On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features



Published Nov 1, 2020
Ruoyu Li Mark Frogley


To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is important. Developing effective online fault detection methodology is important. In this paper, an adaptive filtering technique is applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulse signals of the processed signal are extracted for rotating machine fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the transformed one dimensional feature to identify the gear transmission system with defect. Vibration signals collected from wind turbines in the real operation will be used to demonstrate the effectiveness of the presented methodology.

Abstract 203 | PDF Downloads 114



condition monitoring, fault detection, fault diagnosis, Adaptive filtering, Gear transmission system, Statistical features, Pattern classification, Wind turbine transmission system

Abouhnik, A., and Albarbar, A., “Wind turbine blades condition assessment based on vibration measurements and the level of an empirically decomposed feature”, Energy Conversion and Management, vol. 64, pp. 606-613, 2012.
Antoni, J., and Randall, B., "Unsupervised noise cancellation for vibration signals: part I--evaluation of adaptive algorithms," Mechanical Systems and Signal Processing, vol. 18, pp. 89-101, 2004.
Bechhoefer, E., Li, R., and He, D., “Quantification of condition indicator performance on a split torque gearbox”, Proceedings of the 2009 AHS Forum, Grapevine, TX, May 27-29, 2009.
Bechhoefer, E., Menon, P., and Kingsley, M., “Bearing envelope analysis window selection Using spectral kurtosis techniques”, In 2011 IEEE Conference on Prognostics and Health Management (PHM), pp. 1-6, 2011.
Benesty, J., and Huang, Y., Adaptive signal processing: applications to real-world problems: Springer, 2003.
Greenberg, J. E., "Modified LMS algorithms for speech processing with an adaptive noise canceller," IEEE Transactions on Speech and Audio Processing, vol. 6, pp. 338-351, 1998.
Han, Y., and Song Y.H., "Condition monitoring techniques for electrical equipment—a literature survey", IEEE Transactions on Power Delivery, vol. 18, pp, 4-13, 2003
Inan, O. T., Etemadi, M., Widrow, B., and Kovacs, G., "Adaptive cancellation of floor vibrations in standing ballistocardiogram measurements using a seismic sensor as a noise reference," IEEE Transactions on Biomedical Engineering, vol. 57, pp. 722-727, 2010.
Lei, Y., He, Z., and Zi, Y. “A new approach to intelligent fault diagnosis of rotating machinery”, Expert Systems with Applications, vol. 35, No. 4, 1593-1600, 2008.
Li, R., and He, D., "Development of an Advanced Narrowband Interference Cancellation Method for Gearbox Fault Detection," in AHS 67th Annual Forum and Technology Display, Virginia Beach, Virginia, 2011.
Lu, J., Plataniotis, K. N., and Venetsanopoulos, A. N., Face recognition using LDA-based algorithms., IEEE Transactions on Neural Networks, vol. 14, No.1, pp. 195-200, 2003.
Martinez A., and Kak A., "PCA versus LDA", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001
McInerny, S. A., and Dai, Y., “Basic vibration signal processing for bearing fault detection”, IEEE Transactions on Education, vol. 46, No. 1, 149-156, 2003.
McLachlan, G. J., Discriminant analysis and statistical pattern recognition,vol. 544, Wiley-Interscience, 2004
Medjaher K., Camci F., and Zerhouni N., “Feature Extraction and Evaluation for Health Assessment and Failure Prognostics”, In First European Conference of the Prognostics and Health Management Society., pp. 111-116. 2012.
Norton, M. P., & Karczub, D. G., Fundamentals of noise and vibration analysis for engineers. Cambridge university press. 2003
Rahman, M. Z. U. , Rafi, A.S., and Rama, K.R., "Efficient sign based normalized adaptive filtering techniques for cancellation of artifacts in ECG signals: Application to wireless biotelemetry," Signal Processing, vol. 91, pp. 225-239, 2011.
Ribrant, J., and Bertling, L.M., "Survey of Failures in Wind Power Systems With Focus on Swedish Wind Power Plants During 1997–2005," Energy Conversion, IEEE Transactions on , vol.22, no.1, pp.167,173, 2007
Samanta, B., Al-Balushi, K. R., and Al-Araimi, S. A., “Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Applications of Artificial Intelligence, vol. 16, No. 7, pp. 657-665, 2003
Sambur, M., "Adaptive noise canceling for speech signals," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 26, pp. 419-423, 1978.
Sawalhi, N., Randall, R. B., and Endo, H., “The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis”, Mechanical Systems and Signal Processing, vol. 21, No. 6, pp. 2616-2633., 2007.
Schlechtingen M., Santos I.F., and Achiche S., “Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description”, Applied soft Computing, vol. 13, pp. 259-270, 2013.
SKF, SKF Multilog On-line System IMx-W, www.SKF.com,2011
Swets, D. and Weng, J., "Using discriminant eigenfeatuers for image retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-836, 1996.
Thakor, N. V. and Zhu Y., "Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection," IEEE Transactions on Biomedical Engineering, vol. 38, pp. 785-794, 1991.
Widrow, B., Glover, J., McCool, J., Kaunitz, J., Williams, C.S., Hearn, R.H., Zeidler, J.H., Dong, E., and Goodlin, R.C., "Adaptive noise cancelling: Principles and applications," Proceedings of the IEEE, vol. 63, pp. 1692-1716, 2005.
Yiakopoulos, C.T., Gryllias, K.C., and Antoniadis, I.A., “Rolling element bearing fault detection in industrial enviroments based on a k-mean clustering approach”, Expert Systems with Applications, vol. 38, No. 3, pp. 2888-2911, 2011
Zhu J., He D., and Bechhoefer E., “Survey of lubrication oil condition monitoring, diagnostics, and prognostics techniques”, Proceedings of the 2012 Conference of he Society for Machinery Failure Prevention Technology, pp. 193-212, Dayton, OH, April 24-26, 2012.
Zhu, Y., and Weight J.P., "Ultrasonic nondestructive evaluation of highly scattering materials using adaptive filtering and detection," IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 41, pp. 26-33, 1994.
Wang, W., “Early detection of gear tooth cracking using the resonance demodulation technique”, Mechanical Systems and Signal Processing, vol. 15, No. 5, pp. 887-903, 2001
Endo, H., and Randall, R. B., “Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter”, Mechanical Systems and Signal Processing, vol. 21, No. 2, pp. 906-919, 2007
Wang, K., and Heyns, P. S., “The combined use of order tracking techniques for enhanced Fourier analysis of order components”, Mechanical systems and signal processing, vol. 25, No. 3, pp. 803-811, 2011.
Hyers, R. W., McGowan, J. G., Sullivan, K. L., Manwell, J. F., and Syrett, B. C., “Condition monitoring and prognosis of utility scale wind turbines”, Energy Materials: Materials Science and Engineering for Energy Systems, vol. 1, No. 3, pp. 187-203, 2006.
Nilsson, J., and Bertling, L., “ Maintenance management of wind power systems using condition monitoring systems—life cycle cost analysis for two case studies”, IEEE Transactions on Energy Conversion, vol. 22, No. 1, pp. 223-229, 2007.
Technical Papers