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Master'sNuclear Engineering & Nuclear Fuel Cycle Technologies Applications open

Master’s in Predictive Maintenance and Condition-Based Asset Management (CBM) in Nuclear Plants Engineering

Online master's programme of 1,900 hours in predictive maintenance and condition-based maintenance (CBM) engineering for nuclear plants. 12 modules covering the maintenance rule, reliability-centred maintenance, vibration analysis, thermography, online monitoring, analytics and machine learning, asset management, outage optimisation and critical components, 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

Apply the maintenance rule and equipment reliability programmes.

02

Carry out reliability-centred maintenance analyses.

03

Diagnose equipment using vibration analysis and thermography.

04

Design online monitoring systems.

05

Apply analytics and machine learning to maintenance.

06

Manage assets and their ageing.

07

Optimise refuelling outages using risk criteria.

Profile

Who it's for

  • Equipment reliability engineers.
  • Vibration analysts.
  • Predictive maintenance engineers.
  • Outage planners.
  • Asset management engineers.
  • Plant data scientists.
  • Engineering or science graduates looking to enter the nuclear energy sector.
Standards & tools

What you'll work with

  • 10 CFR 50.65 (requirements for monitoring the effectiveness of maintenance)
  • SAE JA1011 (evaluation criteria for RCM processes)
  • ISO 20816 (measurement and evaluation of machine vibration)
  • ISO 55000 (asset management: overview and principles)
  • INPO AP-913 (equipment reliability process)
  • ISO 18436 (qualification and assessment of condition monitoring personnel)
  • IAEA SSG-48 (ageing management and long-term operation of nuclear power plants)
  • Python
  • MATLAB
  • SKF @ptitude
  • Primavera P6
  • AVEVA PI System
Syllabus

