Edge AI and Managing Risk in the Cloud

Industrial AI

Industrial AI plays a major role in making process predictions

  • Leverage data to generate more value and insight to help operations
  • Consolidating data and deploying via the cloud opens up a plethora of solutions and application possibilities
  • Powerful risk mitigation strategies include deploying edge solutions efficiently

As organisations see greater and greater volumes of data generated from their operations. It is understandable and imperative that this data is leveraged to generate more value and insight that helps operations and asset integrity managers ‘do more, better’.

The good news is that huge amounts of value can be generated by integrating information from assets (IT systems), sensors (OT systems) and design (engineering) systems. In a manufacturing environment, most of these systems are running in multiple networks/units. Even the ownership of this data varies from plant to plant and is also dependent on the user roles.

“In this context, you would need a centralised environment where you can integrate, validate, contextualize the cleaned data and make it accessible centrally – this is where the cloud presents great potential and can yield benefits,” says Charles Blackbeard, Business Development Manager, ABB Ability™ Digital.

Consolidating data and deploying via the cloud opens up a plethora of solutions and application possibilities, including business, asset and sustainability solutions, planning and logistics solutions, operation solutions, supply chain solutions, and advanced supervisory solutions. From an industrial point of view, it also empowers predictive, diagnostics and prescriptive analytics applications. This means that previously siloed information is now available across the entire enterprise.

“Concerns about security are, of course, front of mind when you increase the number of users across a system, more devices connected to the network, and more and more information being stored on the cloud,” says Blackbeard.

There are also a number of data and integration challenges when considering the deployment of cloud solutions:

  • Proprietary controls
  • Multiple data formats
  • No contextual information
  • Quality issues
  • Designed for operations
  • Differences across industrial verticals
  • Directly coupled to applications
  • Isolated networks

There are some powerful risk mitigation strategies, however: For example, deploying edge solutions – whether there is ‘lite edge’ – for secured communication from edge to cloud, or ‘heavy edge’ – for deploying application at the source of data and for enabling distributed computing, faster response time and cost optimisation.

There are several clear benefits to this approach:

  • SAAS based business model: Enables customers to focus on operational efficiency.
  • Building an integrated asset information model using operations, process control, assets and design systems based on a contextualisation engine.
  • Empowering user personas: Solution positioning with a strong focus on controls or process. engineer, manufacturing system engineer, data engineer, integration engineer, system architect
  • Hybrid deployment: Enabling hybrid deployment of containerised solutions, thereby reducing data round trips and enabling faster responses.
  • Cost reduction: Distribute data processing between edge and cloud by filtering and aggregating high-volume data at the edge.
  • Operational resilience: Make the site operation resilient to unreliable networks by enabling hosting of managed application with data at the edge. Synchronise data with the cloud when the connectivity returns.

Web: www.abb.com

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