A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis
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- 作者：M. Grasso ; marcoluigi.grasso@polimi.it" class="auth_mail" title="E-mail the corresponding author ; S. Chatterton steven.chatterton@polimi.it" class="auth_mail" title="E-mail the corresponding author ; P. Pennacchi paolo.pennacchi@polimi.it" class="auth_mail" title="E-mail the corresponding author ; B.M. Colosimo biancamaria.colosimo@polimi.it" class="auth_mail" title="E-mail the corresponding author
- 关键词：Empirical Mode Decomposition ; Combined Mode Functions ; Vibration ; Bearing ; Fault detection
- 刊名：Mechanical Systems and Signal Processing
- 出版年：2016
- 出版时间：15 December 2016
- 年：2016
- 卷：81
- 期：Complete
- 页码：126-147
- 全文大小：5144 K
文摘
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The study proposes a data-driven approach to enhance multi-scale signal decomposition.
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It works by automatically convert the Intrinsic Mode Functions (IMFs) into a minimal number of final modes.
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A probability density-based criterion is proposed to cluster IMFs that share similar properties.
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Benefits in terms of information synthesis and signal characterization can be achieved.
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The methodology is demonstrated by means of real data in a cylindrical roller bearing diagnosis application.