A New Approach to Predict the Pit Depth Extreme Value of a Localized Corrosion Process

Abstract

Depth of pits that propagate during a pitting corrosion process is an important characteristic of the damage of steels; the greater the depth, the more dramatic the damage. For evident reasons of safety and reliability of industrial installations, statistical procedures must be constructed to assess the maximum pit depth to perform proper maintenance from limited inspection data. This paper outlines a new methodology to predict accurately the pit depth extreme value related to a localized corrosion process independently of the nature of the unknown parent distribution of the experimental data. Based on computer calculations and simulations, this methodology combines the Generalized Lambda Distribution (GLD) and the Bootstrap statistical methods. The GLD method was used in this study to determine a modeled distribution that fits the experimental frequency distribution of pit depths produced on a ferritic stainless steel sample during an accelerated corrosion test. This modeled distribution was used to generate, thanks to the Computer-Based Bootstrap Method (CBBM), simulated distributions of corrosion pit depths equivalent to the experimental one. An estimation of the mean with a 90% confidence interval of the maximum pit depth can be finally deduced not only for these simulated samples of equivalent surface size than the experimental one but also for a large scale installation.