scholarly journals Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

Entropy ◽  
2017 ◽  
Vol 19 (9) ◽  
pp. 439 ◽  
Author(s):  
Haikun Shang ◽  
Kwok Lo ◽  
Feng Li
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Partial Discharge ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Mode Decomposition

Degradation Feature Extraction of Rolling Bearings Based on Optimal Ensemble Empirical Mode Decomposition and Improved Composite Spectrum Analysis

2018 ◽  
Author(s):  
Fengli Wang ◽  
Hua Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Single Point ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Significant Feature ◽  
Vibration Signals ◽  
Composite Spectrum ◽  
Optimal Ensemble ◽  
Mode Decomposition

Rolling bearing is a key part of turbomachinery. The performance and reliability of the bearing is vital to the safe operation of turbomachinery. Therefore, degradation feature extraction of rolling bearing is important to prevent it from failure. During rolling bearing degradation, machine vibration can increase, and this may be used to predict the degradation. The vibration signals are however complicated and nonlinear, making it difficult to extract degradation features effectively. Here, a novel degradation feature extraction method based on optimal ensemble empirical mode decomposition (EEMD) and improved composite spectrum (CS) analysis is proposed. Firstly, because only a few IMFs are expected to contain the information related to bearing fault, EEMD is utilized to pre-process the vibration signals. An optimization method is designed for adaptively determining the appropriate EEMD parameters for the signal, so that the significant feature components of the faulty bearing can be extracted from the signal and separated from background noise and other irrelevant components to bearing faults. Then, Bayesian information criterion (BIC) and correlation kurtosis (CK) are employed to select the sensitive intrinsic mode function (IMF) components and obtain fault information effectively. Finally, an improved CS analysis algorithm is used to fuse the selected sensitive IMF components, and the CS entropy (CSE) is extracted as degradation feature. Experimental data on the test bearings with single point faults separately at the inner race and rolling element were studied to demonstrate the capabilities of the proposed method. The results show that it can assess the bearing degradation status and has good sensitivity and good consistency to the process of bearing degradation.

scholarly journals Blade fault diagnosis using empirical mode decomposition based feature extraction method

2019 ◽  
Vol 255 ◽  
pp. 06009
Author(s):  
C. Y. Tan ◽  
W. K. Ngui ◽  
M. S. Leong ◽  
M. H. Lim
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Feature Vector ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Statistical Parameters ◽  
Mode Decomposition ◽  
Parameters Extraction

Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are usually used as the input to the artificial neural network for blade fault diagnosis. However, the application of others time-frequency based feature extraction technique and artificial intelligence approach for blade fault diagnosis is still lacking. In this study, a novel blade fault diagnosis method based on ensemble empirical mode decomposition and extreme learning machine was developed. Bandpass filtering was applied to the raw vibration signals and integrated with the high pass filter to obtain the velocity signal. Synchronous time averaging was then applied to the velocity signals. Three ensemble empirical mode decomposition based feature extraction methods were proposed: direct statistical parameters extraction, intrinsic mode functions averaging statistical parameters extraction and features averaging statistical parameters extraction. The effectiveness of different feature vector sets for blade fault diagnosis was examined. Feature vector set of intrinsic mode functions averaging statistical parameters extraction was found to be more effective for blade fault diagnosis. With the novel proposed method, blade fault diagnosis could be more accurate and precise.

scholarly journals A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1039 ◽  
Author(s):  
Haikun Shang ◽  
Yucai Li ◽  
Junyan Xu ◽  
Bing Qi ◽  
Jinliang Yin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Partial Discharge ◽  
Hybrid Approach ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Fusion Algorithm ◽  
Mode Decomposition ◽  
Adaptive Noise

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

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