Improved Variational Mode Extraction and Its Application in Rolling Bearing Feature Extraction

Variational Mode Extraction (VME) introduces a new criterion based on Variational Mode Decomposition (VMD): the residual signal after extracting a specific mode should have minimal or no energy at that mode’s center frequency. This allows VME to effectively extract modes near the center frequency, reducing the mode aliasing effect seen in VMD. Recursive variational mode extraction (RVME) successfully applies VME to bearing fault diagnosis. However, VME does not fundamentally improve VMD’s estimation of mode bandwidth, leading to biases in the extracted signals and limiting the full extraction of bearing fault characteristics. To address this issue, this paper proposes an Improved Variational Mode Extraction (IVME) method. IVME uses fractional-order derivatives to adjust the L2-norm estimation of mode bandwidth, allowing it to dynamically adapt to signals with varying bandwidths. An empirical formula is provided to relate the penalty factor to the fractional-order derivative. Additionally, IVME adaptively determines the initial center frequency of the target mode based on the convergence trend of VMD’s center frequency. This enables adaptive extraction of bearing fault characteristics. The proposed method is tested on both synthetic and experimental bearing fault signals, with its performance compared to other classical fault feature extraction methods. The results show that IVME outperforms VMD, VME, and fast SK in extracting fault features.

Authors

Institutions

Publication Details

Journal
IEEE Sensors Journal
Published
2025-06-03
DOI
https://doi.org/10.1109/jsen.2025.3573714
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improved Variational Mode Extraction and Its Application in Rolling Bearing Feature Extraction

Nuo Li, Taibo Yang, Zhang Tong-jian, Hang Wang et al.
IEEE Sensors Journal
Machine Fault Diagnosis Techniques
article

Improved Variational Mode Extraction and Its Application in Rolling Bearing Feature Extraction

Nuo Li, Taibo Yang, Zhang Tong-jian, Hang Wang, Zhiyong Duan, Pan He
article en

Abstract

Variational Mode Extraction (VME) introduces a new criterion based on Variational Mode Decomposition (VMD): the residual signal after extracting a specific mode should have minimal or no energy at that mode’s center frequency. This allows VME to effectively extract modes near the center frequency, reducing the mode aliasing effect seen in VMD. Recursive variational mode extraction (RVME) successfully applies VME to bearing fault diagnosis. However, VME does not fundamentally improve VMD’s estimation of mode bandwidth, leading to biases in the extracted signals and limiting the full extraction of bearing fault characteristics. To address this issue, this paper proposes an Improved Variational Mode Extraction (IVME) method. IVME uses fractional-order derivatives to adjust the L2-norm estimation of mode bandwidth, allowing it to dynamically adapt to signals with varying bandwidths. An empirical formula is provided to relate the penalty factor to the fractional-order derivative. Additionally, IVME adaptively determines the initial center frequency of the target mode based on the convergence trend of VMD’s center frequency. This enables adaptive extraction of bearing fault characteristics. The proposed method is tested on both synthetic and experimental bearing fault signals, with its performance compared to other classical fault feature extraction methods. The results show that IVME outperforms VMD, VME, and fast SK in extracting fault features.

IEEE Sensors JournalVol. 25(14)
Harbin Engineering University (CN)
Openalex Percentile: Top 87%
Machine Fault Diagnosis Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.