Adaptive feature selection for rolling bearing condition monitoring

被引：0

作者：

Goreczka, Stefan ^{[1
]}

Strackeljan, Jens ^{[1
]}

机构：

[1] Otto von Guericke Univ, Fak Maschinenbau, Inst Mech, Univ Pl 2, D-30106 Magdeburg, Germany

来源：

8TH INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND MACHINERY FAILURE PREVENTION TECHNOLOGIES 2011, VOLS 1 AND 2 | 2011年

关键词：

rolling element bearing; vibration analysis; condition monitoring; signal processing; feature selection;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

From today's point of view rolling element bearing diagnostic techniques based on vibration analysis seem to be consolidated. Several signal processing methods for noise reduction and aggregation of fault information such as frequency and wavelet filtering were reviewed in detail for the use in condition monitoring. There are many other techniques with more or less credible effect on the observed vibration signal. The adaption of a diagnostic system in the data processing part is time consuming and static regarding the dynamic of changing conditions in and outside the bearing. Choosing a feature selection strategy regarding these aspects is supposed to have a high potential solving problems of this domain. Some current examination results were discussed in this paper.

引用

页码：914 / 924

页数：11

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