QUANTITATIVE RUST-UNDER-PAINT DETECTION UTILIZING NEAR-FIELD MICROWAVE NDE TECHNIQUES

摘要

Near-field microwave NDE systems utilizing open-ended rectangular waveguides constitute a competent candidate to detect and evaluate planner rust layers under paint coatings. Basically, the waveguide illuminates the specimen with microwave signals and monitors the reflected waves. Minute variations in the structure reflect in measurable variation in the reflection coefficient at the waveguide aperture. The functional dependence of reflection coefficient on the rust layer physical properties-i.e. thickness and depth-is exploited in the detection scheme. Upon measuring the reflection coefficient, the inverse problem of rust thickness and depth determination should be solved. This problem is ill-posed in nature and requires sophisticated algorithm to be inverted quantitatively. In this paper, we introduce a Maximum-Likelihood algorithm to be applied in conjunction with multi-frequency measurements to solve the inverse problem. As it will be shown, the multi-frequency measurements provide diversity gain over the uncertainties embedded in the system. The practical potential of the proposed algorithm will be demonstrated in real life rust under-paint detection problem. Finally, the performance of the algorithm in noisy environment will be simulated and analyzed. It will be shown that the proposed algorithm provides significant accuracy with high sensitivity in determining the rust layer's thickness and depth.