Unify for Azure Digital Twins

Accelerate Twin Development and Deployment

Using Element Unify and Azure Digital Twins to monitor emissions

The integration between Microsoft Azure and Element Unify speeds up the process of building digital twins and enhances their effectiveness by simplifying the integration of data from both enterprise applications and operational systems (OT). As a result, customers can gain insights and value much faster, and the integration enables greater scalability.

Accelerate Deployment of Azure Digital Twin Instances

Realize Governance and Maintenance of Models

Leverage and Reuse Existing Azure Digital Twin Models

BUILDING DIGITAL TWINS WITH ELEMENT UNIFY AND
AZURE DIGITAL TWINS – REFERENCE ARCHITECTURE

Partner connectivity applications Ignition and KepServerEX running on AWS Certified industrial PC ingests real-time sensor data and asset model data sent from PLC and industrial historians.

PI Interfaces/Connectors collect data sent from sensors, PLCs, SCADA, etc. into PI Server running at plants or regional data centers. Element Unify AF Management Client retrieves, deploys and manages PI Asset Framework asset models.

Element Unify ingests asset models and tag lists exported from edge applications (i.e. Ignition, Kepware, etc.)

Element Unify P&ID Productivity Client automates harvesting of asset information from graphical engineering designs. EAM and ERP systems (residing either on-premises or in an Amazon VPC), including SAP FLOC and work orders, connect to Element Unify through iPaas layer.

Element Unify is the centralized IT/OT data management platform that integrates, contextualizes, and governs asset data models from OSIsoft PI Asset Framework (bi-directional), Ignition, Kepware running at the edge, and IT data sources like SAP and Maximo, and performs bidirectional model sync with AWS IoT SiteWise.

Element Unify reads existing asset model data from AWS IoT SiteWise and then provisions the new, enriched asset model data back into AWS IoT SiteWise.

Time series data flows into AWS IoT SiteWise from partner connectivity applications running at the edge. AWS IoT SiteWise stores the asset model data and ingests, filters, transforms and processes all incoming data before storing both raw and processed data in a managed time-series data store. AWS IoT SiteWise publishes an MQTT message to AWS IoT Core each time the asset property value updates.

An AWS IoT Core rule publishes these asset propery update messages it receives from AWS IoT SiteWise in near real-time into Amazon Kinesis Data Firehose.

Amazon Kinesis Data Firehose captures data from AWS IoT Core, transforms it, and delivers the data in near real-time to Amazon S3 industrial data lake.

Once real-time and historical data is available in Amazon S3 industrial data lake, Amazon Lookout for Equipment can use the data to detect abnormal equipment behavior, so that machine failures can be detected before failure occur and avoid unplanned downtime. Computed metrics can be written back into Amazon S3 for storage and consumption.

Amazon QuickSight can be used with Amazon Athena to create and publish interactive Bi dashboards.

Integrate and contextualize

Integrate and contextualize data from disparate IT/OT/ET sources using no-code pipelines eliminating manual effort and greatly speeding time to deployment.

element product industrial engines demo graphic

Capture data relationships

Capture data relationships in a semantic data model realizing flexibility and ability to keep model in sync with underlying assets.

element product industrial engines graphic

ELEMENT UNIFY INCLUDES PRE-BUILT CONNECTORS FOR AZURE SERVICES

Unify has a range of pre-built connectors (e.g for Azure Blob Storage, Azure Data Lake Storage, JDBC Connector for Azure, etc.) that make it simple to ingest data or publish models within an Azure cloud context.