POWER May 2022 - 28

CONNECTED PLANT
Advanced Analytics
Applications Power
Predictive Maintenance Efforts
Moving from reactive to predictive maintenance requires the right tools for use by
subject matter experts.
Giro Iuliano
A
s the power industry continues
to advance from preventative to
predictive maintenance, one thing
is clear. Access to robust data and the
ability to use it intelligently are the foundational
elements of effective predictive
maintenance. However, this is not easy
to achieve, and the process must start at
the most basic levels with the ability to
cleanse and contextualize data.
Establishing an asset health model
using a predictive method must focus
on as many variables as possible and
include data from the manufacturer's
asset
specification,
nameplate, past
services performed, and repairs. This
and other data are needed to meet an
asset's failure and performance objectives.
Predictive maintenance also helps
pinpoint corrective actions, and it can
extend asset life.
Implementing this type of predictive
maintenance strategy requires an
understanding of how and why asset
failure occurs, along with early identification
of potential problems or failure.
Many of these conditions are hidden in
the data that is already sitting in an organization's
process historian.
Looking back and understanding the
correlation between data and its variations
can help identify many failure
trends. These maintenance techniques
are an iterative process and may require
additional inputs to refine the
models. They should continue to evolve
and are designed to predict the useful
life of an asset, as well as to determine
maintenance schedules.
Predictive maintenance, in conjunction
with past preventive and reactive
maintenance strategies, provides plant
personnel with the information needed
to cut costs and maximize uptime. A
properly implemented strategy will eliminate
costly downtime and provide optimal
preventive maintenance schedules,
but this is more easily said than done,
28
with some strategies more effective
than others.
Advancing from Reactive to Proactive
Maintenance
Maintenance without adequate planning
negatively affects reliability and operations,
causes equipment to degrade,
and increases operating costs. While organizations
understand this challenge,
they must next determine just when
maintenance should be performed, and
to what extent.
Limiting maintenance saves money
initially but will lead to higher costs in
the long term. On the other hand, while
excessive maintenance will reduce outages
and failures, its higher cost may
appear extreme because it is difficult to
measure the cost savings from avoided
incidents. The key to improved reliability
is directly related to an optimized approach
for operations and maintenance.
Preventive maintenance has become
the industry standard, and it provides
schedules for inspections, testing, repairs,
and replacement of critical assets
and components. It is typically done on a
calendar or time interval and will extend
the asset's life, reduce premature failures,
and increase availability.
This type of time-based maintenance
has also helped establish standards and
techniques that improve the estimated
lifespan of an asset, while providing a
baseline for understanding time-driven
conditions leading to failure. This has
been a vast improvement from purely
reactive maintenance.
Predictive maintenance programs are
the next step and are becoming easier to
implement as utilities take advantage of
increased available data. Analysis of this
data provides valuable insights to determine
equipment condition, and cuts cost
by optimizing when maintenance is performed,
and to what extent. These types
of programs can also be used to predict
asset failures before they occur, avoiding
www.powermag.com
1. Advanced analytics applications provide
the tools required for cleansing, contextualizing,
and analyzing large and complex data
sets to create insights. Courtesy: Seeq
unscheduled downtime.
Utilizing predictive analytics increases
awareness for improvements, provides
accurate forecasting, and helps advance
data-driven decisions for
optimization.
However, the data and context must
be handled properly because with the
wrong tools, subject matter experts
(SMEs) can spend days or weeks conditioning,
verifying, and contextualizing
data. These issues become more serious
as the amount of data grows, along with
its granularity and complexity. Spreadsheets
can no longer handle these data
volumes, requiring a transition to more
robust solutions.
Sophisticated Solutions Empower
Experts
Advanced
analytics
applications
are
purpose built for analyzing the type of
time-series data generated by the types
of assets found in power generation
facilities. These applications provide a
comprehensive solution for managing
predictive models, along with the vast
amounts of data from multiple disparate
systems (Figure 1).
Advanced analytics applications connect
to existing data silos and other
sources, without copying or duplicating
the data from source systems. Once
these data sources are connected, the
application can be used by SMEs for
diagnostic, predictive, and descriptive
analytics. These applications also provide
POWER | May 2022
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POWER May 2022

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