Work

A Radial Basis Function Neural Network Approach for Detecting Wind Turbine Blade Damage via Embedded Accelerometer Data

Year: 2025

Type: article

Abstract: A significant number of wind turbines deployed across the Penghu Islands are exposed to challenging coastal and marine conditions.To address the operational risks associated with such environments, in this study, we introduce a real-time monitoring and fault detection framework aimed at identifying abnormal turbine behavior, particularly those linked to blade damage.The proposed system enhances turbine efficiency and reliability by continuously assessing blade integrity, swiftly detecting irregularities, and supporting near-instantaneous maintenance actions.This capability is especially vital in disaster-prone regions, where turbines may encounter conditions exceeding standard design specifications.The framework integrates sensor-based data acquisition-capturing input from accelerometers, anemometers, hygrometers, thermometers, and barometers-with signal processing and neural network techniques to analyze three-axis vibration data.By identifying distinct patterns associated with blade faults, the system enables timely detection and reporting of malfunctioning units, thereby facilitating effective repair and operational continuity.

Source: Sensors and Materials

DOI: 10.18494/sam5368

Authors: Ming-Hung Hsu, Paul Juinn Bing Tan

Institution: National Penghu University of Science and Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $748