Abstract
In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost. To address these issues, a structured feature selection method based on correlation analysis supplemented with comprehensive visual evaluation was proposed. Unlike generic dimensionality reduction techniques, this approach preserves critical domain-specific information and avoids misinterpretation of fault indicators. By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. The proposed method for reducing condition indicators successfully decreased their number by approximately 3.27 times, simultaneously significantly reducing computational time of prediction, reaching up to 50% reduction for complex models. In doing so, we lowered computational demands and improved classification efficiency without compromising accuracy for ML models. Although feature reduction did not similarly benefit the metrics for DL models, these findings highlight that well-chosen, domain-relevant condition indicators can streamline data input and deliver interpretable, cost-effective PdM solutions suitable for industrial applications.