An Improved EMD Method for Time–Frequency Feature Extraction of Telemetry Vibration Signal Based on Multi-Scale Median Filtering

2014 ◽  
Vol 34 (3) ◽  
pp. 815-830 ◽  
Author(s):  
Mei Li ◽  
Xiong Wu ◽  
Xueyong Liu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Median Filtering ◽  
Time Frequency ◽  
Multi Scale ◽  
Frequency Feature ◽  
Emd Method

Time-Frequency Feature Extraction from Residues of Voiced Phonemes

2004 ◽  
Vol 2 (3) ◽  
pp. 168-173
Author(s):  
E.V. de Payer
Keyword(s):  
Feature Extraction ◽  
Time Frequency ◽  
Frequency Feature

scholarly journals A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1319
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Yucai Li ◽  
Wei Lin ◽  
Jinjuan Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Vibration Signal ◽  
Practical Significance ◽  
Stable Operation ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

scholarly journals EEG-Based 3D Visual Fatigue Evaluation Using CNN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1208 ◽  
Author(s):  
Kang Yue ◽  
Danli Wang
Keyword(s):  
Feature Extraction ◽  
Temporal Structure ◽  
Visual Fatigue ◽  
Eeg Signals ◽  
Visual Evaluation ◽  
Time Frequency ◽  
Multi Scale ◽  
Temporal Features ◽  
Depth Analysis ◽  
Fatigue Evaluation

Visual fatigue evaluation plays an important role in applications such as virtual reality since the visual fatigue symptoms always affect the user experience seriously. Existing visual evaluation methods require hand-crafted features for classification, and conduct feature extraction and classification in a separated manner. In this paper, we conduct a designed experiment to collect electroencephalogram (EEG) signals of various visual fatigue levels, and present a multi-scale convolutional neural network (CNN) architecture named MorletInceptionNet to detect visual fatigue using EEG as input, which exploits the spatial-temporal structure of multichannel EEG signals. Our MorletInceptionNet adopts a joint space-time-frequency features extraction scheme in which Morlet wavelet-like kernels are used for time-frequency raw feature extraction and inception architecture are further used to extract multi-scale temporal features. Then, the multi-scale temporal features are concatenated and fed to the fully connected layer for visual fatigue evaluation using classification. In experiment evaluation, we compare our method with five state-of-the-art methods, and the results demonstrate that our model achieve overally the best performance better performance for two widely used evaluation metrics, i.e., classification accuracy and kappa value. Furthermore, we use input-perturbation network-prediction correlation maps to conduct in-depth analysis into the reason why the proposed method outperforms other methods. The results suggest that our model is sensitive to the perturbation of β (14–30 Hz) and γ (30–40 Hz) bands. Furthermore, their spatial patterns are of high correlation with that of the corresponding power spectral densities which are used as evaluation features traditionally. This finding provides evidence of the hypothesis that the proposed model can learn the joint time-frequency-space features to distinguish fatigue levels automatically.

Time-frequency feature extraction from frequency-modulated sounds in a population of auditory cortical neurons

2008 ◽  
Vol 29 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Shunji Sugimoto ◽  
Yuta Suzuki ◽  
Hiroyuki Tanaka ◽  
Michinori Kubota ◽  
Junsei Horikawa
Keyword(s):  
Feature Extraction ◽  
Cortical Neurons ◽  
Time Frequency ◽  
Frequency Feature

scholarly journals Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

2004 ◽  
Vol 2004 (16) ◽  
Author(s):  
Hamid Hassanpour ◽  
Mostefa Mesbah ◽  
Boualem Boashash
Keyword(s):  
Feature Extraction ◽  
Time Frequency ◽  
Newborn Eeg ◽  
Frequency Feature

scholarly journals Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 152 ◽  
Author(s):  
Nibaldo Rodriguez ◽  
Pablo Alvarez ◽  
Lida Barba ◽  
Guillermo Cabrera-Guerrero
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Work Conditions ◽  
Extraction Methods ◽  
Rolling Element Bearing ◽  
Bearing Failure ◽  
Wavelet Entropy ◽  
Multi Scale ◽  
Diagnosis Accuracy

Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. In the end, both the SWPDE–KELM and the SWPPE–KELM methods are evaluated on two bearing vibration signal databases. We compare these two feature extraction methods to a recently proposed method called stationary wavelet packet singular value entropy (SWPSVE). Based on our results, we can say that the diagnosis accuracy obtained by the SWPDE–KELM method is slightly better than the SWPPE–KELM method and they both significantly outperform the SWPSVE–KELM method.

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