A generic statistical methodology to predict the maximum pit depth of a localized corrosion process

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This paper outlines a new methodology to predict accurately the maximum pit depth related to a localized corrosion process. It combines two statistical methods: the Generalized Lambda Distribution (GLD), to determine a model of distribution fitting with the experimental frequency distribution of depths, and the Computer Based Bootstrap Method (CBBM), to generate simulated distributions equivalent to the experimental one. In comparison with conventionally established statistical methods that are restricted to the use of inferred distributions constrained by specific mathematical assumptions, the major advantage of the methodology presented in this paper is that both the GLD and the CBBM enable a statistical treatment of the experimental data without making any preconceived choice neither on the unknown theoretical parent underlying distribution of pit depth which characterizes the global corrosion phenomenon nor on the unknown associated theoretical extreme value distribution which characterizes the deepest pits.Considering an experimental distribution of depths of pits produced on an aluminium sample, estimations of maximum pit depth using a GLD model are compared to similar estimations based on usual Gumbel and Generalized Extreme Value (GEV) methods proposed in the corrosion engineering literature. The GLD approach is shown having smaller bias and dispersion in the estimation of the maximum pit depth than the Gumbel approach both for its realization and mean. This leads to comparing the GLD approach to the GEV one. The former is shown to be relevant and its advantages are discussed compared to previous methods.

Corrosion damage of 316L steel surface examined using statistical methods and artificial neural network

“…As the model function, we have used the cumulative GEV distribution (see Equations 1 and 2) often considered in the literature. [9][10][11][12][13][14][15][16][17][18] In our case, the variable z in Equations 1and (2) corresponds to the local maximum pit depth (h pd ) determined for each submatrix. Note, that GEV function is reduced to the Weibull distribution for γ < 0, to the Gumbel distribution for γ = 0, and to the Fréchet distribution for γ > 0.…”

“…In Figure 2a, the cumulative probability density of surface heights is shown. These data were fitted using generalized extreme value (GEV) distribution function, defined as [9][10][11][12][13][14][15][16][17][18] :…”

“…[2,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] Application of statistic methods in the investigation of localized corrosion processes allows to predict the maximum pit depth and/or the minimum time to failure of large-scale materials (e.g., oil tank plate, heat exchanger tubes of the boiler, or steel piles in seawater) based on the limited number of relatively small samples. [6][7][8][9][10][11][12][13][14][15][16][17][18] Nowadays, for a quantitative description of complex systems, artificial neural networks (ANN) are used. [19][20][21][22][23][24][25][26][27][28] For example in Mahjani et al [23] three-layered, feed-forward ANNs have been constructed to predict the corrosion current of 316 stainless steel as a function of CuSO 4 concentration, solution pH, and electrode surface area.…”

Mechanism-related modelling of pit evaluation in the CrNiMoV steel in simulated environment of low pressure nuclear steam turbine

“…As the majority of component life is dominated by pitting stage [8], it is critical to accurately predict the evolution of pitting. In the past several years, several models for pit evolution have been developed under the probability framework [9][10][11]. In terms of the simplification of a hemispherical pit, Wei [12] proposed an expression for the evolution of pit depth with exposure time.…”

Modeling pitting corrosion by means of a 3D discrete stochastic model

“…In addition, the formation of a lacy metal cover over a growing pit could occur. This cover provides a diffusion barrier which stabilizes the pit growth since it keeps the bottom of the pit in active dissolution [20].…”