BCS Northampton Event: Condition Monitoring Systems (update)

Tuesday 3rd  May 2016 -  Condition Monitoring Systems

We have pleasure in drawing your attention to the …..

Please arrive around 6.30pm for a 7pm start. 

Condition Monitoring/Asset Management has been employed since the 1950’s.  An example of this is Condition Based Maintenance of rotating machines using monitored condition data.   The advent of cheap computational power and public/private networking has made this area one of the fastest growing technologies today.  

 This talk looks at the field of Condition Monitoring across several industries and how it is revolutionising asset management and predictive maintenance to provide safer transport, protection of ageing assets and hitherto unparalleled optimisation of complex mechanical, electrical and electronic systems.

Please park at the rear of the Newton Building (explain to security via the intercom that your are attending the BCS Event) and enter through the rear of the Newton Building vis the conservatory. Directions to the room will be signed.

The event will be held in Room NW205, The University of Northampton, Newton Building, St Georges Avenue, Northampton, NN2 6JB.

If you'd like to find out more about Computing at the University of Northampton go to: www.computing.northampton.ac.uk. All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with

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

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

Artificial Neural Networks (ANN) and corrosion monitoring

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

S Mabbutt et al 2012 J. Phys.: Conf. Ser. 364 012114

doi:10.1088/1742-6596/364/1/012114