Authors: Linfeng Deng Rongzhen Zhao
Publish Date: 2014/05/01
Volume: 28, Issue: 4, Pages: 1161-1169
Abstract
Feature extraction is the most important step for machine fault diagnosis but useful features are very difficult to extract from the vibration signals especially for intelligent fault diagnosis based on datadriven technique An integral method for fault feature extraction based on local mean decomposition LMD and Teager energy kurtosis TEK is proposed in this paper The raw vibration signals are first processed via LMD to produce a group of product functions PFs Then the Teager energies are computed using the derived PFs Subsequently each Teager energy data set is directly used to calculate the corresponding TEK A vibration experiment was performed on a rotorbearing rig with rubimpact fault to validate the proposed method The experimental results show that the proposed method can extract different TEKs from the mechanical vibration signals under two different operating conditions These TEKs can be employed to identify the normal and rubimpact fault conditions and construct a numericalvalued machine fault decision table which proves that the proposed method is suitable for fault feature extraction of the rotorbearing systemLinfeng Deng received a BS degree in Information and Computation Science from Lanzhou University of Technology China in 2006 He is currently a PhD candidate at Lanzhou University of Technology His primary research interest is machine fault diagnosis
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