JOURNAL ARTICLE

Study on Rolling Element Bearing Fault Diagnosis Methods Based on Ensemble Empirical Mode Decomposition

Zhong Liang LvYi Lin LiuXian Wu HanMin Liu

Year: 2013 Journal:   Applied Mechanics and Materials Vol: 457-458 Pages: 602-607   Publisher: Trans Tech Publications

Abstract

For rotating machinery, a fault diagnosis method is proposed on the basis of the EEMD (Ensemle Emperical Mode Decomposition) and Correlation Coefficient Method. In the Vibration Signals and rotate speed of rotating machinery, the fault diagnosis method is achieved by Spectrum Analysis and real-time monitoring. The original signals are decomposed into several IMF components. Each IMF contains the local feature of the signal. Correlation coefficient method is used to select the appropriate Intrinsic Mode Function. Extract the fault feature through its envelope diagram. Experiment proves the feasibility of this method.

Keywords:
Hilbert–Huang transform Rolling-element bearing Fault (geology) Envelope (radar) Mode (computer interface) Vibration Bearing (navigation) Feature (linguistics) SIGNAL (programming language) Correlation coefficient Decomposition Engineering Basis (linear algebra) Pattern recognition (psychology) Control theory (sociology) Algorithm Structural engineering Computer science Artificial intelligence Mathematics Acoustics Physics Statistics White noise

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Topics

Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
Engineering Diagnostics and Reliability
Physical Sciences →  Engineering →  Mechanics of Materials
Gear and Bearing Dynamics Analysis
Physical Sciences →  Engineering →  Mechanical Engineering
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