Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20824
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines
Authors: Ulmer, Markus
Jarlskog, Eskil
Pizza, Gianmarco
Goren Huber, Lilach
et. al: No
DOI: 10.36001/phmconf.2020.v12i1.1205
10.21256/zhaw-20824
Proceedings: Proceedings of the Annual Conference of the PHM Society 2020
Volume(Issue): 12
Issue: 1
Conference details: 12th Annual Conference of the PHM Society, virtual, 9-13 November 2020
Issue Date: 3-Nov-2020
Publisher / Ed. Institution: PHM Society
ISBN: 978-1-936263-33-2
Language: English
Subjects: Fault Detection; Predictive Maintenance; Deep Learning; Wind Turbines; Machine Learning; Domain adaptation; Convolutional Neural Networks; SCADA data
Subject (DDC): 620: Engineering
Abstract: Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to considerably improve the fault detection performance compared to the scarce data training. Moreover, it is shown to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine with a large and representative training set. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the cross-turbine scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine.
URI: https://digitalcollection.zhaw.ch/handle/11475/20824
Fulltext version: Published version
License (according to publishing contract): CC BY 3.0: Attribution 3.0 Unported
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Published as part of the ZHAW project: Machine Learning Based Fault Detection for Wind Turbines
Appears in collections:Publikationen School of Engineering

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