Data mining corrosion from eddy current non-destructive tests

Abstract

Quicker, more effective methods of corrosion prediction and classification can help to ensure a safe and operational transportation system for both civilian and military sectors. This is especially critical now as transportation providers attempt to meet the increased expense of repairing aging aircraft with smaller budgets. These budget constraints make it imperative to find corrosion and to correctly determine the appropriate time to replace corroded parts. If the part is replaced too soon, the result is wasted resources. However, if the part is not replaced soon enough, it could cause a catastrophic accident. The discovery of models that limit the possibility of a costly accident while optimizing resource utilization would allow transportation providers to efficiently focus their maintenance efforts. While our concern in this study was with aircraft, the results will also be useful to other transportation providers. This paper describes the discovery and comparison of empirical models to predict corrosion damage from non-destructive test (NDT) data. The NDT data were derived from eddy current (EC) scans of the United States Air Force's (USAF) KC-135 aircraft. While we might suspect a link between NDT results and corrosion, up until now this link has not been formally established. Instead, the NDT data have been converted into false color images that are analyzed visually by maintenance operators. The models we discovered are quite complex and suggest that with the appropriate data mining approaches we can sometimes more effectively handle noisy data through more complex models rather than simpler ones. Our results also show that while a variety of modeling techniques can predict corrosion with reasonable accuracy, regression trees are particularly effective in modeling the complex relationships between the EC measurements and the actual amount of corrosion.