@inproceedings{402c843e383a4b60904253f0161b1726,
title = "EEMD Based Multiple Degradation Feature Extraction Method for Electronic Power Panel",
abstract = "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.",
keywords = "EEMD, Electronic power panel, Feature Extraction, Multiple degradation",
author = "Dan Xu and Zhixin Feng",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 ; Conference date: 16-08-2017 Through 18-08-2017",
year = "2017",
month = dec,
day = "9",
doi = "10.1109/SDPC.2017.135",
language = "英语",
series = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "683--687",
editor = "Wei Guo and \{de Oliveira\}, \{Jose Valente\} and Chuan Li and Yun Bai and Ping Ding and Juanjuan Shi",
booktitle = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
address = "美国",
}