ArticleEntropy (Basel, Switzerland)2020
A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A parameter adaptive EEFO VMD method to mitigate noise and trend interference of blast vibration signals.Scientific reports · 2025Article
- Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion.PloS one · 2025Article
- Application of C-InGAN Model in Interpretable Feature of Bearing Fault Diagnosis.Entropy (Basel, Switzerland) · 2024Article
- Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet.Entropy (Basel, Switzerland) · 2023Article
- Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index.Sensors (Basel, Switzerland) · 2022Article
- Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression.Entropy (Basel, Switzerland) · 2021Article
- Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing.Entropy (Basel, Switzerland) · 2021Article
- Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.Sensors (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.
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