Publication type: Conference poster
Type of review: Peer review (abstract)
Title: An AI-based fault detection model using alarms and warnings from the SCADA system
Authors: Pizza, Gianmarco
Notaristefano, Antonio
Fabbri, Gregory Sean
Goren Huber, Lilach
et. al: No
Proceedings: Proceedings of the WindEurope Technology Workshop 2020
Conference details: WindEurope Technology Workshop 2020 : Resource Assessment & Analysis of Operating Wind Farms, online, 8-11 June 2020
Issue Date: 8-Jun-2020
Publisher / Ed. Institution: WindEurope
Language: English
Subjects: Fault detection; Predictive maintenance; Artificial intelligence; Machine learning; Wind turbines; SCADA data; Error logs
Subject (DDC): 006: Special computer methods
620: Engineering
Abstract: Predictive maintenance is a key element for lowering Operation and Maintenance (O&M) costs of wind turbines. Predictive maintenance models are usually based on drivetrain vibration data or operational timeseries from the Supervisory Control And Data Acquisition (SCADA) system, while readily available alarms and warnings from the SCADA system are typically not utilized. In this work we present a novel Artificial Intelligence (AI) based approach for early fault detection of wind turbines using alarms and warnings from the SCADA system.
URI: https://digitalcollection.zhaw.ch/handle/11475/21546
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
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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