Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine

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• 作者：A. Tabrizi (1)
  L. Garibaldi (1)
  A. Fasana (1)
  S. Marchesiello (1)
  
  1. Dynamics & Identification Research Group ; Department of Mechanical and Aerospace Engineering ; Politecnico di Torino ; Corso Duca degli Abruzzi 24 ; 10129 ; Turin ; Italy
  
• 关键词：Roller bearing ; Fault diagnosis ; Empirical mode decomposition (EMD) ; Wavelet packet decomposition (WPD) ; Ensemble empirical mode decomposition (EEMD) ; Support vector machine (SVM) ; Denoising
• 刊名：Meccanica
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：50
• 期：3
• 页码：865-874
• 全文大小：592 KB
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  10. Tabrizi A, Garibaldi L, Fasana A, Marchesiello S (2014) Influence of stopping criterion for sifting process of empirical mode decomposition technique (EMD) on roller bearing fault diagnosis. In: Advances in condition monitoring of machinery in non-stationary operations. Springer, New York (DEU). doi:10.1007/978-3-642-39348-8_33 . ISBN: 978-3-642-39347-1
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• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Mechanics
  Civil Engineering
  Automotive and Aerospace Engineering and Traffic
  Mechanical Engineering
  
• 出版者：Springer Netherlands
• ISSN：1572-9648

文摘

Roller bearings are widely used in rotating machinery and one of the major reasons for machine breakdown is their failure. Vibration based condition monitoring is the most common method for extracting some important information to identify bearing defects. However, acquired acceleration signals are usually noisy, which significantly affects the results of fault diagnosis. Wavelet packet decomposition (WPD) is a powerful method utilized effectively for the denoising of the signals acquired. Furthermore, Ensemble empirical mode decomposition (EEMD) is a newly developed decomposition method to solve the mode mixing problem of empirical mode decomposition (EMD), which is a consequence of signal intermittence. In this study a combined automatic method is proposed to detect very small defects on roller bearings. WPD is applied to clean the noisy signals acquired, then informative feature vectors are extracted using the EEMD technique. Finally, the states of the bearings are examined by labeling the samples using the hyperplane constructed by the support vector machine algorithm. The data were generated by means of a test rig assembled in the labs of the Dynamics and Identification Research Group in the mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and a very small damage size on a roller) were considered to obtain reliable results. It is shown that the combined method proposed is able to identify the states of the bearings effectively.