The MAED and SVM for fault diagnosis of wind turbine system

SOUSSA Abdelkrim, MOUSS Mohamed Djamel, AITOUCHE Samia, MELAKHESSOU Hayet, TITAH Mawloud


Fault diagnosis is the best discipline to control the operation and maintenance costs of the wind turbine system. However, the fault diagnosis of wind turbine finds difficulties with the variation of wind speed and electrical energy (generator torque).

In this work, the proposed fault diagnosis approach is based on the Feature set algorithm, manifold learning and the Support Vector Machine classifier. First, the construction of the feature set is very important step, with the high dimension after application the MAED (Manifold Adaptive Experimental Design) algorithm on the data set. Moreover, the NPE(Neighborhood Preserving Embedding)manifold learning algorithm is applied for dimensionally reduction of feature set by the eigenvectors; it is easy to use as the input for the last step. Finally, the low dimension of eigenvectors is exploited by the Support Vector Machine classifier for recognition fault and making the maintenance decision.

This approach is implanted on the faults of the benchmark wind turbine and gives the best performance.

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Fault diagnosis, Wind turbine, Data-based diagnosis, MAED algorithm, NPE algorithm, SVM classifier.

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F. Zhipeng, L. Ming, Z. Yi, H. Shumin, “Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation”, Renewable Energy journal, vol. 47, pp.112-126, 2012.

F. Zhipeng, L. Ming, “Fault diagnosis of wind turbine planetary gearbox under non stationary conditions via adaptive optimal kernel time-frequency analysis”, Renewable Energy journal, vol. 66, pp.468-477, 2014.

F. Zhipeng, C. Xiaowang, L. Ming, “Iterative generalized synchros-queezing transform for fault diagnosis of wind turbine planetary gearbox under non stationary conditions”, Mechanical Systems and Signal Processing journal, vol. 52-53, pp.360-375, 2015.

A. Kusiak , A. Verma ., “A data-driven approach for monitoring blade pitch faults in wind turbines”, IEEE Transactions on Sustainable Energy journal, vol. 2, no. 1, pp. 87-96, 2011.

Y. Qiu, P.Richardson , Y. Feng, P.Tavner P, G. Erdos, ZS. Viharos, “SCADA alarm analysis for improving wind turbine reliability”, In: European wind energy conference and exhibition conference, Brussels, Belgium. pp. 14-17, 2011.

W. Bartelmus, R. Zimroz, “A new feature for monitoring the condition of gearboxes in non-stationary operating conditions”, Mechanical Systems and Signal Processing journal, vol. 23, no. 5, pp. 1528-1534, 2009.

X. Wang, V. Makis, “Autoregressive model-based gear shaft fault diagnosis using the kolmogorovsmirnov test”, Journal of Sound and Vibration, vol. 327, no. 3, pp. 413-423, 2009.

H. Bingbing; Li. Bing, “A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains”, Measurement Science And Technology journal,vol. 27, no. 2, Article Number: 025017, 2016

Chacon, J. L. Ferrando; Andicoberry, E. Artigao; Kappatos, Vassilios; et al. “An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis”, Journal Of Low Frequency Noise Vibration And Active Control, vol. 35, no. 1,pp. 64-76, MAR 2016.

Qiu, Yingning; Feng, Yanhui; Sun, Juan; et al. “Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data”,IET Renewable Power Generation journal, vol. 10, no. 5, pp.1-8, January 2016.

Yang, Zhi-Xin; Wang, Xian-Bo ;Zhong, Jian-Hua, “Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach”,Energies journal, vol. 9, no. 6, 379, 2016.

Mollasalehi, Ehsan; Sun, Qiao; Wood, David, “Wind Turbine Generator Bearing Fault Diagnosis Using Amplitude and Phase Demodulation Techniques for Small Speed Variations”, BookAdvances in Condition Monitoring of Machinery in Non-Stationary Operations Volume 4 of the seriesApplied Condition Monitoringpp 385-397.

C. Deng,F. H. Xiao. “Manifold Adaptive Experimental Design for Text Categorization”, IEEE Transactions on Knowledge and Data Engineering, journal,vol. 24, no. 4, 2012.

T. F. Cox, M. A. A. Cox. “Multi-dimensionalscaling”, London: Chapman & Hall, 1994.

A. Hyvarinen, E. Oja, “Independent component analysis: algorithms and applications”, Neural Networks journal, vol. 425, no. 13, 411e30, 2000.

H. S. Seung, D. D. Lee,“The manifold ways of perception”. 5500 (290): 2268e9, Science 2000.

JB. Yu, “Bearing performance degradation assessment using locality preserving projections”. Expert Systems with Applications journal, vol. 38, 7440e50, 2011.

W. Y. Liu, Z. F. Wang, J. G. Han, G. F. Wang. “Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM”. Renewable Energy journal, vol. 50, no. 1, e6, 2013.

Z. Huang, H. C. Chen, C. J. Hsu, W. H. Chen, S. S. Wu,“Credit rating analysis with support vector machines and neural networks: a market comparative study”. Decision Support Systems journal,vol. 37, 543e58, 2004.

D.S. Modha, W.S. Spangler. “Feature weighting in k-means clustering”. Machine learning journal, vol. 52, no. 3; pp. 217–237, 2003.

L. Song, A. Smola, A. Gretton, K. Borgwardt, J. Bedo. “Supervised feature selection via dependence estimation”, International Conference on Machine Learning, 2007.

Z. Zhao, H. Liu. “Semi-supervised feature selection via spectral analysis”. In Proceedings of SIAM International Conference on Data Mining (SDM), 2007.

K. L. Hu, J. Q. Yuan, “Statisticalmonitoringoffed-batchprocessusing dynamic multiwayneighborhood preserving embedding”. Chemometrics and Intelligent LaboratorySystems journal, vol. 90, pp. 195–203. 2008.

A. M. Miao, Z. Q. Ge, Z. H. Song, L. Zhou,“Timeneighborhood preserving embedding model and its application for fault detection”. Industrial and Engineering Chemistry Research journal, vol. 52, 13717–13729, 2013.

T. Baoping, S. Tao, L. Feng, D. Lei. “Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine”, Renewable Energy journal, vol. 62, pp. 1-9, 2014.

F. H. Xiao,C. Deng,Y. Shuicheng,H. J. Zhang, “Neighborhood Preserving Embedding”, Tenth IEEE International Conference on Computer Vision, ICCV 2005.

J Hang, J Zhang, M Cheng, “Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine”, - Fuzzy Sets and Systems, Elsevier, 2015.

O. Kadri, L.H. Mouss, M.D. Mouss, “Fault diagnosis of rotary kiln using SVM and binary ACO”, Journal of Mechanical Science and Technology journal, vol. 26, no. 2,pp. 601 - 608, 2012.

O. Dgaard, P. Stoustrup, “Fault tolerant control of wind turbines a benchmark model”, in Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. Sants Hotel, Spain. IFAC, pp. 155–160, June 06-07, 2009.


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