Efficient Plant April 2019 - 11
feature | operating strategies
ARTIFICIAL INTELLIGENCE (AI) is a
hot topic these days. Discussions abound
about how it and/or the Industrial Internet
of Things (IIoT) will change workplaces.
Moreover, countless operations are
beginning to demonstrate real results from
putting AI into practice. At the same time,
the situation can be intimidating for maintenance technicians and engineers who have
already seen a shift in manufacturing. That's
not surprising.
According to the U. S. Bureau of Labor
and Statistics, Washington (bls.gov), the
United States lost 5 million jobs in the manufacturing sector between 2000 and 2015,
even as production increased. Some people
now worry that AI, IIoT, and other technologies related to digital transformation mean
the next wave of job losses is coming. Are
their fears justified?
Definitely not, according to Abhinav
APRIL 2019
Khushraj, co-founder and CEO of Petasense, San Jose, CA (petasense.com).
As Khushraj notes, the fact is most of
those technological advancements will be
deployed on jobs that aren't being done
today. "For example," he said, "plant personnel can't simultaneously look at hundreds of
process variables on a second-by-second
basis to identify small deviations from normal performance. AI can."
SOME DEFINITIONS
John McCarthy, one of the founders of AI,
defined it as "the science and engineering
of making intelligent machines." Khushraj
expanded on that definition with the following points:
Machine learning (ML) is a subset of AI
in which the computer has the ability to
learn and improve without being explicitly
programmed.
Anomaly detection and classification are
two of the most practical applications of
machine-learning techniques used in predictive maintenance (PdM) today.
Anomaly detection is, at its core, classification, except that one of the classes is
considered to be an anomaly and the other is
considered normal. In PdM, anomaly detection could be used to determine whether the
operating condition of a critical machine has
deviated from what was considered normal
in the past. The baseline, or normal, performance can be characterized by observed
values of multiple sensor parameters. For
example, for a pump, you could simultaneously measure suction and discharge pressure,
flow-rate, vibration (in all three directions)
on the drive-end bearing, temperature of
the bearing, and current draw on the motor.
Through the collection of thousands of such
measurements, anomaly detection is able to
EFFICIENTPLANTMAG.COM |
11
http://www.petasense.com
http://www.bls.gov
http://www.EFFICIENTPLANTMAG.COM
Efficient Plant April 2019
Table of Contents for the Digital Edition of Efficient Plant April 2019
Efficient Plant April 2019 - 1
Efficient Plant April 2019 - Cover1
Efficient Plant April 2019 - Cover2
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Efficient Plant April 2019 - Cover3
Efficient Plant April 2019 - Cover4
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