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Master’s in Equipment and Site Digital Twins Engineering

Online master's programme of 1,900 hours in digital twin engineering for equipment and sites. 12 modules covering digital twin concepts, physical modelling, data for the twin, site twins, operations simulation, twins for maintenance and design, platforms, artificial intelligence, cybersecurity and business value, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Define digital twin architectures for equipment and sites.

02

Develop reduced-order physical models.

03

Integrate telematics and sensor data into the twin.

04

Simulate operations and optimise fleets.

05

Apply twins to maintenance and design.

06

Govern and protect the digital twin.

07

Evaluate business value and plan scaling.

Profile

Who it's for

  • Digital twin engineers
  • Simulation engineers
  • Industrial platform architects
  • Industrial data scientists
  • Site modelling specialists
  • Digitalisation project managers
  • Engineering or science graduates looking to enter mining
Standards & tools

What you'll work with

  • ISO 23247 (digital twin framework for manufacturing)
  • ISO 15143-3 (telematics data exchange for machinery)
  • FMI (Functional Mock-up Interface standard)
  • ISO 19650 (BIM information management)
  • IEC 62443 (industrial cybersecurity)
  • Regulation (EU) 2023/2854 (Data Act)
  • Simcenter Amesim
  • AnyLogic
  • Azure Digital Twins
  • Agisoft Metashape
  • MATLAB/Simulink
Syllabus

