Fault Diagnosis of Wind Turbine Blades Using Histogram Features through Nested Dichotomy Classifiers
A. Joshuva1, S. Siva Kumar2, R. Sathish Kumar3, G. Deenadayalan4, R. Vishnuvardhan5
1A. Joshuva, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Kelambakam, Chennai (Tamil Nadu), India.
2S. Siva Kumar, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Kelambakam, Chennai (Tamil Nadu), India.
3R. Sathish Kumar, Department of Automobile Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Kelambakam, Chennai (Tamil Nadu), India.
4G. Deenadayalan, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Kelambakam, Chennai (Tamil Nadu), India.
5R. Vishnuvardhan, Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 193-201 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10320982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1032.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study makes an attempt of classifying different fault conditions which occurs on wind turbine blade due to environmental stress and high wind speed. “Here three bladed horizontal axis variable wind turbine was used for experimental study and different faults like blade crack, hub-blade loose connection, erosion, pitch angle twist and blade bend was considered. This study had been carried out in three phases namely feature extraction, feature selection and feature classification. Initially vibration signals are noted for different blade conditions and required features are obtained using histogram features. Secondly, from the extracted feature, most dominating feature need to be chosen using J48 decision tree classifier. Later, the selected feature is fed into the classifiers like Nested Dichotomy (ND), Class-Balanced Nested Dichotomies (CBND) and Data near Balanced Nested Dichotomy (DNBND) for classification of the faults. These classifiers are compared with respect to their accuracy to suggest a better model for fault diagnosis on blade. The suggested model can be incorporated in real-time system to monitor the condition of wind turbine blade.
Keywords: Fault Diagnosis, Wind Turbine Blade, Condition Monitoring, Histogram Features, Vibration Signals, Nested Dichotomy Classifiers.
Scope of the Article: Refrigeration and Air Conditioning