Electromagnetic ultrasonic diagnosis based on lifting wavelet packet and set empirical mode decomposition

2020 ◽  
Vol 2 (2) ◽  
pp. 43-51
Author(s):  
Cao Jiangying
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Packet ◽  
Ultrasonic Diagnosis ◽  
Mode Decomposition ◽  
Lifting Wavelet

scholarly journals Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianmin Zhou ◽  
Huijuan Guo ◽  
Long Zhang ◽  
Qingyao Xu ◽  
Hui Li
Keyword(s):  
Wavelet Packet ◽  
Assessment Method ◽  
Performance Degradation ◽  
Data Repository ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Mode Decomposition ◽  
Lifting Wavelet ◽  
Envelope Spectrum

Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

Design of high performance copyright protection watermarking based on lifting wavelet transform and bi empirical mode decomposition

2018 ◽  
Vol 77 (19) ◽  
pp. 24593-24614 ◽  
Author(s):  
Nidaa Hasan Abbas ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Sajida Parveen ◽  
Wan Azizun Wan ◽  
Abd. Rahman Bin Ramli
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Copyright Protection ◽  
High Performance ◽  
Lifting Wavelet Transform ◽  
Mode Decomposition ◽  
Lifting Wavelet

A Comparative Study of the Empirical Mode Decomposition and Wavelet Analysis on Their Application for Structural Health Monitoring

2004 ◽  
Author(s):  
Adriana Hera ◽  
Abhijeet Shinde ◽  
Zhikun Hou
Keyword(s):  
Comparative Study ◽  
Empirical Mode Decomposition ◽  
Wavelet Packet ◽  
Noisy Data ◽  
Vibration Signal ◽  
Mode Shapes ◽  
Progressive Damage ◽  
Intrinsic Mode Functions ◽  
Detection Techniques ◽  
Mode Decomposition

The paper presents a comparative study of the effectiveness of three novel damage detection techniques namely Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD) and Wavelet Packet Sifting (WPS). The health condition of a mechanical or civil engineering structure can be assessed by monitoring a change in natural frequencies and mode shapes. CWT method can be used to identify the instantaneous values of these modal parameters by the wavelet ridges. Using the EMD method, intrinsic mode functions (IMF) can be sifted from a vibration signal, whereas a newly-developed WPS technique can decompose a signal into its dominant mono-frequency components. Instantaneous modal information can be extracted by incorporating the EMD and WPS with the Hilbert Transform. These techniques are illustrated for simulated vibration data from a three-degree-of-freedom system subjected to (i) sudden damage and (ii) progressive damage. The aspects related to the implementation algorithms, sensitivity to damage type and the robustness issues in case of noisy data are discussed. In case of progressive damage, all methods performed well. WPS technique performed better in case of sudden damage whereas CWT demonstrated robustness in case of noisy data.

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