Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19071671 | SENSORS |
Keywords | Field | DocType |
Internet of Things (IoT),sensors,deep learning,data mining,long cycle maintenance,convolutional neural network,wind turbine,conditional monitoring | Convolutional neural network,Internet of Things,Support vector machine,Electric power industry,Artificial intelligence,Turbine,Engineering,Deep learning,Data aggregator,Wind power,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 7.0 | 1424-8220 |
Citations | PageRank | References |
1 | 0.43 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chia-Hung Yeh | 1 | 367 | 42.15 |
| Min-Hui Lin | 2 | 3 | 1.82 |
| Chien-Hung Lin | 3 | 20 | 4.15 |
| Cheng-En Yu | 4 | 1 | 0.43 |
| Mei-juan Chen | 5 | 319 | 31.69 |