F2: PROCESS FOR SUPERVISED MACHINE LEARNING
Supervised machine learning
• Model management
• Parallel learning
• Drag and drop
Big Data engineer
Finally, it should also be noted that the Digital Twin is built on a per-asset basis.
In other words, a Digital Twin is not created for an entire rig or refinery. Rather, it can
be created for specific machinery or assets. The issue of scalability and cost effectiveness is a major drawback given current levels of commodity pricing. It may be conceivable that GE provides airplane producers with a Digital Twin model for each new
and expensive jet engine GE sells them, but most of the market in oil and gas is
UNSUPERVISED MACHINE LEARNING
An alternative approach to Simulated Modeling is Industrial Analytics based on Unsupervised Machine Learning.
Let’s go back to the Digital Twin. The Digital Twin technology is based on the so-called Supervised Machine Learning methodology. It “trains” the algorithm on the
underlying asset by providing it with data labels or classifications. When Machine
Learning that is supervised recognizes new data, it then associates it with the data
labels that it has already learned.
With Unsupervised Machine Learning, data labels are not provided to the algorithm.
Instead, vast amounts of data are analyzed and the algorithm itself generates the
The algorithm is looking for abnormal sensor or signal behavior. Once it detects
anomalies the data, correlations, and pattern detections between signals are performed.
This is done to later present the operators with the exact sequence of abnormal events
detected. Once an evolving failure has been detected, a failure alert is generated. This
alert includes information on correlated sensor abnormalities. This valuable information significantly helps in tracking the failure origin.
Apart from methodology, the key difference between Simulated Modeling and
Unsupervised Machine Learning is how it is applied to a production facility. With the
Digital Twin, a unique clone is created for every asset. With Unsupervised Machine
Learning, the algorithm is agnostic with respect to sensor or asset type. The algorithm
is trained to find anomalous sensor behavior (or patterns of anomalous behavior) and
to use this information to provide early warnings of machine degradation or asset
Significantly, Unsupervised Machine Learning can be applied to all the assets in
rig or refinery. In other words, whereas the Digital Twin needs to “learn” each asset
separately, Unsupervised Machine Learn-
ing is analyzing data and looking for
patterns. It is agnostic with respect to
sensor or asset type and can be used by
an entire production facility.
CONCLUSION: CAVEAT EMPTOR
This article summarizes the three major
approaches to Industrial Analytics and
Machine Learning for Asset Maintenance. Given commodity prices and the
current state of assets in the oil and gas
industry, a conservative model for investment in this type of solution is
We are not suggesting that you delay
implementing a Machine Learning for
Asset Maintenance solution. Instead, we
recommend a conservative, fact-based
• When creating the business case for
Industrial Analytics, test each solution
provider by giving them with two to
three years of historical data. Compare
their predictions of machine failure
relative to actual failures.
• Include two to five solution-providers
in the test.
• For final vendor selection and to forecast ROI and TCO, use data that is
based on the pilot instead of relying
on industry benchmarks.
There is an almost universal consensus about the vast economic potential
for Machine Learning for Asset Maintenance in the oil and gas industry. Our
recommendation is to approach solution
and vendor selection using a data-driven
methodology to maximize the likelihood
ABOUT THE AUTHOR
Eitan Vesely is the CEO of
Presenso. He was previously
a hardware specialist and a
support engineer for Applied
Materials where he specialized in software-hard-ware-mechanics interfaces and system
overview. He holds a bachelor of science
degree in mechanical engineering.