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Master’s in Predictive Maintenance and Condition Monitoring Engineering

Online master's programme of 1,900 hours in predictive maintenance and condition monitoring engineering, structured in 12 modules covering vibration analysis, oil analysis, infrared thermography, ultrasound, electrical monitoring, IoT sensors, machine learning, remaining useful life prognosis and digital twins under ISO 17359, ISO 13374 and ISO 20816-1, 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

Select predictive techniques according to failure modes.

02

Diagnose defects through vibration analysis.

03

Interpret oil analyses and thermographic surveys.

04

Deploy IoT sensors and data architectures.

05

Apply machine learning to anomaly detection.

06

Estimate the remaining useful life of components.

07

Implement predictive programmes with measurable returns.

Profile

Who it's for

  • Vibration analysts.
  • Predictive maintenance engineers.
  • Industrial data scientists.
  • Reliability engineers.
  • Thermography technicians.
  • Condition monitoring managers.
  • Engineering or science graduates seeking to enter the mining sector.
Standards & tools

What you'll work with

  • ISO 17359 (condition monitoring: general guidelines)
  • ISO 13374 (condition monitoring data processing)
  • ISO 20816-1 (measurement and evaluation of vibration)
  • ISO 18436-2 (qualification of vibration analysts)
  • ISO 4406 (particle contamination code)
  • ISO 18434-1 (thermography for condition monitoring)
  • ISO 13381-1 (condition prognostics)
  • Python with scikit-learn
  • MATLAB Predictive Maintenance Toolbox
  • Emerson AMS Machine Works
  • Microsoft Azure IoT
Syllabus

