4.7 Article

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

期刊

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 117, 期 -, 页码 255-279

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2018.08.004

关键词

Time-reassigned synchrosqueezing transform; Time-frequency analysis; Reassignment; Group delay; Transient feature extraction; Fault diagnosis

资金

  1. National Natural Science Foundation of China [51575423, 11772244]
  2. National Basic Research Program of China [2015CB057400]
  3. Fundamental Research Funds for the Central University

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据