Introduction: The digital twin opportunity
In industrial operations, a digital twin is a virtual representation of a physical asset, process, or system that spans its entire lifecycle. This mirror image is updated with real-time data and uses simulation, machine learning, and AI-enabled reasoning to glean insights into the operation of the real system being modeled. While the twin provides the “body” (the data structure and visualization), artificial intelligence (AI) acts as the “brain.” The synergy between the two allows industries to evolve from simply seeing what is happening in their operations to predicting and optimizing what will happen next.
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—-wind turbine, jet engine, factory assembly line, etc. One thing a digital twin is not? A static computer 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 that early-stage failures or operational degradations are identified before they happen in the real system.
Alternative performance data can also be input into the model to perform scenario analyses in support of proposed maintenance or system upgrade actions. The digital twin model can then make real-time recommendations about proactive actions intended to avoid failures or performance compromises.
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 thus a continuously-evolving source of value creation.
Digital twins provide a wide range of valuable use cases
Predictive/Prescriptive Maintenance and Anomaly Detection
AI algorithms analyze the massive streams of real-time sensor data flowing into the digital twin. Comparing current data against historical performance enables the model to identify microscopic anomalies human operators would likely miss.
- Early Warning: Detecting a slight increase in vibration or temperature can indicate an impending component failure.
- Remaining Useful Life (RUL): By calculating exactly how many hours of operation are remaining, maintenance can be scheduled during planned downtimes rather than responding to unexpected emergency outages.
Real-Time Process Optimization
Rather than use outdated data for planning, AI-enabled digital twins use live data to adjust operating parameters in real time.
- Dynamic Setpoints: If the temperature in a factory rises, the AI can instruct the digital twin to simulate the effect on chemical processes and automatically adjust HVAC or machinery speeds in the physical plant to maintain output quality.
- Energy Efficiency: AI identifies the most energy-efficient process for a production cycle, reducing costs without sacrificing output volume or quality.
What-If Scenario Simulation
AI-powered models allow engineers to run thousands of simulations within the digital twin environment without risking physical assets, compromising product quality, or reducing productivity.
- Stress Testing: “What happens if we raise output rate by 10%?” The digital twin responds to such queries by simulating mechanical stresses, heat generation, and potential bottlenecks.
- Supply Chain Shocks: Integrating AI with a twin of the end-to-end supply chain enables operators to simulate the impact of a delayed shipment and find the optimal alternate route instantly.
Generative AI and Natural Language Interfaces
Large Language Models (LLMs) can be integrated into the digital twin to create so-called industrial copilots.
- Natural Language Queries: Rather than trying to continuously understand complex dashboards, a technician can ask, “Why is the pressure in Line 7 unstable?,” and the digital twin will model and analyze the specific component to deliver a root cause analysis.
- Automated Documentation: AI automatically creates maintenance logs and standard operating procedures (SOPs) based on data recorded by the digital twin during routine operation and periodic repairs.
Autonomous Operations (Self-Healing Processes)
The ultimate evolution of the digital twin is the closed-loop system, a fully autonomous agentic application in which the twin not only suggests changes, but executes them as well.
- Reinforcement Learning: AI agents optimize process operation by running millions of trials in the virtual twin.
- Self-Correction: When the digital twin detects a deviation in product quality or productivity, the AI automatically recalibrates the physical equipment to correct errors in real-time, achieving an autonomous operating state.
How do digital twins differ from normal behavior models
As asset operators delve deeper into the evaluation of AI-enabled tools for managing their portfolios, the question will inevitably arise concerning just how digital twins differ from normal behavior modeling (NBM). To better understand the relationship between digital twins and NBM, it’s useful to clarify the areas in which these technologies are similar and where they differ.
First, it’s worth taking a moment to distinguish between physics-based digital twins, i.e., the sort provided by OEMs, and data-driven digital twins, to which NBM is closely related. In the former case, because OEM-provided performance models are typically based on well-defined, inflexible performance thresholds, the frequent recurrence of alarms can lead to alert fatigue. Thus, digital twin models based on OEM standards of performance for new systems typically do not modify their alerting thresholds as the system ages and performance evolves.
AI-based NBM systems are well-equipped to deal with these limitations. By continuously updating their understanding of normal system operation, a smaller quantity of alerts are generated that are likely to be actionable (think of NBM as a noise reducer or, alternatively, a signal-to-noise improver). And because NBM models are periodically retrained to reflect the very latest operational performance of the system, the models evolve with time, keeping them 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.
Conclusion: Digital twins are valuable tools for ensuring system performance
While an OEM-provided digital twin can tell you what is happening, an AI-driven digital twin tells you why it’s happening and how to fix it before it becomes a problem.
Digital twins work well with machine learning capabilities to add greater understanding to systems which over time develop new inputs that influence performance. Creating such a dynamic model, versus physics- or rules-based ones, enables simulations that can forecast performance for comparison against what’s actually occurring in the real system.
Industry analysts expect the digital twin market to rise from $24.5-$29.3 Bn in 2025 to as much as $120-$150 Bn by 2030. The manufacturing, automotive, and transportation sectors are expected to be the largest drivers of this growth. 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 critical assets digitally, ensuring continuing performance while providing new ways to optimize future operations.
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