Development of a Methodology to Predict Atmospheric Corrosion Severity Using Corrosion Sensor Technologies

摘要

The cost of corrosion in the US is estimated to be greater than 3% of the GDP. Much of this cost is associated with maintenance practices related to inspection of structurally critical areas and the treatment or replacement of affected parts. The US Department of Defense is seeking to control these costs by performing corrosion maintenance on an as-needed basis using a Condition Based Maintenance Plus (CBM+) approach. While scheduled maintenance intervals are still required to ensure fleet readiness by reducing unexpected maintenance, a CBM+ approach would provide the basis for selecting specific assets needing inspection and possible maintenance. This approach would thus avoid unnecessary down time for assets experiencing less severe conditions and maximizing fleet readiness without compromising safety. A critical need for this approach is the ability to assess cumulative environmental exposure and to relate that exposure to an expected severity of attack. In this work, laboratory and limited field data from corrosion sensors is presented. Predictions of corrosivity as a function of relative humidity, air temperature, surface temperature, and conductance will be shown. Finally, R squared values of various fits will be shown with the goal of guiding the understanding of potential unknown variables needed for improved predictions.