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Review of Artificial Neural Networks (ANN) applied to corrosion monitoring

Mabbutt, S. J., Picton, P., Shaw, P. and Black, S. (2012) Review of Artificial Neural Networks (ANN) applied to corrosion monitoring. Journal of Physics: Conference Series. 364(1), 012114. 1742-6596.

Item Type: Article
Abstract: The assessment of corrosion within an engineering system often forms an important aspect of condition monitoring but it is a parameter that is inherently difficult to measure and predict. The electrochemical nature of the corrosion process allows precise measurements to be made. Advances in instruments, techniques and software have resulted in devices that can gather data and perform various analysis routines that provide parameters to identify corrosion type and corrosion rate. Although corrosion rates are important they are only useful where general or uniform corrosion dominates. However, pitting, inter-granular corrosion and environmentally assisted cracking (stress corrosion) are examples of corrosion mechanisms that can be dangerous and virtually invisible to the naked eye. Electrochemical noise (EN) monitoring is a very useful technique for detecting these types of corrosion and it is the only non-invasive electrochemical corrosion monitoring technique commonly available. Modern instrumentation is extremely sensitive to changes in the system and new experimental configurations for gathering EN data have been proven. In this paper the identification of localised corrosion by different data analysis routines has been reviewed. In particular the application of Artificial Neural Network (ANN) analysis to corrosion data is of key interest. In most instances data needs to be used with conventional theory to obtain meaningful information and relies on expert interpretation. Recently work has been carried out using artificial neural networks to investigate various types of corrosion data in attempts to predict corrosion behaviour with some success. This work aims to extend this earlier work to identify reliable electrochemical indicators of localised corrosion onset and propagation stages
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA418 Corrosion engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science > QA76.87 Neural networks
Creators: Mabbutt, S J, Picton, Philip, Shaw, P and Black, S
Publisher: IOP Science
Faculties, Divisions and Institutes: Faculties > Faculty of Arts, Science & Technology > Engineering
Date: 2012
Date Type: Publication
Page Range: 012114
Journal or Publication Title: Journal of Physics: Conference Series
Volume: 364
Number: 1
Language: English
ISSN: 1742-6596
Status: Published / Disseminated
Refereed: Yes

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