JSIP  Vol.4 No.4 , November 2013
Construction of Wind Turbine Bearing Vibration Monitoring and Performance Assessment System
Abstract: This study is primary to develop relevant techniques for the bearing of wind turbine, such as the intelligent monitoring system, the performance assessment, future trend prediction and possible fault classification etc. The main technique of system monitoring and diagnosis is divided into three algorithms, such as the performance assessment, performance prediction and fault diagnosis, respectively. Among them, the Logistic Regression (LR) is adopted to assess the bearing performance condition, the Autoregressive Moving Average (ARMA) is adopted to predict the future variation trend of bearing, and the Support Vector Machine (SVM) is adopted to classify and diagnose the possible fault of bearing. Through testing, this intelligent monitoring system can achieve real-time vibration monitoring, current performance assessment, future performance trend prediction and possible fault classification for the bearing of wind turbine. The monitor and analysis data and knowledge not only can be used as the basis of predictive maintenance, but also can be stored in the database for follow-up off-line analysis and used as the reference for improvement of operation parameter and wind turbine system design.
Cite this paper: F. Wu, C. Wang, J. Liu, C. Chang and Y. Lee, "Construction of Wind Turbine Bearing Vibration Monitoring and Performance Assessment System," Journal of Signal and Information Processing, Vol. 4 No. 4, 2013, pp. 430-438. doi: 10.4236/jsip.2013.44055.

[1]   P. Tavner, “Offshore Wind Turbines: Reliability, Availability and Maintenance,” The Institution of Engineering and Technology, London, 2012.

[2]   P. A. Lynn, “Onshore and Offshore Wind Energy: An Introduction,” John Wiley & Sons Ltd., Chichester, 2012.

[3]   R. Billinton, R. Karki and A. K. Verma, “Reliability and Risk Evaluation of Wind Integrated Power Systems,” Springer, London, 2013.

[4]   C. R. Farrar and K. Worden, “Structural Health Monitoring: A Machine Learning Perspective,” John Wiley & Sons Ltd., Chichester, 2013.

[5]   C. J. Crabtree, “Condition Monitoring Techniques for Wind Turbines,” Ph.D. Thesis, Durham University, Durham, 2011.

[6]   F. P. G. Márquez, A. M. Tobias, J. M. P. Pérez and M. Papaelias, “Condition Monitoring of Wind Turbines: Techniques and Methods,” Renewable Energy, Vol. 46, 2012, pp. 169-178.

[7]   K. Tracht, G. T. Goch, P. Schuh, M. Sorg and J. F. Westerkamp, “Failure Probability Prediction Based on Condition Monitoring Data of Wind Energy Systems for Spare Parts Supply,” Manufacturing Technology, Vol. 62, 2013, pp. 127-130.

[8]   D. N. P. Murthy and K. A. H. Kobbacy, “Complex System Maintenance Handbook,” Springer, London, 2008.

[9]   R. Manzini, A. Regattieri, H. Pham and E. Ferrari, “Maintenance for Industrial Systems,” Springer, London, 2010.

[10]   H. Czichos, “Handbook of Technical Diagnostics: Fundamentals and Application to Structures and Systems,” Springer, London, 2013.

[11]   V. Palade, C. D. Bocaniala and L. Jain, “Computational Intelligence in Fault Diagnosis,” Springer, London, 2006.

[12]   V S. Nandi, S. Choi and H. Meshgin-Kelk, “Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis,” CRC Press, New York, 2012.

[13]   D. J. Inman, C. R. Farrar, V. L. Junior and V. S. Junior, “Damage Prognosis: For Aerospace, Civil and Mechanical Systems,” John Wiley & Sons Ltd., Chichester, 2005.

[14]   B. L. Song and J. Lee, “Framework of Designing an Adaptive and Multi-Regime Prognostics and Health Management for Wind Turbine Reliability and Efficiency Improvement,” International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013, pp. 142-149.

[15]   G. Vachtsevanos, F. L. Lewis, M. Roemer, A. Hess and B. Wu, “Intelligent Fault Diagnosis and Prognosis for Engineering Systems,” John Wiley & Sons Ltd., New Jersey, 2006.

[16]   M. Kantardzic, “Data Mining: Concepts, Models, Methods, and Algorithms,” 2nd Edition, John Wiley & Sons Ltd., New Jersey, 2011.

[17]   P. Xanthopoulos, P. M. Pardalos and T. B. Trafalis, “Robust Data Mining,” Springer, London, 2013.

[18]   T. Hastie, R. Tibshirani and J. H. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” 2nd Edition, Springer, London, 2009.

[19]   J. Yan, “Degradation Assessment and Fault Modes Classification Using Logistic Regression,” Journal of Manufacturing Science and Engineering, Vol. 127, No. 4, 2005, pp. 912-914.

[20]   Y. N. Jeng, P. G. Huang and Y. C. Cheng, “Decomposition of One-Dimensional Waveform Using Iterative Gaussian Diffusive Filtering Methods,” Proceedings of the Royal Society A, Vol. 464, No. 2095, 2008, pp. 1673-1695.

[21]   P. C. Young, “Recursive Estimation and Time-Series Analysis: An Introduction for the Student and Practitioner,” 2nd Edition, Springer, London, 2011.

[22]   M. Najim, “Modeling, Estimation and Optimal Filtration in Signal Processing,” John Wiley & Sons Ltd., New Jersey, 2010.

[23]   L. Wang, “Support Vector Machines: Theory and Applications,” Springer, New York, 2010.

[24]   S. Abe, “Support Vector Machines for Pattern Classification,” 2nd Edition, Springer, New York, 2010.

[25]   C. Campbell and Y. Ying, “Learning with Support Vector Machines,” Morgan & Claypool Publishers, 2011.

[26]   C. Scheffer and P. Girdhar, “Practical Machinery Vibration Analysis and Predictive Maintenance,” Elsevier, 2004.