Table of Contents Author Guidelines Submit a Manuscript
Advances in Materials Science and Engineering
Volume 2015, Article ID 682786, 11 pages
http://dx.doi.org/10.1155/2015/682786
Review Article

Aluminium Process Fault Detection and Diagnosis

1Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Selangor, Malaysia
2Department of Chemical and Materials Engineering, The University of Auckland, Auckland 1142, New Zealand

Received 18 July 2014; Accepted 17 December 2014

Academic Editor: Charles C. Sorrell

Copyright © 2015 Nazatul Aini Abd Majid 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. Z. Shuiping, L. Jinhong, and D. Lei, “Fault diagnosis system for 350kA pre-baked aluminium reduction cell based on BP neural network,” TMS—Light Metals, pp. 583–587, 2007. View at Google Scholar
  2. L. Banta, C. Dai, and P. Biedler, “Noise classification in the aluminum reduction process,” TMS—Light Metals, pp. 431–435, 2003. View at Google Scholar
  3. R. Bijun, W. Zhao, S. Dai, and S. Chen, “Research of fuzzy control for alumina in Henan HongKong Longquan Aluminium CO.LTD., China,” TMS—Light Metals, pp. 439–442, 2007. View at Google Scholar
  4. A. Meghlaoui, J. Thibault, R. T. Bui, L. Tikasz, and R. Santerre, “Neural networks for the identification of the aluminium electrolysis process,” Computers & Chemical Engineering, vol. 22, no. 10, pp. 1419–1428, 1998. View at Publisher · View at Google Scholar · View at Scopus
  5. V. Yurkov, V. Mann, K. Nikandrov, and O. Trebukh, “Development of aluminium reduction process supervisory control system,” TMS—Light Metals, pp. 263–267, 2004. View at Google Scholar
  6. N. F. Nagem, J. V. Da Fonseca Neto, and C. A. Braga, “Pattern identification for feed control strategy using fuzzy neural algorithm,” in Proceedings of the 11th International Conference on Computer Modelling and Simulation (UKSIM '09), pp. 380–385, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. J. J. Gertler, Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY, USA, 1998.
  8. J. Tessier, C. Duchesne, C. Gauthier, and G. Dufour, “Estimation of alumina content of anode cover materials using multivariate image analysis techniques,” Chemical Engineering Science, vol. 63, no. 5, pp. 1370–1380, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. W. K. Rolland, A. Steinsnes, A. S. Larsen, and K. A. Paulsen, “Haldris—an expert system for process control and supervision of aluminium smelters,” TMS—Light Metals, pp. 437–443, 1991. View at Google Scholar
  10. A. I. Berezin, P. V. Polyakov, O. O. Rodnov, V. L. Yasinski, and P. D. Stont, “FMFA-based expert system for electrolysis diagnosis,” TMS—Light Metals, pp. 429–434, 2005. View at Google Scholar
  11. X. Z. Wang, Data Mining and Knowledge Discovery for Process Monitoring and Control, Springer, London, UK, 1999. View at Publisher · View at Google Scholar
  12. K. Hestetun and M. Hovd, “Detection of abnormal alumina feed rate in aluminium electrolysis cells using state and parameter estimation,” Computer Aided Chemical Engineering, vol. 21, pp. 1557–1562, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis part I: quantitative model-based methods,” Computers & Chemical Engineering, vol. 27, no. 3, pp. 293–311, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, and K. Yin, “A review of process fault detection and diagnosis part III: process history based methods,” Computers & Chemical Engineering, vol. 27, no. 3, pp. 327–346, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Abaffy, R. Aiquel, J. Larez, and J. Gonzalez, “CVG Venalum potline supervisory system,” TMS—Light Metals, pp. 301–305, 2006. View at Google Scholar
  16. S. P. Lu, Control and supervision of the aluminium electrolysis process with expert system [Ph.D. thesis], Quebec University, 2002.
