ArticleEntropy (Basel, Switzerland)2021
Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- An Enhanced Hybrid SVD-DBO-VMD Framework for Rolling Bearing Fault Feature Extraction Using Vibration Data.Sensors (Basel, Switzerland) · 2026Article
- Intelligent Fault Diagnosis Method for Rotating Machinery Based on Recurrence Binary Plot and DSD-CNN.Entropy (Basel, Switzerland) · 2024Article
- Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet.Entropy (Basel, Switzerland) · 2023Article
- A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm.Sensors (Basel, Switzerland) · 2023Article
- A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD.Entropy (Basel, Switzerland) · 2023Article
- A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve.Entropy (Basel, Switzerland) · 2022Article
- Quantitative Identification of Internal and External Wire Rope Damage Based on VMD-AWT Noise Reduction and PSO-SVM.Entropy (Basel, Switzerland) · 2022Article
- Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM.Entropy (Basel, Switzerland) · 2022Article
- Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis.Entropy (Basel, Switzerland) · 2022Article
- Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE-MACNN.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
- A Bearing Fault Diagnosis Method Based on PAVME and MEDE.Entropy (Basel, Switzerland) · 2021Article
- A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM.Entropy (Basel, Switzerland) · 2021Article
- Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.