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Shock and Vibration
Volume 2016 (2016), Article ID 4086324, 12 pages
http://dx.doi.org/10.1155/2016/4086324
Research Article

Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine

Department of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30 Mickiewicza Avenue, 30-059 Krakow, Poland

Received 3 July 2015; Revised 28 August 2015; Accepted 1 September 2015

Academic Editor: Dong Wang

Copyright © 2016 Marcin Strączkiewicz and Tomasz Barszcz. 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. G. W. Van Bussel and M. B. Zaaijer, “Reliability, availability and maintenance aspects of large-scale offshore wind farms,” in Proceedings of the International Conference on Marine Renewable Energy, pp. 119–126, Newcastle, UK, 2001.
  2. D. McMillan and G. W. Ault, “Quantification of condition monitoring benefit for offshore wind turbines,” Wind Engineering, vol. 31, no. 4, pp. 267–285, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Ribrant and L. M. Bertling, “Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005,” IEEE Transactions on Energy Conversion, vol. 22, no. 1, pp. 167–173, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Faulstich, B. Hahn, and P. J. Tavner, “Wind turbine downtime and its importance for offshore deployment,” Wind Energy, vol. 14, no. 3, pp. 327–337, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Jablonski and T. Barszcz, “Validation of vibration measurements for heavy duty machinery diagnostics,” Mechanical Systems and Signal Processing, vol. 38, no. 1, pp. 248–263, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Song, Z.-Y. Li, P. Bellemain, N. Martin, and C. Mailhes, “AStrion data validation of non-stationary wind turbine signals,” in Proceedings of the 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Oxford, UK, June 2015.
  7. L. F. Villa, A. Reñones, J. R. Perán, and L. J. de Miguel, “Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation,” Mechanical Systems and Signal Processing, vol. 25, no. 6, pp. 2157–2168, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. R. B. Randall, “A new method of modeling gear faults,” ASME Journal of Mechanical Design, vol. 104, no. 2, pp. 259–267, 1982. View at Publisher · View at Google Scholar · View at Scopus
  9. P. D. McFadden, “Determining the location of a fatigue crack in a gear from the phase of the change in the meshing vibration,” Mechanical Systems and Signal Processing, vol. 2, no. 4, pp. 403–409, 1988. View at Publisher · View at Google Scholar · View at Scopus
  10. J. D. Smith, Gear Noise and Vibration, Marcel Dekker, New York, NY, USA, 2nd edition, 2003.
  11. J. Mączak, “Local meshing plane as a source of diagnostic information for monitoring the evolution of gear faults,” in Engineering Asset Lifecycle Management: Proceedings of the 4th World Congress on Engineering Asset Management (WCEAM 2009), 28–30 September 2009, pp. 661–670, Springer, London, UK, 2010. View at Publisher · View at Google Scholar
  12. A. Belsak and J. Flasker, “Method for detecting fatigue crack in gears,” Theoretical and Applied Fracture Mechanics, vol. 46, no. 2, pp. 105–113, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. W. J. Staszewski and G. R. Tomlinson, “Application of the wavelet transform to fault detection in a spur gear,” Mechanical Systems and Signal Processing, vol. 8, no. 3, pp. 289–307, 1994. View at Publisher · View at Google Scholar · View at Scopus
  14. I. Howard, S. Jia, and J. Wang, “The dynamic modelling of a spur gear in mesh including friction and a crack,” Mechanical Systems and Signal Processing, vol. 15, no. 5, pp. 831–853, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Bartelmus, “Gearbox dynamic modelling,” Journal of Theoretical and Applied Mechanics, vol. 39, no. 4, pp. 989–999, 2001. View at Google Scholar
  16. W. J. Wang and P. D. McFadden, “Application of wavelets to gearbox vibration signals for fault detection,” Journal of Sound and Vibration, vol. 192, no. 5, pp. 927–939, 1996. View at Publisher · View at Google Scholar · View at Scopus
