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Diploma in Partial Twins and Degradation Assessment

Online postgraduate diploma of 800 hours in partial twins and degradation assessment. 8 modules covering physical, reduced-order and hybrid degradation models, Bayesian data assimilation, remaining useful life prognosis and twin deployment under ISO 23247, ISO 13381-1 and FMI 3.0, with an assigned technical tutor.

Duration6–8 months
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 the appropriate scope of a partial digital twin.

02

Model degradation mechanisms with physical laws.

03

Build reduced-order models for fast execution.

04

Combine physics and data in hybrid models.

05

Update the twin through Bayesian data assimilation.

06

Estimate remaining life with uncertainty propagation.

07

Deploy interoperable, verified partial twins.

Profile

Who it's for

  • Digital twin engineers who want to add degradation modelling and prognosis to their twins.
  • Structural integrity engineers looking to move from periodic assessment to continuously updated models.
  • Prognostics and health management (PHM) engineers.
  • Simulation engineers who need fast reduced-order and hybrid models.
  • Industrial data scientists working with physics-based models.
  • Reliability engineers.
  • Engineering or science graduates seeking to enter the energy sector.
Standards & tools

What you'll work with

  • ISO 23247 (Digital twin framework for manufacturing)
  • ISO 13381-1 (Prognostics: general guidelines)
  • Modelica Association FMI 3.0 (Functional Mock-up Interface)
  • ASME V&V 10 (Verification and validation in computational solid mechanics)
  • API 579-1/ASME FFS-1 (Fitness-for-service)
  • IEC 63278-1 (Asset Administration Shell)
  • ASTM E1049 (Fatigue cycle counting)
  • MATLAB and Simulink
  • Ansys Twin Builder
  • Python with PyTorch and FilterPy
  • OpenModelica
  • PyMC
Syllabus

8 modules

  1. 1.1Full twin versus partial twin
  2. 1.2Scope by component and by phenomenon
  3. 1.3Degradation mechanisms: wear, fatigue and corrosion
  4. 1.4Creep and thermal ageing
  5. 1.5Degradation of batteries and electronics
  6. 1.6Observable and hidden variables
  7. 1.7Fidelity required by the decision
  8. 1.8Architecture of a degradation twin
  9. 1.9Use cases in industrial assets
  10. 1.10Workshop: selecting the scope of a partial twin
  1. 2.1Paris law for crack growth
  2. 2.2Miner's rule and cumulative damage
  3. 2.3Rainflow cycle counting
  4. 2.4Archard wear models
  5. 2.5Arrhenius model for ageing
  6. 2.6Electrochemical battery models
  7. 2.7Reduced-order thermal models
  8. 2.8Physical parameters and their identification
  9. 2.9Limitations of physical models
  10. 2.10Practical exercise: fatigue damage with rainflow and Miner
  1. 3.1The need for fast models
  2. 3.2Proper orthogonal decomposition (POD)
  3. 3.3Surrogate models with Gaussian processes
  4. 3.4Neural networks as surrogates
  5. 3.5Reduced finite element models
  6. 3.6Design of experiments sampling
  7. 3.7Validation of the reduced model
  8. 3.8Real-time execution
  9. 3.9Model reduction tools
  10. 3.10Lab: Gaussian process surrogate
  1. 4.1Correcting physical models with data
  2. 4.2Physics-informed neural networks (PINN)
  3. 4.3Grey-box models
  4. 4.4Residual learning
  5. 4.5Physical constraints in training
  6. 4.6Extrapolation beyond the data
  7. 4.7Comparison with purely data-driven models
  8. 4.8Interpretability of hybrid models
  9. 4.9Selecting the approach according to the data
  10. 4.10Practical exercise: hybrid tool-wear model
  1. 5.1Bayesian parameter estimation
  2. 5.2Extended and unscented Kalman filters
  3. 5.3Particle filters
  4. 5.4Updating with inspection data
  5. 5.5Model calibration with MCMC
  6. 5.6Observability of the degradation state
  7. 5.7Virtual state sensors
  8. 5.8Twin update frequency
  9. 5.9Detection of regime changes
  10. 5.10Lab: particle filter for crack growth
  1. 6.1Defining the failure threshold
  2. 6.2Remaining useful life prognosis
  3. 6.3Uncertainty propagation
  4. 6.4Prognostic metrics: alpha-lambda and PH
  5. 6.5Future operating scenarios
  6. 6.6Operating decisions to extend life
  7. 6.7Risk-based inspection
  8. 6.8Maintenance planning
  9. 6.9Communicating probabilistic results
  10. 6.10Case study: remaining life of an industrial battery
  1. 7.1Software architecture of the twin
  2. 7.2Integration with plant data
  3. 7.3Interoperability with FMI and FMU
  4. 7.4Asset Administration Shell
  5. 7.5Execution at the edge or in the cloud
  6. 7.6Validation, verification and credibility
  7. 7.7Model version management
  8. 7.8Costs and return
  9. 7.9Scaling to asset fleets
  10. 7.10Workshop: architecture of a degradation twin
  1. 8.1Asset and degradation mechanism
  2. 8.2Available operating and inspection data
  3. 8.3Baseline physical model
  4. 8.4Reduced or hybrid model
  5. 8.5Data assimilation
  6. 8.6Remaining life prognosis
  7. 8.7Validation with field data
  8. 8.8Deployment proposal for the degradation twin
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training 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 each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB and Simulink, Ansys Twin Builder, Python with PyTorch and FilterPy, OpenModelica and PyMC, in free, academic or evaluation versions. Practical work is based on real procedures for Bayesian calibration of degradation models, remaining life prognosis with uncertainty and validation of reduced models against full simulation. Assessment is continuous by module plus the final project.

Standards covered: ISO 23247, ISO 13381-1, Modelica Association FMI 3.0, ASME V&V 10, API 579-1/ASME FFS-1, IEC 63278-1, ASTM E1049.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 23247, ISO 13381-1 and FMI 3.0 applied to real cases, not as a theoretical annex.
Industry software:MATLAB and Simulink, Ansys Twin Builder, Python with PyTorch and FilterPy, OpenModelica and PyMC, in free, academic or evaluation versions.
Final project:a degradation twin for a real asset, from physical model to remaining life prognosis and deployment proposal, reviewed by your tutor.
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.

Asset and mechanism
Select an asset, its degradation mechanism and the available operating and inspection data.
Physical degradation model
Build the baseline physical model, for example fatigue damage with rainflow counting and Miner's rule.
Reduced or hybrid model
Develop a fast surrogate or hybrid physics-data model and validate it against full simulation.
Assimilation and prognosis
Update the twin with a particle filter and estimate remaining useful life with uncertainty.
Final project
Validate with field data and present a deployment proposal for the degradation twin, reviewed by your tutor.
Career opportunities

Where this takes you

Digital twin engineerStructural integrity engineerPrognostics and health management (PHM) engineerSimulation engineerIndustrial data scientistReliability engineer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB and Simulink, Ansys Twin Builder, Python with PyTorch and FilterPy, OpenModelica and PyMC, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the 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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