Wind Turbine Gearbox Failure Identification with Deep Neural Networks

IEEE Transactions on Industrial Informatics

Volumn 13, Issue 3, 2017, Pages 1360-1368

  Wang, Long ^a   Zhang, Zijun ^a   Long, Huan ^a   Xu, Jia ^b   Liu, Ruihua ^b  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b China Longyuan Power Group Co Ltd   (China)

Author keywords

Condition monitoring; data mining; deep neural network (DNN); lubricant pressure; wind turbine gearbox

Indexed keywords

CONDITION MONITORING; DATA ACQUISITION; DATA MINING; GEARS; NEAREST NEIGHBOR SEARCH; REGRESSION ANALYSIS; WIND POWER; WIND TURBINES;

COMPARATIVE ANALYSIS; COMPUTATIONAL RESULTS; DATA MINING ALGORITHM; EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS; FAILURE IDENTIFICATION; K-NEAREST NEIGHBORS; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; WIND TURBINE GEARBOXES;

DEEP NEURAL NETWORKS;

EID: 85027729414     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2016.2607179     Document Type: Article

References (41)

1. J. Nilsson and L. Bertling, "Maintenance management of wind power systems using condition monitoring systems-Life cycle cost analysis for two case studies, " IEEE Trans. Energy Convers., vol. 22, no. 1, pp. 223-229, Mar. 2007.
2. I. Staffell and R. Green, "How does wind farm performance decline with age?" Renewable Energy, vol. 66, pp. 775-786, 2014.
3. A. Zaher, S. McArthur, D. Infield, and Y. Patel, "Online wind turbine fault detection through automated SCADA data analysis, " Wind Energy, vol. 12, no. 6, pp. 574-593, 2009.
4. P. Tavner et al., "Study of weather and location effects on wind turbine failure rates, " Wind Energy, vol. 16, no. 2, pp. 175-187, 2013.
5. Z. Hameed, Y. Hong, Y. Cho, S. Ahn, and C. Song, "Condition monitoring and fault detection of wind turbines and related algorithms: A review, " Renewable Sustainable Energy Rev., vol. 13, no. 1, pp. 1-39, 2009.
6. B. Lu, Y. Li, X. Wu, and Z. Yang, "A review of recent advances in wind turbine condition monitoring and fault diagnosis, " in Proc. IEEE Power Electron. Mach. Wind Appl., 2009, pp. 1-7.
7. A. A. Salem, A. Abu-Siada, and S. Islam, "Condition monitoring techniques of the wind turbines gearbox and rotor, " Int. J. Elect. Energy, vol. 2, no. 1, pp. 53-56, 2014.
8. W. Liu et al., "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review, " Renewable Sustainable Energy Rev., vol. 44, pp. 466-472, 2015.
9. Y. Lei, D. Han, J. Lin, and Z. He, "Planetary gearbox fault diagnosis using an adaptive stochastic resonance method, " Mech. Syst. Signal Process., vol. 38, no. 1, pp. 113-124, 2013.
10. J. Li, X. Chen, Z. Du, Z. Fang, and Z. He, "A new noise-controlled secondorder enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis, " Renewable Energy, vol. 60, pp. 7-19, 2013.
11. I. Antoniadou, G. Manson, W. Staszewski, T. Barszcz, and K. Worden, "A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions, " Mech. Syst. Signal Process., vol. 64, pp. 188-216, 2015.
12. H. Luo et al., "Effective and accurate approaches for wind turbine gearbox condition monitoring, " Wind Energy, vol. 17, no. 5, pp. 715-728, 2014.
13. Z. Feng and M. Liang, "Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis, " Renewable Energy, vol. 66, pp. 468-477, 2014.
14. W. Teng, X. Ding, X. Zhang, Y. Liu, and Z. Ma, "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform, " Renewable Energy, vol. 93, pp. 591-598, 2016.
15. J. Rafiee, F. Arvani, A. Harifi, and M. Sadeghi, "Intelligent condition monitoring of a gearbox using artificial neural network, " Mech. Syst. Signal Process., vol. 21, no. 4, pp. 1746-1754, 2007.
16. Z. Zhang, A. Verma, and A. Kusiak, "Fault analysis and condition monitoring of the wind turbine gearbox, " IEEE Trans. Energy Covers., vol. 27, no. 2, pp. 526-535, Jun. 2012.
