A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

  • Authors

    • A. Joshuva
    • V. Sugumaran
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20833
  • Condition monitoring, wind turbine blade, statistical features, machine learning, vibration signals.

  • This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.

     

     

  • References

    1. [1] Shokrieh MM, Rafiee R, “Simulation of fatigue failure in a full composite wind turbine bladeâ€, Composite Structures, Vol. 74, No. 3, (2006), pp. 332-342.

      [2] Chehouri A, Younes R, Ilinca A, Perron J, “Review of performance optimization techniques applied to wind turbinesâ€, Applied Energy, Vol. 142, (2015), pp. 361-388.

      [3] Kusiak A, Verma A, “A data-driven approach for monitoring blade pitch faults in wind turbinesâ€, IEEE Transactions on Sustainable Energy, Vol. 2, No. 1, (2011), pp. 87-96.

      [4] Abouhnik A, Albarbar A, “Wind turbine blades condition assessment based on vibration measurements and the level of an empirically decomposed featureâ€, Energy Conversion and Management, Vol. 64, (2012), pp. 606-613.

      [5] Godwin JL, Matthews P, “Classification and detection of wind turbine pitch faults through SCADA data analysisâ€, IJPHM Special Issue on Wind Turbine PHM, (2013).

      [6] Pratumnopharat P, Leung PS, Court RS, “Wavelet transform-based stress-time history editing of horizontal axis wind turbine bladesâ€, Renewable Energy, Vol. 63, (2014), pp. 558-575.

      [7] Roth-Johnson P, Wirz RE, Lin E, “Structural design of spars for 100-m biplane wind turbine bladesâ€, Renewable Energy, Vol. 71, (2014), pp. 133-155.

      [8] VuÄina D, Marinić-Kragić I, Milas Z, “Numerical models for robust shape optimization of wind turbine bladesâ€, Renewable Energy, Vol. 87, (2016), pp. 849-862.

      [9] Ha JM, Oh H, Park J, Youn BD, “Classification of operating conditions of wind turbines for a class-wise condition monitoring strategyâ€, Renewable Energy, Vol. 103, (2017), pp. 594-605.

      [10] Joshuva A, Sugumaran V, “Fault diagnostic methods for wind turbine: A reviewâ€, Asian Research Publishing Network (ARPN) Journal of Engineering and Applied Sciences, Vol. 11, No. 7, (2016), pp. 4654-4668.

      [11] Joshuva A, Sugumaran V, “Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithmâ€, Indian Journal of Science and Technology, Vol. 9, No. 48, (2016).

      [12] Amarnath M, Sugumaran V, Kumar H, “Exploiting sound signals for fault diagnosis of bearings using decision treeâ€, Measurement, Vol. 46, No. 3, (2013), pp. 1250-1256.

      [13] Mitchell TM, “Machine learningâ€, Burr Ridge, IL: McGraw Hill. (1997).

      [14] Witten IH, Frank E, “Data Mining: Practical machine learning tools and techniquesâ€, (2005).

      [15] Villacampa O, “Feature Selection and Classification Methods for Decision Making: A Comparative Analysisâ€, (2015).

      [16] Sakthivel NR, Sugumaran V, Babudevasenapati S, “Vibration based fault diagnosis of monoblock centrifugal pump using decision treeâ€, Expert Systems with Applications, Vol. 37, No. 6, (2010), pp. 4040-4049.

      [17] Sugumaran V, Ramachandran K, “Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learningâ€, Expert Systems with Applications, Vol. 38, No. 5, (2011), pp. 4901-4907.

      [18] Joshuva A, Sugumaran V, “A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative studyâ€, ISA transactions, Vol. 67, (2017), pp. 160-172.

      [19] Cleary JG, Trigg LE, “K*: An instance-based learner using an entropic distance measureâ€, InProceedings of the 12th International Conference on Machine learning, Vol. 5, (1995), pp. 108-114.

      [20] Atkeson CG, Moore AW, Schaal S, “Locally weighted learning for controlâ€, InLazy learning, (1997), pp. 75-113.

      [21] Gutin G, Yeo A, Zverovich A, “Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSPâ€, Discrete Applied Mathematics, Vol. 117, No. 1, (2002), pp. 81-86.

      [22] Altman NS, “An introduction to kernel and nearest-neighbor nonparametric regressionâ€, The American Statistician, Vol. 46, No. 3, (1992), pp. 175-185.

      [23] Aha DW, Kibler D, Albert MK, “Instance-based learning algorithmsâ€, Machine learning, Vol. 6, No. 1, (1991), pp. 37-66.

      [24] Zheng Z, Webb GI, “Lazy learning of Bayesian rulesâ€, Machine Learning, Vol. 41, No. 1, (2000), pp. 53-84.

      [25] Quinlan JR, “C4. 5: programs for machine learningâ€, (2014).

      [26] Burke DS, Brundage JF, Redfield RR, Damato JJ, Schable CA, Putman P, Visintine R, Kim HI, “Measurement of the false positive rate in a screening program for human immunodeficiency virus infectionsâ€, New England Journal of Medicine, Vol. 319, No. 15, (1988), pp. 961-964.

      [27] Peck R, Devore JL, “Statistics: The exploration & analysis of dataâ€, (2011).

      [28] Powers DM, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationâ€, (2011).

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    Joshuva, A., & Sugumaran, V. (2018). A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach. International Journal of Engineering & Technology, 7(4.10), 190-196. https://doi.org/10.14419/ijet.v7i4.10.20833