Intelligent identification of wear mechanism via on-line ferrograph images

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• 作者：Tonghai Wu (1)
  Yeping Peng (1)
  Chenxing Sheng (2)
  Jiaoyi Wu (1)
  
• 关键词：wear mechanism ; characteristic wear debris ; ferrography ; image processing
• 刊名：Chinese Journal of Mechanical Engineering
• 出版年：2014
• 出版时间：March 2014
• 年：2014
• 卷：27
• 期：2
• 页码：411-417
• 全文大小：612 KB
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• 作者单位：Tonghai Wu (1)
  Yeping Peng (1)
  Chenxing Sheng (2)
  Jiaoyi Wu (1)
  
  1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing Systems, Xi鈥檃n Jiaotong University, Xi鈥檃n, 710049, China
  2. Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, 430063, China
  
• ISSN：2192-8258

文摘

Condition based maintenance(CBM) issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.