| |
Intelligent Discrimination of Failure Modes in Thermal Barrier Coatings: Wavelet Transform and Neural Network Analysis of Acoustic Emission Signals
- 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
- 参考文献:1. Miller, RA (1987) Current status of thermal barrier coatings: an overview. Surf Coat Technol 30: pp. 1-11 CrossRef
2. Mumm, DR, Evans, AG, Spitsberg, IT (2001) Characterization of a cyclic displacement instability for a thermally grown oxide in a thermal barrier system. Acta Mater 49: pp. 2329-2340 CrossRef 3. Padture, NP, Gell, M, Jordan, EH (2002) Thermal barrier coating for gas-turbine engine applications. Science 296: pp. 280-284 CrossRef 4. De, R, Ananthakrishna, G (2006) Dynamics of the peel front and the nature of acoustic emission during peeling of an adhesive tape. Phys Rev Lett 97: pp. 165503 CrossRef 5. Hutchinson, JW, He, MY, Evans, AG (2000) The influence of imperfections on the nucleation and propagation of buckling driven delaminations. J Mech Phys Solids 48: pp. 709-734 CrossRef 6. Hutchinson, JW, Suo, Z (1992) Mixed mode cracking in layered materials. Adv Appl Mech 29: pp. 63-191 CrossRef 7. Yang, L, Zhou, YC, Lu, C (2011) Damage evolution and rupture time prediction in thermal barrier coatings subjected to cyclic heating and cooling: an acoustic emission method. Acta Mater 9: pp. 6519-6529 CrossRef 8. Yao, WB, Dai, CY, Mao, WG, Lu, C, Yang, L, Zhou, YC (2011) Acoustic emission analysis on tensile failure of air plasma-sprayed thermal barrier coatings. Surf Coat Technol 206: pp. 3803-3807 CrossRef 9. Yang, L, Zhong, ZC, Zhou, YC, Lu, C (2014) Quantitative assessment of the surface crack density in thermal barrier coatings. Acta Mech Sinica 30: pp. 167-174 CrossRef 10. Renusch, D, Sch眉tze, M (2007) Measuring and modeling the TBC damage kinetics by using acoustic emission analysis. Surf Coat Technol 202: pp. 740-744 CrossRef 11. Ma, XQ, Takemoto, M (2001) Quantitative acoustic emission analysis of plasma sprayed thermal barrier coatings subjected to thermal shock tests. Mater Sci Eng A 308: pp. 101-110 CrossRef 12. Fu, L, Khor, KA, Ng, HW, Teo, TN (2000) Non-destructive evaluation of plasma sprayed functionally graded thermal barrier coatings. Surf Coat Technol 130: pp. 233-239 CrossRef 13. Ma, X, Cho, S, Takemoto, M (2001) Acoustic emission source analysis of plasma sprayed thermal barrier coatings during four-point bend tests. Surf Coat Technol 139: pp. 55-62 CrossRef 14. Yang, L, Zhou, YC, Mao, WG, Lu, C (2008) Real-time acoustic emission testing based on wavelet transform for the failure process of thermal barrier coatings. Appl Phys Lett 93: pp. 231906 CrossRef 15. Gutkin, R, Green, C, Vangrattanachai, S, Pinho, ST, Robinson, P, Curtis, PT (2011) On acoustic emission for failure investigation in CFRP: pattern recognition and peak frequency analyses. Mech Syst Signal Pr 25: pp. 1393-1407 CrossRef 16. Benson, PM, Vinciguerra, S, Meredith, PG, Young, RP (2008) Laboratory simulation of volcano seismicity. Science 322: pp. 249-252 CrossRef 17. Wu, JD, Liu, CH (2008) Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Syst Appl 35: pp. 1200-1213 CrossRef 18. Wu, JD, Wang, YH, Chiang, PH, Bai, MR (2009) A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network. Expert Syst Appl 36: pp. 49-56 CrossRef 19. Kim, EY, Lee, YJ, Lee, SK (2012) Heath monitoring of a glass transfer robot in the mass production line of liquid crystal display using abnormal operating sounds based on wavelet packet transform and artificial neural network. J Sound Vib 331: pp. 3412-3427 CrossRef 20. Sasikumar, T, RajendraBoopathy, S, Usha, K, Vasydev, ES (2009) Failure strength prediction of unidirectional tensile coupons using acoustic emission peak amplitude and energy parameter with artificial neural networks. Compos Sci Technol 69: pp. 1151-1155 CrossRef 21. Oliveira, RD, Marques, AT (2008) Health monitoring of FRP using acoustic emission and artificial neural networks. Compos Struct 86: pp. 367-373 CrossRef 22. Chui, CK (1992) An introduction to wavelets. Academic Press, San Diego 23. Newland DE (1993) An introduction to random vibrations, spectral and wavelet analysis. Longman Scientific & Technical, Essex 24. Daubechies, I (1990) The wavelet transform, time frequency localization and signal analysis. IEEE T Inform Theory 36: pp. 961-1005 CrossRef 25. Khamedi, R, Fallahi, OAR (2010) Effect of martensite phase volume fraction on acoustic emission signals using wavelet packet analysis during tensile loading of dual phase steels. Mater Des 31: pp. 2752-2759 CrossRef 26. Duan, ZP, Eischen, JW, Herrmann, G (1986) Harmonic wave propagation in nonhomogeneous layered composites. J Appl Mech 53: pp. 108-115 CrossRef
- 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.
| |
NGLC 2004-2010.National Geological Library of China All Rights Reserved.
Add:29 Xueyuan Rd,Haidian District,Beijing,PRC. Mail Add: 8324 mailbox 100083
For exchange or info please contact us via email.
| |