A Novel Probabilistic Long-Term Fault Prediction Framework Beyond SCADA Data - With Applications in Main Bearing Failure

Jürgen Herp*, Niels L. Pedersen, Esmaeil S. Nadimi

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

Prognostic and health monitoring addresses the issue of detecting faults and monitoring the current state of a wind turbine. Details about the fault's progression, and from there, the remaining useful lifetime, are key features in monitoring and industrial operation and maintenance planning. In order to avoid increase in operation and maintenance cost, as well as subjective human involvement, we present an online and automated monitoring framework for prediction of the remaining useful lifetime based on deep learning models. This framework includes training and re-training of predictive models with minimal oversight by the operators.

Further, we explore the dependency of various models' predictive abilities based on the input variables available, such as SCADA and secondary measurements. Especially deep learning approaches, such as neural networks, benefit greatly from the volume of data that can be extracted from modern-day turbines. This work utilizes upon the volume of data to present a case study on main bearing failures for 108 turbines. In the presented setting, predictions of the remaining useful lifetime of more than 90 days can be expected on average, outperforming the closest state-of-the-art estimate by almost a factor of two on average.
Original languageEnglish
Book seriesJournal of Physics: Conference Series (Online)
Volume1222
Number of pages10
ISSN1742-6596
DOIs
Publication statusPublished - 2019
EventWind Europe Conference and Exhibition 2019 - Bilbao, Spain
Duration: 2. Apr 20194. Apr 2019

Conference

ConferenceWind Europe Conference and Exhibition 2019
Country/TerritorySpain
CityBilbao
Period02/04/201904/04/2019

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