ArticleEntropy (Basel, Switzerland)2021
Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Fault Feature Extraction for RV Reducers Based on IWHO-VMD and Effective Mode Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- Entropy-Driven Adaptive Decomposition and Linear-Complexity Score Attention: An AI-Powered Framework for Crude Oil Financial Market Forecasting.Entropy (Basel, Switzerland) · 2026Article
- Vibration characterization of rolling bearings with compound fault features under multiple interference factors.PloS one · 2024Article
- Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis.Entropy (Basel, Switzerland) · 2022Article
- Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
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Abstract
The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy (
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