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Research of Fault Feature Information Extraction Based on the Empirical Mode Decomposition

Author: WuXiaoJuan
Tutor: LiHongSheng
School: Wuhan University of Technology
Course: Control Theory and Control Engineering
Keywords: Information Extraction Empirical Mode Decomposition (EMD) Intrinsic mode functions (IMF) Continuation Filtering
CLC: TP18
Type: Master's thesis
Year: 2006
Downloads: 492
Quote: 12
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Abstract


Fault diagnosis research equipment operating status information changes and to further the science of identification equipment operating status . Fault diagnosis , fault feature extraction is a bottleneck of its research , and signal processing technology is the core of extracting characteristic information . In practical applications , the large number of signals are non- stationary and nonlinear random signal . Non-stationary signal processing technology : short-time Fourier transform (STFT), the time-frequency analysis theory (Wigner-Vill distribution ) and wavelet transform ( WT ) , but these methods have some defects . In 1998 , the Chinese-American NEHuang et al proposed a new deal with the nonlinear and non-stationary data processing method - empirical mode decomposition method , referred to as the EMD method . In this thesis, to unbalanced rotor rotating machinery and rotor misalignment fault signal as the research object , carry out research based on the the EMD fault characteristics of information extraction , the main findings are as follows : get up and down the EMD decomposition process , due to the use of the cubic spline interpolation function envelope average, endpoint . Without any treatment , cubic spline endpoints will produce a significant swing , this swing will affect the accuracy of fault signal feature information extracted . There has been some solutions , these methods can be effective in controlling significant wobble . This article explores add extreme point symmetric extension algorithm combining experimental data to prove the correctness of this method , which improved EMD analysis methods . Use of the improved algorithm the simulation vibration signal analysis, extraction of the characteristic information of the fault signal . Signal EMD and IMF combination filtering method based on EMD . By simulation and real data analysis , proved effective filtering method based on EMD extracting characteristic information . Bently rotor test stand as an experimental system , the vibration signal acquisition the various rotor failure state , the extraction of fault features of rotor imbalance , misalignment fault signal feature extraction . With the the ActiveX components DDE technology mixed programming of MATLAB and VB enabling the EMD - based fault feature information extraction software design .

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