  17. V. Uraikul, C. W. Chan, and P. Tontiwachwuthikul, “Artificial intelligence for monitoring and supervisory control of process systems,” Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 115–131, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Andrews, J. Diederich, and A. B. Tickle, “Survey and critique of techniques for extracting rules from trained artificial neural networks,” Knowledge-Based Systems, vol. 8, no. 6, pp. 373–389, 1995. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Kourti, “Application of latent variable methods to process control and multivariate statistical process control in industry,” International Journal of Adaptive Control and Signal Processing, vol. 19, no. 4, pp. 213–246, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. J. F. MacGregor, H. Yu, S. G. Muñoz, and J. Flores-Cerrillo, “Data-based latent variable methods for process analysis, monitoring and control,” Computers & Chemical Engineering, vol. 29, no. 6, pp. 1217–1223, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Kano and Y. Nakagawa, “Recent developments and industrial applications of data-based process monitoring and process control,” Computer Aided Chemical Engineering, vol. 21, pp. 57–62, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. C. WikströmA, C. Albanoa, L. Erikssona et al., “Multivariate process and quality monitoring applied to an electrolysis process: part I. Process supervision with multivariate control charts,” Chemometrics and Intelligent Laboratory Systems, vol. 42, pp. 221–231, 1998. View at Google Scholar
  23. J. Tessier, C. Duchesne, G. P. Tarcy, C. Gauthier, and G. Dufour, “Analysis of a potroom performance drift, from a multivariate point of view,” TMS—Light Metals, pp. 319–324, 2008. View at Google Scholar
  24. J. Tessier, T. G. Zwirz, G. P. Tarcy, and R. A. Manzini, “Multivariate statistical process monitoring of reduction cells,” TMS—Light Metals, pp. 305–310, 2009. View at Google Scholar
  25. J. Tessier, C. Duchesne, G. P. Tarcy, C. Gauthier, and G. Dufour, “Increasing potlife of Hall-Héroult reduction cells through multivariate on-line monitoring of preheating, start-up, and early operation,” Metallurgical and Materials Transactions B, vol. 41, no. 3, pp. 612–624, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. I. Miletic, S. Quinn, M. Dudzic, V. Vaculik, and M. Champagne, “An industrial perspective on implementing on-line applications of multivariate statistics,” Journal of Process Control, vol. 14, no. 8, pp. 821–836, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. D. Harris, Y. Gao, M. Taylor, J. Chen, and M. Hautus, “Operational decision making in aluminium smelters,” in Engineering Psychology and Cognitive Ergonomics, Springer, Berlin, Germany, 2009. View at Google Scholar
  28. M. A. Stam, M. P. Taylor, J. J. J. Chen, A. Mulder, and R. Rodrigo, “Common behaviour and abnormalities in aluminium reduction cells,” TMS—Light Metals, pp. 589–593, 2008. View at Google Scholar
  29. Z. Shuiping and Z. Qiuping, “A supervision system for aluminium reduction cell,” TMS—Light Metals, pp. 463–468, 2003. View at Google Scholar
  30. N. A. A. Majid, M. P. Taylor, J. J. J. Chen, M. A. Stam, A. Mulder, and B. R. Young, “Aluminium process fault detection by Multiway Principal Component Analysis,” Control Engineering Practice, vol. 19, no. 4, pp. 367–379, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. N. A. A. Majid, M. P. Taylor, J. J. J. J. Chen, W. Yu, and B. R. Young, “Diagnosing faults in aluminium processing by using multivariate statistical approaches,” Journal of Materials Science, vol. 47, no. 3, pp. 1268–1279, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. M. P. Taylor and J. J. J. Chen, “Advances in process control for aluminium smelters,” Materials and Manufacturing Processes, vol. 22, no. 7-8, pp. 947–957, 2007. View at Publisher · View at Google Scholar
  33. M. Vajta and L. Tikasz, “Adaptive prediction of anode effects in aluminium reduction cells,” in Selected Papers from the IFAC Symposium, pp. 311–315, Pergamon, Oxford, UK, 1987.
  34. F. Stevens Mcfadden, G. P. Bearne, P. C. Austin, and B. J. Welch, “Application of advanced process control to aluminium reduction cells—a review,” TMS—Light Metals, pp. 1233–1242, 2001. View at Google Scholar
  35. L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems, Springer, London, UK, 2001.
