pwr_february-2025 - 28

PREDICTIVE MAINTENANCE
Cut Downtime and Costs with
Predictive Maintenance (PdM):
Here's How
Predictive maintenance (PdM) is transforming power plant operations by leveraging
artificial intelligence (AI), data analytics, and automation to prevent costly equipment
failures. Learn about real-world applications, challenges in adoption, and the
significant cost savings achieved through advanced monitoring and proactive
maintenance strategies.
Aaron Larson
n an era where power outages can
ripple through communities and industries,
causing significant economic
impact and disrupting essential
services, the reliability of power generation
equipment has never been more
crucial. Yet, across the fleet of aging
power plants worldwide, maintenance
teams are caught in a delicate balance
between preventing catastrophic failures
and avoiding unnecessary downtime
for inspections and repairs. This is
where predictive maintenance (PdM) is
revolutionizing the industry's approach to
equipment health.
Gone are
I
the days when power
plant operators relied solely on scheduled
maintenance intervals and reactive
repairs. Today's PdM technologies
harness the power of sensors, data
analytics, and artificial intelligence
(AI) to detect potential failures weeks
or even months before they occur.
For an industry where unplanned turbine
downtime can cost hundreds of
thousands of dollars in lost generation
production, this shift from reactive to
predictive maintenance isn't just an
operational improvement-it's a competitive
necessity.
Consider this, at any given moment,
a typical combined cycle power plant
(Figure 1) has thousands of critical
components operating under extreme
temperatures and pressures. Each bearing,
blade, and valve tells its own story
through vibration patterns, temperature
fluctuations, and performance metrics.
By listening to these mechanical whispers
and interpreting them through advanced
analytics, maintenance teams
can now predict and prevent failures
with unprecedented accuracy.
28
Combining the Right Tool with the
Right Expertise
Orangeville, Utah-based Taber International
is an engineering and automation
consulting group that offers fully customized
solutions in smart automation, optimization,
data visualization, and process
control. It works with existing hardware
and systems, and also deploys new systems
from the ground up, providing endto-end
software platforms.
The company was founded in 2006,
and initially focused on combustion optimization
and intelligent sootblowing
applications for coal-fired electric utility
generators. Over the years, it expanded
its scope to include ancillary systems
in thermal power plants and diversified
into other industries including chemicals,
manufacturing, water and wastewater,
oil and gas, and others.
Taber has deployed applications
within the Griffin AI Toolkit, a no-code
interface and companion rapid prototyping
run-time engine, which provides an
excellent platform for improving maintenance
practices. With these tools and
Taber's help, many power plants have
replaced time-interval or " gut-feel "
based maintenance approaches with
condition-based solutions.
" Taber's expertise in the power industry
combined with the capabilities of
the Griffin AI Toolkit provide insight into
a wide range of conditions, " Jake Tuttle,
CEO of Taber International, told POWER.
" We've had success in recognizing
condenser and heat exchanger fouling
throughout the fouling process, keying
into subtle performance degradation
during the early stages of formation, allowing
for minor corrective actions to be
taken at much lower cost. "
In other cases, Tuttle said the compawww.powermag.com
1.
A modern combined cycle power plant
has literally thousands of sensors installed
to monitor temperatures, pressures, flows,
levels, vibrations, water quality, and more.
Data from these sensors is vitally important
to predictive maintenance programs. Source:
Envato
ny has been able to recognize the immediate
onset of tube leaks in steam power
plants, before acoustic monitoring or
other systems detected it. One such
example occurred in a fossil power plant
while load ramping. During one ramp
from minimum to maximum load, the
system recognized a change in operating
state once the unit reached full load.
No other systems reported any change.
Eight hours later the tube had a massive
failure and caused the unit to come offline
immediately. Other examples include
bearing fatigue in motors, sensor
calibration and measurement errors, fan
performance deterioration, oil and lubricant
leaks, equipment filter pluggage,
general performance degradation, mechanical
stress failures, and more.
Challenges to Implementation
Yet, as game-changing as Taber's technology
is, it can be difficult to get a
team of power plant managers, engineers,
operators, and technicians to
move beyond past practices. " The bigPOWER
| February 2025
http://www.powermag.com
pwr_february-2025

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