Digital Twin Solutions for Smarter Industrial Operations

Wiki Article

Digital transformation is changing the way industrial organizations design, operate, maintain, and manage complex assets. Modern facilities generate large amounts of information through engineering systems, sensors, inspection programs, maintenance records, and operational equipment. The challenge is turning this information into useful engineering insight.

Digital twin technology provides a framework for connecting physical assets with their virtual representations and real-time information. Instead of relying only on static drawings or isolated databases, organizations can create digital environments that combine engineering models, operational data, simulation, analytics, and artificial intelligence.

ProSIM provides digital twin engineering services that combine engineering knowledge with digital technologies such as reduced-order modelling, artificial intelligence, machine learning, Industrial Internet of Things systems, hybrid modelling, and Building Information Modeling. ProSIM Digital Twin Services

Understanding the Digital Twin Concept

A digital twin is a virtual representation of a physical asset, system, or facility that can be connected to information generated by its real-world counterpart.

The physical asset may contain sensors that measure temperature, pressure, vibration, flow, speed, or other operating parameters. These measurements can be transferred to a digital environment where they are analysed using engineering models and computational techniques.

The virtual model can then provide information about current operating conditions, potential problems, expected performance, and possible future scenarios.

This creates a connection between physical operations and digital engineering.

Digital Twin Versus a Traditional 3D Model

A 3D model primarily represents the geometry of an object or facility.

A digital twin can contain much more information.

For example, a 3D model of a pump can show its physical dimensions and location. A digital twin can additionally contain operating data, vibration measurements, maintenance history, performance information, and predictive analytics.

This distinction makes digital twins particularly valuable for industrial asset management.

The digital model becomes a source of operational and engineering intelligence rather than simply a visualization tool.

Engineering-Based Digital Twins

Industrial equipment operates according to physical principles.

Pumps, turbines, compressors, pressure vessels, piping systems, heat exchangers, and other equipment respond to pressure, temperature, mechanical loads, fluid flow, vibration, and other physical conditions.

A digital twin can incorporate these engineering principles into its virtual representation.

ProSIM combines engineering simulation and digital technologies to develop digital twin solutions for industrial applications. ProSIM Engineering Digital Twin Capabilities

This engineering foundation can help make digital twin predictions more meaningful for complex equipment.

Reduced Order Modelling

High-fidelity engineering simulations can be computationally expensive.

Finite Element Analysis and Computational Fluid Dynamics, for example, can provide detailed information about structural and fluid behaviour but may require considerable processing time.

Reduced Order Models provide a way to simplify complex simulations while retaining the characteristics required for a particular application.

ProSIM develops Reduced Order Models intended to convert computationally intensive physics-based simulations into faster models suitable for real-time applications. ProSIM Reduced Order Modelling

These models can potentially be deployed in cloud environments or closer to equipment using edge computing infrastructure.

Real-Time Simulation

Real-time simulation is important when engineering information needs to be available quickly.

A detailed simulation that takes hours to complete may not be suitable for continuous equipment monitoring.

A reduced-order model can provide results much faster, allowing organizations to use engineering-based simulations as part of ongoing operational analysis.

Potential applications include equipment monitoring, process optimization, fault detection, performance analysis, and predictive maintenance.

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning can provide another important layer within a digital twin.

Industrial equipment produces large amounts of sequential data. Machine learning algorithms can analyse historical and current measurements to identify patterns associated with equipment performance and degradation.

ProSIM integrates AI and machine learning with Industrial IoT data for applications such as predictive maintenance, early detection of equipment problems, and remaining useful life estimation. ProSIM AI and Machine Learning Digital Twin Solutions

The combination of engineering models and machine learning can provide a broader understanding of equipment behaviour.

Predictive Maintenance

Traditional maintenance programs often rely on fixed schedules.

While scheduled maintenance remains important, not every component deteriorates at the same rate. Operating conditions, environmental exposure, loading, and equipment history can affect degradation.

Digital twin technology can support condition-based and predictive maintenance by analysing equipment data continuously.

If the system detects a pattern that may indicate degradation, engineers can investigate the asset before the issue develops into a larger failure.

This can help maintenance teams prioritize resources and focus attention on equipment showing meaningful changes.

