Ai-based wind turbine predictive maintainance
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Date
2026-06
Journal Title
Journal ISSN
Volume Title
Publisher
AIKTC
Abstract
Wind turbines play a key role in renewable energy generation, but their mechanical components—
such as blades, bearings, and gearboxes—are prone to failures under extreme
environmental stresses such as variable winds, vibrations, and temperature fluctuations.
These breakdowns result in significant operational downtime, escalating maintenance
costs, and reducing overall energy efficiency. This project introduces an innovative AIdriven
predictive maintenance framework to mitigate these challenges by enabling early
fault detection and proactive interventions. The system processes multi-modal sensor
data, including vibration signals from accelerometers, torque measurements from transducers,
and acoustic emissions from microphones, to identify incipient issues like bearing
degradation or blade fissures prior to major disruptions. The methodology integrates
advanced signal processing for time-frequency feature extraction (e.g., RMS, FFT, and
wavelet transforms) with a hybrid AI model. This combines machine learning algorithms—
Random Forest for non-linear pattern recognition and XGBoost for optimized
gradient boosting—with deep learning via CNN-LSTM networks to capture spatial and
temporal dependencies in time-series data. A real-time interactive dashboard, built on
modular software pipelines, visualizes turbine status, generates predictive insights, and issues
prioritized maintenance alerts to technicians.The anticipated results include a fault
detection accuracy that exceeds 90%, along with a 30–40 percentage decrease in unplanned
downtime compared to traditional scheduled maintenance approaches. By improving
reliability and minimizing economic losses, this solution advances sustainable
wind energy operations, offering a scalable blueprint for industry-wide adoption.