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Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Multivariate weighted multiscale permutation entropy for complex time series
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Overview of dynamic modelling and analysis of rolling element bearings with localized and distributed faults
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An improved method for single image super-resolution based on deep learning
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Sequential approximation optimization assisted particle swarm optimization for expensive problems
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Financial time series analysis based on fractional and multiscale permutation entropy
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Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition
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Identification of electromechanical oscillatory modes based on variational mode decomposition
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Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy
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Data decomposition method combining permutation entropy and spectral substitution with ensemble empirical mode decomposition
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An optimized VMD method and its applications in bearing fault diagnosis
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Regularization incremental extreme learning machine with random reduced kernel for regression
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Virtual machine placement strategy using cluster-based genetic algorithm
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Multiscale permutation entropy analysis of electrocardiogram
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Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions
inferredcites →https://doi.org/10.1016/j.renene.2020.04.041openalex-snapshot
Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis
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Further understanding of rolling contact fatigue in rolling element bearings - A review
inferredcites →https://doi.org/10.1016/j.ymssp.2017.08.038openalex-snapshot
Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings
inferredcites →https://doi.org/10.1016/j.ymssp.2019.01.012openalex-snapshot
Permutation entropy and Lyapunov exponent: Detecting and monitoring the chaotic edge of a closed planar under-actuated system
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
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Transformer winding type recognition based on FRA data and a support vector machine model
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Fast Single-Image Super-Resolution via Deep Network With Component Learning
inferredcites →https://doi.org/10.1109/tsp.2013.2288675openalex-snapshot
Variational Mode Decomposition
inferredcites →https://doi.org/10.1155/2020/4032628openalex-snapshot
Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score
inferredcites →https://doi.org/10.1186/s10033-019-0428-5openalex-snapshot
Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers
inferredcites →https://doi.org/10.17531/ein.2020.4.17openalex-snapshot
Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes
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Deep learning enabled intelligent fault diagnosis: Overview and applications
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Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis
inferredcites →https://doi.org/10.3390/e20050387openalex-snapshot
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inferredcites →https://doi.org/10.3390/e22010027openalex-snapshot
A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis
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Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM
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An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems
inferredcites →https://doi.org/10.3390/rs11020126openalex-snapshot
Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Fault Diagnosis Technique Based on Multi-Domain Features and Reliff-Bayes-Knn
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Analysis of Vibration Signals for High-Speed Bearing Operation and Development of Fault Diagnosis System
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Rolling Bearing Fault Diagnosis Method Based on GWO-VMD-SVM
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High-low frequency features fusion and integrated classification SCNs for intelligent fault diagnosis of rolling bearing
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MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
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MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios
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A novel scheme based on modified hierarchical time-shift multi-scale amplitude-aware permutation entropy for rolling bearing condition assessment and fault recognition
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