Work

Wind Turbine Blades Fault Diagnosis Based on Vibration Signals Utilizing Convolutional Neural Network

Year: 2024

Type: conference-paper

Abstract: Wind energy is a key contributor to renewable energy production, with wind turbines playing a critical role in its generation. However, the operational efficiency of wind turbines is often compromised by blade faults, leading to reduced performance, expensive maintenance, and potential safety hazards. Traditional methods of diagnosing faults in wind turbine blades are limited in both accuracy and efficiency. This study proposes a novel fault diagnosis approach using Convolutional Neural Networks (CNNs), a powerful deep learning technique for data analysis. The dataset comprises four sets of vibration signals representing both healthy and faulty wind turbine blades, covering fault conditions such as blade cracks, surface degradation, imbalance, and blade twist. Our proposed CNN-based method aims to accurately detect and classify these faults, enabling proactive maintenance and improving the overall reliability and efficiency of wind energy systems.

Source: International Conference on Decision Aid Sciences and Applications (DASA)

DOI: 10.1109/dasa63652.2024.10836616

Authors: Mouaad Bougoffa, Samir Benmoussa, Mohand Djeziri, Thierry Contaret

Institutions: Badji Mokhtar-Annaba University, Centre National de la Recherche Scientifique, Université de Toulon, Aix-Marseille Université, Institut des Matériaux, de Microélectronique et des Nanosciences de Provence

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: green

Funder: Ministère de l'Europe et des Affaires Étrangères