Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

2011 ◽  
Vol 330 (15) ◽  
pp. 3766-3782 ◽  
Author(s):  
Wenxian Yang ◽  
Richard Court ◽  
Peter J. Tavner ◽  
Christopher J. Crabtree
Keyword(s):  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Mode Decomposition ◽  
Bivariate Empirical Mode Decomposition

scholarly journals The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection

2017 ◽  
Vol 7 (2) ◽  
pp. 145 ◽  
Author(s):  
Huanguo Chen ◽  
Jianyang Shen ◽  
Wenhua Chen ◽  
Chuanyu Wu ◽  
Chunshao Huang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Chatter Detection ◽  
Grinding Chatter ◽  
Mode Decomposition ◽  
Bivariate Empirical Mode Decomposition

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

2018 ◽  
pp. 13-33 ◽  
Author(s):  
Rajeev Sharma ◽  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Automated Classification ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis ◽  
Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

scholarly journals Bivariate Empirical Mode Decomposition

2007 ◽  
Vol 14 (12) ◽  
pp. 936-939 ◽  
Author(s):  
Gabriel Rilling ◽  
Patrick Flandrin ◽  
Paulo Goncalves ◽  
Jonathan M. Lilly
Keyword(s):  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Bivariate Empirical Mode Decomposition
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