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Fault prognosis for wind turbines’ main bearing based on SCADA data

Author
Encalada-Dávila, Á.; Puruncajas, B.; Tutivén, C.; Vidal, Y.
Type of activity
Presentation of work at congresses
Name of edition
10th International Conference on Structural Health Monitoring of Intelligent Infrastructure
Date of publication
2021
Presentation's date
2021
Book of congress proceedings
SHMII-10th: International Conference on Structural Health Monitoring of Intelligent Infrastructure: Porto, Portugal, 30 June-2 July 2021: proceedings
First page
1
Last page
8
Project funding
Development and validation of intelligent monitoring systems, pitch and structural damping control strategies for floating offshore wind turbines
Grup de recerca acreditat: Control, Modelització, Identificació i Aplicacions
Repository
https://web.fe.up.pt/~shmii10/ficheiros/papers_finais/proc_14_ABS_276_1610024176.pdf Open in new window
Abstract
The rapid growth of large-scale wind turbines (WT) has changed the requirements in terms of operation and maintenance strategies. WTs are required to be safe and profitable systems. A great option is failure prognosis, aiming to reduce maintenance and operating costs, and forecast service life based on condition. In this work, the analysis of the data from the supervisory control and data acquisition (SCADA), already present in industrial sized WTs, and work orders (repair and maintenance data) ...
Keywords
Convolutional neural network, Fault prognosis, Main bearing, SCADA data, Wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

Participants