Efficient Plant November 2017 - 8

column | implementations

AI and
Reliability:
How Much,
How Fast?
Dr. Klaus M. Blache
Univ. of Tennessee Reliability
& Maintainability Center
(RMC)

AI use is limited to specific tasks. General-task
application will only occure when scientists figure
out how to actually use neural networks.

8

| EFFICIENTPLANTMAG.COM

I

NTEREST IN ARTIFICIAL Intelligence (AI) has been gaining speed
due to big data, the cloud, increased
computing power, greater connectivity, and advancements in sensors and
signal processing. Machine perception,
i.e., using cameras to recognize objects,
has been around for years. Today's machine-learning systems can do much
more.
For purposes of this column, the
term "machine learning" refers to
computer algorithms that improve
with experience. Think of enhanced
speech and facial-recognition technology, Tesla autonomous vehicles, IBM
Watson, and reliability and predictive
maintenance (PdM) modeling efforts.
Another example comes from Lufthansa
Airlines, which maintains more than
1,000 planes and is using machine
learning in real-time data collection and
decision making. Recommended failureavoidance actions come as a result of
error messages and sensor data, among
other things.
Less than two years ago, I visited
Lufthansa Technik (airplane maintenance, production, and development)
near Hamburg, Germany, where they
were still mostly "talking" about machine learning. Now, these operations
are leveraging it to reduce downtime,
component failures, and cost.
Around the same time, my study on
evaluating the state of reliability-related
modeling in North America found that
fewer than one in 100 companies were
sufficiently using the analytical tools to
gain competitive advantage. Then again,
some companies that are technically
capable often don't have sufficiently
granular or quality data. I see this situation changing in the next 5+ years in all

parts of industry and government.
Yes, companies are currently setting
up "virtual twins" to emulate their critical assets. Visual models and algorithms
are being used to simulate manufacturing capabilities and predict failures
and when to do maintenance repairs
or replacements, as well as for product
performance/customer experience. At
the same time, the world-renowned
scientist Stephen Hawking has expressed
his concern to BBC News that "artificial
intelligence could spell the end for the
human race." Maybe, but not in the near
future. Risk-versus-progress decisions
will always be with us.
Progress in AI is inevitable. Thus, it's
better to embrace it, understand it, use it
to improve, and be part of controlling it
to the extent necessary. Today, AI is still
very specific or applied, i.e., controlling
drones and cars. AI that can do anything, or most general tasks, is still only
in the movies. The time to be concerned
is when scientists figure out how to use a
neural network, like the human brain.
What we need to improve immediately is the ability to apply big data and
analytics to practical applications for
daily use. We should also take the opportunity we have to engage Millennials
who like to be in this space.
If you have examples of successful
machine learning, I would like to hear
from you. EP

Based in Knoxville, 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.

NOVEMBER 2017


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