Feature extraction and power quality event classification using Curvelet transform and optimized extreme learning machine

2021 ◽  
Author(s):  
Indu Sekhar Samanta ◽  
Pravat Kumar Rout ◽  
Satyasis Mishra
Keyword(s):  
Feature Extraction ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Curvelet Transform ◽  
Event Classification ◽  
Learning Machine

scholarly journals Power Quality Event Detection Using a Fast Extreme Learning Machine

Energies ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 145 ◽  
Author(s):  
Ferhat Ucar ◽  
Omer Alcin ◽  
Besir Dandil ◽  
Fikret Ata
Keyword(s):  
Power Quality ◽  
Extreme Learning Machine ◽  
Event Detection ◽  
Learning Machine

scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yongbin Liu ◽  
Bing He ◽  
Fang Liu ◽  
Siliang Lu ◽  
Yilei Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Phase Point ◽  
Rolling Bearings ◽  
Point Distribution ◽  
Useful Life ◽  
Learning Machine

Rolling bearings play a pivotal role in rotating machinery. The degradation assessment and remaining useful life (RUL) prediction of bearings are critical to condition-based maintenance. However, sensitive feature extraction still remains a formidable challenge. In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR) and joint with approximate diagonalization of Eigen-matrices (JADE). Firstly, the original features are extracted from bearing vibration signals in time and frequency domain. Secondly, the PSR is applied to embed the original features into high dimensional phase space. The between-class and within-class scatter (SS) are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculatedSS. Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process. Finally, the advanced features are input into the extreme learning machine (ELM) to train the RUL prediction model. A set of experimental case studies are carried out to verify the effectiveness of the proposed method. The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.

scholarly journals Local Block Multilayer Sparse Extreme Learning Machine for Effective Feature Extraction and Classification of Hyperspectral Images

2019 ◽  
Vol 57 (8) ◽  
pp. 5580-5594 ◽  
Author(s):  
Faxian Cao ◽  
Zhijing Yang ◽  
Jinchang Ren ◽  
Weizhao Chen ◽  
Guojun Han ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine ◽  
Local Block

IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine

2022 ◽  
pp. 137-152
Author(s):  
Ganesh Kumar R. ◽  
Srilatha Toomula ◽  
D. Paulraj ◽  
Jebin Bose S. ◽  
Thulasi Bikku ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Learning Machine
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