Predictive maintenance is crucial for minimizing unplanned downtime in industrial machinery. This research proposes a hybrid deep learning approach using Convolutional LSTM Networks (Conv-LSTM) for fault detection in wind turbine gearboxes. The Conv-LSTM model combines convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks for temporal modeling, enabling it to capture intricate patterns in multivariate sensor data. The approach was evaluated on the AI4I Predictive Maintenance dataset from Kaggle, containing real-world sensor readings from an operational wind turbine gearbox. The Conv-LSTM architecture processes raw sensor data through convolutional and LSTM layers trained jointly to learn hierarchical representations of the gearbox dynamics. Extensive experiments demonstrated the model`s outstanding performance, achieving an impressive 97.9% accuracy in classifying whether a fault condition exists in the gearbox and a corresponding loss of 0.0059 after ten epochs of training. This high predictive accuracy allows wind farm operators to anticipate potential gearbox failures proactively, enabling timely maintenance and minimizing costly downtime. The proposed approach contributes to the efficiency and sustainability of wind energy operations.

Predictive Maintenance, Convolutional Neural Network, Long Short-Term Memory, Engine Failure, Industrial Machinery, Sensor Data

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