data-centric safety critical element management
TRANSCRIPT
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www.advisian.com
April 2017
Data-centric Safety Critical Element ManagementKevin GanPrincipal, Asset Integrity & Digital Advisory
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A Safety Critical Element (SCE) is classified as an equipment, structure or system whose failure could cause or contribute to a major accident, or whose purpose is to prevent or mitigate the effect of a major accident.
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Effective management of Safety Critical Elements must be a primary focus
1. Bowtie / MAH Workshops 2. Barrier Assessments 3. Performance Standards
To identify events, threats and consequences
Typical steps to identifying SCEs include:
To categorise SCEs into groups.
An example of a barrier would be Shutdown Systems and one
of the SCE groups would be ESDVs.
Developed to maintain the integrity and ensure the functions of the SCEs are
intact.
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Today, there are software packages that will visualise and integrate bowtie models and barriers to make life easier during design and operations.
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Though we still face issues managing Safety Critical Elements
SCEs are missing from
the asset register or so it appears because
they’re not identified clearly
in the system
There is no methodology in place when
classifying SCEs, leading to wrong data
There are too many SCEs,
leading to high, unnecessary maintenance
costs and man-hours
There is a lack of accurate
reporting on the status of
SCE integrity - correct decisions cannot be easily
made when it comes to the
integrity of assets
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The answer? Good data management.
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Good data management can help answer:
What is the probability that a SCE
will fail soon?
What is its remaining useful life?
What are the causes of failure?
What maintenance
should be performed to
fix them?
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• Reducing operational risk• Identifying failures before they occur• Reducing unnecessary maintenance operations• Controlling cost of maintenance• Lowering inventory costs • Discovering trends of various maintenance problems
It offers predictive maintenance through the Internet of Things
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But the key questions remain
Are we considering predictive
maintenance as
part of our maintenance
strategy?
Are we implementing
it correctly, if at all?
Are we making full use of the
data available?
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Validate model by pulling real-time data.Integrate model within
the organisation to start the optimisation
process.
Steps to implementing Predictive Maintenance
IdentifyOutcome
DetermineData Sources
DataIntegration
Model Development
Validate and Integrate
1 2 3 4 5
What do we want to predict?
For example, performance
data, maintenance logs, sensors, the weather
etc.
Data needs to be normalised into a single,
consistent system.
To identify meaningful
patterns using machine learning
techniques.
Once set up, maintenance needs can be anticipated and unscheduled downtime can
be avoided.
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Visual Integrity ManagementVisual Integrity Management is the next level of SCE maintenance and operations management - we now have the capability to connect multiple systems and data sources into a virtual asset
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Engineers can now visit remote platforms, take readings, measurements and assess maintenance requirements without having to leave the office.
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We need to start exploring solutions with the tools and technologies (e.g. SCE management processes, predictive analytics, visual integrity management etc.) that are available to us today.
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FOR MORE INFORMATION CONTACT
Kevin Gan | Asset Integrity & Digital Enterprise Consultant