Implementing Rotation Forest for Wind Turbine Blade Fault Diagnosis
A. Joshuva1, G. Deenadayalan2, S. Siva Kumar3, R. Sathish Kumar4, R. Vishnuvardhan5
1A. Joshuva, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Kelambakam, Chennai (Tamil Nadu), India.
2G. Deenadayalan, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Kelambakam, Chennai (Tamil Nadu), India.
3S. Siva Kumar, Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Old Mahabalipuram Road, Padur, Kelambakam, Chennai (Tamil Nadu), India.
4R. Sathish Kumar, Department of Automobile 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: 185-192 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10310982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1031.0982S1119
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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: Wind energy is one of the important renewable energy resources because of its reliability due to the development of the technology and relative less cost. “The wind energy are converted into electrical energy using rotating blades which are connected to the generator. Due to environmental conditions and large structure, the blades are subjected to various faults and cause the lack of productivity. The downtime can be reduced when they are diagnosed periodically using structural health monitoring. These are considered as a pattern recognition problem which consist of three phases, namely feature extraction, feature selection and feature classification. In this research, statistical features are extracted from vibration signals, feature selection are carried out using J48 algorithm and the feature classification is done with a rotation forest algorithm.
Keywords: Fault Diagnosis, Structural Health Monitoring, Statistical Features, J48 Algorithm, Rotation Forest (RF) Algorithm.
Scope of the Article: Solid and Structural Mechanics