Variable Filtered-Waveform Variational Mode Decomposition and Its Application in Rolling Bearing Fault Feature Extraction

Variational Mode Decomposition (VMD) serves as an effective method for simultaneously decomposing signals into a series of narrowband components. However, its theoretical foundation, the classical Wiener filter, exhibits limited adaptability when applied to broadband signals. This paper proposes a n...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 27; no. 3; p. 277
Main Authors Li, Nuo, Wang, Hang
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 07.03.2025
MDPI
Subjects
Adaptability
Algorithms
Bandwidths
bearing fault diagnosis
Bearings
Broadband
Broadband transmission
Chirp signals
Decomposition
envelope spectral entropy
Fault diagnosis
Fault lines
mode mixing
Narrowband
Regularization methods
Roller bearings
Signal processing
variational mode decomposition
Waveforms
Wavelet transforms
wideband signal
Wiener filter
Wiener filtering
United Kingdom
mode mixing
Wiener filter
variational mode decomposition
wideband signal
bearing fault diagnosis
envelope spectral entropy
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Summary:Variational Mode Decomposition (VMD) serves as an effective method for simultaneously decomposing signals into a series of narrowband components. However, its theoretical foundation, the classical Wiener filter, exhibits limited adaptability when applied to broadband signals. This paper proposes a novel Variable Filtered-Waveform Variational Mode Decomposition (VFW-VMD) method to address critical limitations in VMD, particularly in handling broadband and chirp signals. By incorporating fractional-order constraints and dynamically adjusting filter waveforms, the proposed algorithm effectively mitigates mode mixing and over-smoothing issues. The mathematical framework of VFW-VMD is formulated, and its decomposition performance is validated through simulations involving both synthetic and real-world signals. The results demonstrate that VFW-VMD exhibits superior adaptability in extracting broadband signals and effectively captures more rolling bearing fault features. This work advances signal processing techniques, enhancing capability and significantly improving the performance of practical bearing fault diagnostic applications.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e27030277