NDLI: Decomposition of mechanical signals

Content Provider	IEEE Xplore Digital Library
Author	Cao Jun Wang Xingsong
Copyright Year	2009
Description	Author affiliation: School of Mechanical Engineering, Southeast University, Nanjing 211189 (Cao Jun; Wang Xingsong)
Abstract	Time frequency transformations have gained increasing attention for the characterization of non-stationary signals in a broad spectrum of science and engineering applications. Signals encountered in rotary machine systems can be broadly classified as being either stationary or nonstationary. This study evaluates the performance of the traditional method-polynomial fit filtering with Fourier spectrum analysis and the new developed method-empirical mode decomposition with Hilbert transform (EMD+HT), in mechanical signal decomposition. The former method is based on the sense of least squares, thus insensitive to noise. However, it demands a predetermined time scale, which is unchangeable once fixed, while EMD is adaptive with multi-resolution and univocal. One shortcoming of the latter approach-sensitive to noise, is alleviated by the wavelet threshold de-noising method. A synthetic signal as well as a path error signal of precision working table is analyzed using the two methods. Evaluation is made upon the mode mixing phenomenon and illusive components problem included in EMD with proposed indicators, which confirms the validity of this method used in mechanical signal decomposition.
Starting Page	2020
Ending Page	2025
File Size	711427
Page Count	6
File Format	PDF
ISBN	9781424446483
ISSN	1553572X
e-ISBN	9781424446506
DOI	10.1109/IECON.2009.5414915
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-03
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal analysis Fourier transforms Signal resolution Filtering Wavelet transforms Signal processing Noise reduction Data mining Time frequency analysis Data analysis signal decomposition polynomial fit filtering Hilbert-Huang transform wavelet de-noising
Content Type	Text
Resource Type	Article

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