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    Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals

    Access Status
    Fulltext not available
    Authors
    Yang, L.
    Kang, H.
    Zhou, Y.
    He, L.
    Lu, Chunsheng
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Yang, L. and Kang, H. and Zhou, Y. and He, L. and Lu, C. 2014. Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals. Experimental Mechanics. 55 (2): pp. 321-330.
    Source Title
    Experimental Mechanics
    DOI
    10.1007/s11340-014-9956-1
    ISSN
    0014-4851
    School
    Department of Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/41285
    Collection
    • Curtin Research Publications
    Abstract

    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.

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