Chapter 2
Why Use a Digital Twin?
Digital twins are used in a variety of applications like anomaly detection, asset management, and fleet management.
These common applications drive the intended use of the digital twins. In a characteristic smart connected system topology as shown below, the digital twins could be executed on the smart asset, at the edge, or on the IT/OT layers depending on the response time that the application requires.
For example, predictive maintenance generally would require making real-time or time-sensitive decisions. When you implement multiple digital twins for different applications, you can deploy each one differently into the system topology.
Example
To optimize maintenance schedules, use remaining useful life estimation (RUL).
Example
To optimize current operations, use simulation to create future “what-if” scenarios.
Digital Twin Applications
Predictive Maintenance
In industrial automation and machinery applications, you can use digital twin models to determine remaining useful life and the most opportune time to service or replace equipment.
Baker Hughes predictive maintenance alarm system.
Operations Optimization
Use variables such as weather, fleet size, energy costs, or performance factors to trigger hundreds or thousands of what-if simulations to evaluate readiness or necessary adjustments to current system set points. This approach lets you optimize system operations to mitigate risk, reduce cost, or gain system efficiencies.
Monte Carlo simulations to evaluate potential asset behavior.
Anomaly Detection
The digital twin model runs in parallel to the real assets and immediately flags operational behavior that deviates from expected (simulated) behavior.
For example, a petroleum company may stream sensor data from offshore oil rigs that operate continuously. The digital twin model will look for anomalies in the operational behavior to help avoid catastrophic damage.
Prototype of industrial IoT deployment on oil rig. (Image courtesy: National Oilwell Varco)
Benefits of Using Digital Twins
Asset History
An important feature of a digital twin is that it captures the real asset’s history. Recall that the digital twin model is updated periodically to represent the real asset’s current state. Over time, these past states become the asset’s history.
The type of information included in this history might differ based on how you’re using the digital twin and what’s captured in the current state.
For example, if you're using the digital twin for fault classification, the history captured by each digital twin can be the operational data from the specific pump and its healthy and faulty state.
In the future, you can compare the operational data from one pump to the digital twin histories of other pumps to understand how they behaved under similar faults and the effect on the fleet's efficiency.
Scenario A: Order parts and run the pump in a suboptimal state until they arrive.
Scenario B: Spend more to expedite shipping to get the parts quickly and reduce time spent running suboptimally.
Maintenance Strategies
The ability to monitor the whole fleet using digital twins brings additional advantages in terms of planning operational events and improving maintenance strategies.
Imagine a situation in which one of the pumps is expected to fail soon. Using digital twins, you can assess how this will affect the efficiency of the fleet and what it will cost you.
Based on this analysis, you can either:
- Scenario A: Order replacements and run your pump in a suboptimal state until you get the new parts
- Scenario B: Pay more for shipping and get the parts immediately to schedule maintenance as soon as possible
Simulate Future Scenarios
As the digital twins help you understand the history of the assets, they also help you with future planning. You can use digital twins to simulate hundreds of future scenarios to see how factors such as weather, fleet size, or different operating conditions affect the performance.
This approach will help you manage your assets and optimize operations by informing your maintenance staff about the expected failures in advance so they can plan for future repairs and replacements.