12 modules · 1900 h

  1. 1.1P-F curve and detection interval
  2. 1.2Condition-based maintenance
  3. 1.3Available monitoring techniques
  4. 1.4Selection of techniques by failure mode
  5. 1.5Return on predictive investment
  6. 1.6Condition monitoring standards
  7. 1.7Organisation of a predictive programme
  8. 1.8Analyst competencies
  9. 1.9Common implementation errors
  10. 1.10Case study: selecting techniques for an excavator
  1. 2.1Mechanical vibration in rotating machines
  2. 2.2Transducers and their mounting
  3. 2.3FFT spectral analysis
  4. 2.4Unbalance, misalignment and looseness
  5. 2.5Bearing defects
  6. 2.6Envelope analysis
  7. 2.7Gear defects
  8. 2.8Vibration in variable-speed machines
  9. 2.9Severity according to ISO 20816
  10. 2.10Lab: bearing diagnosis using envelope analysis
  1. 3.1Oil analysis programme
  2. 3.2Elemental spectrometry
  3. 3.3Particle counting to ISO 4406
  4. 3.4Ferrography and wear particles
  5. 3.5Viscosity and acid number
  6. 3.6Water and fuel in oil
  7. 3.7Soot and oxidation
  8. 3.8Limits and trends
  9. 3.9Integration with vibration analysis
  10. 3.10Practical exercise: interpreting trends for a diesel engine
  1. 4.1Infrared radiation and temperature
  2. 4.2Thermal imaging cameras and their selection
  3. 4.3Emissivity and correction factors
  4. 4.4Thermography of electrical systems
  5. 4.5Thermography of mechanical components
  6. 4.6Thermography of hydraulic systems
  7. 4.7Thermographic inspection routes
  8. 4.8Severity criteria
  9. 4.9Preparing thermographic reports
  10. 4.10Lab: thermographic inspection of an electrical panel
  1. 5.1Airborne ultrasound inspection
  2. 5.2Detection of air and gas leaks
  3. 5.3Ultrasound-assisted lubrication
  4. 5.4Detection of electrical discharges
  5. 5.5Acoustic emission in structures
  6. 5.6Detection of active cracks
  7. 5.7Equipment and sensors
  8. 5.8Measurement procedures
  9. 5.9Interpretation of results
  10. 5.10Practical exercise: ultrasound-guided bearing lubrication
  1. 6.1Motor current signature analysis
  2. 6.2Rotor and stator defects
  3. 6.3Power quality
  4. 6.4Motor insulation
  5. 6.5Battery monitoring
  6. 6.6Power electronics in electric machines
  7. 6.7Monitoring of diesel engines
  8. 6.8Process pressures and temperatures
  9. 6.9Combined diagnosis using several techniques
  10. 6.10Case study: diagnosis of a traction motor
  1. 7.1Wireless vibration sensors
  2. 7.2Temperature and pressure sensors
  3. 7.3Industrial data gateways
  4. 7.4MQTT and OPC UA protocols
  5. 7.5Edge computing
  6. 7.6Cloud platforms
  7. 7.7Manufacturer telematics
  8. 7.8Data quality and sampling rate
  9. 7.9Sensor cybersecurity
  10. 7.10Lab: deploying wireless sensors
  1. 8.1Preparation of condition data
  2. 8.2Feature extraction
  3. 8.3Anomaly detection
  4. 8.4Fault classification
  5. 8.5Supervised and unsupervised models
  6. 8.6Deep learning for time series
  7. 8.7Model validation
  8. 8.8Explainability for technicians
  9. 8.9Deploying models in production
  10. 8.10Practical exercise: anomaly detection in Python
  1. 9.1Concept of remaining useful life
  2. 9.2Physics-based models
  3. 9.3Data-driven models
  4. 9.4Hybrid prognostic models
  5. 9.5Health indicators
  6. 9.6Prognostic uncertainty
  7. 9.7Maintenance decision-making
  8. 9.8Integration with planning
  9. 9.9Cases of major components
  10. 9.10Lab: life prognosis for a bearing
  1. 10.1Equipment criticality analysis
  2. 10.2Definition of routes and frequencies
  3. 10.3Selection of technologies
  4. 10.4Integration with the CMMS
  5. 10.5Training and certification of analysts
  6. 10.6Change management
  7. 10.7Programme indicators
  8. 10.8Programme audit
  9. 10.9Continual improvement of the programme
  10. 10.10Case study: predictive programme for a mining fleet
  1. 11.1Concept of the digital twin
  2. 11.2Component simulation models
  3. 11.3Real-time data integration
  4. 11.4Fleet twins
  5. 11.5Operating scenarios
  6. 11.6Maintenance optimisation
  7. 11.7Commercial digital twin platforms
  8. 11.8Implementation challenges
  9. 11.9Return on investment
  10. 11.10Case study: digital twin of a transmission
  1. 12.1Selection of the asset or fleet
  2. 12.2Failure mode analysis
  3. 12.3Selection of techniques
  4. 12.4Data architecture
  5. 12.5Diagnostic model
  6. 12.6Life prognosis
  7. 12.7Implementation plan
  8. 12.8Economic analysis of the programme
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 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, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with scikit-learn, MATLAB Predictive Maintenance Toolbox, Emerson AMS Machine Works and Microsoft Azure IoT, in free, academic or evaluation versions. Testing is studied through real procedures for diagnosis through vibration analysis, thermographic inspections and oil analysis programmes. Assessment is continuous by module plus the final Master's Thesis.

Standards covered: ISO 17359, ISO 13374, ISO 20816-1, ISO 18436-2, ISO 4406, ISO 18434-1, ISO 13381-1.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 17359, ISO 13374 and ISO 20816-1 applied to real cases, not as a theoretical annex.
Industry software:Python with scikit-learn, MATLAB Predictive Maintenance Toolbox, Emerson AMS Machine Works and Microsoft Azure IoT, in free, academic or evaluation versions.
Master's Thesis with defence:A complete predictive maintenance programme for an asset or fleet, from failure mode analysis to diagnostic model, life prognosis and economic case, 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.

Failure mode analysis
Select the asset or fleet and analyse its failure modes.
Techniques and data architecture
Select the monitoring techniques and design the data architecture.
Diagnosis and prognosis
Build the diagnostic model and the remaining useful life prognosis.
Implementation and economics
Draw up the implementation plan and the economic analysis.
Master's Thesis
Present the complete predictive maintenance project, defended before a panel.
Career opportunities

Where this takes you

Vibration analystPredictive maintenance engineerIndustrial data scientistReliability engineerThermography technicianCondition monitoring manager
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's 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 with scikit-learn, MATLAB Predictive Maintenance Toolbox, Emerson AMS Machine Works and Microsoft Azure IoT, 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 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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