Intelligent Discrimination of Failure Modes in Thermal Barrier Coatings: Wavelet Transform and Neural Network Analysis of Acoustic Emission Signals

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• 作者：L. Yang (1) (2)
  H. S. Kang (1) (2)
  Y. C. Zhou (1) (2)
  L. M. He (3)
  C. Lu (4)
  
  1. Key Laboratory of Low Dimensional Materials & Application Technology (Ministry of Education) ; Xiangtan University ; Xiangtan ; Hunan ; 411105 ; China
  2. School of Materials Science and Engineering ; Xiangtan University ; Xiangtan ; Hunan ; 411105 ; China
  3. Beijing Institute of Aeronautical Materials ; Beijing ; 100095 ; China
  4. Department of Mechanical Engineering ; Curtin University ; Perth ; WA ; 6845 ; Australia
  
• 关键词：Thermal barrier coatings ; Acoustic emission ; Failure mode ; Wavelet transform ; Neural network
• 刊名：Experimental Mechanics
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：55
• 期：2
• 页码：321-330
• 全文大小：1,956 KB
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• 刊物类别：Engineering
• 刊物主题：Mechanical Engineering
  Theoretical and Applied Mechanics
  Characterization and Evaluation Materials
  Structural Mechanics
  Engineering Fluid Dynamics
  Engineering Design
  
• 出版者：Springer Boston
• ISSN：1741-2765

文摘

To identify failure modes in thermal barrier coatings (TBCs), we propose a method of processing acoustic emission signals based on the wavelet packet transform and neural networks. The results show that there are four typical failure modes in TBCs: surface cracks, sliding interface cracks, opening interface cracks, and substrate deformation. These failure modes can be discriminated by the wavelet energy coefficients that parameterize their characteristic frequency bands. By using the energy coefficient vector as an input, the back-propagation neural network has a self-learning ability to cluster signals with the same order features. In comparison with experiments, this processing method is effective for intelligently discriminating the failure modes of TBCs.