Early Structural Health Monitoring via Electronic Control Scanning and Computer Vision Data Fusion

This work presents an advanced Structural Health Monitoring (SHM) system for the refined identification of structural micro-defects, achieving a relative dimensional error of less than 1% for characterizing high-aspect-ratio damage geometries. The system integrates coaxial microscopic imaging with a precision motorized scanning stage. To ensure high-fidelity measurements in early-stage warning applications, depth is determined using a focus variation method driven by a robust data fusion strategy. By capturing a sequence of images along the Z-axis, the focal planes of the defect’s surface orifice and internal base are automatically identified using a data fusion algorithm based on a consensus evaluation of three parallel sharpness metrics (Tenengrad, Laplacian, and Brenner variants). The Z-axis scanning module, featuring encoder feedback and bi-directional compensation, achieves a repeated positioning error of ±0.5µm. For lateral damage assessment, the system’s high magnification provides an effective sampling resolution of 0.09µm. The equivalent diameter of the focused orifice image is calculated through a robust data fusion pipeline involving adaptive thresholding, morphological filtering, and sub-pixel ellipse fitting, which serves as a highly sensitive indicator for early-stage structural deformation. The entire process can be completed within five minutes, demonstrating a rapid, highly accurate, and localized optical inspection solution that generates high-precision dimensional data crucial for Digital Twin modeling in aerospace and precision engineering.