12 modules · 1900 h

  1. 1.1Definitions and levels of digital twin
  2. 1.2Component, machine and site twins
  3. 1.3Model, data and bidirectional connection
  4. 1.4ISO 23247 reference architecture
  5. 1.5Use cases in machinery and construction sites
  6. 1.6Digital twin maturity
  7. 1.7Expected business value
  8. 1.8Common mistakes in projects
  9. 1.9Map of technology suppliers
  10. 1.10Case study: defining the scope of a fleet twin
  1. 2.1Multibody models of machines
  2. 2.21D hydraulic models
  3. 2.3Simplified thermal models
  4. 2.4Reduced-order models
  5. 2.5Wear and damage models
  6. 2.6Calibrating models with tests
  7. 2.7Co-simulation of subsystems
  8. 2.8The FMI standard for model exchange
  9. 2.9Real-time computational performance
  10. 2.10Lab: reduced-order model of a hydraulic circuit
  1. 3.1Machine data sources
  2. 3.2Telematics and ISO 15143-3
  3. 3.3Additional field sensors
  4. 3.4Synchronising data and models
  5. 3.5Data quality and cleansing
  6. 3.6Time-series databases
  7. 3.7Contextualisation with metadata
  8. 3.8Data governance
  9. 3.9Latency and update frequency
  10. 3.10Practical exercise: integrating telematics data into a model
  1. 4.13D site models
  2. 4.2Drone photogrammetry
  3. 4.3Terrestrial laser scanning
  4. 4.4Digital terrain models
  5. 4.5Integration with BIM on site
  6. 4.6Open-pit mine twins
  7. 4.7Updating the model with progress
  8. 4.8Immersive site visualisation
  9. 4.9Geospatial exchange formats
  10. 4.10Lab: terrain model from a drone flight
  1. 5.1Discrete-event simulation
  2. 5.2Simulation of loading and haulage fleets
  3. 5.3Agent-based simulation
  4. 5.4Production scenarios
  5. 5.5Fleet optimisation
  6. 5.6Validation with real data
  7. 5.7Bottleneck analysis
  8. 5.8Simulation of interference on site
  9. 5.9Simulation tools
  10. 5.10Practical exercise: simulation of a haulage fleet
  1. 6.1Component state estimation
  2. 6.2Cumulative damage in structures
  3. 6.3Remaining useful life prognosis
  4. 6.4Model-based anomaly detection
  5. 6.5Maintenance planning with the twin
  6. 6.6Management of repairable components
  7. 6.7Integration with CMMS
  8. 6.8Cases in mining and construction
  9. 6.9Return on investment in maintenance
  10. 6.10Case study: twin of a dumper transmission
  1. 7.1Using field data in engineering
  2. 7.2Feedback loop to design
  3. 7.3Virtual validation of changes
  4. 7.4Virtual durability testing
  5. 7.5Twins in software development
  6. 7.6Hardware-in-the-loop with models
  7. 7.7Twins for type-approval
  8. 7.8Managing product variants
  9. 7.9Integration with PLM
  10. 7.10Practical exercise: virtual validation of a design change
  1. 8.1Industrial digital twin platforms
  2. 8.2Cloud and edge architecture
  3. 8.3Microservices and APIs
  4. 8.4Cloud simulation engines
  5. 8.5Web and 3D visualisation
  6. 8.6Integration with ERP and MES
  7. 8.7System scalability
  8. 8.8Operating costs
  9. 8.9Platform selection
  10. 8.10Lab: architecture of a cloud-based twin
  1. 9.1Hybrid physics and data models
  2. 9.2Physics-informed neural networks
  3. 9.3Reinforcement learning on the twin
  4. 9.4Optimisation of operating parameters
  5. 9.5Synthetic data generation
  6. 9.6Explainability of recommendations
  7. 9.7Uncertainty of predictions
  8. 9.8Validation of AI models
  9. 9.9Deployment in production
  10. 9.10Case study: fuel consumption optimisation with reinforcement learning
  1. 10.1Risks of bidirectional connection
  2. 10.2Security of data and models
  3. 10.3Role-based access control
  4. 10.4Intellectual property of models
  5. 10.5The European Data Act
  6. 10.6Personal data protection
  7. 10.7Version management of the twin
  8. 10.8Auditing the twin
  9. 10.9Accountability for decisions
  10. 10.10Practical exercise: access policy for a site twin
  1. 11.1Selecting use cases
  2. 11.2Proof of concept and pilot
  3. 11.3Value indicators
  4. 11.4Scaling to fleets and sites
  5. 11.5Change management
  6. 11.6Team competencies
  7. 11.7Supplier partnerships
  8. 11.8Twin-based business models
  9. 11.9Lessons learned from implementations
  10. 11.10Case study: scaling plan for a mine twin
  1. 12.1Definition of the use case
  2. 12.2Twin architecture
  3. 12.3Physical or data-driven model
  4. 12.4Integration of real data
  5. 12.5Scenario simulation
  6. 12.6Validation of the twin
  7. 12.7Value analysis
  8. 12.8Implementation plan
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the programme can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Simcenter Amesim, AnyLogic, Azure Digital Twins, Agisoft Metashape and MATLAB/Simulink, in free, academic or evaluation versions. Testing is studied through real procedures for mining fleet simulation, terrain modelling with drones and model validation with field data. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: ISO 23247, ISO 15143-3, FMI (Functional Mock-up Interface), ISO 19650, IEC 62443, Regulation (EU) 2023/2854.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 23247, ISO 15143-3 and the FMI standard applied to real cases, not as a theoretical annex.
Industry software:Simcenter Amesim, AnyLogic, Azure Digital Twins, Agisoft Metashape and MATLAB/Simulink, in free, academic or evaluation versions.
Master's Thesis with defence:A complete digital twin of real equipment or a real site, from architecture to implementation plan, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Use case and architecture
Define the use case and design the twin architecture.
Model and data integration
Build the physical or data-driven model and connect real data.
Scenario simulation and validation
Simulate operating scenarios and validate the twin.
Value and implementation plan
Analyse the business value and plan the implementation.
Master's Thesis
Present the complete digital twin project, defended before a panel.
Career opportunities

Where this takes you

Digital twin engineerSimulation engineerIndustrial platform architectIndustrial data scientistSite modelling specialistDigitalisation project manager
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Simcenter Amesim, AnyLogic, Azure Digital Twins, Agisoft Metashape and MATLAB/Simulink, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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