Unsupervised Segmentation of Wear Surface Defects in Hydroturbine Bearing Pads Guided by Local Anomaly Scores

Vision-based defect detection on bearing-pad wear surfaces is essential for quantifying damage geometry and assessing condition in hydroturbine units. Compared with 2D color images, depth images can suppress disturbances caused by complex textures, surface color variations, and specular reflections, thereby providing a more reliable basis for precise damage localization. Nevertheless, depth-based damage segmentation under a large field of view remains challenging, mainly due to fine-scale texture noise and weak defect saliency; moreover, robust defect probability estimation is often hindered by limited labeled data. To address these challenges, this paper proposes an unsupervised defect segmentation framework for hydroturbine friction components guided by local anomaly score distributions. First, a salient damage detection module is developed based on topography–texture separation, which mitigates the interference of local micro-texture noise on defect segmentation. Then, a normal reference dataset is constructed using defect-free bearing-pad depth images, and an unsupervised network is employed as the core to generate anomaly score representations of potential damage regions for coarse localization. Finally, the obtained anomaly score distribution is used as adaptive weights to fuse depth-based defect cues with morphological processing, enabling self-adaptive refinement of the damage regions. Experiments on real depth images acquired from hydroturbine bearing pads demonstrate that the proposed method achieves accurate defect extraction and reliable geometric quantification. Quantitative evaluations on the testing set yield a mean surface area error of 9.39% ± 4.25% and a volume error of 4.91% ± 2.85%, with best-case errors dropping as low as 3.67% and 1.03%, respectively. Crucially, these results demonstrate that our framework goes beyond mere visual detection; by operating entirely without pixel-level annotations, it offers a highly practical tool for diagnosing specific lubrication failure modes and driving predictive maintenance in actual hydroturbine engineering.