  36. P. Milgram, H. Takemura, A. Utsumi, and F. Kishino, “Augmented reality: a class of displays on the reality-virtuality continuum,” in Telemanipulator and Telepresence Technologies, vol. 2351 of Proceedings of SPIE, Boston, Mass, USA, October 1994. View at Publisher · View at Google Scholar
  37. A. Y. C. Nee, S. K. Ong, G. Chryssolouris, and D. Mourtzis, “Augmented reality applications in design and manufacturing,” CIRP Annals—Manufacturing Technology, vol. 61, no. 2, pp. 657–679, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Mizell, “Boeing’s wire bundle assembly project,” in Fundamentals of Wearable Computers and Augmented Reality, W. Barfield and T. Caudell, Eds., Lawrence Erlbaum Associates, New York, NY, USA, 2001. View at Google Scholar
  39. H. Regenbrecht, G. Baratoff, and W. Wilke, “Augmented reality projects in the automotive and aerospace industries,” IEEE Computer Graphics and Applications, vol. 25, no. 6, pp. 48–56, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. F. Echtler, F. Sturm, K. Kindermann et al., “The intelligent welding gun: augmented reality for experimental vehicle construction,” in Virtual and Augmented Reality Applications in Manufacturing, S. K. Ong and A. Y. C. Nee, Eds., Springer, 2004. View at Google Scholar
  41. N. Navab, “Developing killer apps for industrial augmented reality,” IEEE Computer Graphics and Applications, vol. 24, no. 3, pp. 16–20, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. G. Yashuang, M. Taylor, J. J. J. Chen, P. Lavoie, and M. Hautus, “Advanced supervisory control of smelters,” in Proceedings of the 10th Australasian Aluminium Smelting Technology Conference, 2011.
  43. D. Cheng, Y. Wang, H. Hua, and M. M. Talha, “Design of an optical see-through head-mounted display with a low f-number and large field of view using a freeform prism,” Applied Optics, vol. 48, no. 14, pp. 2655–2668, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. Z. Zheng, X. Liu, H. Li, and L. Xu, “Design and fabrication of an off-axis see-through head-mounted display with an x-y polynomial surface,” Applied Optics, vol. 49, no. 19, pp. 3661–3668, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  45. M. Hakkarainen, C. Woodward, and M. Billinghurst, “Augmented assembly using a mobile phone,” in Proceedings of the 7th IEEE International Symposium on Mixed and Augmented Reality (ISMAR '08), pp. 167–168, Cambridge, UK, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. B. Stutzman, D. Nilsen, T. Broderick, and J. Neubert, “MARTI: mobile augmented reality tool for industry,” in Proceedings of the WRI World Congress on Computer Science and Information Engineering (CSIE '09), pp. 425–429, Los Angeles, Calif, USA, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. M. Kitagawa and T. Yamamoto, “3D puzzle guidance in augmented reality environment using a 3D desk surface projection,” in Proceedings of the IEEE Symposium on 3D User Interface (3DUI '11), pp. 133–134, Singapore, March 2011.
  48. B. Schwald, J. Figue, E. Chauvineau et al., “STARMATE: using augmented reality technology for computer guided maintenance of complex mechanical elements,” in Proceedings of the eBusiness and eWork Conference, pp. 17–19, Venice, Italy, October 2001.
  49. S. Feiner, B. Macintyre, and D. Seligmann, “Knowledge-based augmented reality,” Communicationsof the ACM, vol. 36, pp. 53–62, 1993. View at Google Scholar
  50. S. Henderson and S. Feiner, “Opportunistic tangible user interfaces for augmented reality,” IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 1, pp. 4–16, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  51. P. Savioja, P. Jarvinen, T. Karhela, P. Siltanen, and C. Woodward, “Developing a mobile service-based augmented reality tool for modern maintenance work,” in Proceedings of the 2nd International Conference on Virtual Reality, pp. 554–563, Beijing, China, 2007.
  52. P. Harmo, A. Halme, P. Virekoski, M. Halinen, and H. Pitkanen, “Etala-virtual reality assisted telepresence system for remote maintenance,” in Proceedings of the 1st IFAC conference on Mechatronic Systems, Darmstadt, Germany, 2000.
  53. J. Didier and D. Roussel, “Augmented reality assistance in train maintenance tasks,” in Proceedings of the Workshop on Industrial Augmented Reality (ISMAR '05), 2005.