of operation, or within a known time range between
1000 and 1500 hours. Life data analysis can support a
company’s maintenance planning, through estimating a
component’s reliability or probability of failure at a
given time; as well as its mean life span. Using the
sensor data to inform this type of analysis allows
assessment to be made, with confidence limits, on an
asset’s current condition and to make probabilistic
predictions of its future condition based on envisaged
operational scenarios.
Next steps
The sensor itself uses boron-doped diamond (BDD)
electrodes, made of polycrystalline diamond. The
electrodes offer long term stability and high sensitivity.
The University of Southampton has completed a ‘proof
of concept’ test on the sensor, demonstrating that it is
capable of sensing within an occluded environment that
is analogous to the environment where corrosion under
insulation occurs. It is capable of sensing for the
presence of metal-cations in tiny (nanolitre) solution
volumes, and is able to establish localised solution
chemistry.
Sensors are to be deployed on assets, following a
requirements capture process that identifies where
damage assessments need to be made. This process
involves asset mapping, breaking the asset down into
subsections and systems, and prioritising these
elements in relation to how their failure might impact
on the capability and availability of the asset. A
baseline assessment is made of the current state of the
subsection or system, and sensors and models are used
in specific degradation hotspots, with sensors in other
areas of the asset used to calibrate the damage models.
Through minimising the amount of external access and
disturbance required, the risk of damage being caused
to the insulation or surface is similarly minimised.
Conclusion
Following on from the proof of concept testing, the
sensor is now ready to be tested in real life
environments, in both the upstream and downstream
sectors. Data will be gathered in these environments,
which can be used to substantiate the value of the
modelling software. Positive results in this testing stage
will enable the sensor to be commercially launched, to
benefit the downstream industry's fight against CUI.
References
1. WOOD, M. H., VETERE ARELLANO, A. L. and VAN WIJK, L.,
Corrosion-Related Accidents in Petroleum Refineries:
Lessons learned from accidents in EU and OECD countries,
European Commission Joint Research Centre Scientific and
Policy Reports, EUR 26331 EN, 2013,
.
ec.europa.eu/repository/bitstream/JRC84661/lbna26331enn.
pdf.
2. KOCH, G., et al., International Measures of Prevention,
Application, and Economics of Corrosion Technologies Study,
NACE International, 2016,
.
