Digital Twin Solutions for Smarter Industrial Operations
Wiki Article
Industrial facilities are becoming increasingly connected, data-driven, and complex. Equipment operators and engineering teams now have access to information from sensors, inspection systems, maintenance records, engineering simulations, and operational databases. The challenge is to bring these different sources of information together in a useful and reliable way.
Digital twin technology provides a framework for doing exactly that. A digital twin creates a virtual representation of a physical asset, system, or facility and connects that representation with real-world data. When combined with engineering simulation, artificial intelligence, machine learning, and Industrial Internet of Things technologies, a digital twin can provide valuable insight into equipment performance and asset condition.
ProSIM offers digital twin services that combine engineering expertise with digital technologies, including Reduced Order Models, AI and machine learning, IIoT integration, hybrid modelling, and BIM-based digital information. ProSIM Digital Twin Services
What Is a Digital Twin?
A digital twin is a virtual representation of a physical object or system that can be connected to information from its real-world counterpart.
Consider an industrial compressor as an example. A conventional engineering drawing may show its dimensions and connections. A 3D model may provide a detailed representation of its geometry.
A digital twin can go much further.
It can combine the equipment's engineering characteristics with sensor measurements, operating history, maintenance information, performance data, and analytical models.
The result is a digital environment that can help engineers understand how the physical asset is performing and how it may behave under different conditions.
Digital Twins in Industrial Engineering
Digital twin technology is particularly useful in industries where assets are expensive, complex, safety-critical, or difficult to replace.
Power plants, oil and gas facilities, process plants, offshore installations, manufacturing systems, and other industrial facilities contain equipment that must operate reliably for long periods.
A digital twin can help connect engineering design information with operational experience.
This creates an opportunity to use the same digital environment throughout different stages of an asset's lifecycle.
Connecting Physical and Digital Assets
The value of a digital twin comes from the connection between the physical asset and its virtual counterpart.
Sensors installed on equipment can collect operational information such as:
⦁ Temperature
⦁ Pressure
⦁ Vibration
⦁ Flow
⦁ Speed
⦁ Load
⦁ Energy consumption
⦁ Equipment status
This information can be transferred to a digital platform for analysis.
The digital twin can then compare current conditions with expected behaviour and identify changes that may require further investigation.
Beyond Traditional 3D Modelling
A 3D model is an important component of many digital engineering projects, but it is not automatically a digital twin.
A 3D model primarily represents physical geometry.
A digital twin can incorporate geometry together with operational information, engineering calculations, historical data, sensor measurements, and predictive models.
This distinction is particularly important for industrial organizations because operational decisions often require more information than geometry alone can provide.
Reduced Order Models
Detailed engineering simulations can be computationally demanding.
Finite Element Analysis, Computational Fluid Dynamics, and other high-fidelity methods may require significant processing resources. While these methods can provide detailed results, they are not always practical for continuous real-time applications.
Reduced Order Models provide a way to simplify complex physics-based models while retaining the characteristics needed for a specific application.
ProSIM develops Reduced Order Models to convert computationally intensive engineering simulations into faster numerical models suitable for real-time applications. ProSIM Reduced Order Modelling Services
These models can support faster simulation and decision-making.
Real-Time Engineering Simulation
Real-time simulation can be valuable when operational conditions are constantly changing.
Instead of waiting for a detailed simulation to complete, a reduced model can provide rapid predictions based on current inputs.
This can support equipment monitoring, performance evaluation, operational optimization, and predictive maintenance.
The model can be deployed close to the equipment or through cloud infrastructure depending on the requirements of the application.
Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning provide another important component of digital twin technology.
Industrial equipment can produce enormous amounts of historical and real-time data. Machine learning algorithms can examine these datasets to identify patterns associated with normal operation, degradation, or abnormal behaviour.
ProSIM integrates AI and machine learning into digital twin solutions for applications such as predictive maintenance, early problem identification, and remaining useful life estimation. ProSIM AI and ML Digital Twin Solutions
The objective is to transform large amounts of operational data into actionable engineering information.
Predictive Maintenance
Predictive maintenance uses equipment information to identify potential problems before they become major failures.
Traditional preventive maintenance may involve servicing equipment according to a fixed schedule. Predictive approaches instead consider actual equipment condition.
For example, a gradual increase in vibration may indicate developing mechanical problems. A change in temperature or pressure may also indicate that equipment behaviour has changed.