12 modules · 1900 h

  1. 1.1The maintenance rule 10 CFR 50.65
  2. 1.2Classification of systems and components
  3. 1.3Maintenance strategies
  4. 1.4Online and outage maintenance
  5. 1.5Equipment performance indicators
  6. 1.6Equipment reliability programmes: INPO AP-913
  7. 1.7Relationship with nuclear safety
  8. 1.8Configuration management
  9. 1.9Maintenance costs
  10. 1.10Case study: analysis of an equipment reliability programme
  1. 2.1RCM methodology
  2. 2.2SAE JA1011 and JA1012
  3. 2.3Functions and functional failures
  4. 2.4Component FMEA
  5. 2.5Failure consequences
  6. 2.6Task selection
  7. 2.7Interval optimisation
  8. 2.8EPRI PM Basis Database
  9. 2.9Living programme review
  10. 2.10Practical: RCM analysis of a feedwater pump
  1. 3.1Vibration fundamentals
  2. 3.2Transducers and mounting
  3. 3.3Spectral analysis
  4. 3.4Unbalance, misalignment and looseness
  5. 3.5Bearing defects
  6. 3.6Gear defects
  7. 3.7Phase and orbit analysis
  8. 3.8ISO 10816 and ISO 20816
  9. 3.9Turbine generators
  10. 3.10Lab: spectral diagnosis with pump data
  1. 4.1Principles of infrared thermography
  2. 4.2Electrical thermography
  3. 4.3Mechanical thermography
  4. 4.4Airborne ultrasound
  5. 4.5Oil analysis
  6. 4.6Wear particle analysis
  7. 4.7Electric motor monitoring: MCSA
  8. 4.8Motor-operated valve diagnostics
  9. 4.9Integration of techniques
  10. 4.10Practical: thermographic report of an electrical panel
  1. 5.1Permanent monitoring systems
  2. 5.2Wireless sensors
  3. 5.3Transformer monitoring
  4. 5.4Turbine monitoring
  5. 5.5Online instrument calibration monitoring
  6. 5.6Sensor drift detection
  7. 5.7Monitoring and diagnostic centres
  8. 5.8Data architecture
  9. 5.9Cybersecurity of monitoring systems
  10. 5.10Case study: design of a monitoring centre
  1. 6.1Preparation of plant data
  2. 6.2Empirical reference models
  3. 6.3MSET and estimation techniques
  4. 6.4Anomaly detection
  5. 6.5Fault classification
  6. 6.6Remaining useful life prognostics
  7. 6.7Explainability
  8. 6.8Commercial platforms
  9. 6.9Model validation
  10. 6.10Lab: empirical reference model in Python
  1. 7.1ISO 55000 and asset management
  2. 7.2Component life plans
  3. 7.3Obsolescence management
  4. 7.4Long-term operation
  5. 7.5Ageing management programmes
  6. 7.6Investment planning
  7. 7.7Life cycle cost analysis
  8. 7.8Risk and criticality
  9. 7.9Decision-making
  10. 7.10Workshop: life plan for a main transformer
  1. 8.1Planning of refuelling outages
  2. 8.2Critical path
  3. 8.3Online versus outage maintenance
  4. 8.4Shutdown risk management
  5. 8.5Reducing outage duration
  6. 8.6Contractor coordination
  7. 8.7Outage indicators
  8. 8.8Lessons learned
  9. 8.9Planning tools
  10. 8.10Practical: critical path analysis of an outage
  1. 9.1The maintenance rule and oversight
  2. 9.2Configuration risk assessment
  3. 9.3Technical specifications
  4. 9.4Surveillance testing
  5. 9.5Maintenance of safety equipment
  6. 9.6Regulatory inspections
  7. 9.7Maintenance documentation
  8. 9.8Safety culture in maintenance
  9. 9.9Human factors
  10. 9.10Case study: risk assessment with a risk monitor
  1. 10.1Reactor coolant pumps
  2. 10.2Safety valves
  3. 10.3Emergency diesel generators
  4. 10.4Turbine generator
  5. 10.5Transformers
  6. 10.6Heat exchangers
  7. 10.7Safety instrumentation
  8. 10.8Medium-voltage electrical equipment
  9. 10.9Structures and supports
  10. 10.10Lab: failure analysis of an emergency diesel generator
  1. 11.1Predictive maintenance roadmap
  2. 11.2Organisation and competences
  3. 11.3Technology suppliers
  4. 11.4Return on investment
  5. 11.5Effectiveness indicators
  6. 11.6Change management
  7. 11.7International experience
  8. 11.8Integration with the digital twin
  9. 11.9Trends
  10. 11.10Workshop: business case for online monitoring
  1. 12.1Selection of the system or component
  2. 12.2RCM analysis
  3. 12.3Selection of predictive techniques
  4. 12.4Monitoring design
  5. 12.5Analytical model
  6. 12.6Economic assessment
  7. 12.7Implementation plan
  8. 12.8Conclusions
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
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 the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is Python, MATLAB, SKF @ptitude, Primavera P6 and AVEVA PI System, in free, academic or evaluation versions. Exercises are based on real procedures for equipment diagnosis through vibration analysis, RCM analysis of systems, and empirical anomaly detection modelling. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: 10 CFR 50.65 (maintenance rule), SAE JA1011, ISO 20816, ISO 55000, INPO AP-913, ISO 18436 (condition monitoring qualification), IAEA SSG-48.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of 10 CFR 50.65, SAE JA1011 and ISO 20816 applied to real cases, not as a theoretical annex.
Industry software:Python, MATLAB, SKF @ptitude, Primavera P6 and AVEVA PI System, in free, academic or evaluation versions.
Master's Thesis with defence:A predictive maintenance programme for a plant system covering RCM analysis, technique selection, monitoring design, analytical model and economic assessment, 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.

RCM analysis
Carry out an RCM analysis of a feedwater pump.
Vibration diagnosis
Diagnose a pump fault from vibration spectra.
Anomaly detection model
Build an empirical reference model for anomaly detection in Python.
Outage critical path
Analyse the critical path of a refuelling outage with risk criteria.
Master's Thesis
Design a predictive maintenance programme for a nuclear plant system, defended before a panel.
Career opportunities

Where this takes you

Equipment reliability engineerVibration analystPredictive maintenance engineerOutage plannerAsset management engineerPlant data scientist
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer able to run 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 Python, MATLAB, SKF @ptitude, Primavera P6 and AVEVA PI System, 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 university 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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