Research on Bearing Incipient Failure Degradation State Recognition Method Based on Fast Spectral Kurtosis and Multi-point Optimal Minimum Entropy Deconvolution Adjusted

2021 ◽  
Author(s):  
Zixia Fu ◽  
Yingjie Zhao ◽  
Hao Yin
Keyword(s):  
Minimum Entropy ◽  
Recognition Method ◽  
State Recognition ◽  
Spectral Kurtosis ◽  
Degradation State

scholarly journals Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 701 ◽  
Author(s):  
Beige Ye ◽  
Taorong Qiu ◽  
Xiaoming Bai ◽  
Ping Liu
Keyword(s):  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Sample Entropy ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
State Recognition ◽  
Driving Fatigue

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

scholarly journals Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD

2018 ◽  
Vol 36 (1) ◽  
pp. 20-27
Author(s):  
Haixu Jiang ◽  
Ke Zhang ◽  
Jingyu Wang ◽  
Meibo Lü
Keyword(s):  
Measured Data ◽  
Variational Mode Decomposition ◽  
Telemetry Data ◽  
Recognition Method ◽  
State Recognition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Sample Data ◽  
Orbital Spacecraft ◽  
Weak Anomaly

Considering the difficulty in identifying the in-orbital spacecraft weak anomaly, a spacecraft anomaly state recognition method based on Morphological variational mode decomposition and JRD distance is proposed. First of all, the telemetry data of the spacecraft is decomposed into multi-scale modal functions with different frequencies via morphological variational modal decomposition. Then the Rényi entropy of each modal function is extracted, which is regarded as the feature of telemetry data. Finally, the recognition of spacecraft anomaly state is realized by comparing the JRD distance between the sample data and the measured data. The proposed method is verified by means of the telemetry data of the weak anomaly speed of a satellite reaction wheel. The simulation results demonstrate that the proposed method can effectively identify the anomaly of the spacecraft and has obvious advantage in recognition speed.

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