Time-reassigned Multisynchrosqueezi​ng Transform

横向/纵向项目，业务范围包括但不限于，生物/地震/机械/振动/声音/语音等多种领域中的信号处理问题。（最新文章源码以及c或者python版本，可联系， 手机号：[phone] 微信号：yg694722435 邮箱：[e-mail]

The impulse features in a condition monitoring (CM) signal usually imply the occurrence of a defect in a rotating machine. To accurately capture the impulse components in a CM signal, a concentrated time-frequency analysis (TFA) method based on time-reassigned synchrosqueezing transform (TSST) is proposed. Firstly, the limitation of the TSST method in dealing with strong frequency-varying signals is explored. Secondly, an iteration procedure is introduced to address the blurry time frequency representation problem of TSST. The convergence of the iteration algorithm is also analyzed. Finally, an algorithm is proposed to extract the impulse features for signal reconstructions, which are also useful for an accurate diagnosis of the fault type. A simulated noise-contaminated signal and three sets of experimental data are employed in the study to evaluate the performance of the proposed method. Results obtained from this study confirm that the proposed method has a better performance in dealing with impulsive-like signals than other TFA methods.

Codes for the paper "Time-reassigned Multisynchrosqueezing Transform for Bearing Fault Diagnosis of Rotating Machinery", 10.1109/TIE.2020.2970571. It can be found on

https://ieeexplore.ieee.org/document/8984752

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这篇文章，我个人感觉最大的贡献在于如何去理解瞬态信号。对一些存在时间极短的瞬态信号，确实不再适合使用时域模型进行分析。然而，频域模型却不受此影响。后面的模态分解算法其实也很有趣。以时频掩码的方式重构信号，常用于语音信号处理，瞬态信号分析中比较罕见。