FactoryTalk Anaytics Augmented Modeler

Enable OT Personnel with Ready-to-use Machine Learning Applications for Manufacturing

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Enable OT Personnel with Ready-to-use Machine Learning Applications for Manufacturing

OT engineers interact intimately with plant data, processes, and equipment on a daily basis. Owing to their unique domain expertise in plant operations, they are best suited to bring out tangible improvement opportunities on the plant floor. However, they lack access to an easy-to-use machine learning solution that can help them support continuous improvement objective with operational performance for targeted manufacturing use cases.

FactoryTalk® Analytics™ Augmented Modeler is a visually interactive workflow solution to let plant engineers easily build, deploy and monitor targeted machine learning solutions in your plant—without the need for data scientists. The power of our FactoryTalk Analytics Augmented Modeler application is that the data science is built in—users are not required to pick the right ML algorithm from a suite of options. The application pre-delivers three powerful machine learning applications—Anomaly Detection, Predictive Maintenance and Predictive KPIs—that have specific individual value in manufacturing operations. The machine learning applications are built on top of our FactoryTalk Analytics products so that industrial connectivity, scalability and product security is built in.

FactoryTalk Analytics Augmented Modeler is designed to help OT professionals drive rapid machine learning solutions with minimal data science effort or training—accelerating time to value.

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Targeted Machine Learning Apps that Are Ready-to-use

FactoryTalk Analytics Augmented Modeler is a workflow wrapper to build, deploy and monitor machine learning solutions. It comprises three powerful applications for machine learning development in your operations.

Anomaly Detection

With Anomaly Detection, applications are trained on normal operating data to identify problematic unit operations or lines, be alerted on unusual operations, and identify leading indicators before significant losses occur. Anomaly Detection offers clues to help prevent costly shutdowns or minimize unexpected performance deviations.

Predictive Maintenance

Predictive Maintenance applications are trained on identified patterns of past failure data to detect and recognize potential future equipment failures, indicate failure urgency and may include an Anomaly Detection model to detect new types of equipment failures. Through Predictive Maintenance, you have the information for making intelligent prioritization decisions: defer unnecessary maintenance cycles, avoid expensive equipment wear and reduce unplanned equipment failure or downtime.

Predictive KPIs

FactoryTalk Analytics Augmented Modeler software also supports Predictive KPIs trained on measured lab qualities or various plant performance indicators. Examples include: what is making energy costs unusually high, or quality or productivity poor now? This allows you to improve quality, efficiency, yield or productivity with higher plant insight into performance deviations.

Visually Interactive Machine Learning

FactoryTalk Analytics Augmented Modeler provides a visually interactive workflow for plant engineers to successfully evaluate data sources, prepare data visually, and extend pre-built machine learning applications to solve plant-specific challenges. They can discover and ingest business data from a common data lake or database in a simple manner, use visual tools to be guided through data wrangling, and push ML applications to a ready edge, on-premise or cloud.

FactoryTalk Analytics Augmented Modeler

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FactoryTalk Analytics Augmented Modeler

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Industry Use Cases

Life Sciences

Product quality is so essential in the life sciences industries—as well as other industries, like food and beverage—that issues with ingredients, processing, purity or cleaning can create significant regulatory or market costs. Developing solutions that capture the uniqueness of each facility is key to avoid nuisance alarms and false positives. The Anomaly Detection application can be used to learn the multivariate interactions that map to ‘good’ patterns and intelligently alert when deviations become significant. Plant specialists can realize unprecedented value by readily adjusting alarm tolerances to the desired level of oversight on useful models.

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Mining

In the harsh, broadly distributed environments of the mining industry, equipment including mills, kilns, flotation towers, filters and smelters can fail over time due to misalignment, ineffective cooling, lubrication, overloading or losses. Predictive maintenance apps can provide condition-based monitoring on hundreds of equipment simultaneously and dispatch maintenance activities to help avoid extensive future outages. In addition, the Anomaly Detection app can identify inconsistencies and abnormal operations in integrated systems, while providing indications of what has failed— even with unlabeled failure data.

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Chemical

In the chemical industry, measurements in plants—especially that produce vinyl chloride monomer or sulfuric acid—tend to fail and processes can become unstable due to extreme operating conditions. Similarly, in the Oil & Gas industry, there can be several anomalies across wellheads connected by a shared reservoir or methane concentration can shift away from design in a gas processing plant. For both industries, the Anomaly Detection app can learn on data filtered to ‘good’ operations and help identify issues early before significant deviations occur.

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