While no technology can prevent normal equipment wear
or the need for maintenance, Steve Tonissen, vice president,
SmartSignal, believes Predictive Analytics and the latest
advances in Predictive Diagnostics can detect impending
problems early and allow plants to take control of their
operations

Predictive Analytics provides early and
actionable real-time warnings of
impending equipment and process
problems. These warnings enable operators
to fix what needs to be fixed but not fix what
doesn’t. They allow plants to move from
reactive and time-based maintenance to
proactive and predictive maintenance. Plants,
therefore, improve their availability and
reliability, increase efficiency, and reduce
maintenance costs.

Predictive Analytics works by
understanding the essential element—that
every piece of equipment is unique. It
develops a set of fingerprints for each
individual piece of equipment across all
known loads, ambient conditions, and
operating contexts. It calculates the proper
operational relationships among all relevant
parameters, such as loads, temperatures,
pressures, vibration readings, ambient
conditions, and more. It then takes actual
real-time sensor readings and compares
them to that particular machine’s normal
fingerprints. Based upon the differences
between real time and normal,
along with their persistence,
Predictive Analytics detects
and isolates abnormal
behaviour, in the context of
operating conditions. It then
posts these ‘incidents’ and
provides exception-based
notifications of developing
problems to users. It does
this automatically,
continuously, and
relentlessly, 24h/day.

Said differently,
Predictive Analytics can
determine that, even
though a temperature
reading is in the middle of the
minimum/maximum range, the sensor value
is abnormal for a particular piece of
equipment in the context of its individual
operating conditions.

Instead of plant personnel sorting through
vast amounts of data to extract meaningful
nuggets, Predictive Analytics operates on a
real-time model, identifying and flagging
these subtle changes from expected
behaviour that have been verified to be
actionable issues. Doing so, it identifies
sensor, equipment, and operational issues—
and sometimes can identify issues weeks and
months before failure.With these early
warnings, operators can schedule
appropriate maintenance or plan further
investigation in context of the overall plant
schedule. Hence, they avoid surprise
equipment failures.

Predictive Analytic technology is scalable
to all critical rotating, non-rotating, and
process equipment, across the plant, across
the fleet, and across industries. It currently is
being used in power generation – coal,
combined cycle, nuclear, wind, hydro – and
in oil and gas – upstream, gas transportation,
and downstream. About 50% of the US
Power Gen fleet is using it, along with some
leading oil and gas super majors, and its use
is expanding globally.

Moving up the P-F curve
Perhaps the easiest way of thinking about the
advantages of Predictive Analytics is to
review it in context of the P-F curve.

Reliability engineers use a P-F curve to
visualise the activities of managing
maintenance and repair activities against the
cost of equipment failure. Key points on the
curve represent Potential Failure (P) and
Functional Failure (F). Potential Failure
occurs when events lead to component
damage that needs repair. Functional Failure
occurs when equipment performance no
longer meets design conditions and must be
shut down for repair.

Before Predictive Analytics, with some
traditional condition-monitoring tools, this
could be a short time envelope, as indicated
in Figure 2 (below).

Given the customised equipment models
that automatically adapt to changes in load,
ambient conditions, and operating contexts,
though, Predictive Analytics provides an
accurate assessment of the condition of each
individual piece of equipment and, therefore,
early warning of developing issues.

Quite simply, Predictive Analytics enables
operators to move ‘up the curve’, providing
extended lead time and enabling operators to
fix small problems before they grow large or
catastrophic. See Figure 3 (above).

Predictive diagnostics
Predictive Diagnostics builds on the powerful
foundation of Predictive Analytics, as
described above. But, whereas Predictive
Analytics tells you what is going to fail,
Predictive Diagnostics goes further and also
tells you what is the apparent cause of the
failure and what is the priority of the
impending failure.

Predictive Diagnostics was made possible
by the collection and analysis of data from
hundreds of millions of machine hours and
tens of thousands of incidents across
equipment types from the world’s largest base
of equipment operating data. This in-depth
analysis of 10 years’ data resulted in the
identification of fault patterns in context of
operating behaviour. From here, with user
input, a new technology was developed that
expanded detection of equipment problems
to diagnosis and prioritisation of them based
on severity.

There are an unlimited number of root
causes for failures. Predictive Diagnostic
algorithms can pinpoint failure effects. If it’s
in the data, Predictive Diagnostics will find it
– and diagnose it to one of the pre-identified
performance or mechanical fault patterns.

When a problem is detected, the
SmartSignal SHIELD Predictive Diagnostic
software automatically shoots an email to the
customer, 24/7, with identification of the
problem, diagnostic guidance, and clear
prioritisation based upon severity.

Predictive Diagnostics alerts the operator
as to whether an item warrants immediate
corrective action or represents a future
maintenance concern. Detection of minor
problems is key to preventing larger ones, as
the plant is able to closely track and monitor
the problems. The software continually
monitors the equipment and will adjust the
priority as the number of deviating sensors
and the degree of deviation change.

In a plant or fleet with multiple problems,
Predictive Diagnostic notifications give
maintenance crews the information they need
to prioritise their work and focus on the most
important issues first. Plant personnel work on
the right equipment at the right time, making
sure they have the right parts and resources
available to do the job. They reduce their parts
and labour costs by planning their outages
instead of being forced into unplanned events
– and they reduce maintenance duration and
increase maintenance intervals. In addition,
they avoid the higher risk of catastrophic
failures that come with forced outages, since, at
that point, equipment has passed its potential
failure point.

Here’s an example of how the increasing
priority of a developing equipment issue
identified by Predictive Diagnostics enabled a
plant to receive early warning of a combustor
hot spot.

Predictive diagnostics of a
condenser tube leak
The failure fingerprints of a condenser tube
leak typically present as spikes in chemistry
parameters. In this case, Predictive
Diagnostics was able to provide early
notification of a developing leak, based on a
combination of deviations of two parameters.

Initially, the issue was rated 4 on a 1 to 5
priority scale, (5 being the lowest rating). A
day later, the priority escalated from 4 to 3,
based on a 3rd parameter contributing to the
diagnosis. The notification was forwarded
from SmartSignal’s Availability and
Performance Center to the plant.With this
advance notification, the plant was able to
repair the leak during a subsequent minor
outage, preventing corrosion in the boiler
and much more serious outages later on.

How to execute?
Predictive Diagnostics can be implemented on
all critical equipment in one plant or an entire
fleet within a matter of weeks to a few months,
depending upon size of deployment. It can be
flexibly integrated into a user’s processes and
culture, and it integrates with a user’s data
infrastructure, thermal performance software,
RCM system, and other tools.

Just as every piece of equipment is unique,
so, too, is every operation. So, users can
obtain services that meet their needs. And, if
needs change, so can the services. A customer
plant or fleet can host the software itself or
use the SmartSignal in-house Availability and
Performance Center (APC). The APC
engineers provide flexible to full services in
deployment, model maintenance, and
monitoring. They can monitor the software
and communicate with customers when the
software identifies abnormalities that require
action. SmartSignal identifies, diagnoses,
prioritises, and verifies customer problems
and works with customers to validate, solve,
document, and capture knowledge. This
service option ensures that Predictive
Diagnostics is executed quickly and properly,
and customers benefit from the Best Practices
of the APC and other APC users.