Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations

Renewable Energy ◽

10.1016/j.renene.2016.12.013 ◽

2018 ◽

Vol 116 ◽

pp. 55-73 ◽

Author(s):

Zhixiong Li ◽

Yu Jiang ◽

Qiang Guo ◽

Chao Hu ◽

Zhongxiao Peng

Keyword(s):

Wind Turbines ◽

Crack Detection ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

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Multi-Dimensional Variational Mode Decomposition Applied to Intrinsic Vibration Mode Extraction for Bearing Crack Detection in Wind Turbines with Large Speed Variation

35th Wind Energy Symposium ◽

10.2514/6.2017-1169 ◽

2017 ◽

Author(s):

Zhixiong Li ◽

Jiang Yu ◽

Chao Hu ◽

Zhongxiao Peng

Keyword(s):

Wind Turbines ◽

Crack Detection ◽

Vibration Mode ◽

Variational Mode Decomposition ◽

Speed Variation ◽

Mode Decomposition ◽

Mode Extraction ◽

Intrinsic Vibration ◽

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Wavelet Denoising for the Vibration Signals of Wind Turbines Based on Variational Mode Decomposition and Multiscale Permutation Entropy

IEEE Access ◽

10.1109/access.2020.2975875 ◽

2020 ◽

Vol 8 ◽

pp. 40347-40356 ◽

Author(s):

Xuejun Chen ◽

Yongming Yang ◽

Zhixin Cui ◽

Jun Shen

Keyword(s):

Wind Turbines ◽

Wavelet Denoising ◽

Permutation Entropy ◽

Variational Mode Decomposition ◽

Vibration Signals ◽

Mode Decomposition

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A parameter optimized variational mode decomposition method for rail crack detection based on acoustic emission technique

Nondestructive Testing And Evaluation ◽

10.1080/10589759.2020.1785447 ◽

2020 ◽

pp. 1-29

Author(s):

Xin Zhang ◽

Tiantian Sun ◽

Yan Wang ◽

Kangwei Wang ◽

Yi Shen

Keyword(s):

Acoustic Emission ◽

Decomposition Method ◽

Crack Detection ◽

Variational Mode Decomposition ◽

Acoustic Emission Technique ◽

Mode Decomposition

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Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Applied Soft Computing ◽

10.1016/j.asoc.2020.106515 ◽

2020 ◽

Vol 95 ◽

pp. 106515 ◽

Author(s):

Zifei Xu ◽

Chun Li ◽

Yang Yang

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Convolutional Neural Networks ◽

Wind Turbines ◽

Rolling Bearing ◽

Variational Mode Decomposition ◽

Deep Convolutional Neural Networks ◽

Mode Decomposition

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Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy

Energy ◽

10.1016/j.energy.2019.03.057 ◽

2019 ◽

Vol 174 ◽

pp. 1100-1109 ◽

Author(s):

Xuejun Chen ◽

Yongming Yang ◽

Zhixin Cui ◽

Jun Shen

Keyword(s):

Fault Diagnosis ◽

Wind Turbines ◽

Variational Mode Decomposition ◽

Energy Entropy ◽

Mode Decomposition

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Reduced-order Model Predictions of Wind Turbines via Mode Decomposition and Sparse Sampling

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1602 ◽

2021 ◽

Author(s):

Ala' E. Qatramez ◽

Daniel Foti

Keyword(s):

Wind Turbines ◽

Sparse Sampling ◽

Reduced Order Model ◽

Order Model ◽

Reduced Order ◽

Mode Decomposition ◽

Model Predictions

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Review for "Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression"

10.1002/2050-7038.12364/v2/review1 ◽

2020 ◽

Author(s):

Muhammad Adil

Keyword(s):

Power Quality ◽

Ridge Regression ◽

Variational Mode Decomposition ◽

Kernel Ridge Regression ◽

Mode Decomposition ◽

Power Quality Disturbance

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Review for "Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression"

10.1002/2050-7038.12364/v1/review2 ◽

2019 ◽

Keyword(s):

Power Quality ◽

Ridge Regression ◽

Variational Mode Decomposition ◽

Kernel Ridge Regression ◽

Mode Decomposition ◽

Power Quality Disturbance

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A Novel Approach for the Reduction of 50Hz Noise in Electrocardiogram Using Variational Mode Decomposition

Current Signal Transduction Therapy ◽

10.2174/1574362412666170307092351 ◽

2017 ◽

Vol 12 (1) ◽

pp. 39-48 ◽

Author(s):

Suchetha Manikandan ◽

Kumaravel Natesan

Keyword(s):

Variational Mode Decomposition ◽

Novel Approach ◽

Mode Decomposition

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An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method

Water Resources Management ◽

10.1007/s11269-021-02879-3 ◽

2021 ◽

Author(s):

Fugang LI ◽

Guangwen MA ◽

Shijun CHEN ◽

Weibin HUANG

Keyword(s):

Short Term Memory ◽

Entropy Method ◽

Ensemble Modeling ◽

Variational Mode Decomposition ◽

Term Memory ◽

Modeling Approach ◽

Energy Entropy ◽

Mode Decomposition ◽

Reservoir Inflow

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