2017 Frank Newman Speller Award: Knowledge-Based Predictive Analytics in Corrosion

Abstract

Corrosion researchers have developed many approaches to predicting the occurrence of different corrosion modes. Four types of predictive analytics can be identified: data-centric correlative analysis, theory-based semi-empirical models, expert-knowledge-based models, and theory-based, multi-scale models. However, most new corrosion failures have been serendipitous discoveries, rather than anticipated through a systematic process. This paper reviews stress corrosion cracking (SCC) of carbon steel in non-aqueous electrolytes and in aqueous solutions of oxyanions, to understand whether using the appropriate predictive analytic strategy may have helped anticipate the failures. In all of these cases of SCC, some information was available in related environments prior to field failures, but a framework was lacking to identify the connections and anticipate failures. Data-centric predictive analytics would not have helped anticipate the failures because of the low frequency of the phenomena and the lack of prior failure data. A better predictive analytic strategy will need methods to integrate diverse sources of knowledge into a theoretical framework. Predictive analytics also must have a probabilistic component because both the knowledge and data are uncertain. The paper provides a conceptual approach to developing such a predictive analytics framework.