  17. W. J. Staszewski, K. Worden, and G. R. Tomlinson, “Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition,” Mechanical Systems and Signal Processing, vol. 11, no. 5, pp. 673–692, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Zimroz and A. Bartkowiak, “Investigation on spectral structure of gearbox vibration signals by principal component analysis for condition monitoring purposes,” Journal of Physics: Conference Series, vol. 305, Article ID 012075, pp. 1–11, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. P. D. Samuel and D. J. Pines, “A review of vibration-based techniques for helicopter transmission diagnostics,” Journal of Sound and Vibration, vol. 282, no. 1-2, pp. 475–508, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. P. D. Samuel and D. J. Pines, “Constrained adaptive lifting and the CAL4 metric for helicopter transmission diagnostics,” Journal of Sound and Vibration, vol. 319, no. 1-2, pp. 698–718, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. X. Zhao, M. J. Zuo, and Z. Liu, “Diagnosis of pitting damage levels of planet gears based on ordinal ranking,” in Proceedings of the 2011 IEEE Conference on Prognostics and Health Management, pp. 1–8, Denver, Colo, USA, June 2011.
  22. Z. Cheng, N. Hu, F. Gu, and G. Qin, “Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis,” Transactions of the Canadian Society for Mechanical Engineering, vol. 35, no. 3, pp. 403–417, 2011. View at Google Scholar
  23. Z. Chen, Z. Zhu, and Y. Shao, “Fault feature analysis of planetary gear system with tooth root crack and flexible ring gear rim,” Engineering Failure Analysis, vol. 49, pp. 92–103, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. D. M. Blunt and J. A. Keller, “Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis,” Mechanical Systems and Signal Processing, vol. 20, no. 8, pp. 2095–2111, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Zhao, M. J. Zuo, Z. Liu, and M. R. Hoseini, “Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking,” Measurement, vol. 46, no. 1, pp. 132–144, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. J. P. T. Koch and C. M. Vicuña, “Dynamic and phenomenological vibration models for failure prediction on planet gears of planetary gearboxes,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 36, no. 3, pp. 533–545, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. T. Barszcz and R. B. Randall, “Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine,” Mechanical Systems and Signal Processing, vol. 23, no. 4, pp. 1352–1365, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Zimroz and A. Bartkowiak, “Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions,” Mechanical Systems and Signal Processing, vol. 38, no. 1, pp. 237–247, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. W. Bartelmus and R. Zimroz, “A new feature for monitoring the condition of gearboxes in non-stationary operating conditions,” Mechanical Systems and Signal Processing, vol. 23, no. 5, pp. 1528–1534, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Lei, J. Lin, M. J. Zuo, and Z. He, “Condition monitoring and fault diagnosis of planetary gearboxes: a review,” Measurement, vol. 48, no. 1, pp. 292–305, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Lei, D. Kong, J. Lin, and M. J. Zuo, “Fault detection of planetary gearboxes using new diagnostic parameters,” Measurement Science and Technology, vol. 23, no. 5, Article ID 055605, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Jablonski and T. Barszcz, “Instantaneous circular pitch cyclic power (ICPCP)—a tool for diagnosis of planetary gearboxes,” Key Engineering Materials, vol. 518, pp. 168–173, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. D. Astolfi, F. Castellani, and L. Terzi, “Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm,” Diagnostyka, vol. 15, no. 2, pp. 71–78, 2014. View at Google Scholar · View at Scopus
  35. J. R. Stack, T. G. Habetler, and R. G. Harley, “Effects of machine speed on the development and detection of rolling element bearing faults,” IEEE Power Electronics Letters, vol. 1, no. 1, pp. 19–21, 2003. View at Publisher · View at Google Scholar · View at Scopus
  36. W. Yang, R. Court, and J. Jiang, “Wind turbine condition monitoring by the approach of SCADA data analysis,” Renewable Energy, vol. 53, pp. 365–376, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. R. Zimroz, W. Bartelmus, T. Barszcz, and J. Urbanek, “Wind turbine main bearing diagnosis—a proposal of data processing and decision making procedure under non stationary load condition,” Key Engineering Materials, vol. 518, pp. 437–444, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. Z. Hameed, Y. S. Hong, Y. M. Cho, S. H. Ahn, and C. K. Song, “Condition monitoring and fault detection of wind turbines and related algorithms: a review,” Renewable and Sustainable Energy Reviews, vol. 13, no. 1, pp. 1–39, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Urbanek, M. Strączkiewicz, and T. Barszcz, “Joint power-speed representation of vibration features: application to wind turbine planetary gearbox,” in Proceedings of the 3rd International conference on Condition Monitoring of Machinery in Non-Stationary Operations, Ferrara, Italy, May 2013.
