Spectrogram-driven unsupervised autoencoder with isolation forest and one-class SVM for lab-scale wind turbine blade fault detection

Wind turbine blades are critical components, and their structural integrity is essential for uninterrupted operation and minimizing downtime. Although various methods are used to monitor the health of wind turbine blades, several research challenges persist, such as the reliance on manual feature engineering and the limited availability of large amounts of labeled data. In this study, a novel approach is proposed that will overcome the limitations of manual feature extraction and label data challenges. In the proposed work, time series vibration signals from the blade are first converted into spectrograms and passed through a CNN-based autoencoder that is trained solely on healthy data to learn a compact latent representation. Anomalies are then flagged in three complementary ways: (i) by thresholding the autoencoder’s reconstruction error, (ii) by applying an Isolation Forest to the latent features, and (iii) by evaluating the same features with a One-Class SVM. The outputs of these detectors are subsequently benchmarked, providing a systematic comparison of their ability to discriminate between vibration-induced faults, such as cracks and erosion, and normal operation on a controlled test-rig dataset, the autoencoder achieves 97.2 % accuracy, outperforming the Isolation Forest and One-Class SVM by 8%–27%. These results demonstrate that zero-label, deep-feature pipelines can deliver reliable and scalable blade-fault detection, paving the way for more cost-effective predictive maintenance in wind farms. • Introduces a label-free spectrogram-based autoencoder for blade fault detection. • Benchmarks AE, Isolation Forest, and One-Class SVM in a unified framework. • Learns fault-sensitive latent features without manual vibration feature design. • Achieves 97.2% accuracy for crack and erosion detection on lab-scale data. • Provides a scalable baseline for label-free blade health monitoring in wind farms.