Machine learning for long cycle maintenance prediction of wind turbine

Chia Hung Yeh*, Min Hui Lin, Chien Hung Lin, Cheng En Yu, Mei Juan Chen


研究成果: 雜誌貢獻期刊論文同行評審

31 引文 斯高帕斯(Scopus)


Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance.

期刊Sensors (Switzerland)
出版狀態已發佈 - 2019 4月 1

ASJC Scopus subject areas

  • 分析化學
  • 生物化學
  • 原子與分子物理與光學
  • 儀器
  • 電氣與電子工程


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