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- 저자 : Rongzhen Zhao (검색결과 1 건)
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Linfeng Deng,Rongzhen Zhao 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.4
Feature extraction is the most important step for machine fault diagnosis, but useful features are very difficult to extract from the vibrationsignals, especially for intelligent fault diagnosis based on data-driven technique. An integral method for fault feature extraction basedon local mean decomposition (LMD) and Teager energy kurtosis (TEK) is proposed in this paper. The raw vibration signals are firstprocessed 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 arotor-bearing rig with rub-impact fault to validate the proposed method. The experimental results show that the proposed method canextract different TEKs from the mechanical vibration signals under two different operating conditions. These TEKs can be employed toidentify the normal and rub-impact fault conditions and construct a numerical-valued machine fault decision table, which proves that thismethod is suitable for fault feature extraction of the rotor-bearing system.
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