Journal
MEASUREMENT SCIENCE AND TECHNOLOGY
Volume 22, Issue 5, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0957-0233/22/5/055704
Keywords
modulation signal; local mean decomposition; phase unwrapping; signal-to-noise ratio; fault diagnosis
Funding
- National Natural Science Foundation of China [51035007]
- National S&T Major Projects of China [2009ZX04014-015]
- National Basic Research Program of China [2009CB724405]
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Since machinery fault vibration signals are usually multicomponent modulation signals, how to decompose complex signals into a set of mono-components whose instantaneous frequency (IF) has physical sense has become a key issue. Local mean decomposition (LMD) is a new kind of time-frequency analysis approach which can decompose a signal adaptively into a set of product function (PF) components. In this paper, a modulation feature extraction method-based LMD is proposed. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. The computed IF and IA are displayed together in the form of time-frequency representation (TFR). Modulation features can be extracted from the spectrum analysis of the IA and IF. In order to make the IF have physical meaning, the phase-unwrapping algorithm and IF processing method of extrema are presented in detail along with a simulation FM signal example. Besides, the dependence of the LMD method on the signal-to-noise ratio (SNR) is also investigated by analyzing synthetic signals which are added with Gaussian noise. As a result, the recommended critical SNRs for PF decomposition and IF extraction are given according to the practical application. Successful fault diagnosis on a rolling bearing and gear of locomotive bogies shows that LMD has better identification capacity for modulation signal processing and is very suitable for failure detection in rotating machinery.
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