POWER September 2017 - 17

application of artificial intelligence (AI)
and algorithms is now fueling advances
in the Industrial Internet of Things
(IIoT). The term " unsupervised " indicates
that the algorithms do not need to be
trained on the underlying asset that is
monitored. There is no need to use a simulated
clone of the physical asset, such
as a " Digital Twin. "
To apply machine learning to asset
maintenance, turbine asset sensor data
is continuously streamed to the cloud
where AI algorithms analyze it in real
time. Of course, this assumes real-time
communications links, which will need to
be considered for remote locations.
The algorithms are trained to detect
anomalies in all the asset's signals. Correlations
and pattern detections among
signals are performed. Instead of looking
for breaches of manually set control
limits, the algorithms detect abnormal
behavior that is indicative of asset degradation
and failure.
Automated Model Selection
One of the challenges with machine learning
is the selection of the algorithm used
to detect the abnormal sensor behavior
that is indicative of machine degradation
or failure. In the past, there was
reliance on data scientists to select the
most relevant model from a library of algorithms.
However, with the massive amount
that must be analyzed in real time, the
ability to automatically select an algorithm
is critical.
In the case of Presenso, our automated
machine and deep learning (MDL) engine
(Figure 3) selects the optimal algorithms
and hyperparameters in a data-driven
way without any human intervention. The
auto-MDL includes dozens of machine
learning algorithms, tens of preprocessing
methods, and all their respective hyperparameters,
yielding a total of hundreds of
hyperparameters. This process is enabled
by computational power of the cloud.
Machine and Sensor Agnostic
One of the overlooked advantages of unsupervised
machine learning is that it is
machine or sensor agnostic. This means a
wind turbine can have more than 8,000
unique components, ranging from hydraulic
systems and power converters to fall
protection. As long as sensor data is generated,
the machine learning algorithm
analyzes all the signal data and detects
abnormal patterns without consideration
of the following:
■ Asset age. The algorithm does not difSeptember
2017 | POWER
3. Automated machine and deep learning engine. Presenso's automated machine
and deep learning engine selects optimal algorithms and hyperparameters without human
intervention, yielding hundreds of hyperparameters as it uses the computational power of the
cloud. Courtesy: Presenso
ferentiate whether the turbine is two
years old or 15 years old.
■ Vendor. Vendor information is not
relevant to the performance of the algorithm.
Therefore, sensor data from
the following manufacturers can be
analyzed: Acciona, Alstom, Dongfang,
Enercon, Gamesa, GE Energy, Goldwind,
Nordex, Repower, Siemens, Sinovel,
Suzlon, and Vestas. Note
that both
GE (Predix) and Siemens (MindSphere)
are also major players in the so-called
Digital Twin market. The impact is that
a single solution can be applied to all
turbine types without the need for multiple
vendor-specific systems.
■ Part or asset type. All sensor data
from all the following assets can be analyzed,
including blades, gearbox, generator,
and coupling and bolt tightness.
■ Onshore versus offshore. The algorithm
does not differentiate between
offshore and onshore facility location.
The Impact of Machine Learning on
Asset Maintenance Costs
As asset reliability increases, there is
less money spent on reactive maintenance.
Conversely, an underinvestment in
predictive and preventive maintenance increases
the need for reactive maintenance
caused by unplanned machine breakdown.
When machine learning algorithms
detect asset degradation and breakdown
ahead of time, resources can be shifted
away from over-reliability investments
such as unnecessary maintenance. As asset
reliability investments are rationalized
and the detection of degradation is
improved, the frequency of unscheduled
downtime is reduced. This lowers total
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maintenance cost as a percentage of replacement
asset value.
The unsupervised machine learning
solution for asset maintenance applies
the latest advances in AI to detect abnormal
data patterns throughout an entire
facility, thereby lowering the cost of
reliability maintenance. A cloud-based
solution does not require new hardware
or sensors and can be deployed remotely,
without the need for human input
from facility staff.
Based on estimates from Germany, the
UK, and Denmark, O&M costs are estimated
to be between 1.2 c€ and 1.5 c€ per
kWh of wind power produced, over the total
lifetime of a turbine. In the U.S., O&M
costs are less expensive (best practice is
$0.01 per kWh). Between 43% and 54% of
this is allocated to overhead, administrative
costs, insurance, and rental income.
Applying machine learning to the asset
maintenance mix to reduce replacement
part and labor costs has the potential to
significantly impact the financial performance
of a wind farm.
The cost savings for maintenance and
labor can be extensive. Using both U.S.
and European data, a 20% reduction in
the repair and maintenance portion of
O&M (assumed to be between 46% and
57% of overall O&M costs) would yield annual
cost savings of $11,383 for a 2.5-MW
turbine and $34,148 for a 7.5-MW turbine.
Limiting O&M expenses is a high priority
for the wind power sector. If executed
effectively, wind farm operators
can now use automated machine learning
to turn under-utilized sensor data
into a way to lower overall turbine asset
maintenance costs. ■
-Eitan Vesely is CEO of Presenso.
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POWER September 2017

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