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FactoryTalk Analytics LogixAI

Empower OT Personnel With Out-of-the-box AI for Anomaly Detection and Soft Sensors
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Many existing analytics technologies require deep expertise in both data science and industrial processes, which prevents many OT professionals from taking advantage of basic analytics at the plant floor. What if you could harness the power of machine learning to predict anomalies or estimate operational variables in your production processes? And what if you could do this without the help of a data scientist?

Our FactoryTalk® Analytics™ LogixAI® module changes the game by applying analytics within the controller application to achieve process improvement. This add-on module for ControlLogix® controllers fits directly into a control chassis and streams controller data over the backplane to build predictive models. It can continuously monitor a production operation, detecting anomalies against its derived understanding, empowering operators and technicians to easily apply machine learning concepts to get ahead of product quality issues and help protect process integrity—all without having to develop, validate or sustain home-grown machine learning algorithms.


Powerful Modeling Engine

The power of FactoryTalk Analytics LogixAI comes from a modeling engine that is targeted for use cases around anomaly detection and soft sensing. For anomaly detection, the predictions enable a customer to act before a deviation from a set point. For soft sensing, the predictions enable a customer to predict operational values in place of a manual reading or where it is not possible to deploy a sensor or instrument. In both cases, customers can benefit from improving quality yield, increasing asset utilization, and honing in on specific learnings for their operational excellence journey.

FactoryTalk Analytics LogixAI: Machine Learning and Logix
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FactoryTalk Analytics LogixAI: Machine Learning and Logix

What if you could predict anomalies to stay ahead of downtime or losses?

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Process Optimization Use Cases

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Anomaly Detection

Learn what's normal and find deviations

In many manufacturing industries, blades are used for cutting operations and their dulling can be correlated with certain drive parameters. For instance, drive torque can be considered a controlled variable and could be associated with manipulated variables like conveyor speed. A model could be built to learn the physical nature of the process impacting the drive torque resulting in an anomaly detected when the blade is anticipated to dull. The proactive alert can be used to schedule downtime to replace the blade.

Predictvie maintenance

Soft Sensor

Estimate values from incomplete data

Quality parameters, like final humidity within packaged products, can correlate to longer shelf life and higher customer satisfaction. To measure actual values on a finished product, manufacturers are forced to rely upon performing manual measurements that destroy sellable products. Instead, embedding the machine learning intelligence of the FactoryTalk Analytics LogixAI Add-On module into the control chassis gives the ability to analyze variables from line assets like sprayers, dryers and burners to predict a measurement, virtually. Workers can then be notified of problems by configuring alarms on a human machine interface (HMI) or dashboard.


What’s Your Learning Appetite?

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