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NDI Architecture

Corrosion is considered the most significant form of damage for on aging aircraft—impacting both maintenance costs and readiness. Currently, there is an urgent need to improve on established non-destructive inspection methods that increase detection reliability and accuracy in multi-layer structures.

The goal of the Non Destructive Inspection Data and Evidence Fusion Program (NDI) initiative was to investigate methods for increasing the accuracy of non-destructive inspection imaging technology. KBSI used our advanced data fusion technologies to acheive this goal.

Using both ultrasonic and eddy current images of KC-135 lap joint coupons, KBSI developed image processing techniques, both classical and wavelet-based, for the pre-processing of image data prior to corrosion quantification and fusion. Once the image pre-processing was complete, we improved and automated corrosion quantification using an artificial neural network. In the final stage of the project, we developed a pixel level NDI data fusion model to further improve the quantification of hidden corrosion.

By improving on the accuracy and reliability of NDI imaging, the U.S. Military anticipates millions of dollars in savings from speedier corrosion assessments and inspections on their aging aircraft fleet and assets.