Remaining Useful Life Prediction

One of the objectives of predictive maintenance is estimating remaining useful life.

Remaining useful life refers to the expected amount of service an asset or component can provide before reaching a defined condition or performance limit.

AI and machine learning can use historical operating information and sensor measurements to identify degradation trends.

ProSIM includes remaining useful life estimation within its digital twin and predictive analytics capabilities. ProSIM Remaining Useful Life Prediction

Engineering validation remains important when predictions are used to support critical maintenance or safety decisions.

Industrial IoT Integration

Industrial Internet of Things technology enables physical equipment to communicate operational information to digital systems.

Sensors can collect information from equipment continuously.

Depending on the application, this can include:

⦁ Temperature
⦁ Pressure
⦁ Flow rate
⦁ Vibration
⦁ Speed
⦁ Load
⦁ Position
⦁ Energy consumption
⦁ Equipment operating status

A digital twin can process this information and use it as an input to simulations and analytical models.

ProSIM provides IIoT integration capabilities for collecting and analysing sequential sensor data from equipment and facilities. ProSIM IIoT Integration for Digital Twins

Hybrid Digital Twin Models

A purely data-driven model is dependent on the quality and range of available historical data.

Fitness for service companies However, industrial equipment can encounter operating conditions that are not well represented in historical datasets.

Physics-based models can provide additional information because they are based on established engineering relationships.

Hybrid models combine physical principles with AI and machine learning.

ProSIM uses hybrid modelling approaches that combine engineering physics with data-driven techniques for industrial digital twin applications. ProSIM Hybrid Digital Twin Modelling

This approach can be valuable where both engineering understanding and operational data are important.

BIM and Digital Twin Integration

Building Information Modeling provides structured information about buildings, structures, equipment, and facilities.

When BIM is connected with digital twin technologies, the resulting environment can combine spatial information with operational and maintenance data.

This is particularly useful for large facilities where equipment and systems are distributed across multiple buildings or plant areas.

ProSIM integrates BIM information with digital twin concepts to connect engineering and structural information with operational and maintenance records. ProSIM BIM and Digital Twin Integration

As-Built Digital Information

Accurate information about the existing physical facility is essential for effective digitalization.

Engineering drawings may change during construction, and facilities can undergo numerous modifications during their operating life.

An as-built digital representation can provide a more accurate basis for digital twin development.

It can include information about equipment locations, piping systems, structures, and other plant components.

When this information is connected with live operational data, engineers can gain a more complete view of the physical facility.

Spatial Intelligence

Industrial asset information becomes more useful when it is connected to physical location.

Knowing that a particular pump is experiencing abnormal vibration is useful. Knowing exactly where that pump is located within a large facility provides additional operational context.

BIM and 3D plant information can provide this spatial connection.

Maintenance personnel can use the digital environment to locate equipment, review associated information, and understand relationships between assets.

Cloud-Based Digital Twin Applications

Cloud computing provides scalable resources for storing and analysing industrial data.

A cloud-based digital twin can support centralized access to models, operational information, analytics, and engineering results.

This can be particularly useful for organizations managing multiple facilities or geographically distributed assets.

Cloud deployment can also simplify collaboration between engineering, operations, maintenance, and management teams, subject to the organization's security and infrastructure requirements.

Edge Computing

Not every digital twin calculation needs to take place in the cloud.

Some applications require rapid responses or need to continue operating when network connectivity is limited.

Edge computing places computational resources closer to the physical equipment.

ProSIM's Reduced Order Model approach supports deployment on edge hardware as well as cloud environments. ProSIM Digital Twin Deployment Capabilities

The choice between cloud, edge, or hybrid deployment depends on the specific application.

Root-Cause Analysis

When equipment performance changes unexpectedly, engineers need to determine the underlying cause.

A digital twin can bring together historical sensor data, engineering models, operational records, and maintenance information.

This allows teams to investigate how equipment behaviour changed over time.

ProSIM describes virtual timelines that combine sensor and physical data to support root-cause analysis following equipment failures. ProSIM Digital Twin Root-Cause Analysis

This type of analysis can help organizations understand failure mechanisms and improve future maintenance Fitness for service companies strategies.

