@inproceedings{b10484eccd1a43cc9f44bb0f7cb6853a,
title = "Data-driven online monitoring of wind turbines",
abstract = "Condition based maintenance is a modern approach to maintenance which has been successfully used in several industrial sectors. A specific problem in wind turbine maintenance is that failures of certain parts may be caused by the malperformance or failure of other parts. This mandates for approaches that can produce timely warnings by combining sensor data from different sources. More concretely, in this paper, we present a hybrid statistical approach to condition based maintenance by combining regression analysis with tools from statistical process control. Our approach improves the wind turbine maintenance practice by using adaptive alarm thresholds for the monitored parameters, whilst correcting for environmental factors or for other relevant parameters. We illustrate our approach with a case study demonstrating that we are able to predict upcoming failures much earlier than the current practice.",
keywords = "Condition based monitoring, Control charts, Regression analysis, Wind turbine",
author = "Thomas Kenbeek and Stella Kapodistria and {Di Bucchianico}, Alessandro",
year = "2019",
month = mar,
day = "12",
doi = "10.1145/3306309.3306330",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery, Inc",
pages = "143--150",
booktitle = "Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2019",
address = "United States",
note = "12th EAI International Conference on Performance Evaluation Methodologies and Tools, (VALUETOOLS 2019), VALUETOOLS2019 ; Conference date: 13-03-2019 Through 15-03-2019",
}