ArticleSensors (Basel, Switzerland)2021
Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Gearbox Fault Diagnosis Based on Adaptive Variational Mode Decomposition-Stationary Wavelet Transform and Ensemble Refined Composite Multiscale Fluctuation Dispersion Entropy.Sensors (Basel, Switzerland) · 2024Article
- A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN.Environmental science and pollution research international · 2024Article
- Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis.Entropy (Basel, Switzerland) · 2023Article
- Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine.Entropy (Basel, Switzerland) · 2022Article
- Fault Diagnosis of Balancing Machine Based on ISSA-ELM.Computational intelligence and neuroscience · 2022Article
- An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm-Based PNN for Fault Diagnosis of Rolling Bearings.Computational intelligence and neuroscience · 2022Article
- Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD-RCMDE-SSA-SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.
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