Digital twins are digital representations of real-world assets that are in use. They reflect the state of assets and enable real-time access to pertinent historical data. Engineers will find these up-to-date representations essential in adjusting to the demands of the Industry 4.0 age.
Teams can use digital twins to analyze operational data to improve and evaluate present and future performance and increase automation and efficiency. Engineers can save money and speed up development as a result of this.
Digital twins offer numerous benefits
One of the most important advantages of digital twins is that they supply large amounts of data to engineers, making better operational decisions. What’s crucial is that engineers may select which types of data to acquire for distinct digital twins. This takes advantage of the fact that both digital twins continually depict a real asset’s current state while keeping track of past states that make up the asset’s history.
So, how would an engineer put this to use in the real world? They can, for example, generate a digital twin that captures the history of a single pump’s operating data from its healthy to problematic states for fault classification. Engineers will be able to compare operational data history acquired by a digital twin for one type of pump to another in the future when deciding which type of pump to utilize for a certain use case. This helps customers to comprehend the impact of defects on each pump and the effect on efficiency, allowing them to select a pump that is more suited to their demands.
Another benefit is that digital twins assist engineers in bettering their predictive maintenance plan. When a single pump is on the verge of failing, for example, teams can use the digital twin asset history to determine how this problem will affect the efficiency of the entire fleet of pumps and the potential cost. This information assists engineering leaders and teams determine whether to order a new item with standard or accelerated delivery, the latter of which will cost more but may cost-effective in the long run. Data insights from the twin can also aid teams in determining the remaining usable life of the equipment operating normally and determining the optimal time to service or replace it entirely.
Engineers can use digital twin data to simulate future situations to learn how weather, operational environments, fleet size, and other factors influence equipment performance. This enables them to manage and optimize assets to reduce risk, lower costs, and improve system efficiency.
The use of digital twins for anomaly detection has a lot of potentials. In this scenario, a digital twin works alongside a real asset and alerts engineers in real-time if operational behavior deviates from expected parameters. An oil corporation, for example, would feed data from sensors on continually operating offshore rigs, and their digital twin models would detect irregularities. Engineers can detect possible equipment damage as it occurs, avoiding future disturbance and lowering expenses.
In-the-field Use of Digital Twins
Consider the case of an oil and gas business with three well sites in different regions to see how engineers might profit from digital twins in practice. Assume that the organization operates oil and gas extraction pumps at each location. The organization can use a digital twin to perform predictive maintenance on many pumps. Engineers might create a computer model that updates itself based on real-time data from sensors and pump operating circumstances. The readings are record in the digital twin model, producing the current state. Employees can use this information to:
⚙️ Reduce the amount of time that your equipment is unavailable.
Valves, seals, and plungers are all parts of a pump that can fail. Staff can use digital twins to detect faults ahead of time, reducing equipment downtime.
⚙️ Keep track of inventory.
Engineers can use digital twins to identify developing defects and determine which pieces of a system require repair or replacement, allowing for better inventory management.
⚙️ Enhance your operational planning
Pumps may have comparable functionality at each well site location, but environmental factors, such as temperature, may alter operations performance. The company can use digital twins to track the entire fleet, model future scenarios, and compare pumps. Engineers can use this information to find ways to increase efficiency and operational planning.
Developing Digital Twins
There are various approaches that engineers entrusted with producing digital twins and enjoying the benefits for their organization should be aware of.
⚙️Model-based on data
A data-driven approach should be used by organizations that want to optimize maintenance plans by calculating the remaining usable life. The type of data from an asset will determine which data-driven model to choose. Engineers can use similarity models if they have entire histories of related machines. Survival models can be used if only failure data is provided.
They can utilize a degradation model if they don’t have any failure data but know the safety threshold. The degradation model is regularly updated utilizing data from the pump and measured by various sensors such as vibration, pressure, and flow to calculate the remaining useful life.
⚙️ Model-based on physics
A physics-based model should be used if a corporation wants to simulate future scenarios and see how its fleet will behave. Engineers transfer data from an asset into the model, connecting mechanical and hydraulic components. To keep the model up to current, its parameters are calculated and modified using the incoming data. The behavior of assets can then be tested under several fault kinds and scenarios.
⚙️ Kalman Filters
Another model that mixes data and physics that engineers should explore is Kalman filters, an algorithm that estimates unknown variables based on measurements taken over time. This is a fantastic alternative for engineers seeking digital twin models that establish a history of a pump’s degradation and update the current state regularly to depict the asset’s current condition.
Design teams must build a distinct digital twin for each asset once leaders have agreed on the models to utilize. Engineers must generate a new digital twin for each object at a different place, initialized with the parameters of that unique piece.
The overall number of twins necessary is determined by the application and level of precision required. When modeling a system of systems, a digital twin for each system of components may or may not be required. For example, to run both failure prediction and fault categorization, design teams must construct models that fulfill both functions.
Because of the numerous advantages digital twins provide, engineers should make exploiting this technology a high priority as they convert their organizations to Industry 4.0 goals. Engineers can use digital twins to gain significant insights about equipment performance, helping them make better decisions in all parts of their work.
Employees may improve efficiency, reduce equipment downtime, and prepare for the future by having up-to-date representations of all assets. These enhancements mean that staff will be able to save development time, save money, and eventually help the company’s bottom line.