Detection of Wind Turbine Faults Using a Data Mining Approach-Reference-Cited by-同舟云学术

Detection of Wind Turbine Faults Using a Data Mining Approach

Published:2016-09 Issue:3 Volume:142 Page:04015021
ISSN:0733-9402
Container-title:Journal of Energy Engineering
language:en
Short-container-title:J. Energy Eng.

Author:

Yang Hsu-Hao¹,Huang Mei-Ling²,Huang Po-Chung³

Affiliation:

1. Professor, Dept. of Industrial Engineering and Management, National Chin-Yi Univ. of Technology, Taichung City 41170, Taiwan (corresponding author).

2. Professor, Dept. of Industrial Engineering and Management, National Chin-Yi Univ. of Technology, Taichung City 41170, Taiwan.

3. M.S. Student, Dept. of Industrial Engineering and Management, National Chin-Yi Univ. of Technology, Taichung City 41170, Taiwan.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Waste Management and Disposal,Energy Engineering and Power Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EY.1943-7897.0000286

Reference22 articles.

1. Bayba A. J. Siegel D. N. and Tom K. (2012). “Application of auto-associative neural networks to health monitoring of the CAT 7 diesel engine.” ARL-TN-0472 U.S. Army Research Laboratory Adelphi MD.

2. Bulunga M. L. (2012). “Change-point detection in dynamical systems using auto-associative neural networks.” Master’s thesis Stellenbosch Univ. Stellenbosch South Africa.

3. On damage diagnosis for a wind turbine blade using pattern recognition

4. Feature extraction using auto-associative neural networks

5. Kim K. Parthasarathy G. Uluyol O. Foslien W. Sheng S. and Fleming P. (2011). “Use of SCADA data for failure detection in wind turbines.” Proc. 5th Intl. Conf. on Energy Sustainability and 9th Fuel Cell Science Engineering and Technology Conf. American Society of Mechanical Engineers Washington DC 2071–2079.

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