POWER May 2022 - 29

CONNECTED PLANT
improved
real-time
collaboration, enabling
greater vision and insights across
teams, and ultimately accelerating the
communication of insights across the
broader organization.
With advanced analytics applications,
ensuring data integrity, and
cleansing and contextualizing time-series
data, is much quicker and easier.
This type of data is often noisy, including
small spikes and erroneous values,
which can complicate the analysis.
Cleansing data with smoothing filters
to eliminate or reduce the noise is a
necessary first step to contextualization.
In addition to signal smoothing,
SMEs can leverage advanced analytics
applications to remove outliers,
time shift data to adjust for different
intervals, and eliminate data that is not
relevant, such as when an asset is not
operating or is in startup mode.
As the power industry continues to
look for solutions around predictive
analytics, machine learning, and data
science, it must efficiently utilize the
expertise of its existing workforce.
The collaboration and knowledge of
process engineers and experts will
be required to assemble a data-driven
analytics effort to rapidly distribute
insights and improve outcomes. With
advanced analytics applications, these
and other issues can be addressed
by connecting to data and landing insights,
eventually leading to improved
business and production results, as
shown in the following examples.
Bearing Failure Prediction
Rotating machinery presents challenges
when establishing proper maintenance
and servicing schedules. Specifically,
bearings can fail with little notice, making
scheduled maintenance very difficult.
System interdependence often
means that a failure of one bearing results
in subsequent failure of others.
SMEs can use an advanced analytics
application to cleanse equipment data,
identify normal operating conditions,
calculate statistical thresholds, and
identify any deviations or abnormalities.
In one scenario, data scientists used
data cleansed and contextualized by
their SMEs to build a bearing health status
signal. This was done by running an
anomaly detection algorithm, with results
shared across the company.
Maintenance is now notified of abnormal
operation days before a catastrophic
failure. This early warning time
enables proactive greasing of bearings
May 2022 | POWER
2. View of the anomaly detection algorithm
overlaid with the original failure detection calculations.
The red condition begins to show
signs of failure days before the yellow condition
occurs. Courtesy: Seeq
to extend run life, and to provide time
for advanced parts ordering and resource
allocation.
The data that was used to configure
the bearing prediction model was already
residing within the process data
historian, and it was correlated with the
data from periodic vendor testing that
was stored as CSV files and uploaded
to the advanced analytics application.
The data was then structured into an asset
hierarchy, enabling calculations to be
quickly propagated from one bearing to
many others.
The anomaly detection signals were
pushed back to the advanced analytics
application, and SMEs used this information
for daily monitoring to identify
potential bearing failures earlier. The results
were added into a dashboard. A
graph was also created to provide more
detailed information related to the predicted
time of bearing failure (Figure 2).
This information was used to schedule
proactive maintenance in advance of
bearing failure.
Pump Health Monitoring
Pump health monitoring has become increasingly
important and has led to better
methods for measuring performance,
along with the ability to gauge pump
health in near real time. Implementing
a condition-based monitoring analysis to
monitor pump health across hundreds of
assets requires access to historical data
for creating a predictive maintenance
forecast to detect failures before they
occur. The state of overall pump health
is informed by monitoring performance
and identifying any degrading conditions
or risks, allowing for prioritization of
maintenance activities.
Using Seeq, required data is accessed
from asset management systems to define
models for normal operating conditions.
SMEs then cleanse the data to
remove outliers, noise, and dropouts.
Calculations and conditions are then
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3. The daily health score for this pump
provides a detailed view showing leading indicators
that could contribute to poor performance,
and eventually failure. Courtesy: Seeq
applied to create a histogram (Figure 3)
displaying the time each pump is in a certain
mode of operation, such as:
■ Good or Poor.
■ Percent time calculation in different
poor modes of operation.
■ Percent magnitude of violation during
the poor modes of operation.
These results are displayed for reporting
and collaboration. Using Seeq, dashboards
with automatic updates to show
pump health across all assets were
created to aid troubleshooting efforts.
Cleansed data was also provided to reliability
engineers for performing root
cause analysis.
Advanced Tools Are Available Today
Predictive analytics adds insight and
clarity, and it is quickly becoming more
attainable due to the confluence of big
data and advanced analytics applications.
These applications are revolutionizing
the way data is analyzed, making it
possible for SMEs, engineers, and data
scientists to make strategic decisions.
Advanced analytic applications have
made it simpler for a wider range of
personas to leverage predictive analytics
and perform faster analyses. These
applications also enable organizations
to expand predictive analytics to identify
patterns using machine learning, requiring
less operator intervention for recognition
between cause and result, while
automatically adjusting models to deal
with new data. With advanced analytics
applications, these and other benefits
are realized through the rapid iteration
and refinement of analyses, providing
more accurate and timely insights. ■
-Giro Iuliano is an industry principal
at Seeq Corp. He has nearly 20 years
of sales and business development
experience, having worked for a
decade with power and utilities
customers at OSIsoft, and five
plus years at Oracle and Itron.
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http://www.powermag.com
POWER May 2022

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