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Digital Twins: Models of the Real World Powered by Industrial AI

Seemingly everyone is talking about how a digital twin will improve business as part of an overall Industrial AI strategy. But what does the phrase really mean, and how can a digital twin help manufacturers, shippers, and industrial companies? Simply put, a digital twin is a virtual representation of an object or system that spans its entire lifecycle; this mirror image is updated from real-time data and uses simulation, machine learning, and AI-enabled reasoning to glean insights into the operation of the real system being modeled. 

This identical but virtual reproduction of an entire physical system or device runs in parallel with the analogous system or device. Rather than a computer program meant to forecast performance in advance, a digital twin is a tool for predicting and/or maintaining operational performance in real-time, working in concert with its physical counterpart.

 

How do digital twins work?

A digital twin can be created for any system or device—-a jet engine, wind turbine, factory assembly line, etc. One thing a digital twin is not? A basic computerized simulation. The digital twin operates using real-time data inputs from sensors on the actual system or device rather than predicted or OEM-provided values for important performance parameters. This means early stage failures or operational degradations can be identified before they happen in the real system.

Alternative performance data can be input into the model to conduct scenario analyses in support of proposed maintenance actions. The digital twin model can also make real-time recommendations about proactive actions intended to avoid failures or performance compromises. 

 

Digital twins fueled by artificial intelligence have varied applications

Digital twins have a number of important uses:

  • Monitor real-world conditions and operating performance of a physical system.
  • Model system performance prior to actual production or modification of a physical asset.
  • Improve operation of physical systems in the field.
  • Predict maintenance needs and prevent failures.
  • Model proposed maintenance or physical system changes prior to implementation.
  • Identify potential operating hazards.

 Digital twins work well with machine learning capabilities like those employed by Avathon to add greater understanding to systems which over time develop new inputs that influence performance. Creating such a model, often physics- or rules-based, enables simulations that can forecast performance for comparison against what’s actually occurring in the real system. 

A sample of current digital twin applications provides a sense for the scale and scope of benefits the technology is delivering across the industrial world.

  1. Siemens Agent-based Turbine Operations & Maintenance (ATOM) system emulates the global maintenance, repair, and overhaul (MRO) of their fleet of gas turbines. 
  2. GE develops digital twins of all its aerospace products. Built into each physical engine are hundreds of sensors that supply temperature, pressure, and other performance data to the model for each unique engine. Potential performance issues identified by the model are then shared with maintenance crews at the site where the physical engine is operating.
  3. CNH Industrial uses digital twin technology to model its vehicle assembly lines and identify effective maintenance practices on the line. The system provides detailed information about the economic and production consequences of different maintenance policy configurations.

  Digital twins are two-way streets: sensor performance data flows from the physical system into the model, and performance improvement recommendations flow from the model back into the physical system. This bidirectional flow allows the physical system to operate better over time. The cyclical nature of the data flow also allows the digital twin model to constantly improve its own analytical abilities. The digital twin is an ever-improving source of value going forward. 

 

The interplay of digital twins and artificial intelligence

To better understand the relationship between AI and digital twins, it’s important to acknowledge a couple of potential issues from the outset.

First, because model outputs are typically based on well-defined, inflexible performance thresholds, the frequent recurrence of alarms can lead to alert fatigue. Second, digital twin models are based on OEM standards of performance for new systems and do not modify their alerting thresholds as the system ages and its performance evolves. 

AI-based normal behavior modeling (NBM) is well-equipped to deal with both of these limitations. By continuously updating its understanding of normal system operation, a far smaller quantity of meaningful alerts are generated. And because NBM models are periodically updated to reflect the very latest operational performance of the system, the model evolves with time, keeping it relevant and accurate.

Finally, whereas digital twins are typically focused on a single piece of equipment or asset (e.g., a turbine), NBM models are effective at modeling entire complex systems comprising dozens or even hundreds of individual assets. These factors make AI-powered NBM predictive maintenance models excellent complements to digital twins.

 

The future of digital twins

Industry analysts expect the digital twin market to rise from $6.9 billion in 2022 to as much as $73.5 billion by 2027, representing a CAGR of 60.6%. The healthcare and pharmaceutical sectors are the largest drivers of the market. And so, with the simultaneous growth of the digital twin and AI marketplaces, it makes sense to regard the two as companion technologies, each ideally equipped to maximize the benefits provided by the other.

With Avathon, industrial users replicate physical assets digitally; these digital twins provide new ways to optimize and monitor operations. Customers can also visualize asset performance to identify and correct operational issues. Get started with Avathon’s AI-powered digital twin solution today.  

 

 

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