Manufacturing, energy and transportation companies are increasingly benefiting from Industry 4.0 applications after years of hype. The emergence of digital twins is one of the main reasons engineering and design teams are actualising these benefits for their enterprises.
What is a Digital Twin?
A digital twin is a real-time digital representation of a real-world physical asset in operation that reflects the asset’s current condition and provides relevant historical data. Companies can leverage digital twins to analyse the real-time performance, optimise operations, predict future behaviour, or refine control of its assets, such as pumps, engines, power plants, manufacturing lines, or fleets of vehicles.
Why use a Digital Twin?
There are numerous ways engineering teams can design and deploy digital twins to deliver value to their companies and optimise future operations.
Digital twins capture the physical asset’s history – being 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 differs based on how the digital twin is being used and what’s captured in the current state. For example, a digital twin used for fault classification, will capture a history that includes a specific pump’s operational data from its healthy and faulty state. In the future, engineers can then compare the operational data from that 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.
The ability to monitor a whole fleet using digital twins brings advantages to planning operational events and improving maintenance strategies.
For example, when a specific pump is nearing failure, the digital twins can assess how this will affect the efficiency of the fleet and potential costs. This informs the company when making the decision between ordering a new part and waiting for it to arrive or paying more for expedited ship to get the part as soon as possible.
Simulate Future Scenarios
Companies can use digital twins to simulate future scenarios to see how factors such as weather, fleet size, or different operating conditions affect performance. This approach helps manage assets and optimise operations by informing maintenance schedules or flagging expected failures in advance.
Digital twins can be leveraged by companies for a variety of applications, including anomaly detection, operations optimisation and predictive maintenance.
The digital twin model runs in parallel to the real assets and flags operational behaviour that deviates from expected behaviour in real-time. For example, a petroleum company may stream sensor data from offshore oil rigs that operate continuously while the digital twin model looks for anomalies in the operational behaviour to flag potential equipment damage.
Companies can apply variables such as weather, fleet size, energy costs, or performance factors to trigger hundreds or thousands of simulations to evaluate readiness or necessary adjustments to current system set points. This approach lets you optimise system operations to mitigate risk, reduce cost, or gain system efficiencies. For example, Mathworks worked with a beverage and industry food producer to create a digital twin that supported not just design optimisation but also fault testing and predictive maintenance.
In industrial automation and machinery applications, companies can use digital twin models to determine remaining useful life and the most opportune time to service or replace equipment.
In a characteristic smart connected system topology as shown in Fig. 1, the digital twins could be executed on the smart asset, at the edge, or on the IT/OT layers depending on the required response time of the application. For example, predictive maintenance, a common Industry 4.0 application, generally requires making real-time or time-sensitive decisions – meaning the digital twin should be integrated directly with the asset or at the edge.
How does a Digital Twin work?
The following example describes a company that has three well sites at different locations where it operates multiple pumps to extract oil and gas from the ground and wants to apply predictive maintenance to a digital twin on its multiple pumps.
Engineers can build a digital model that gets updated with the incoming data transmitted from sensors and current operating conditions of the pump. As seen in Fig. 2, the digital twin model takes these readings and outputs the current state of the pump that is analysed by the company’s staff to unlock several benefits including:
- Equipment Downtime Reduction: Each pump contains valuable components such as valves, seals, and plungers. The digital twin helps reduce downtime by enabling staff to prevent failures by predicting them in advance.
- Inventory Management: Engineers can also leverage the digital twin to identify faults that develop and get insights into what parts may need repair or replacement – enabling better parts inventory management.
- Fleet Management, What-If Simulations, and Operational Planning: In the three well-site locations, all the pumps may have similar functionality; however, each location has different environmental factors, such as temperature, that affect how the pumps operate. The digital twin enables the company to monitor the whole fleet, simulate future scenarios and make comparisons to identify ways to increase efficiency and improve operational planning.
How to Build a Digital Twin
Engineers will be increasingly asked to develop digital twins for their company given the above benefits. Here are three methods design teams must keep in mind as they prepare, build and apply their digital twin models.
A company looking to optimise maintenance schedules by estimating remaining useful life (RUL) will use a data-driven model as the type of the data from the asset will determine which model teams will be using. Similarity models can be used if the company has complete histories from similar machines. If only failure data is available, then survival models can be used, and if failure data is not available, but the safety threshold is known, a degradation model can be used. If failure data is not available but you know of a safety threshold, you can use degradation models to estimate RUL. In this RUL scenario, the degradation model is constantly updated using the data from the pump measured by different sensors such as pressure, flow, and vibration.
If a company wants to simulate future scenarios and monitor how the fleet will behave under those scenarios it would use a physics-based model, which is created by connecting mechanical and hydraulic components. This model is fed with data from an asset, and its parameters are estimated and tuned with this incoming data to keep the model up to date. Engineers can then inject different types of faults and simulate the pump’s behaviour under different fault conditions.
How to Apply Digital Twins
Design teams need to create a unique digital twin for every individual asset. This means that for each asset at different location, teams must create a unique digital twin that has been initialised with the specific asset’s parameters. The total number of unique twins will depend on the application. If teams are modeling a system of systems, they may or may not need a twin for each system of components depending on your required level of precision. For example, if the intention is to run failure prediction and fault classification, design teams need to create different models that serve these different purposes.
Delivering Value with Digital Twin
The flexibility and various potential benefits of digital twins makes them a top priority for companies transitioning to Industry 4.0. Having an up-to-date representation of real operating assets lets engineering and design teams unlock insights in data to optimise, improve efficiencies, automate, and evaluate future performance – all delivering cost savings and shorter development timelines.