Efficient Plant December 2017 - 54

column | industrial internet of things

Duke Power
Optimizes
PdM Service
Grant Gerke
Contributing Editor

Duke Energy Renewables
division manages or owns
more than 4,000 MW of
wind power in six states in
the United States.

| EFFICIENTPLANTMAG.COM

AINTENANCE IS A
pure cost for manufacturers, food producers,
processors, and utilities. Due to this
persistent fact, changes or investments
in maintenance approaches come
slowly, but advanced predictive- and
condition-monitoring technologies are
pushing manufacturing leaders to new
thoughts on "fixed costs."
Internal company debates on transitioning to predictive maintenance
(PdM) include management buy-in,
project scopes, workforce strategies, and,
of course, return-on-investment (ROI).
Over the past two years, this column
has shown how first-movers have been
implementing smaller pilot projects to
provide management with quick results
while keeping workforce demands to a
minimum.
However, early adopters are examining new approaches-after evaluating
IIoT (Industrial Internet of Things) pilots-with remote monitoring services or
by leveraging predictive platforms. Price
Waterhouse Cooper's 2016 Global Industry 4.0 Survey showed "early adopters are
expecting to forecast revenue gains of
more than 30% and greater than 30% in
cost reductions at the same time."
While this PWC survey isn't speaking directly to optimizing revenues or
new business outcomes resulting from
IIoT investments, utility companies
such as Duke Energy Corp., Charlotte,
NC (duke-energy.com), are evaluating
how PdM investments could lead to this
scenario.
Similar to General Electric's model
of testing its new monitoring platform
on their assets, Duke Energy recently
announced a partnership with Sentient
Science, Buffalo, NY (sentientscience.

com), to implement a "digitalization
strategy to continue to safely manage
and reduce the cost of maintaining Duke
Energy's wind turbines."
Duke Energy Renewables, owner/
manager of more than 4,000 MW of
wind assets, now uses Sentient Science's
DigitalClone Live software to diagnose
gearbox failures and provide corrective
actions for wind turbines. The software
helps identify and predict when cracks
will appear in the microstructure of
rotating mechanical components.
"We want to ensure that our technicians and asset managers have state-ofthe-art tools," stated Jeff Wehner, vice
president , Operations, Renewables at
Duke Energy. "After we have some experience in our fleet, we expect to offer this
enhanced service to other operators who
contract with us."
The utility giant recently used the predictive software to analyze 109 Winergy,
4410.4 GE 1.5-MW-rated gearbox machines and benchmarked these findings
against its internal monitoring tools, such
as oil debris and vibration monitoring.
Winergy Drive Systems Corp. is located
in Elgin, IL (winergy-group.com).
The monitoring software indicated
that 11 specific wind turbines would
show gearbox damage in the next 12
months, according to Sentient Science.
Duke Energy Renewables service personnel climbed towers and did borescope
examinations and confirmed that all
seven gearbox inspections had observable damage. The software also correctly
identified the damaged subcomponents
on five turbines.
To learn more about IIoT applications,
visitefficientplantmag.com/category/automation/iiot/. EP
ggerke@efficientplantmag.com

DECEMBER 2017

http://www.winergy-group.com http://www.duke-energy.com http://www.visitefficientplantmag.com/category/au http://www.EFFICIENTPLANTMAG.COM

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