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

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 i...

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Main Authors: Yang, L., Kang, H., Zhou, Y., He, L., Lu, Chunsheng
Format: Journal Article
Published: Springer 2014
Online Access:http://hdl.handle.net/20.500.11937/41285
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author Yang, L.
Kang, H.
Zhou, Y.
He, L.
Lu, Chunsheng
author_facet Yang, L.
Kang, H.
Zhou, Y.
He, L.
Lu, Chunsheng
author_sort Yang, L.
building Curtin Institutional Repository
collection Online Access
description 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.
first_indexed 2025-11-14T09:06:51Z
format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:06:51Z
publishDate 2014
publisher Springer
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spelling curtin-20.500.11937-412852017-09-13T14:11:35Z Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals Yang, L. Kang, H. Zhou, Y. He, L. Lu, Chunsheng 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/41285 10.1007/s11340-014-9956-1 Springer restricted
spellingShingle Yang, L.
Kang, H.
Zhou, Y.
He, L.
Lu, Chunsheng
Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
title Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
title_full Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
title_fullStr Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
title_full_unstemmed Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
title_short Intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
title_sort intelligent discrimination of failure modes in thermal barrier coatings: wavelet transform and neural network analysis of acoustic emission signals
url http://hdl.handle.net/20.500.11937/41285