Improved human identification method based on electrocardiogram using ensemble empirical mode decomposition and Teager Energy Operator

2017 ◽  
Author(s):  
Yanjun Deng ◽  
Zhidong Zhao ◽  
Yefei Zhang ◽  
Diandian Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Human Identification ◽  
Energy Operator ◽  
Ensemble Empirical Mode Decomposition ◽  
Identification Method ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Teager Energy

Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Zhipeng Feng ◽  
Ming J. Zuo ◽  
Rujiang Hao ◽  
Fulei Chu ◽  
Jay Lee
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
Characteristic Frequency ◽  
Energy Operator ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Teager Energy

Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the local damage of rolling element bearings. A new method based on ensemble empirical mode decomposition (EEMD) and the Teager energy operator is proposed to extract the characteristic frequency of bearing fault. The signal is firstly decomposed into monocomponents by means of EEMD to satisfy the monocomponent requirement by the Teager energy operator. Then, the intrinsic mode function (IMF) of interest is selected according to its correlation with the original signal and its kurtosis. Next, the Teager energy operator is applied to the selected IMF to detect fault-induced impulses. Finally, Fourier transform is applied to the obtained Teager energy series to identify the repeating frequency of fault-induced periodic impulses and thereby to diagnose bearing faults. The principle of the method is illustrated by the analyses of simulated bearing vibration signals. Its effectiveness in extracting the characteristic frequency of bearing faults, and especially its performance in identifying the symptoms of weak and compound faults, are validated by the experimental signal analyses of both seeded fault experiments and a run-to-failure test. Comparison studies show its better performance than, or complements to, the traditional spectral analysis and the squared envelope spectral analysis methods.

scholarly journals Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 309 ◽  
Author(s):  
Tao Zhang ◽  
Xinhua Wang ◽  
Yingchun Chen ◽  
Zia Ullah ◽  
Haiyang Ju ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Side Lobe ◽  
Ensemble Empirical Mode Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Teager Energy

During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

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