Intelligent Multimodal Monitoring of Renewable Energy Systems Using AI Fusion Frameworks

Abstract

The integration of Artificial Intelligence (AI) for fault detection and diagnosis (FDD) in renewable energy systems has become essential for handling complex, multimodal data from wind turbine installations. This study introduces a multimodal AI framework that evaluates feature-level (early) and decision-level (late) fusion strategies. Sensor data are organized into electrical (currents, voltages, power) and mechanical (torque, speed) modalities, enabling both modality-specific learning and cross-modal analysis via early and late fusion. This paper makes several key contributions. First, it introduces the proposed Adaptive Polynomial Kolmogorov–Arnold Network (AdaptPolyKAN) model for multi-class fault classification in wind energy conversion systems (WECS). Second, it develops a robust preprocessing pipeline that integrates wavelet denoising and Z-score normalization to enhance the quality and consistency of multimodal sensor data. Third, it provides a comprehensive comparison between AdaptPolyKAN and six other machine learning models Neural Networks (NN), Support Vector Machine (SVM), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) highlighting the benefits brought by the proposed preprocessing and fusion procedures. Fourth, it presents an in-depth analysis of early and late fusion strategies, offering practical insights into their effectiveness for WECS fault diagnosis. Experimental results demonstrate that early and late fusion significantly enhance classification accuracy and robustness compared to single-modality or single-classifier approaches. The proposed AdaptPolyKAN framework achieves near-perfect accuracy (99.99% with early fusion) while remaining computationally efficient, making it suitable for real-time condition monitoring and predictive maintenance.