@inproceedings{2438dccfa3204d56b37d672819ad82d1,
title = "An integrated feature extraction algorithm for condition monitoring of railway point machine",
abstract = "Railway point machines are crucial parts of rail transit systems. To monitoring their health conditions, how to extract relevant features is extremely important. This paper proposes an integrated method for feature extraction, which utilizes Empirical Mode Decomposition and Singular Value Decomposition (EMD-SVD) to process the signals of point machines. In this method, the power supply signal is first decomposed to a number of intrinsic mode functions and a residue to form a feature matrix. Singular values of this feature matrix are obtained by SVD, at which point Mahalanobis distance (MD) is employed. This method can avoid the segemetation of signals and process them entirely without any manual intervention. Its performance and effectiveness has also been validated via a test rig.",
keywords = "Empirical mode decomposition, Fault detection, Mahalanobis distance, Railway points, Singular value decomposition",
author = "Zhipeng Wang and Limin Jia and Yong Qin",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016 ; Conference date: 19-10-2016 Through 21-10-2016",
year = "2017",
month = jan,
day = "16",
doi = "10.1109/PHM.2016.7819893",
language = "英语",
series = "Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qiang Miao and Zhaojun Li and Zuo, \{Ming J.\} and Liudong Xing and Zhigang Tian",
booktitle = "Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016",
address = "美国",
}