An Integrated Health Condition Detection Method for Rolling Bearings Using Time-Shift Multi-Scale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

2021 ◽  
Author(s):  
Jiancheng Gong ◽  
Xiaoqiang Yang ◽  
Ke Feng ◽  
Wuqiang Liu ◽  
Fuming Zhou ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detection Method ◽  
Health Condition ◽  
Time Shift ◽  
Permutation Entropy ◽  
Rolling Bearings ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Integrated Health

scholarly journals Time-Frequency Analysis and Application of a Vibration Signal of Tunnel Excavation Blasting Based on CEEMD-MPE-HT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Band ◽  
Frequency Analysis ◽  
Frequency Characteristic ◽  
Permutation Entropy ◽  
Natural Vibration Frequency ◽  
Characteristic Parameters ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.

Multi-scale eigenvalues Empirical Mode Decomposition for geomagnetic signal filtering

Measurement ◽  
2019 ◽  
Vol 146 ◽  
pp. 885-891 ◽  
Author(s):  
Nan Qiao ◽  
Li-hui Wang ◽  
Qing-ya Liu ◽  
Hong-qi Zhai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Filtering ◽  
Multi Scale ◽  
Mode Decomposition

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

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