.png){kind=icon}
Return
Variational generalized nonlinear mode decomposition: Algorithm and applications
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ymssp.2023.110913.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently proposed variational signal decomposition methods like adaptive chirp mode decomposition (ACMD) and generalized dispersive mode decomposition (GDMD) have attracted much attention in various fields. However, these methods are difficult to simultaneously separate chirp components and dispersive components. This paper proposes a variational generalized nonlinear mode decomposition (VGNMD) framework to address this issue. The VGNMD first introduces an adaptive time-frequency fusion and clustering (ATFFC) scheme to improve noise robustness and resolution of time-frequency distribution (TFD) of signal in a noisy environment and to accurately obtain the TFD of each mode. Then, a mode-type discrimination criterion is established to categorize modes into chirp modes or dispersive modes based on their time-frequency (TF) ridges. Finally, with these TF ridges as initial instantaneous frequencies (IFs) or initial group delays (GDs), a variational optimization algorithm is applied to accurately reconstruct the modes and refine their IFs or GDs. Simulated examples and real-life applications to bat echolocation signal analysis and railway wheel/rail fault diagnosis are considered to show the effectiveness of the VGNMD. The results indicate that the proposed approach can accurately extract chirp modes and dispersive modes simultaneously, and is well-suitable for analyzing nonlinear signals with discontinuous TF patterns.
Keywords:
Adaptive chirp mode decomposition
Fault diagnosis
Generalized dispersive mode decomposition
Group delay
Instantaneous frequency
Time-frequency fusion
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W
Organization
Cited Papers
Identification of Two Novel Members of the Tentative Genus Wukipolyomavirus in Wild Rodents.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveTime-Reassigned Multisynchrosqueezing Transform for Bearing Fault Diagnosis of Rotating Machinery
PREAI
ShareSave
ShareSaveOptimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defectsMEASUREMENT
MEASUREMENTIF5.6
PREAI
ShareSaveShareSave
View more