Detector-Guided Multi-View Visual Monitoring of Bolt Loosening in Hydropower Generator Rotors

Hydropower-generator rotors contain numerous closely spaced bolted joints, making full-coverage contact instrumentation impractical, while single-view vision methods are vulnerable to missing or ambiguous evidence during rotation. This study proposes a detector-guided multi-view visual monitoring framework that separates region-of-interest (ROI) localization from explicit geometric interpretation. On a simulated hydropower-generator rotor platform operating at 15 rpm, YOLO-family detectors localize candidate bolttop, boltside, and starmarker regions. Quality-retained top-view ROIs yield the image-space angular indicator θimg from the relative orientation of nut-side and disk-side anti-loosening lines; side-view ROIs yield the pixel-space thread-exposure indicator Lpx from exposed-thread endpoints and, when marker geometry is sufficiently visible, an auxiliary angular cue. A star-shaped reference marker organizes accepted frame-level observations into approximate rotation intervals, while hierarchical checks of ROI completeness, endpoint availability, image quality, geometric plausibility, and temporal membership retain both usable evidence and explicit rejection reasons. YOLO11n achieved precision 0.9987, recall 1.0000, mAP50 0.9950, and mAP50–95 0.7798 for laboratory ROI localization. After geometric screening, evidence availability was 16.7% for the top-view branch and 76.3% for the side-view branch. In a supplementary 169-image operational field subset, the principal ROI model achieved precision 0.9782, recall 0.9942, mAP50 0.9946, and mAP50–95 0.7948. The field results support appearance-level localization under complex rotor-bolt conditions, and the framework provides a traceable, reliability-aware basis for organizing, screening, and interpreting multi-view evidence in hydropower-generator rotors and similar rotating structures.