  40. G. Żak, J. Obuchowski, A. Wylomańska, and R. Zimroz, “Application of ARMA modelling and alpha-stable distribution for local damage detection in bearings,” Diagnostyka, vol. 15, no. 3, pp. 3–10, 2014. View at Google Scholar · View at Scopus
  41. M. A. Timusk, M. G. Lipsett, J. McBain, and C. K. Mechefske, “Automated operating mode classification for online monitoring systems,” Journal of Vibration and Acoustics, vol. 131, pp. 131–141, 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Straczkiewicz, P. Wiciak, A. Jabłoński, and T. Barszcz, “Machinery in highly changing operations: on designation of operational states,” in Proceedings of the 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, pp. 1–12, Oxford, UK, June 2015.
  43. A. Hajnayeb, S. E. Khadem, and M. H. Moradi, “Design and implementation of an automatic condition-monitoring expert system for ball-bearing fault detection,” Industrial Lubrication and Tribology, vol. 60, no. 2, pp. 93–100, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. B. Samanta and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings using time-domain features,” Mechanical Systems and Signal Processing, vol. 17, no. 2, pp. 317–328, 2003. View at Publisher · View at Google Scholar · View at Scopus
  45. M. Cocconcelli, R. Rubini, R. Zimroz, and W. Bartlemus, “Diagnostics of ball bearings in varying-speed motors by means of artificial neural network,” in Proceedings of the 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, pp. 760–771, Cardiff, UK, June 2011.
  46. J. Rafiee, F. Arvani, A. Harifi, and M. H. Sadeghi, “Intelligent condition monitoring of a gearbox using artificial neural network,” Mechanical Systems and Signal Processing, vol. 21, no. 4, pp. 1746–1754, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. A. Azadeh, M. Saberi, A. Kazem, V. Ebrahimipour, A. Nourmohammadzadeh, and Z. Saberi, “A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization,” Applied Soft Computing Journal, vol. 13, no. 3, pp. 1478–1485, 2013. View at Publisher · View at Google Scholar · View at Scopus
  48. D. V. S. S. S. Sarma and G. N. S. Kalyani, “ANN approach for condition monitoring of power transformers using DGA,” in Proceedings of the IEEE Region 10 Conference (TENCON '04), vol. 3, pp. 444–447, IEEE, November 2004. View at Publisher · View at Google Scholar
  49. Z. Tian, “An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring,” Journal of Intelligent Manufacturing, vol. 23, no. 2, pp. 227–237, 2012. View at Publisher · View at Google Scholar · View at Scopus
  50. A. R. Bahmanyar and A. Karami, “Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs,” International Journal of Electrical Power and Energy Systems, vol. 58, pp. 246–256, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. D. Crivelli, M. Guagliano, and A. Monici, “Development of an artificial neural network processing technique for the analysis of damage evolution in pultruded composites with acoustic emission,” Composites Part B: Engineering, vol. 56, pp. 948–959, 2014. View at Publisher · View at Google Scholar · View at Scopus
  52. Z. Zhang and K. Wang, “Wind turbine fault detection based on SCADA data analysis using ANN,” Advances in Manufacturing, vol. 2, no. 1, pp. 70–78, 2014. View at Publisher · View at Google Scholar
  53. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362, MIT Press, Cambridge, Mass, USA, 1986. View at Google Scholar
  54. A. Jablonski, T. Barszcz, M. Bielecka, and P. Breuhaus, “Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems,” Measurement, vol. 46, no. 1, pp. 727–738, 2013. View at Publisher · View at Google Scholar · View at Scopus
  55. C. Cempel, “Limit value in the practice of machine vibration diagnostics,” Mechanical Systems and Signal Processing, vol. 4, no. 6, pp. 483–493, 1990. View at Publisher · View at Google Scholar · View at Scopus
  56. T. Barszcz and M. Straczkiewicz, “Novel intuitive hierarchical structures for condition monitoring system of wind turbines,” Diagnostyka, vol. 14, no. 3, pp. 53–60, 2013. View at Google Scholar · View at Scopus
  57. ISO, “Mechanical vibration—evaluation of machine vibration by measurements on non-rotating parts—part I: general guidelines,” ISO 10816, International Organization for Standardization, Geneva, Switzerland, 1995. View at Google Scholar