subject matter experts (SMEs), fleet operators can benefit from online monitoring
with a much smaller capital and human
resource investment. This Lean M&D approach can be an enabler for smaller fleet
operators who cannot afford the cost of a
dedicated team of experts and monitoring staff. As the Lean M&D approach becomes more widely deployed, increasing
efficiency will follow from the ability to
capture and share valuable diagnostic and
prognostic expertise across the industry.
Taken together, these new analytics become a core element of harnessing the Industrial Internet in the power generation
Randy Bickford is president of
Expert Microsystems Inc.
One of the key factors enabling Lean
M&D is the availability of solutions designed to scale easily across the Industrial Internet. These solutions are designed
to operate in the same way and perform
the same services when running on a network edge device, on an engineering laptop or within a corporate or public cloud.
This creates multiple points of entry for
introducing powerful analytics that are
interoperable across a deployment. Many
new users benefit from solutions that can
be run in full function mode on a single
desktop or laptop. Few of the APR solutions deployed today offer this option
and most require a large upfront information technology (IT) investment that
has priced many smaller power generating companies out of the APR market. An
ability to develop monitoring solutions
locally and then scale up to the cloud or
down to the device or control platform,
when needed, is a new paradigm that is
an enabling factor for Lean M&D.
The primary value of an APR-based
solution is that it can be used to charac-
terize plant operating anomalies in detail.
Similar function can also be established
using first principle models, such as a heat
balance, when the variables of interest
can be modeled based on physical, ther-
modynamic or electrical principles. The
rise of APR solutions is mostly attribut-
able to the fact that it is extremely easy to
create an APR model of these same phys-
ical, thermodynamic or electrical prin-
ciples using machine learning methods.
Figure 1 on page 32 illustrates how APR is
used to transform an observed data signal
into a residual signal that has very useful
properties for online monitoring. The
APR model uses patterns in a set of signals
to estimate the expected value of each of
its input signals. The deviation between
the observed and expected signals, of-
ten called the residual signal, will have a
near zero mean and predictable statistics
when the monitored system is operating
normally and the APR model matches the
data. When the monitored system moves
away from normal operation, the change
in properties of the residual signal can be
characterized to define a set of symptoms
that describe the change in behavior.
It is no surprise that all APR solutions
are not equally capable of creating accurate expected data signals for plant operating data signals. However, the accuracy
of the APR model’s expected data signal is
very important when implementing Lean
M&D. More accurate APR predictions
translate directly to earlier problem detection and a more accurate initial diagnosis.
More accurate APR predictions also mean
fewer false alarms and lower staffing costs
for alarm management. Managers of most
large fleet remote monitoring centers cite
false alarm management as the single
greatest cost and inefficiency within their
operations. Reducing the false alarm rate
and improving the accuracy of problem
detection and diagnosis is essential for
moving to a Lean M&D implementation.
What then are the attributes that support a highly accurate APR solution?
First, the APR algorithm itself controls
the quality of the predicted values based
on the observed values of the plant data.
Most algorithms in use today are proprietary, but in general, those that use regression based methods will interpolate
the expected data values more accurately
than those that use cluster distance based
methods or principle component based
methods. A summary of several key features of a highly accurate solution are listed in Figure 2 on page 32.
Accurate APR solutions will also provide excellent support to help a user avoid
the “garbage in-garbage out” problem.
It is no surprise that a poorly designed
APR model will be less accurate than a
well-designed model. One key attribute
of a well-designed model is that there is
good correlation within the set of modeled plant data signals. In other words,
there are actual patterns in the data for
the model to learn and work with. Another key attribute of a well-designed model
is the historical data used for calibration