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Postgraduate DiplomaAerospace, UAM, Drones & Space Applications open

Diploma in Structural Monitoring (SHM) and Aircraft Health

Online postgraduate diploma of 800 hours in Structural Health Monitoring (SHM) and Aircraft Health. 8 modules covering sensors, guided waves, loads and usage monitoring, signal processing and machine learning, reliability and prognostics, and certification under SAE ARP6461, 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

Explain the levels and benefits of structural health monitoring.

02

Select suitable sensors for each type of damage and structure.

03

Apply guided waves to detect damage in plates.

04

Estimate loads and consumed life from flight data.

05

Develop damage-detection algorithms with machine learning.

06

Assess reliability and remaining useful life prognosis.

07

Define the certification route and maintenance credits for an SHM system.

Profile

Who it's for

  • Structural health monitoring engineers looking to deepen their expertise in aircraft applications.
  • Predictive maintenance engineers moving towards condition-based maintenance.
  • Data scientists working on aircraft health and fleet data.
  • Aeronautical sensor engineers who want to understand the full SHM chain.
  • Structures engineers interested in damage tolerance and monitoring.
  • Fleet management engineers responsible for availability and maintenance planning.
  • Engineering or science graduates aiming to join aircraft manufacturers.
Standards & tools

What you'll work with

  • SAE ARP6461 (guidelines for SHM implementation on aircraft)
  • SAE ARP4754A (development of civil aircraft and systems)
  • MIL-HDBK-1823A (NDE system reliability and probability of detection)
  • ATA MSG-3 (maintenance programme development)
  • CS-25 (EASA), CS 25.571 (damage tolerance and fatigue evaluation of structure)
  • RTCA DO-178C (airborne software)
  • RTCA DO-160G (environmental conditions and test procedures for airborne equipment)
  • ISO 13374 (condition monitoring data processing)
  • MATLAB
  • Python (scikit-learn, TensorFlow)
  • Abaqus
  • LabVIEW
  • COMSOL Multiphysics
Syllabus

8 modules

  1. 1.1From scheduled to condition-based maintenance
  2. 1.2SHM levels: detection, localisation, quantification and prognosis
  3. 1.3Active versus passive SHM
  4. 1.4Economic and availability benefits
  5. 1.5Integration with HUMS and fleet management
  6. 1.6Use cases in aeroplanes, helicopters and drones
  7. 1.7Barriers to certification
  8. 1.8Technology roadmap
  9. 1.9European research initiatives
  10. 1.10Case study: SHM business case in a fleet programme
  1. 2.1Strain gauges and fibre Bragg grating networks
  2. 2.2PZT piezoelectric transducers
  3. 2.3Acoustic emission sensors
  4. 2.4Comparative vacuum monitoring (CVM) sensors
  5. 2.5Corrosion sensors
  6. 2.6Wireless sensor networks
  7. 2.7Integration in composites and its effects
  8. 2.8Sensor durability and reliability
  9. 2.9Power supply and energy harvesting
  10. 2.10Lab: interrogation of an FBG network
  1. 3.1Lamb waves in plates
  2. 3.2Dispersion curves
  3. 3.3Frequency and mode selection
  4. 3.4Excitation and reception with PZT
  5. 3.5Baseline signals and temperature compensation
  6. 3.6Damage indices
  7. 3.7Damage imaging by tomography
  8. 3.8Tests on stiffened panels
  9. 3.9Detection limits
  10. 3.10Practical exercise: delamination detection with guided waves
  1. 4.1Operational loads monitoring
  2. 4.2Load–strain transfer models
  3. 4.3Neural networks for load estimation
  4. 4.4Fatigue life consumption
  5. 4.5Individual aircraft tracking
  6. 4.6Hard landing monitoring
  7. 4.7Helicopter usage and component life
  8. 4.8Integration with flight records
  9. 4.9Validation against tests
  10. 4.10Workshop: estimating consumed life from flight data
  1. 5.1Filtering, normalisation and feature extraction
  2. 5.2Time–frequency analysis and wavelets
  3. 5.3Dimensionality reduction with PCA
  4. 5.4Unsupervised anomaly detection
  5. 5.5Supervised damage classification
  6. 5.6Deep learning applied to SHM
  7. 5.7Environmental and operational influence
  8. 5.8Structural digital twin
  9. 5.9Big data management
  10. 5.10Lab: damage detection with PCA and machine learning
  1. 6.1Probability of detection for SHM
  2. 6.2MAPOD models
  3. 6.3Remaining useful life prognosis
  4. 6.4Particle filters for damage growth
  5. 6.5Uncertainty quantification
  6. 6.6False alarms and their cost
  7. 6.7Maintenance decision-making
  8. 6.8Integration with the MSG-3 programme
  9. 6.9System performance indicators
  10. 6.10Practical exercise: remaining useful life prognosis with a particle filter
  1. 7.1SAE ARP6461 and SHM guidelines
  2. 7.2Maintenance credits
  3. 7.3Integration into structural design
  4. 7.4Software and hardware to DO-178C and DO-254
  5. 7.5Data management and cybersecurity
  6. 7.6In-service certification experience
  7. 7.7SHM in drones and eVTOL
  8. 7.8Approval by the authority
  9. 7.9Changes to the maintenance programme
  10. 7.10Case study: approval route for a CVM system
  1. 8.1Selection of the target structure
  2. 8.2Detection requirements
  3. 8.3Sensor selection and placement
  4. 8.4Processing algorithm
  5. 8.5Reliability estimation
  6. 8.6Integration with maintenance
  7. 8.7Cost–benefit analysis
  8. 8.8Certification route
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

A 100% online programme on the SEIUM Institute 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 you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. You work with MATLAB, Python (scikit-learn, TensorFlow), Abaqus, LabVIEW and COMSOL Multiphysics, in free, academic or evaluation versions. Tests are studied on real procedures: damage detection with guided waves in composite panels, estimation of consumed life from in-flight load data, and reliability analysis of a monitoring system. Assessment is continuous by module plus the final project.

Standards covered: SAE ARP6461, SAE ARP4754A, MIL-HDBK-1823A, ATA MSG-3, CS-25 (EASA) / CS 25.571, RTCA DO-178C, RTCA DO-160G, ISO 13374.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of SAE ARP6461, SAE ARP4754A and MIL-HDBK-1823A applied to real cases, not as a theoretical annex.
Industry software:MATLAB, Python (scikit-learn, TensorFlow), Abaqus, LabVIEW and COMSOL Multiphysics, in free, academic or evaluation versions.
Final project:an SHM system project for a target structure, from detection requirements to certification route, 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.

Sensor network interrogation
Interrogate an FBG sensor network and characterise its response.
Guided-wave damage detection
Detect a delamination in a composite panel using guided waves.
Consumed life estimation
Estimate the fatigue life consumed from real flight data.
Remaining useful life prognosis
Forecast damage growth and remaining life with a particle filter.
Final project
Design a complete SHM system for a target structure, with reliability, cost–benefit and certification route, reviewed by your tutor.
Career opportunities

Where this takes you

Structural health monitoring engineerPredictive maintenance engineerAircraft health data scientistAeronautical sensor engineerStructures engineerFleet management 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, Python (scikit-learn, TensorFlow), Abaqus, LabVIEW and COMSOL Multiphysics, 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 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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