TY - JOUR
T1 - Time-reassigned synchrosqueezing transform
T2 - The algorithm and its applications in mechanical signal processing
AU - He, Dong
AU - Cao, Hongrui
AU - Wang, Shibin
AU - Chen, Xuefeng
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/2/15
Y1 - 2019/2/15
N2 - 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.
AB - 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.
KW - Fault diagnosis
KW - Group delay
KW - Reassignment
KW - Time-frequency analysis
KW - Time-reassigned synchrosqueezing transform
KW - Transient feature extraction
UR - https://www.scopus.com/pages/publications/85051391588
U2 - 10.1016/j.ymssp.2018.08.004
DO - 10.1016/j.ymssp.2018.08.004
M3 - 文章
AN - SCOPUS:85051391588
SN - 0888-3270
VL - 117
SP - 255
EP - 279
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
ER -