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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1671
JournalSensors (Switzerland)
Volume19
Issue number7
DOIs
Publication statusPublished - 2019 Apr 1

Keywords

  • Conditional monitoring
  • Convolutional neural network
  • Data mining
  • Deep learning
  • Internet of Things (IoT)
  • Long cycle maintenance
  • Sensors
  • Wind turbine

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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