A digital twin can combine these measurements with engineering models and historical data to support earlier investigation.
Remaining Useful Life Estimation
Another important application is estimating remaining useful life.
Equipment gradually deteriorates as a result of operating conditions, fatigue, corrosion, wear, creep, thermal cycling, vibration, and other mechanisms.
Historical and real-time data can be analysed to identify degradation patterns.
ProSIM includes remaining useful life estimation within its AI and digital twin capabilities. ProSIM Remaining Useful Life Capabilities
Such predictions can support maintenance planning and asset-management decisions, while engineering assessment remains important for critical equipment.
Industrial Internet of Things
Industrial Internet of Things systems provide the data connection between physical assets and digital platforms.
IIoT sensors and connected devices can continuously collect information from equipment throughout a facility.
A digital twin can use this information to maintain an updated representation of the physical system.
ProSIM provides IIoT integration for collecting and analysing sequential data from sensors installed across equipment and facilities. ProSIM IIoT Digital Twin Integration
This creates the data foundation needed for many digital twin applications.
Hybrid Modelling
Industrial digital twins can use either physics-based models or data-driven models, but combining both approaches can provide additional advantages.
Physics-based models are built around established engineering principles.
Machine learning models learn patterns from historical and operational data.
Hybrid modelling combines these two approaches.
ProSIM develops hybrid models that combine physics-based engineering with AI and machine learning. ProSIM Hybrid Modelling Services
This approach can help create models that use both engineering knowledge and real-world operating information.
Why Engineering Physics Is Important
Industrial systems follow physical laws.
A pressure vessel responds to mechanical stresses. A pipeline experiences pressure and thermal loads. A heat exchanger transfers heat according to physical relationships. A rotating machine responds to mechanical and dynamic forces.
A purely statistical model may identify patterns without explicitly representing these physical relationships.
By incorporating engineering principles into a digital twin, organizations can add an additional layer of technical understanding to their predictive models.
BIM and Digital Twin Integration
Building Information Modeling provides structured digital information about buildings, structures, equipment, and facilities.
Connecting BIM with digital twin technology allows engineering and operational information to be linked with the physical location of assets.
For large industrial facilities, this can be particularly valuable.
Instead of reviewing maintenance information separately from engineering drawings, users can connect equipment information with its location and surrounding systems.
ProSIM integrates BIM information into digital twin applications to connect engineering and structural information with operational and maintenance data. ProSIM BIM Digital Twin Integration
As-Built Digital Representation
An accurate representation of the existing facility is an important foundation for digital twin development.
Industrial facilities often change after their original construction. Equipment may be replaced, piping may be modified, and new systems may be installed.
As-built digital information can help capture the current condition of the facility.
When this information is combined with operational data, engineers can develop a more accurate digital representation of the physical plant.
Spatial Asset Management
Large industrial facilities can contain thousands of individual components.
Knowing the location of equipment can be critical when responding to maintenance requirements.
A BIM-enabled digital twin can connect asset data with physical locations.
Maintenance teams can use this information to understand where equipment is located, what systems are connected to it, and what relevant engineering or operational information is available.
Cloud-Based Digital Twins
Cloud computing provides scalable infrastructure for digital twin applications.
Large volumes of sensor data, simulation results, engineering documents, and analytical models can be stored and processed through cloud environments.
Cloud deployment can also support collaboration between engineering and operations teams in different locations.
The appropriate architecture depends on factors such as data volume, connectivity, cybersecurity requirements, response time, and organizational infrastructure.
Edge Computing for Digital Twins
Some digital twin applications require very rapid responses.
In these cases, processing data closer to the physical equipment can reduce communication delays.
Edge computing places computational resources near the equipment or facility where the data is generated.
ProSIM's Reduced Order Model approach supports deployment in edge environments as well as cloud platforms. ProSIM Edge and Cloud Digital Twin Deployment
A combination of cloud and edge computing can be used when both centralized analytics and fast local processing are required.
Root-Cause Analysis
When equipment fails or begins operating abnormally, determining the cause can require analysis of multiple information sources.
Fitness for service companies Engineers may need to review sensor readings, operating history, maintenance records, inspection information, and engineering calculations.
A digital twin can bring these data sources together.
