Research on Hyper Pipes and Voting Feature Intervals Classifier for Condition Monitoring of Wind Turbine Blades Using Vibration Signals
A. Joshuva1, S. Siva Kumar2, R. Vishnuvardhan3, G. Deenadayalan4, R. Sathish Kumar5
1A. Joshuva, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
2S. Siva Kumar, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
3R. Vishnuvardhan, Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (Tamil Nadu), India.
4G. Deenadayalan, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai (Tamil Nadu), India.
5Sathish Kumar, Department of Automobile 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: 310-319 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10490982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1049.0982S1119
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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: Renewable energy is viewed as a vital energy field due to the present energy devastations. Among the vital substitutions being considered, wind energy is a strong challenger as a result of its reliability. “To yield wind energy more effectively, the structure of wind turbines has designed bigger, making protection and restoration works difficult. Because of different natural conditions, wind turbine blades are exposed to vibration and it prompts failure. If the failure is not analyzed initially, then it will haste dreadful destruction of the turbine structure. To increase safety perceptions, to decrease down time and to cut down the repeat of unpredictable breakdowns, the wind turbine blades must be examined from time to time to guarantee that they are in great condition. In this paper, a three bladed wind turbine was preferred and using vibration source through statistical features, the state of a wind turbine blade is inspected. The fault classification is carried out using machine learning techniques like hyperpipes (HP) and voting feature intervals (VFI) algorithm. The performance of these algorithms is compared and better algorithm is suggested for fault prediction on wind turbine blades.
Keywords: Condition Monitoring, Fault Diagnosis, Voting Feature Interval Algorithm, Hyperpipes, Statistical Features, Vibration Signals.
Scope of the Article: Refrigeration and Air Conditioning