From refined products to heavy crude, Four-Path Ultrasonic Flow Meters offers the capability to minimize measurement uncertainty of liquid hydrocarbons. Attendees work to build a machine learning (ML) predictive quality management (PQM) solution on AWS to proactively predict the health of the ultrasonic flow meters. This is done using the ML Data Readiness Package based on KNIME, from AWS Marketplace. Another PQM example for attendees to explore uses features extracted from motor current measured with a current probe and an oscilloscope on two phases measured under different speeds, load moments, and load forces. ML is used to proactively classify whether the motor has intact or defective components. A third PQM example involves using raw process sensor data from a hydraulic test rig with a primary working and a secondary cooling-filtration circuit, connected via the oil tank. They then use ML on AWS to proactively predict the cooler condition, hydraulic accumulator condition, internal pump leakage condition, and valve condition.

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Machine Learning and Predictive
Quality Management
Madhu Raman
WW Lead ML Industry Programs
Amazon Web Services
A I M 3 1 1

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Edouard Kachelmann
Solutions Architect
Amazon Web Services
Machine Learning and Predictive
Quality Management
Madhu Raman
WW Lead ML Industry Programs
Amazon Web Services
A I M 3 1 1

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning and Predictive
Quality Management
A I M 3 1 1
Madhu Raman
WW Lead ML Industry Programs
Amazon Web Services
Edouard Kachelmann
Solutions Architect
Amazon Web Services
Vikrant Kahlir
Enterprise Solutions Architect
Amazon Web Services

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Agenda
Ultrasonic flow meter PQM
AMI access, setup, and launch
Data ingest and transformation
Machine learning for PQM
Solution deployment

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Builder session repeats
Tuesday, November 27
Machine Learning and Predictive Quality Management
8:30 – 9:30 a.m. | Mirage, Grand Ballroom B, Table 5, T1
Day of Week, Month Day
Session Title
Time – Time | Location
Day of Week, Month Day
Session Title
Time – Time | Location

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Related breakouts
Tuesday, November 27
Machine Learning and Excess Stock Inventory Prediction
1 – 2 p.m. | Aria West, Level 3, Starvine 3, Table 9, T1
Monday, November 26
ADT 303 ML for Real-Time Self-Service Trend Detection & Root Cause Analysis
11:30 a.m. – 12:30 p.m. | Aria West, Level 3, Starvine 3, Table 2, T1
Tuesday, November 27
AIM 309 Machine Learning and Predicting Risk
11 a.m. – noon | Aria West, Level 3, Starvine 3, Table 5, T1

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Framing our predictive quality management problem
• From refined products to heavy crude, four-path ultrasonic flow meters
offer the capability to minimize measurement uncertainty of liquid
hydrocarbons.
• Electronics detect changing flow dynamics and help operators pinpoint
issues, including blockages upstream of the meter and abnormal flow
profiles, to minimize measurement uncertainty.
• There is a major focus on cost-reduction in the O&G and P&U industries.
This has initiated increased focus on metering. If a flow meter is not
healthy, the measurement uncertainty may increase significantly in the
period before repair or replacement. This increases the risk of loss of
income from the exported oil, risks related to measurement uncertainty,
and meter failure and production shutdown.

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Framing our predictive quality management problem
• We are predicting the health of the
ultrasonic flow meter (USM) based on
the measurement of parameters
reported by it while in operation.
• Data adopted from UCI Machine
Learning Repository, contribution from
Coventry University and National
Engineering Laboratory UK
Citation: K. S. Gyamfi, J. Brusey, A. Hunt, E. Gaura,
“Linear dimensionality reduction for classification
via a sequential Bayes error minimisation with an
application to flow meter diagnostics,” Expert
Systems with Applications (IF: 3.928), September
2017.
1. Profile factor, symmetry, and crossflow
2. Flow velocity in each of the four paths
3. Speed of sound in each of the four paths
4. Signal strength at both ends of each of the four paths
5. Signal quality at both ends of each of the four paths
6. Gain at both ends of each of the four paths
7. Transit time at both ends of each of the four paths

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Build on
You can access our
resource site, which has
everything you need
to build the solution
example described in this
session. You can use
these resources now or
at a later time to build
on AWS.
1. Head over to the URL page.
Use the Step by step Lab
Instructions to build your
solution example.
2. FAQ provides deployment
instructions.
http://2018-reinvent-builders-session-aim311.s3-website-us-east-1.amazonaws.com/

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Access
The analytics platform
AMI will be used in
today’s session.
Deployment
instructions for your
ML workflows using
Server AMI is included
in the step-by-step
instructions.
Follow links at URL
shown for setup and
access instructions.
http://2018-reinvent-builders-session-aim311.s3-website-us-east-1.amazonaws.com/

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How to solve
We will follow these
steps:
1. Ingest and partition your
data
2. Learn from the data and
create a model
3. Apply predictor model and
determine a score
Connect to S3 data
and transform
+
Partition data and
train your model
+ Apply and score your
model
Build a decision tree model.

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Ingest & transform
We ingest the data and
associate “colors” with
classes of outcomes
before partitioning into
70% training and 30%
test sets.
1. Green is healthy
2. Purple implies gas
injection detected
3. Blue implies installation
effects have not yet died
out
4. Red implies that the
health is deteriorating

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Partitioning
• Take from top: Puts the
topmost rows into the first
output table and the
remainder in the second table.
• Linear sampling: Includes the
first and the last row and
selects the remaining rows
linearly over the whole table.
• Draw randomly: Random
sampling of all rows; you may
optionally specify a fixed seed.
• Stratified sampling: Check this
button if you want stratified
sampling. You may optionally
specify a fixed seed.

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Model-building
We will use a
classification decision
tree in main memory.
The target attribute
must be nominal. The
other attributes used
for decision-making
can be either nominal
or numerical.

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Model prediction
You can examine the
decision tree view.

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Evaluation
You can examine how
much better than a
random toss of a coin
your model has
predicted.
We will now use
XGBoost, another type
of machine learning
algorithm, to learn and
predict from the same
data.

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Server setup
The KNIME server is
also available for trial
on AWS Marketplace.
Once you launch, you
will find a number of
examples.

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Deploy
Examples help you
understand how batch,
RESTful, and various
other capabilities can
be set up visually.

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Deploy
The ML workflow can
be executed manually
or automated to be
driven by an event or
to respond to an input
via a RESTful interface.

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Deploy
You could simply refer
to your KNIME Server
on AWS via a mount
point within your
KNIME Analytics
Platform.

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Deploy
Deployment of your
ML workflow would be
as simple as one-click
drag and drop from
your workspace to start
execution on your
server!
Check out the FAQ
with the Builder
Session material for
more information on
deployment and other
helpful tips.

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Key takeaways and next steps
• What we just did on AWS with KNIME does not require data science
and ML experience for you to use on your data
• If you wish to discuss a specific use case for your business, please reach
out to AWS ML Industry Programs (madraman@amazon.com)
• Please complete our session survey: AIM311 Machine Learning and
Predictive Quality Management

29. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Madhu Raman
madraman@amazon.com
Edouard Kachelmann
kedouard@amazon.com
Vikrant Kahlir
awsvik@amazon.com

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