S Mabbutt, P Picton, P Shaw and S Black (2012) Review of Artificial Neural Networks (ANN) applied to corrosion monitoring J. Phys.: Conf. Ser. 364 012114 doi:10.1088/1742-6596/364/1/012114
To read the paper go to: http://iopscience.iop.org/1742-6596/364/1/012114/pdf/1742-6596_364_1_012114.pdf
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.
To read the paper go to: http://iopscience.iop.org/1742-6596/364/1/012114/pdf/1742-6596_364_1_012114.pdf
Related postings:
PREDICTING LEATHER HANDLE LIKE AN EXPERT BY ARTIFICIAL NEURAL NETWORKS
Subjective Measurement of Leather Handle
A selection of 2012 relevant publications involving staff involved with the STRiPe group
Journals
- Maunder, R., Turner, S., Sneddon, S. and Crouch, A. (2012) Editorial. Enhancing the Learner Experience in Higher Education. 4(1), pp. 1-2. 2041-3122.
- More details can be found here.
- Kariyawasam K., A., Turner S., Hill G. (2012) "Is it Visual? The importance of a Problem Solving Module within a Computing course", Computer Education, Volume 10, Issue 166, May 2012, pp. 5-7, ISSN: 1672-5913.
- More details can be found here.
- Hill G., Turner S. (2012) "Referencing within Code in Software Engineering Education!", Computer Education, Volume 10, Issue 166, May 2012, pp. 1-4, ISSN: 1672-5913.
- More details can be found here.
Conference
- Turner, S. and Al-Sherbaz, A. (2012) "What's the problem with problem-solving?" Seminar Presentation presented to: Insights into the future of learning and teaching at Northampton, University of Northampton, 3rd December 2012
- More details can be found here.
- Bailey D and Adams J (2012) "NORTHANTS ENGINEERING TRAINING PARTNERSHIP (NETP), A MODEL FOR SUSTAINABLE, INDUSTRY – UNIVERSITY ENGAGEMENT" International Conference on Engineering Education 2012 Turku Finland July 30 – August 3, 2012.
- More details can be found here.
- Picton P (2012) "TEACHING ULTRASONICS USING SPREADSHEETS" International Conference on Engineering Education 2012 Turku Finland July 30 – August 3, 2012
- More details can be found here.
- Salamaliki-Simpson R, Kaczmarczyk S, Picton P, Turner S (2006) Non-Linear Modal Interactions in a Suspension Rope System with Time-Varying Length Journal of Applied Mechanics and Materials Vol. 5-6 pp 217-224 ISSN 1660-9336
Abstract:
This paper focuses on the investigation of the autoparametric coupling effects and modal
interactions in a suspension rope system with a time varying length. Equations of motion of a
multi-degree-of-freedom discrete, non-stationary and non-linear model are presented and are used to analyze the dynamic response of an elevator suspension rope system under resonance conditions. The equations of motion involve quadratic and cubic non-linear terms which are responsible for the modal interaction between the lateral and longitudinal oscillations of the rope and the car motions. The model takes into account the periodic excitations caused by motion of the host structure. The
results confirm that adverse responses may arise and internal autoparametric resonance phenomena
may occur
http://www.scientific.net/AMM.5-6.217
- Terumichi Y, Kaczmarczyk S, Turner S, Yoshizawa M, Ostachowicz W (2003) Modelling, Simulation and Analysis Techniques in the Prediction of Non-stationary Vibration Response of Hoist Ropes in Lift Systems Materials Science Forums Vol. 440-441 pp 497-504
http://www.scientific.net/MSF.440-441.49
A paper has recently being published by members of the department of engineering, University of Northampton reviewing neural networks applied to corrosion conditioning monitoring.
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.
Reference
Review of Artificial Neural Networks (ANN) applied to corrosion monitoring
A paper has been recently published that combines artificial intelligence and leather science research looking at modelling the subjective assessment of leather handle.
Abstract
This study developed an artificial neural network model to predict the subjective assessment of leather handle by an expert using its measurable physical characteristics. A statistical method was applied to prune the inputs of the network and the “error band” conception was proposed during training.
More details can be found at:
Wang, Y., Picton, P., Turner, S. and Attenburrow, G (2011) Predicting Leather Handle like an Expert Artificial Neural Networks, Applied Artificial Intelligence, Volume 25, Issue 2 February 2011 , pages 180 - 192 ISSN: 0883-9514 DOI:10.1080/08839514.2011.545218
