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Master’s in Predictive Maintenance Engineering

Online master's programme of 1,900 hours in predictive maintenance engineering, structured in 12 modules covering maintenance strategies, vibration analysis, complementary condition monitoring techniques, sensors and data acquisition, signal processing, machine learning, remaining useful life prognosis, predictive platforms and reliability, 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

Design condition-based and reliability-based maintenance strategies.

02

Diagnose mechanical faults using vibration analysis.

03

Apply complementary condition monitoring techniques.

04

Process signals and extract features for diagnostics.

05

Develop machine-learning models for fault detection.

06

Estimate the remaining useful life of critical assets.

07

Implement a predictive programme with economic evaluation.

Profile

Who it's for

  • Predictive maintenance engineers looking to add data-driven methods to their toolkit.
  • Vibration analysts who want to move into prognosis and machine learning.
  • Reliability engineers responsible for critical asset strategies.
  • Industrial data scientists applying models to condition data.
  • Maintenance managers building or scaling a predictive programme.
  • Condition monitoring technicians seeking engineering-level expertise.
  • Engineering or science graduates aiming to work in the process industry.
Standards & tools

What you'll work with

  • ISO 17359 (condition monitoring and diagnostics of machines, general guidelines)
  • ISO 13374-1 (data processing, communication and presentation for condition monitoring)
  • ISO 20816-1 (measurement and evaluation of machine vibration)
  • ISO 18436-2 (qualification and assessment of vibration analysts)
  • ISO 55001 (asset management systems, requirements)
  • ISO 13379-1 (data interpretation and diagnostics techniques)
  • IEC 60812 (failure modes and effects analysis, FMEA)
  • EN 13306 (maintenance terminology)
  • MATLAB Predictive Maintenance Toolbox
  • Python with scikit-learn
  • SKF @ptitude
  • FLIR Thermal Studio
  • Grafana
Syllabus