Operational Optimization

Digital twins can also be used to evaluate potential operating strategies.

Engineers can create virtual scenarios to examine how changes in operating parameters may affect equipment or system performance.

This provides an opportunity to evaluate alternatives before implementing them in the physical facility.

Digital twin models can therefore support performance optimization while reducing the need for trial-and-error experimentation on operating equipment.

Closed-Loop Optimization

Advanced digital twin systems can move beyond monitoring and prediction toward optimization.

A model can analyse current operating conditions and calculate parameters that may improve a selected performance objective.

ProSIM describes closed-loop optimization capabilities in which digital twin systems can calculate optimal operating parameters and provide them to control networks. ProSIM Closed-Loop Digital Twin Optimization

Applications involving control-system integration require appropriate engineering validation, cybersecurity, safeguards, and operational controls.

Digital Twins for Aging Industrial Assets

Many industrial facilities operate for decades.

As equipment ages, organizations need accurate information about condition, degradation, maintenance history, and remaining service capability.

Digital twins can provide a central digital environment for bringing this information together.

When combined with structural integrity assessments, inspection data, engineering simulations, and predictive analytics, digital twin technology can support more informed lifecycle decisions.

This can be especially valuable for power plants, oil and gas facilities, process plants, and other asset-intensive industries.

Custom Digital Twin Development

Industrial organizations do not all have the same requirements.

A standard digital twin architecture may not be suitable for specialized equipment or unique operating environments.

Custom development can involve creating specialized models, generating training data, integrating sensors, developing analytical workflows, or connecting existing engineering systems.

ProSIM provides digital twin research and development services for customized applications, including framework prototyping, synthetic data generation, and sensor integration experiments. ProSIM Digital Twin R&D Services

Benefits of Digital Twin Technology

A properly developed digital twin can support several areas of industrial engineering and operations.

Potential benefits include:

⦁ Better equipment monitoring
⦁ Earlier identification of abnormal behaviour
⦁ Predictive maintenance support
⦁ Remaining useful life estimation
⦁ Faster engineering analysis
⦁ Improved operational decision-making
⦁ Virtual testing of operating scenarios
⦁ Better asset information management
⦁ Improved root-cause analysis
⦁ More effective lifecycle management
⦁ Integration of engineering and operational information

The actual benefits depend on the quality of the data, engineering models, sensors, software architecture, and implementation strategy.

Industries Using Digital Twin Technology

Digital twins are particularly relevant to industries where equipment is complex, expensive, safety-critical, or difficult to replace.

Applications can be developed for:

⦁ Nuclear power
⦁ Thermal power
⦁ Oil and gas
⦁ Offshore facilities
⦁ Process industries
⦁ Heavy engineering
⦁ Manufacturing
⦁ Renewable energy
⦁ Infrastructure
⦁ Large commercial facilities

The technology can be adapted to different levels of complexity, from individual equipment models to complete facility-level digital environments.

Building an Effective Digital Twin

Developing a useful digital twin requires careful planning.

The first step is normally identifying a clear business or engineering objective. The organization then needs to determine what physical assets should be represented, what information is available, and what additional sensors or data sources are required.

The engineering model must be validated, and the digital system needs suitable data processing, storage, analytics, cybersecurity, and integration capabilities.

A digital twin should therefore be treated as an engineering and technology program rather than simply a software installation.

Conclusion

Digital twin technology provides a powerful framework for connecting physical industrial assets with engineering models, real-time data, artificial intelligence, and predictive analytics.

Reduced Order Models can make complex simulations faster and more suitable for operational applications. AI and machine learning can help identify patterns in equipment data, while Industrial IoT systems provide continuous information from physical assets. Hybrid modelling can combine engineering physics with real-world operating data, and BIM integration can connect digital information with the physical location of equipment.

ProSIM provides digital twin services 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 Services

For organizations operating complex industrial facilities, digital twins can become an important component of modern asset management. When supported by accurate engineering models, reliable sensor data, validated analytics, and appropriate digital infrastructure, they can help organizations better understand equipment behaviour, anticipate potential problems, evaluate operating scenarios, and make more informed decisions throughout the asset lifecycle.