EEMD Based Multiple Degradation Feature Extraction Method for Electronic Power Panel

The electric power panel is widely used in aircraft engineering. Extracting the degradation feature of power panel is very important for degradation modeling and RUL estimation. This paper employed an Ensemble Empirical Mode Decomposition (EEMD) based method to extract the degradation statistics feature of power panel. First, the degradation data is decomposed into independent Intrinsic Mode Functions (IMFs) by EEMD. Each IMF contains the information of degradation and fluctuation. The FFT and Chow test method is applied to extract the information of periodicity and mutability characteristic respectively. Based on this, the characteristic series of each IMF can be constructed. Secondly, the main characteristic is identified by correlation coefficients analysis which is utilized to identify the main characteristic by comparing the characteristic series and corresponding IMFs. Finally, the results are carried out and compared with that of using wavelet packet decomposition (WPD) to verify the efficiency.

[1]  Jasmin Kevric,et al.  Biomedical Signal Processing and Control , 2016 .

[2]  Leontios J. Hadjileontiadis,et al.  Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features , 2014, IEEE Journal of Biomedical and Health Informatics.

[3]  Ling Tang,et al.  An Integrated Data Characteristic Testing Scheme for Complex Time Series Data Exploration , 2013, Int. J. Inf. Technol. Decis. Mak..

[4]  Mohammed Imamul Hassan Bhuiyan,et al.  Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting , 2017, Comput. Methods Programs Biomed..

[5]  Jianzhou Wang,et al.  A hybrid forecasting approach applied to wind speed time series , 2013 .

[6]  D. Andrews,et al.  Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis , 1992 .

[7]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[8]  Yi Zhang,et al.  Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction , 2017 .

[9]  Rajesh Patel,et al.  Effective Extraction of Visual Event-Related Pattern by Combining Template Matching With Ensemble Empirical Mode Decomposition , 2017, IEEE Sensors Journal.

[10]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Ling Tang,et al.  A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting , 2015 .

[12]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

[13]  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.

[14]  Sangjin Cho,et al.  Extraction of Significant Features using Empirical Mode Decomposition and Its Application , 2013 .

[15]  F. Fisher.  Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note , 1970 .