Enhancing Wind Turbine Diagnostics With SCADA‐Vibration Fusion, Contrastive Learning, and Linear Predictive Coefficients

Wind energy plays a pivotal role in the transition to sustainable power generation. However, maintaining the reliability and efficiency of wind turbine (WT) remains a significant challenge due to complex operational conditions and the high cost associated with unexpected failures. Effective condition monitoring (CM) and predictive maintenance (PM) strategies are critical to mitigate these risks. This study presents a data‐driven fault detection framework that fuses supervisory control and data acquisition (SCADA) data with high‐frequency vibration signals using deep learning techniques to enhance diagnostic performance. Unlike conventional normal behavior models that rely exclusively on healthy data for training, the proposed framework incorporates limited labeled fault data when available. As only a few types of faults and a few samples are typically available in real‐world scenarios, the approach does not assume a complete representation of all possible fault conditions. Instead, it is designed to generalize beyond the specific faults seen during training. This is demonstrated by training the model on healthy conditions and only two known fault types (with labeled data available) and testing it on a third, previously unseen fault type. In particular, Siamese networks with contrastive and reconstruction learning are employed to improve feature representation and anomaly detection. Two distinct methodologies are compared: the first utilizes a binary cross‐entropy (BCE) loss function to classify the healthy or faulty status of the WT, while the second uses a triplet loss function for multiclass representation learning. Both methodologies generate low‐dimensional representations of the input features, also known as embeddings. The resulting feature embeddings are passed through a k ‐means clustering algorithm to improve fault separation and identification. Statistical features are extracted from SCADA data to capture key trends or event information, while the linear prediction coefficient (LPC) method, which models a signal by predicting future values based on its past samples, is applied to the vibration data for better fault characterization. The proposed approach is evaluated using the publicly available ETH Zurich dataset from an Aventa AV‐7 turbine. Experimental results indicate that the fusion of SCADA and vibration‐based diagnostics, in combination with contrastive and representation learning, substantially improves the predictive accuracy and generalization of fault detection models.