Just as glass cleaner improves the view in a mirror, Bentley Systems' OpenPlant and Siemens' Comos teams have used their complimentary software to jointly develop PlantSight cloud- and browser-based services, which enable up-to-date and as-operated digital twins for synchronizing real equipment, applications and facilities with their engineering representations for more efficient operations.
"The drive to extend existing assets is always there, but it's difficult to know what assets you have, capture their data, know how they’re performing, compare them to the rest of the fleet and put them in historical context," says Ken Adamson, vice president, business development, Bentley Systems. "PlantSight puts this information right at hand and resolves issues between data streams, aligning it to add value at a better price."
Because production processes are constantly changing, PlantSight coordinates physical reality and engineering information to create holistic digital contexts, allowing consistently understood digital devices across disparate data sources. It gives operators highly trustworthy, quality information for improved reliability and continuous readiness. Users get the best data from all sources, allowing them to quickly identify what's wrong and correct it. These digital twins also are continually updated and evergreen in a portal environment, so users can link in from anywhere, view them in 2-D schematics, 3-D models or whatever format they want while continuing to make informed decisions.
Adamson adds that PlantSight has automatic and dynamic updates, responds to users' needs as they perceive them, and lets them seamlessly add functions without involving IT. This practice allows immediate ROI because users can quickly pinpoint problems with information supporting maintenance activities and can respond much faster. PlantSight's cloud deployment also means it can be more personalized and set up in the unique ways that make the most sense to each user and their process."
Using reality modeling—a form of photogrammetry that uses digital photos supplemented with laser scans to create accurate, 3-D reality models—to capture data from existing physical plants in context, PlantSight can integrate the 3-D reality model with other models or information from Bentley, Siemens, Aveva, Hexagon, AutoCAD or Excel spreadsheets.
Next, PlantSight uses machine learning (ML) and artificial intelligence (AI) techniques to identify and recognize components and associate devices with tag numbers in its asset registry, creating navigation hot spots, so that the 3-D plant rendering is made with minimal effort.
"PlantSight harvests and aggregates data and models, mapping them to a consistent set of definitions," adds Adamson. "For instance, a 'valve' always has the same definition when the user seeks it, no matter the data source. If, however, there are inconsistencies in the data, users can intercede and interject changes. PlantSight then delivers visualizations via cloud services and mobile devices. For example, it can interact with plant data historians to display an alarm, but also show what's happening around it.
"This flexibility to show data types in different ways is what increases insights and improves decisions. PlantSight also shows what's happening between different models and interfaces, all on one screen without requiring users to find maintenance manuals or search through piping and instrumentation diagrams. PlantSight lets users follow the ball down to individual components, link to reliability systems to better plan maintenance and work with software like Siemens XHQ dashboard to perform more analytics."
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