Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 219710, 12 pages
http://dx.doi.org/10.1155/2015/219710
Research Article

Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

1State Key Laboratory of Synthetical Automation for Process Industries, Northeast University, Shenyang 110004, China
2Information Science and Engineering School, Northeastern University, Shenyang 110004, China
3College of Science, Liaoning Industry University, Jinzhou 121000, China
4College of Resources and Civil Engineering, Northeastern University, Shenyang 110004, China

Received 6 November 2014; Revised 9 January 2015; Accepted 14 January 2015

Academic Editor: Sangmin Lee

Copyright © 2015 Dong Xiao 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. D. Xiao, J. C. Wang, and H. X. Tian, “Quality prediction and control of reducing pipe based on EOS-ELM-RPLS mathematics modeling method,” Journal of Applied Mathematics, vol. 2014, Article ID 298218, 13 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Lévesque, S. E. Kruger, G. Lamouche et al., “Thickness and grain size monitoring in seamless tube-making process using laser ultrasonics,” NDT & E International, vol. 39, no. 8, pp. 622–626, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Lv, Y. R. Li, and H. G. Wang, “On-line monitoring and fault diagnosis for mill drives,” Heavy Machinery, vol. 2002, no. 2, pp. 56–59, 2002. View at Google Scholar
  4. M. Reggio, F. McKenty, L. Gravel, J. Cortes, G. Morales, and M.-A. Ladron de Guevara, “Computational analysis of the process for manufacturing seamless tubes,” Applied Thermal Engineering, vol. 22, no. 4, pp. 459–470, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Jin, Fault Diagnosis of Large DC Motor Strip Mill, University of Science and Technology of China, 2000.
  6. L. Lei and H. L. Zhang, “Continuous rolling-tube unit on-line supervision system based on virtual instrument technology,” Natural Science Journal of Xiangtan University, vol. 26, no. 1, pp. 102–105, 2004. View at Google Scholar
  7. C. H. Zhao and F. R. Gao, “Fault-relevant Principal Component Analysis (FPCA) method for multivariate statistical modeling and process monitoring,” Chemometrics and Intelligent Laboratory Systems, vol. 133, no. 1, pp. 1–12, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Yu, “Local and nonlocal preserving projection for bearing defect classification and performance assessment,” IEEE Transactions on Industrial Electronics, vol. 59, no. 5, pp. 2363–2376, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. E. Orchard, and G. Vachtsevanos, “A probabilistic fault detection approach: application to bearing fault detection,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 2011–2018, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process,” Journal of Process Control, vol. 22, no. 9, pp. 1567–1581, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. S. X. Ding, “Data-driven design of monitoring and diagnosis systems for dynamic processes: a review of subspace technique based schemes and some recent results,” Journal of Process Control, vol. 24, no. 2, pp. 431–449, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Lei, J. Lin, Z. He, and M. J. Zuo, “A review on empirical mode decomposition in fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 35, no. 1-2, pp. 108–126, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Bedoui, R. Faleh, H. Samet, and A. Kachouri, “Electronic nose system and principal component analysis technique for gases identification,” in Proceedings of the 10th International Multi-Conference on Systems, Signals & Devices (SSD '13), pp. 1–6, IEEE, Hammamet, Tunisia, March 2013. View at Publisher · View at Google Scholar
  14. G. Georgoulas, M. O. Mustafa, I. P. Tsoumas et al., “Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines,” Expert Systems with Applications, vol. 40, no. 17, pp. 7024–7033, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Evans and J. Kennedy, “Integration of adaptive neuro fuzzy inference systems and principal component analysis for the control of tertiary scale formation on tinplate at a hot mill,” Expert Systems with Applications, vol. 41, no. 15, pp. 6662–6675, 2014. View at Publisher · View at Google Scholar
  16. C. M. Ringle, M. Sarstedt, R. Schlittgen, and C. R. Taylor, “PLS path modeling and evolutionary segmentation,” Journal of Business Research, vol. 66, no. 9, pp. 1318–1324, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. E. M. Abdel-Rahman, O. Mutanga, J. Odindi, E. Adam, A. Odindo, and R. Ismail, “A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data,” Computers and Electronics in Agriculture, vol. 106, pp. 11–19, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Lu, I. Castillo, L. Chiang, and T. F. Edgar, “Industrial PLS model variable selection using moving window variable importance in projection,” Chemometrics and Intelligent Laboratory Systems, vol. 135, no. 15, pp. 90–109, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. N. Lu, F. Gao, and F. Wang, “Sub-PCA modeling and on-line monitoring strategy for batch processes,” AIChE Journal, vol. 50, no. 1, pp. 255–259, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. X.-T. Doan, R. Srinivasan, P. M. Bapat, and P. P. Wangikar, “Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: a case study with rifamycin B fermentation,” Journal of Biotechnology, vol. 132, no. 2, pp. 156–166, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Emparán, R. Simpson, S. Almonacid, A. Teixeira, and A. Urtubia, “Early recognition of problematic wine fermentations through multivariate data analyses,” Food Control, vol. 27, no. 1, pp. 248–253, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Dong, Y. Yao, and F. Gao, “Phase analysis and identification method for multiphase batch processes with partitioning multi-way principal component analysis (MPCA) model,” Chinese Journal of Chemical Engineering, vol. 20, no. 6, pp. 1121–1127, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. P. Nomikos and J. F. MacGregor, “Multi-way partial least squares in monitoring batch processes,” Chemometrics and Intelligent Laboratory Systems, vol. 30, no. 1, pp. 97–108, 1995. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. S. Qi, P. Wang, X. J. Gao, and Y. J. Gong, “Batch process monitoring and fault diagnosis based on improved multi-way principal component analysis,” CIESC Journal, vol. 85, no. 1, pp. 82–93, 2009. View at Google Scholar