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March
2017
HYDROCARBON
ENGINEERING
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In order to make better business decisions, the IIoT
offers companies the ability to:
Aggregate data from existing sources.
Create additional data sources in a cost effective way.
Gain visibility into new data.
Identify patterns.
Derive insight through analytics.
Through this approach, previously unsolved problems,
as well as new ones, can be solved with assets
communicating and providing real-time usage data to allow
plants to carry out predictive maintenance and process
optimisation.
Industry-leading companies are transforming their
operations by utilising proven solutions in the areas of
process and event data collection, combined process and
asset-centric analytics, and visualisation technology to
continuously and automatically collect, organise and
analyse data. Indeed, advanced analytics is one of the pillars
of the IIoT, connecting people, processes and assets to
optimise business results. It can transform work processes
from manual and reactive to automatic and proactive,
helping users avoid unplanned downtime, and improve
performance and safety.
An IIoT-enabled plant uses a combination of advanced
sensors, automation systems, and cloud technologies
integrated with current systems and data analytics to
become smarter. This provides the ability to locate data in a
cloud environment where it can be accessed and analysed
with analytical tools. For example, an equipment vibration
reading would be sent to the plant’s distributed control
system (DCS) as a single value, whereas rich dynamic data
stored in the cloud would allow engineers to study the
harmonic signature of a bearing or shaft to determine the
root cause of a pending asset failure. Currently, in most
cases, dynamic data is only employed by specialists in
custom applications – limiting its accessibility by other
users in the plant.
In terms of predictive maintenance and process
performance, IIoT-based solutions enable industrial
enterprises to proactively manage their assets and make
more informed decisions through analytics at the edge.
Production and maintenance strategies can be combined for
optimal overall performance and executed based on how
assets are expected to function tomorrow – not solely
according to a specific periodicity or on particular present
conditions.
Another key driver of the IIoT is a reduction in the level
of information technology (IT) skills and expertise required
to support standalone applications, so that companies can
focus on their core competency of running and managing
operations.
Making the most of plant data
Major automation suppliers have developed innovative
technologies that deliver real-time process and
asset-centric analytics, performance calculations, event
detection and collaboration for plant management,
engineering, maintenance, center of excellence (COE)
experts, and operations. These solutions are designed for
online continuous monitoring of equipment and process
health, enabling industrial facilities to predict and prevent
asset failures and poor operational performance.
Today’s tools for real-time process performance
monitoring provide statistical calculations and embedded
performance models which, when paired with near
real-time surveillance of instruments, processes and
equipment, allow users to accurately assess asset
performance. They offer a clear window into plant
processes – continuously monitoring operating conditions,
and enabling decisions and actions to prevent production
loss, minimise downtime, and reduce maintenance expenses.
The latest developments in the field of plant equipment
and process health monitoring leverage secure, managed,
and hardened edge-to-cloud platforms, while focusing on
data science and analytics, and applying ‘digital twin’
patterns to drive their analytic models. With the help of
external experts, these solutions enable industrial firms to
extract meaningful insights from their data. This leads to
improved decision-making and addresses such issues as
safety improvement, asset management and optimisation of
operations. As a result, process plants are becoming more
agile, driving increased revenue and keeping the focus on
what matters most – production.
By modelling first principle compressor performance
and baseline performance, for example, current
performance can be continuously compared to detect both
sudden changes and long-term degradation. These events
have successfully been demonstrated to trigger
maintenance activity, such as chemical injection to clear
fouling or a compressor wash, or to initiate further action if
required.
Unlike condition monitoring solutions focused solely on
the equipment’s physical condition, the latest data analytics
and asset monitoring solutions use performance
degradation as a leading indicator of potential problems.
With the IIoT, identification of performance degradation
and course of action are continuously improved since both
process and equipment data are used, not only for a specific
compressor but also for all compressors of similar design
and service. Some tools employ pre-defined best practice
templates for a wide range of equipment types, including
Figure 2.
Real-time process performance monitoring
provides an expanded view of operations to help
plant personnel maintain the health of critical assets.