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Complexity
Volume 2018, Article ID 7356189, 9 pages
https://doi.org/10.1155/2018/7356189
Research Article

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

1The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China
2Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China
3Ministry of Education, Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
4National Demonstration Center for Experimental Machinery Education, Wuhan University of Science and Technology, Wuhan 430081, China
5Smart Materials and Structures Laboratory, Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA

Correspondence should be addressed to Gangbing Song; ude.hu@gnosg

Received 11 August 2017; Accepted 3 January 2018; Published 31 January 2018

Academic Editor: Michele Scarpiniti

Copyright © 2018 Changming Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Selva, O. Cherrier, V. Budinger, F. Lachaud, and J. Morlier, “Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks,” Engineering Structures, vol. 56, pp. 794–804, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. D. M. Peairs, G. Park, and D. J. Inman, “Improving Accessibility of the Impedance-Based Structural Health Monitoring Method,” Journal of Intelligent Material Systems and Structures, vol. 15, no. 2, pp. 129–139, 2016. View at Publisher · View at Google Scholar
  3. S. Ritdumrongkul, M. Abe, Y. Fujino, and T. Miyashita, “Quantitative health monitoring of bolted joints using a piezoceramic actuator-sensor,” Smart Materials and Structures, vol. 13, no. 1, pp. 20–29, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. Y.-K. An and H. Sohn, “Integrated impedance and guided wave based damage detection,” Mechanical Systems and Signal Processing, vol. 28, pp. 50–62, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. G. Lu, Y. Li, T. Wang et al., “A multi-delay-and-sum imaging algorithm for damage detection using piezoceramic transducers,” Journal of Intelligent Material Systems and Structures, vol. 9, pp. 1–10, 2016. View at Google Scholar
  6. H. Xiao, J. Zheng, and G. Song, “Severity evaluation of the transverse crack in a cylindrical part using a PZT wafer based on an interval energy approach,” Smart Materials and Structures, vol. 25, no. 3, article 035021, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Wang, G. Song, Z. Wang, and Y. Li, “Proof-of-concept study of monitoring bolt connection status using a piezoelectric based active sensing method,” Smart Materials and Structures, vol. 22, article 87001, 2013. View at Publisher · View at Google Scholar
  8. Q. Feng, Q. Kong, and G. Song, “Damage detection of concrete piles subject to typical damage types based on stress wave measurement using embedded smart aggregates transducers,” Measurement, vol. 88, pp. 345–352, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Ghassemi Kakroudi, E. Yeugo-Fogaing, M. Huger, C. Gault, and T. Chotard, “Influence of the thermal history on the mechanical properties of two alumina based castables,” Journal of the European Ceramic Society, vol. 29, no. 15, pp. 3197–3204, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Liu, Z. Wang, and Y. Li, “Innovative method on simulating the damage mechanism of the refractory,” ISIJ International, vol. 53, no. 7, pp. 1275–1279, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Briche, N. Tessier-Doyen, M. Huger, and T. Chotard, “Investigation of the damage behaviour of refractory model materials at high temperature by combined pulse echography and acoustic emission techniques,” Journal of the European Ceramic Society, vol. 28, no. 15, pp. 2835–2843, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Liu, Z. Wang, and Y. Li, “Damage pattern recognition and feature extraction of mgO-C refractory,” ISIJ International, vol. 53, no. 7, pp. 1280–1285, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Schmitt, Y. Berthaud, and J. Poirier, “Tensile behaviour of magnesia carbon refractories,” Journal of the European Ceramic Society, vol. 20, no. 12, pp. 2239–2248, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Chotard, J. Soro, H. Lemercier, M. Huger, and C. Gault, “High temperature characterisation of cordierite-mullite refractory by ultrasonic means,” Journal of the European Ceramic Society, vol. 28, no. 11, pp. 2129–2135, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Xu, P. Gong, J. Xie, H. Shi, G. Chen, and G. Song, “An acoustic emission based multi-level approach to buried gas pipeline leakage localization,” Journal of Loss Prevention in the Process Industries, vol. 44, pp. 397–404, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Li, S. C. M. Ho, D. Patil, and G. Song, “Acoustic emission monitoring and finite element analysis of debonding in fiber-reinforced polymer rebar reinforced concrete,” Structural Health Monitoring, 2016. View at Google Scholar
  17. W. Li, Q. Kong, S. C. M. Ho et al., “Feasibility study of using smart aggregates as embedded acoustic emission sensors for health monitoring of concrete structures,” Smart Materials and Structures, vol. 25, no. 11, article 115031, 2016. View at Google Scholar
  18. J. Vandenbussche, P. Lee, and J. Peuteman, “Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform,” Applied Sciences, vol. 4, no. 2, pp. 99–127, 2014. View at Publisher · View at Google Scholar
