Wind Turbine Failure Prediction Using Time-Series Analysis and Deep Learning: A Predictive Maintenance Approach

Wind Turbine Failure Prediction Using Time-Series Analysis and Deep Learning: A Predictive Maintenance Approach

Authors- Assistant Professor Dr. Pankaj Malik, Naina Manghani, Nandini Chawda, Krati Patidar, Tasneem Khan

Abstract-Wind turbines are essential for renewable energy production, but their operation is often interrupted by unexpected failures of critical components, leading to costly downtimes and maintenance. Predictive maintenance (PdM) offers a promising solution by forecasting potential failures before they occur, thereby minimizing unplanned outages and improving operational efficiency. This paper presents a deep learning-based approach for predicting wind turbine failures using time-series sensor data. Specifically, we employ Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), which excels in modeling sequential dependencies in time-series data. Our method predicts imminent failures and estimates the Remaining Useful Life (RUL) of critical turbine components, such as gearboxes and blades, based on sensor readings from operational turbines. The results demonstrate that the LSTM-based model outperforms traditional machine learning techniques, achieving higher accuracy in failure prediction and lower error rates in RUL estimation. This predictive maintenance approach can significantly enhance turbine reliability, optimize maintenance schedules, and reduce operational costs.

DOI: /10.61463/ijset.vol.12.issue6.403