Ai-based wind turbine predictive maintainance
| dc.contributor.author | Shaikh, Afzal [Guide] | |
| dc.contributor.author | Qureshi, Noaman [23DEE16] | |
| dc.contributor.author | Ramteke, Alok [23DEE17] | |
| dc.contributor.author | Sayed, Tuba [23DEE18] | |
| dc.contributor.author | Shaikh, Abdul Aziz [23DEE19] | |
| dc.date.accessioned | 2026-09-03T10:38:50Z | |
| dc.date.available | 2026-09-03T10:38:50Z | |
| dc.date.issued | 2026-06 | |
| dc.description.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. | |
| dc.identifier.uri | http://aiktc.ndl.gov.in/handle/123456789/4621 | |
| dc.language.iso | en_US | |
| dc.publisher | AIKTC | |
| dc.title | Ai-based wind turbine predictive maintainance | |
| dc.type | Project Report |