Efficient Plant Nov./Dec. 2022 - 23

department | tech horizons
wireless sensors, cloud computing, big data,
and IoT between 30% and 38%, and machine
learning, collaborative robotics, 3D printing,
and augmented reality between 8% and 18%.
Machine learning was at 10%. 100% equals
fully adopted.
A few years back, Gartner found that
only 13% of survey respondents already used
some form of digital twin. At the same time,
62% were working on it or had plans to do so
(Digital twins becoming mainstream: Gartner
| CXO Insight Middle East (cxoinsightme.
com). I think that more organizations are
experimenting with some machine learning
on assets, but not referring to it as a digital
twin, since it's early in their learning.
Th ere are numerous benefi ts that can be
attained by applying digital twins, including:
 Helps reduce unplanned downtime and
related costs. Also, this puts people fi xing
things in less-frequent emergency situations,
which will reduce safety risk.
 Tracks failures in real time.
 Provides more time to intervene on pending
issues. It's a way to better identify when
each asset/component is most likely to fail,
extending your time on the P-F curve.
 Simulates what maintenance actions
provide the best outcome.
 Becomes your focal point for ongoing
improvement, with all the current knowledge
of the asset performance now in one place.
 Off ers the option to link digital twins
when appropriate.
Th ere are some challenges that need to be
overcome:
 confi rming data quality (includes data
relevance)
 handling massive amounts of data
 acquiring real-time data and keeping it
secure
 integrating and combining asset models
and plant functions is complex.
NOV/DEC 2022
North America digital adoption level in TPM practices
60
50
40
30
20
10
Graph shows the current state of adoption of digital-transformation technology in North
American plants. Source: Univ. of Tennessee Reliability & Maintainability Center
Digital twins provide an opportunity to
create good failure models, improve assets,
and simulate scenarios for ongoing learning,
insights, and question answering. Like
all tools and technologies, the digital twin
should add value to the business. Once we get
better answers to the " what if " questions, we
may fi nd out that we haven't known enough
to even ask the correct questions. Digital
twins can also be applied to predictive maintenance.
If you develop a model of a pump,
machine, or production line, you can use it to
support predictive maintenance.
As you move from reactive to a more
proactive process (preventive, predictive,
condition-based), a digital twin provides you
with a model that knows asset performance
and changes (real time) with plant-fl oor asset
changes for better predictability. You also
have a model that can run any number of
simulations to improve predictability based
on factors such as hours of operation, age,
temperature, and fl ow rate. Since a digital
twin is constantly learning and improving, it
provides great opportunity for new insights.
Th is approach delivers all the benefi ts of a
best-practice reliability and maintainability
process. In addition, it can improve
machinery and equipment performance at
the systems-thinking level. EP
Based in Knoxville, Dr. Klaus M.
Blache is director of the Reliability &
Maintainability Center at the Univ. of
Tennessee, and a research professor in
the College of Engineering. Contact him
at kblache@utk.edu.
EFFICIENTPLANTMAG.COM | 23
Remote control/
monitoring
Wireless
sensors
Cloud
computing
Big data
IoT (Internet of
Things)
Machine
learning
Collaborative
robotics
3D printing
Augmented
reality
http://www.EFFICIENTPLANTMAG.COM
Efficient Plant Nov./Dec. 2022

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