17. G. Smith, "SCADA in wind farms, " in Proc. IEE Colloquium Instrum, Electric. Supply Ind., 1993, pp. 11/1-11/2.
18. Y. Feng, Y. Qiu, C. J. Crabtree, H. Long, and P. J. Tavner, "Monitoring wind turbine gearboxes, " Wind Energy, vol. 16, no. 5, pp. 728-740, 2013.
19. M. C. Garcia, M. A. Sanz-Bobi, and J. del Pico, "SIMAP: Intelligent system for predictive maintenance: Application to the health condition monitoring of awindturbine gearbox, " Comput. Ind., vol. 57, no. 6, pp. 552-568, 2006.
20. Y. Wang and D. Infield, "Supervisory control and data acquisition databased non-linear state estimation technique for wind turbine gearbox condition monitoring, " IET Renewable Power Generation, vol. 7, no. 4, pp. 350-358, 2013.
21. Y. Park and M. Kellis, "Deep learning for regulatory genomics, " Nature Biotechnol., vol. 33, no. 8, pp. 825-826, 2015.
22. V. Mnih et al., "Human-level control through deep reinforcement learning, " Nature, vol. 518, no. 7540, pp. 529-533, 2015.
23. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning, " Nature, vol. 521, no. 7553, pp. 436-444, 2015.
24. B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey, "Predicting the sequence specificities of DNA-And RNA-binding proteins by deep learning, " Nature Biotechnol., vol. 33, pp. 831-838, 2015.
25. D. Silver et al., "Mastering the game of go with deep neural networks and tree search, " Nature, vol. 529, no. 7587, pp. 484-489, 2016.
26. Y. Bengio and O. Delalleau, "On the expressive power of deep architectures, " in Proc. Int. Conf. Algorithmic Learn. Theory, 2011, pp. 18-36.
27. N. Srivastava et al., "Dropout: A simple way to prevent neural networks from overfitting, " J Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
28. N. S. Altman, "An introduction to kernel and nearest-neighbor nonparametric regression, " Amer. Statistician, vol. 46, no. 3, pp. 175-185, 1992.
29. R. Tibshirani, "Regression shrinkage and selection via the lasso, " J. Roy. Statistical Soc. Series B, vol. 73, pp. 267-288, 1996.
30. A. E. Hoerl and R. W. Kennard, "Ridge regression: Biased estimation for nonorthogonal problems, " Technometrics, vol. 12, no. 1, pp. 55-67, 1970.
31. A. Smola and V. Vapnik, "Support vector regression machines, " in Proc. Adv. Neural Inf. Process Syst., 1997., vol. 9, pp. 155-161.
32. B. Kröse, B. Krose, P. van der Smagt, and P. Smagt, An Introduction to Neural Networks. Amsterdam, the Netherlands: Univ. Amsterdam Press, 1993.
33. T. G. Dietterich, "Ensemble methods in machine learning, " in Multiple Classifier Systems. Berlin, Germany: Springer, 2000, pp. 1-15.
34. G. E. Dahl, T. N. Sainath, and G. E. Hinton, "Improving deep neural networks for LVCSR using rectified linear units and dropout, " in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., 2013, pp. 8609-8613.
35. Y. Bengio, "Practical recommendations for gradient-based training of deep architectures, " in Neural Networks: Tricks of the Trade. Berlin, Germany: Springer, 2012, pp. 437-478.
36. B. Recht, C. Re, S. Wright, and F. Niu, "Hogwild: A lock-free approach to parallelizing stochastic gradient descent, " in Proc. Adv. Neural Inf. Process Syst., 2011, vol. 24, pp. 693-701.
37. G. Schwarz, "Estimating the dimension of a model, " Ann. Statist., vol. 6, no. 2, pp. 461-464, 1978.
38. R. J. Tibshirani and J. Taylor, "Degrees of freedom in lasso problems, " Ann. Statist., vol. 40, no. 2, pp. 1198-1232, 2012.
39. D. C. Montgomery, Introduction to Statistical QualityControl. NewYork, NY, USA: Wiley, 2007.
40. S. S. Prabhu and G. C. Runger, "Designing a multivariate EWMA control chart, " J. Quality Technol., vol. 29, no. 1, pp. 8-15, 1997.
41. Y. Abu-Mostafa, M. Magdon-Ismail, and H. Lin, Learning from Data. Berlin, Germany: AMLBook, 2012.