Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis Using Vibration Signals
A. Joshuva1, R. Vishnuvardhan2, G. Deenadayalan3, R. Sathish Kumar4, S. Siva Kumar5
1A. Joshuva, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
2R. Vishnuvardhan, Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (Tamil Nadu), India.
3G. Deenadayalan, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
4R. Sathish Kumar, Department of Automobile Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
5S. Siva Kumar, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 320-331 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10500982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1050.0982S1119
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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: The fast developing wind industry has revealed a requirement for more multifaceted fault diagnosis system in the segments of a wind turbine. “Present wind turbine researches concentrate on enhancing their dependability quality and decreasing the cost of energy production, especially when wind turbines are worked in off-shore places. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform dynamic energy of wind into useable power and due to environmental conditions, it get damage often and cause lack in productivity. The main objective of this study is to carry out a fault identification model for wind turbine blade using a machine learning approach through vibration data to classify the blade condition. Here five faults namely, blade bend, hub-blade loose connection, blade cracks, blade erosion and pitch angle twist have been considered. Machine learning approach has three steps namely feature extraction, feature selection and feature classification. Feature extraction was carried out by statistical analysis followed by feature selection using J48 decision tree algorithm. Feature classification was done using twelve rule based classifiers using WEKA. The results were compared with respect to the classification accuracy and the computational time of the classifier.
Keywords: Condition Monitoring, Wind Turbine Blade, Statistical Features, Rule based Classifiers, Vibration Signals.
Scope of the Article: Refrigeration and Air Conditioning