Digital twin course in turbines
Online course of 300 hours in digital twins for turbines, structured in 6 modules covering digital twin concepts, physical turbine modelling, data and connectivity, model-data fusion, and diagnostics, prognostics and maintenance, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand the concepts and levels of a turbine digital twin.
Build thermodynamic and degradation models of turbines.
Manage sensor data using quality and security criteria.
Fuse physical models and data using estimation techniques.
Apply the digital twin to diagnostics and prognostics.
Assess the value of the twin in fleet maintenance.
Develop a prototype turbine digital twin.
Who it's for
- Digital twin engineers building models of rotating machinery.
- Engine health monitoring engineers at airlines and OEMs.
- Industrial data scientists working with turbine time series.
- Turbine performance engineers using cycle models.
- Predictive maintenance engineers planning shop visits.
- IoT platform architects designing data pipelines for engines.
- Engineering or science graduates looking to enter engine manufacturing.
What you'll work with
- ISO 23247 (series) (digital twin framework for manufacturing)
- ISO/IEC 30173 (digital twin concepts and terminology)
- ISO 13374 (series) (condition monitoring and diagnostics, data processing)
- ISO 17359 (condition monitoring and diagnostics of machines, general guidelines)
- ISO 13381-1 (prognostics, general guidelines)
- ISO 20816-4 (mechanical vibration of gas turbines)
- SAE ARP1587 (aircraft gas turbine engine health management system guide)
- GasTurb
- Ansys Twin Builder
- MATLAB/Simulink
- T-MATS (NASA)
- Python with scikit-learn
6 modules
1.1Digital twin, digital model and digital shadow1.2Maturity and fidelity levels1.3Engine life cycle and use of the twin1.4Physical, digital and connection architecture1.5Use cases: diagnostics, prognostics and design1.6Fleet twins versus unit twins1.7Industrial suppliers and ecosystems1.8Return on investment and business value1.9Standardisation and fleet databases1.10Workshop: defining use cases for a fleet turbine
2.10D thermodynamic cycle models2.2Compressor and turbine maps2.3Map scaling and adaptation2.4Dynamic transient models2.5Thermal models of hot-section components2.6Creep and fatigue life models2.7Reduced-order modelling of CFD and FEM models2.8Component degradation models2.9Secondary and oil system models2.10Lab: performance model in GasTurb
3.1Engine instrumentation and sampling rate3.2In-flight and ground data recording3.3Data quality and outlier treatment3.4Correction to standard conditions3.5IoT architectures and edge computing3.6Cloud platforms for time series3.7Cybersecurity of engine data3.8Interoperability and data standards3.9Data governance and ownership3.10Practical: cleaning an engine data set
4.1Adaptive gas path analysis4.2Extended and unscented Kalman filters4.3Health parameter estimation4.4Hybrid physics-machine learning models4.5Neural networks for time series4.6Uncertainty quantification4.7Continuous calibration of the twin4.8Validation against test-bed data4.9Sensor drift detection4.10Case study: compressor efficiency estimation with a Kalman filter
5.1Anomaly detection and alarms5.2Fault isolation by component5.3Remaining useful life and prognostics5.4Condition-based maintenance5.5Planning of washes and shop visits5.6Fleet optimisation and availability5.7Twins for industrial and power-generation turbines5.8Governance and validation of the twin5.9Integration with maintenance management systems5.10Case study: remaining life estimation of a hot-section module
6.1Definition of scope and use cases6.2Selection of physical models6.3Data architecture6.4Fusion and estimation algorithms6.5Health indicators and alerts6.6Validation with test data6.7Operator dashboard6.8Deployment plan6.9Report and presentation6.10Final project: final review with the tutor
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 GasTurb, Ansys Twin Builder, MATLAB/Simulink, T-MATS (NASA) and Python with scikit-learn, in free, academic or evaluation versions. Testing is studied through real procedures: building an adaptive performance model with flight data, health parameter estimation with a Kalman filter, and remaining life prognostics for hot-section components. Assessment is continuous by module, plus the final project.
Standards covered: ISO 23247 (series), ISO/IEC 30173, ISO 13374 (series), ISO 17359, ISO 13381-1, ISO 20816-4, SAE ARP1587.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification 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.
Frequently asked questions
The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with GasTurb, Ansys Twin Builder, MATLAB/Simulink, T-MATS (NASA) and Python with scikit-learn, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute certificate, 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.
No prior qualification is required; you need 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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