scholarly journals An Online Degradation Feature Extraction Technique for Shore Bridge Gearbox Based on Morphological Fractal Dimension and Sliding Window Weibull Fitting

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Dejian Sun ◽  
Bing Wang ◽  
Xiong Hu ◽  
Wei Wang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Energy Spectrum ◽  
Scale Parameter ◽  
Sliding Window ◽  
Performance Degradation ◽  
Extraction Technique ◽  
Vibration Energy ◽  
Fractal Curve ◽  
Feature Extraction Technique

Shore bridge and other port cranes have some working condition characters including high speed, heavy load, and large impact. In order to solve the degradation feature extraction issue of hoisting mechanism gearbox, an online degradation feature extraction technique based on morphological fractal dimension and sliding window Weibull fitting is proposed. Firstly, taking the vibration energy spectrum collecting from the gearbox as the online data source, the fractal dimension of the vibration energy spectrum during an analysis period is calculated and a fractal evolution curve is obtained. A three-parameter Weibull fitting on the fractal curve within a sliding window after setting the window’s width and step size is performed. The scale parameter of the Weibull fitting model is introduced as the performance degradation feature. The effectiveness of the technique is verified by the full-life vibration data of hoisting gearbox from Shanghai Port Group. The results show that the morphological fractal dimension is able to describe the fractal complexity of the vibration energy spectrum. The scale parameter of Weibull distribution is able to reflect the performance degradation trend of fractal curve smoothly, which lays a theoretical foundation for further solving the problem of online health assessment.

scholarly journals Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
R. Salazar-Varas ◽  
Roberto A. Vazquez
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
High Variability ◽  
Training Data ◽  
Extraction Technique ◽  
Eeg Signals ◽  
Data Set ◽  
Cutoff Frequencies ◽  
Public Data ◽  
Feature Extraction Technique

In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%.

scholarly journals A Novel Degradation Feature Extraction Technique Based on Improved Base-Scale Entropy

2021 ◽  
Vol 26 (1) ◽  
pp. 41-48
Author(s):  
Zhenyi Chen ◽  
Changzhuan Shao ◽  
Xiong Hu ◽  
Bing Wang ◽  
Daobing Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Approximate Entropy ◽  
Signal Amplitude ◽  
Amplitude Distribution ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Entropy Method ◽  
Extraction Technique ◽  
Information Amount ◽  
Feature Extraction Technique

In order to track the performance degradation trend accurately, a novel degradation feature extraction technique is proposed based on improved base-scale entropy. A unified base scale is proposed and a new symbol standard is defined to overcome the disadvantages of the base-scale entropy method, so as to symbolize signal amplitude to characterize information amount under different degradation conditions quantitatively. A lifetime dataset of rolling bearing from the IMS Bearing Experiment Center is introduced. For instance, analysis and some entropy-based techniques including fuzzy entropy, approximate entropy and sample entropy are imported for comparison. The results show that the improved basic-scale technique is able to characterize information amount of the signal amplitude distribution, so that the characterizing performance degradation degree of bearing shows a proportional relationship. When comparing the entropy-based techniques, the improved base-scale entropy technique has a faster calculation speed and better algorithm stability.

S-DCTNet: Security-oriented biometric feature extraction technique

2021 ◽  
Author(s):  
Mohamed Yassine Haouam ◽  
Abdallah Meraoumia ◽  
Lakhdar Laimeche ◽  
Issam Bendib
Keyword(s):  
Feature Extraction ◽  
Extraction Technique ◽  
Biometric Feature ◽  
Feature Extraction Technique

A hybrid WD-EEMD sEMG feature extraction technique for lower limb activity recognition

2021 ◽  
pp. 1-1
Author(s):  
Ankit Vijayvargiya ◽  
Vishu Gupta ◽  
Rajesh Kumar ◽  
Nilanjan Dey ◽  
Joao Manuel R. S. Tavares
Keyword(s):  
Feature Extraction ◽  
Activity Recognition ◽  
Lower Limb ◽  
Extraction Technique ◽  
Feature Extraction Technique
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