ProSIM describes the use of virtual timelines containing sensor and physical data to assist with root-cause analysis after equipment failures. ProSIM Digital Twin Root-Cause Analysis
This can provide engineers with a more structured view of how equipment behaviour changed before an incident.
Operational Performance Optimization
Digital twins can also be used to evaluate operating conditions before making changes to physical equipment.
Engineers can use the virtual environment to test different scenarios and examine potential outcomes.
This can support optimization of equipment performance, energy use, operating parameters, and process conditions.
The advantage is that many scenarios can be evaluated Fitness for service companies digitally before changes are implemented in the physical facility.
Closed-Loop Optimization
More advanced digital twin systems can use model predictions to recommend optimized operating parameters.
ProSIM describes closed-loop optimization capabilities in which calculated operating parameters can be supplied to control networks. ProSIM Closed-Loop Optimization
Applications that interact with industrial control systems require careful engineering validation, cybersecurity measures, safeguards, and appropriate operational controls.
Digital Twin for Asset Lifecycle Management
The benefits of a digital twin can extend across the entire lifecycle of an industrial asset.
During design, it can support engineering simulation and digital coordination.
During construction, it can help provide a structured representation of the facility.
During commissioning and operation, it can connect equipment with operational information.
During maintenance, it can support condition monitoring and predictive analysis.
During later modifications, the digital representation can provide useful information about existing conditions.
This makes digital twins relevant to both new projects and existing facilities.
Digital Twin Research and Development
Some industrial applications require customized solutions.
A particular machine may not have an existing digital twin model. Sensor data may require a specialized architecture, or the organization may need a custom machine learning model.
ProSIM provides digital twin research and development capabilities including customized framework prototyping, synthetic data generation, and sensor integration experimentation. ProSIM Digital Twin R&D Services
This can help organizations develop digital twin applications around specific engineering requirements.
Industries That Can Benefit From Digital Twins
Digital twin technology can be applied across many industrial sectors.
Potential applications include:
⦁ Nuclear power
⦁ Thermal power
⦁ Oil and gas
⦁ Offshore engineering
⦁ Process industries
⦁ Heavy engineering
⦁ Manufacturing
⦁ Renewable energy
⦁ Infrastructure
⦁ Large-scale industrial facilities
The design of the digital twin should be adapted to the operational objectives and technical requirements of each industry.
Key Benefits of Digital Twin Technology
When appropriately designed and implemented, digital twins can support:
1. Real-time asset monitoring
2. Predictive maintenance
3. Early detection of abnormal behaviour
4. Remaining useful life estimation
5. Engineering simulation
6. Operational optimization
7. Root-cause analysis
8. Asset lifecycle management
9. Integration of engineering and operational data
10. Improved visibility of complex facilities
The effectiveness of these applications depends heavily on data quality, sensor reliability, model accuracy, validation, and implementation strategy.
Developing a Successful Digital Twin
A digital twin project should begin with a clearly defined objective.
Organizations should determine what asset or system needs to be represented, what information is currently available, what additional data is required, and what decisions the digital twin is expected to support.
The engineering model should be validated against appropriate data. Sensor systems must provide reliable measurements, while AI and machine learning models need suitable datasets and ongoing evaluation.
Cybersecurity and access control are also important considerations, especially when digital twins are connected to operational technology networks.
Conclusion
Digital twin technology is becoming an important component of modern industrial engineering and asset management.
By connecting physical assets with virtual models, sensors, engineering simulations, AI, machine learning, IIoT systems, and BIM information, organizations can develop a more comprehensive understanding of how their facilities and equipment operate.
Reduced Order Models can make complex engineering simulations faster and more suitable for real-time applications. AI and machine learning can support predictive maintenance and remaining useful life estimation. Hybrid models can combine engineering physics with real-world operational data, while BIM integration can connect digital information with the physical location of assets.
ProSIM provides digital twin solutions covering Reduced Order Models, AI/ML, IIoT integration, hybrid modelling, BIM integration, predictive maintenance, remaining useful life estimation, and customized digital twin research and development. ProSIM Digital Twin Solutions
For engineering organizations, EPC contractors, manufacturers, plant operators, and asset owners, a well-designed digital twin can provide a valuable digital foundation for monitoring, analysis, prediction, and optimization. As industrial systems become increasingly connected, combining engineering expertise with digital technologies can help organizations manage complex assets more effectively throughout their operational lifecycle.