  19. A. Martini, M. Troncossi, and A. Rivola, “Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements,” Applied Sciences, vol. 7, no. 1, article 7010002, 2017. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Liu, Z. Wang, Y. Li et al., “Damage pattern recognition of refractory materials based on bp neural network,” LNCS, pp. 431–440, 2012. View at Google Scholar
  21. A.-B. A. E. Mohamad and Z. Chen, “Experimental and numerical analysis of the compressive and shear behavior for a new type of self-insulating concrete masonry system,” Applied Sciences, vol. 6, no. 9, article 6090245, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. G. Lacidogna, P. Cutugno, G. Niccolini, S. Invernizzi, and A. Carpinteri, “Correlation between earthquakes and AE monitoring of historical buildings in seismic areas,” Applied Sciences (Switzerland), vol. 5, no. 4, pp. 1683–1698, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. V. Christlein, D. Bernecker, F. Hönig, A. Maier, and E. Angelopoulou, “Writer Identification Using GMM Supervectors and Exemplar-SVMs,” Pattern Recognition, vol. 63, pp. 258–267, 2017. View at Publisher · View at Google Scholar · View at Scopus
  24. Q. Jiang, B. Huang, and X. Yan, “GMM and optimal principal components-based Bayesian method for multimode fault diagnosis,” Computers & Chemical Engineering, vol. 84, pp. 338–349, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. S. K. Al-Jumaili, K. M. Holford, M. J. Eaton, and R. Pullin, “Parameter Correction Technique (PCT): a novel method for acoustic emission characterisation in large-scale composites,” Composites Part B: Engineering, vol. 75, pp. 336–344, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Ahmed, A. Temko, W. Marnane, G. Lightbody, and G. Boylan, “Grading hypoxic-ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine,” Clinical Neurophysiology, vol. 127, no. 1, pp. 297–309, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. M. A. Wright and B. D. Intwala, “The effect of elevated temperatures on the mechanical properties of B-Al composites,” Journal of Materials Science, vol. 8, no. 7, pp. 957–963, 1973. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Sharifi and R. Langari, “Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models,” Mechanical Systems and Signal Processing, vol. 85, pp. 638–650, 2017. View at Publisher · View at Google Scholar · View at Scopus
  29. H. Li, Z. Chen, Y. Sun, and H. . Karimi, “Stabilization for a class of nonlinear networked control systems via polynomial fuzzy model approach,” Complexity, vol. 21, no. 2, pp. 74–81, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. X. Yuan, Z. Ge, H. Zhang, Z. Song, and P. Wang, “Soft sensor for multiphase and multimode processes based on Gaussian mixture regression,” in Proceedings of the 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014, vol. 47, pp. 1067–1072, August 2014. View at Scopus
  31. H. C. Siu, J. A. Shah, and L. A. Stirling, “Classification of anticipatory signals for grasp and release from surface electromyography,” Sensors, vol. 16, no. 11, article no. 1782, 2016. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Hamel, A. Boulkroune, and A. Bouzeriba, “Function vector synchronization based on fuzzy control for uncertain chaotic systems with dead-zone nonlinearities,” Complexity, vol. 21, no. S1, pp. 234–249, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. X. Zhao, W. Li, L. Zhou et al., “Application of support vector machine for pattern classification of active thermometry-based pipeline scour monitoring,” Structural Control and Health Monitoring, vol. 22, no. 6, pp. 903–918, 2015. View at Publisher · View at Google Scholar · View at Scopus
  34. Q. Hou, W. Jiao, L. Ren, H. Cao, and G. Song, “Experimental study of leakage detection of natural gas pipeline using FBG based strain sensor and least square support vector machine,” Journal of Loss Prevention in the Process Industries, vol. 32, pp. 144–151, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Turgay, “Bayesian change detection based on spatial sampling and Gaussian mixture model,” Pattern Recognition Letters, vol. 32, no. 12, pp. 1635–1642, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. B. Zhang, C. Zhang, and X. Yi, “Active curve axis Gaussian mixture models,” Pattern Recognition, vol. 38, no. 12, pp. 2351–2362, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Shang, M. Chen, H. Ji, and D. Zhou, “Recursive transformed component statistical analysis for incipient fault detection,” Automatica, vol. 80, pp. 313–327, 2017. View at Publisher · View at Google Scholar · View at Scopus
  38. American Standard, C1161, 2002. Standard Test Method for Flexural Strength of Advanced Ceramics at Ambient Temperature, ASTM International, West Conshohocken, PA, USA, 2002. View at Publisher · View at Google Scholar
  39. A. Benavent-Climent, A. Gallego, and J. M. Vico, “An acoustic emission energy index for damage evaluation of reinforced concrete slabs under seismic loads,” Structural Health and Monitoring, vol. 11, no. 1, pp. 69–81, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. D. G. Aggelis, D. V. Soulioti, E. A. Gatselou, N.-M. Barkoula, and T. E. Matikas, “Monitoring of the mechanical behavior of concrete with chemically treated steel fibers by acoustic emission,” Construction and Building Materials, vol. 48, pp. 1255–1260, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. M. K. Elbatanouny, P. H. Ziehl, A. Larosche, J. Mangual, F. Matta, and A. Nanni, “Acoustic emission monitoring for assessment of prestressed concrete beams,” Construction and Building Materials, vol. 58, pp. 46–53, 2014. View at Publisher · View at Google Scholar · View at Scopus