The document introduces FactoryTalk Analytics LogixAI, which uses artificial intelligence and predictive analytics to help manufacturers discover new insights from industrial data. It can perform descriptive, diagnostic, predictive, and prescriptive analytics to monitor equipment performance, identify issues, predict faults and quality problems, and recommend actions. The key benefit is it can build these predictive models without requiring data science expertise by automating the data preparation and model training process. The presentation also provides an overview of how FactoryTalk Analytics LogixAI works and its different modes of operation for anomaly detection and acting as a virtual sensor.

1. Automate the discovery
of new insights with
FactoryTalk® Analytics™ LogixAI™

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Agenda
FactoryTalk®
Analytics™
LogixAI™
Hands-On Lab
1 2

4. The discovery, interpretation and
communication of meaningful patterns in
data. Analytics relies on the application of
statistics, computer programming and
operations research to quantify performance.
ANALYTICS

5. Scalable analytics
The key differentiator for Rockwell Automation
DEVICESYSTEMENTERPRISE
Am I running ok? Why did a fault happen? I predict a fault will
happen soon.
What action helps to avoid
the fault?
Is Line 1 running ok? Why is Line 1
quality poor?
I predict that Line 1 quality is
moving out of tolerance.
What action helps the operator to
avoid poor quality?
What plant
performed the best?
Why is site A throughput
below plan?
Will I meet plan today?
Tomorrow?
How can I change operations
to improve profitability? Yield? Quality?
DESCRIPTIVE
PRESCRIPTIVE
PREDICTIVEDIAGNOSTIC

6. Scalable analytics
Where FactoryTalk® Analytics™ LogixAI™ fits into the ecosystem
DEVICESYSTEMENTERPRISE
Am I running ok? Why did a fault happen? I predict a fault will
happen soon.
Is Line 1 running ok? Why is Line 1
quality poor?
I predict that Line 1 quality is
moving out of tolerance.
What plant
performed the best?
Why is site A throughput
below plan?
Will I meet plan today?
Tomorrow?
How can I change operations
to improve profitability? Yield? Quality?
+
What action helps to avoid
the fault?
What action helps the operator to
avoid poor quality?
DESCRIPTIVE PRESCRIPTIVEPREDICTIVEDIAGNOSTIC

7. Building predictive models and change management
3% 5%
4%
9%
19%
60%
Building training sets Other
Refining algorithms Mining data for patterns
Collecting data sets Cleaning and organizing data
What
data scientists
spend the most
time doing
Project definition
Data exploration
Data preparation
Model creation, testing & validation
Scoring & deployment
Model management
Other
13%
18%
25%
23%
12%
9%
8%
Groups spend this percentage of time on
each phase in a predictive analytics project
Based on 166 responses, adapted from Eckerson (2007)

8. FactoryTalk® Analytics™ LogixAI™
Descriptive | Diagnostic | Predictive | Prescriptive
Configure Identify data Model Monitor Integrate
DATA SCIENCE EXPERTISE NOT REQUIRED

9. How FactoryTalk® Analytics™ LogixAI™ works
You select output variable that you want to model and monitor
You select potential inputs or state variables that could impact your output variable
FactoryTalk® Analytics™ LogixAI™ automated modeling cleanses the data and determines the relationships as
a mathematical function
You train the model using the User-Defined Tag Structure
You set the model to calculate predictions
Use your automation skills to integrate predictions
Summary

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FactoryTalk® Analytics™ LogixAI™
Modes of operation
Anomaly Detection Virtual Sensor
Learn what's normal, find deviations from normal.
Anomalous behavior that is not understood and cannot be
alarmed upon through typical automation system capabilities
can cause scrap, defects, poor quality or undesirable
operations. A predictive model can be utilized to support
understanding if an output variable could exceed acceptable
operational boundaries. Pinpointing slow changing deviations
that cannot be detected by other means enables proactive
thinking to address conditions before production is impacted.
Estimate values from incomplete data.
A predictive model can act as a virtual sensor (aka soft
sensor) by estimating output values. Having a predicted,
estimated output value reduces the need for manual effort
required to capture and record readings. A virtual sensor can
reduce the need to instrument the operation with expensive or
difficult to implement instruments or sensors. A virtual sensor
can be used to check the reliability of a physical sensing
device, where environmental conditions may cause
degradation of the physical sensor devices’ reading (i.e.
caused by heat, humidity, debris). Ultimately, virtual sensors
can enable tight control or proactive deployment of resources
when the estimated output variable is expected to reach an
out of specification condition.

11. FactoryTalk® Analytics™
LogixAI™
Hands-On Experience

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