Applying empirical mode decomposition (EMD) and entropy to diagnose circuit breaker faults

Abstract In this paper, a new method to extract characteristic parameters from vibration signal of HV (high voltage) CB (circuit breakers) is proposed based on the empirical mode decomposition (EMD)-entropy. First, the normal signal and several fault signals are decomposed with empirical mode, and then extraction and selection the IMF function. Second, calculating the signal envelope after selection in IMF using Hilbert method. Finally, on each envelope signals using time integral of entropy, acquiring the EMD-characteristic entropy vector. The fault signal characteristic entropy vector and normal signal's comparison determine fault types. In practice, this method is very convenient, and can be applied directly to diagnose the fault signals and its species.

[1]  Yi Shen,et al.  A Ridge Ensemble Empirical Mode Decomposition Approach to Clutter Rejection for Ultrasound Color Flow Imaging , 2013, IEEE Transactions on Biomedical Engineering.

[2]  Huaizhi Su,et al.  Structure analysis for concrete‐faced rockfill dams based on information entropy theory and finite element method , 2012 .

[3]  Chen Weigen.  Applying wavelet packet energy entropy and neural networks to diagnose circuit breaker faults , 2008 .

[4]  Ziwei Pan,et al.  A new bearing fault diagnosis method based on MM and EMD , 2010, 2010 3rd International Congress on Image and Signal Processing.

[5]  Martin Novak,et al.  Fault diagnosis of rotating machinery based on wavelet transforms and neural network , 2010, 2010 International Conference on Applied Electronics.

[6]  Zhongyi Hu,et al.  Does restraining end effect matter in EMD-based modeling framework for time series prediction? Some experimental evidences , 2014, Neurocomputing.

[7]  Tang Baiwen.  Application of EMD and Duffing Oscillator to Fault Line Detection in Un-effectively Grounded System , 2013 .

[8]  Rafał Rakoczy,et al.  The Characterization of the Residence Time Distribution in a Fluid Mixer by Means of the Information Entropy , 2014 .

[9]  V. A. Menegatto,et al.  Weighted Fourier–Laplace transforms in reproducing kernel Hilbert spaces on the sphere , 2014 .

[10]  Manon Thibault De Chanvalon.  Quantum symmetry groups of Hilbert modules equipped with orthogonal filtrations , 2013, 1304.3718.

[11]  Zhao Guang-zhou.  Analysis of Bearing Vibration Characteristics Using Hilbert-Huang Transformation , 2006 .

[12]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[13]  Laijun Sun,et al.  A new fault diagnosis method for HV circuit breakers based on wavelet packet-neural network , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[14]  A.K. Tafreshi,et al.  Empirical Mode Decomposition In Epileptic Seizure Prediction , 2008, 2008 IEEE International Symposium on Signal Processing and Information Technology.

[15]  Xia Yanqing.  Fault diagnosis based on bispectrum and Hilbert-Huang transform for circuit breaker , 2013 .

[16]  Mingliang Liu,et al.  A New Method to Fault Diagnosis for Circuit Breakers Based on Characteristic Entropy of Wavelet Packet , 2010 .