform is verified by simulation and experimental studies. (C) 2018 Elsevier Ltd. All rights reserved.
机译:同步压缩变换(SST)是机械信号处理中一种有效的后处理时频分析(TFA)方法。它提高了由具有缓慢变化的瞬时频率(IF)的多个分量组成的非平稳信号的时频(TF)表示的集中度。但是,对于TF脊曲线快速变化甚至与频率轴几乎平行的组件,SST仍然会遭受TF模糊。在本文中,我们介绍了一种称为时间重分配同步压缩变换(TSST)的TFA方法,该方法可对TF脊曲线与频率轴几乎平行的类脉冲信号实现高度集中的TFR。此外,与标准TF重新分配方法(例如重新分配的短时傅立叶变换和重新分配的小波变换)相比,TSST能够进行信号重建。在TSST算法中,计算群时延估计器而不是IF估计器。此外,TF系数在时间方向上而不是在SST上在频率方向上重新分配。然后,我们比较了在不同高斯窗长和线性调频率下SST和TSST之间的浓度,然后分别讨论了SST和TSST的各自应用范围。此外,我们描述了用于TSST的实际实现的有效数值算法。发现SST适合于表征小izing率的信号,而TSST在大chi率条件下表现更好。因此,TSST更能够提取类脉冲信号的瞬态特征。最后,通过仿真和实验研究验证了TSST及其逆变换的有效性。 (C)2018 Elsevier Ltd.保留所有权利。

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