12 modules · 1900 h

  1. 1.1. Corrective, preventive and predictive maintenance
  2. 1.2. Condition-based maintenance
  3. 1.3. Reliability-centred maintenance
  4. 1.4. P-F curve and early detection
  5. 1.5. Asset criticality
  6. 1.6. MTBF, MTTR and availability indicators
  7. 1.7. Life cycle costs
  8. 1.8. Asset management according to ISO 55000
  9. 1.9. Maintenance maturity
  10. 1.10. Case study: criticality analysis of a plant
  1. 2.1. Fundamentals of mechanical vibration
  2. 2.2. Sensors: accelerometers and proximity probes
  3. 2.3. Spectral analysis with FFT
  4. 2.4. Unbalance and misalignment
  5. 2.5. Looseness and resonances
  6. 2.6. Bearing defects and envelope analysis
  7. 2.7. Gear defects
  8. 2.8. Severity according to ISO 20816
  9. 2.9. Phase and waveform analysis
  10. 2.10. Lab: bearing diagnosis with envelope spectrum
  1. 3.1. Infrared thermography of equipment
  2. 3.2. Oil and particle analysis
  3. 3.3. Ultrasound and leak detection
  4. 3.4. Motor current signature analysis
  5. 3.5. Partial discharges in machines
  6. 3.6. Acoustic emission techniques
  7. 3.7. Pressure and flow monitoring
  8. 3.8. Automated visual inspection
  9. 3.9. Combining techniques
  10. 3.10. Practical: thermographic inspection of an electrical panel
  1. 4.1. Selecting sensors for monitoring
  2. 4.2. MEMS versus piezoelectric sensors
  3. 4.3. Sampling frequency and anti-aliasing
  4. 4.4. Portable and online acquisition systems
  5. 4.5. Wireless vibration sensors
  6. 4.6. Sensor mounting
  7. 4.7. Power supply and battery life
  8. 4.8. Integration with PLC and SCADA
  9. 4.9. Quality of the acquired signal
  10. 4.10. Workshop: designing an online monitoring system
  1. 5.1. Digital signal filtering
  2. 5.2. Statistical indicators in the time domain
  3. 5.3. Joint time-frequency analysis
  4. 5.4. Wavelet transform for transients
  5. 5.5. Spectral kurtosis and kurtogram
  6. 5.6. Order analysis at variable speed
  7. 5.7. Envelope demodulation
  8. 5.8. Feature extraction
  9. 5.9. Dimensionality reduction
  10. 5.10. Practical: feature extraction in Python
  1. 6.1. Fault classification
  2. 6.2. Unsupervised anomaly detection
  3. 6.3. Neural networks for time series
  4. 6.4. Autoencoders for deviation detection
  5. 6.5. Random forest and boosting models
  6. 6.6. Class imbalance and scarce data
  7. 6.7. Validation of diagnostic models
  8. 6.8. Model interpretability
  9. 6.9. Transfer between machines
  10. 6.10. Lab: fault classification with bearing data
  1. 7.1. Degradation models
  2. 7.2. Physics-based and data-driven approaches
  3. 7.3. Statistical survival models
  4. 7.4. Particle filters for prognosis
  5. 7.5. LSTM networks for useful life
  6. 7.6. Prognosis uncertainty
  7. 7.7. Prognosis evaluation metrics
  8. 7.8. Benchmark datasets
  9. 7.9. Integration with maintenance decisions
  10. 7.10. Practical: useful life estimation with C-MAPSS data
  1. 8.1. Architecture of a predictive system
  2. 8.2. Edge computing for vibration
  3. 8.3. Cloud maintenance platforms
  4. 8.4. Integration with CMMS and ERP
  5. 8.5. Asset digital twins
  6. 8.6. Dashboards and alerts
  7. 8.7. Condition data integration standards
  8. 8.8. Cybersecurity of the monitoring system
  9. 8.9. Scaling to multiple plants
  10. 8.10. Case study: integrating alerts with a CMMS
  1. 9.1. Electric motors and pumps
  2. 9.2. Compressors and fans
  3. 9.3. Gearboxes and transmissions
  4. 9.4. Turbines and generators
  5. 9.5. Wind turbine drivetrains
  6. 9.6. Power transformers
  7. 9.7. Machine tools and spindles
  8. 9.8. Bogies and railway equipment
  9. 9.9. Continuous process assets
  10. 9.10. Workshop: predictive plan for a pumping station
  1. 10.1. Selection of assets and techniques
  2. 10.2. Defining thresholds and alarms
  3. 10.3. Inspection routes
  4. 10.4. Organisation of the reliability team
  5. 10.5. Analyst training and certification
  6. 10.6. Root cause analysis
  7. 10.7. Return on investment
  8. 10.8. Managing cultural change
  9. 10.9. Continuous improvement of the programme
  10. 10.10. Case study: return calculation for a predictive programme
  1. 11.1. Failure mode and effects analysis
  2. 11.2. Fault trees
  3. 11.3. Weibull analysis
  4. 11.4. Reliability block diagrams
  5. 11.5. Maintainability and design
  6. 11.6. Field failure data analysis
  7. 11.7. Optimisation of maintenance intervals
  8. 11.8. Spare parts management
  9. 11.9. Human reliability in maintenance
  10. 11.10. Practical: Weibull fitting with failure data
  1. 12.1. Selection of the asset and its context
  2. 12.2. Criticality and failure mode analysis
  3. 12.3. Monitoring system design
  4. 12.4. Data acquisition and processing
  5. 12.5. Development of diagnostic models
  6. 12.6. Prognosis and decision rules
  7. 12.7. Economic evaluation of the programme
  8. 12.8. Conclusions on the predictive programme
  9. 12.9. Master's Thesis report
  10. 12.10. Master'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. No fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, SKF @ptitude, FLIR Thermal Studio and Grafana, in free, academic or evaluation versions. Testing is studied through real procedures: bearing diagnosis with vibration analysis, thermographic inspection of installations, and remaining useful life estimation with degradation data. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 17359, ISO 13374-1, ISO 20816-1, ISO 18436-2, ISO 55001, ISO 13379-1, IEC 60812, EN 13306.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 17359, ISO 13374-1 and ISO 20816-1 applied to real cases, not as a theoretical annex.
Industry software:MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, SKF @ptitude, FLIR Thermal Studio and Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:A complete predictive programme for a real asset, from criticality analysis and monitoring design to diagnostic models, prognosis and economic evaluation, 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.

Asset and criticality
Select the asset, analyse its context and carry out a criticality and failure mode analysis.
Monitoring system
Design the monitoring system and acquire and process the condition data.
Diagnostic models
Develop machine-learning models to detect and classify faults.
Prognosis and decisions
Estimate remaining useful life and define the maintenance decision rules.
Master's Thesis
Evaluate the programme economically and present the results, defended before a panel.
Career opportunities

Where this takes you

Predictive Maintenance EngineerVibration AnalystReliability EngineerIndustrial Data ScientistMaintenance ManagerCondition Monitoring Technician
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An 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 MATLAB Predictive Maintenance Toolbox, Python with scikit-learn, SKF @ptitude, FLIR Thermal Studio and Grafana, 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's 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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