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Master'sNaval, Offshore & Sports Nautical Applications open

Master’s in Telemetry, Maritime IoT and Predictive Maintenance Engineering

Online master's programme of 1,900 hours in telemetry engineering, maritime IoT and predictive maintenance. 12 modules covering marine sensors, onboard IoT architectures, ship-to-shore communications, vibration analysis, predictive models and class requirements, 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 sensors suited to the marine environment.

02

Design onboard IoT architectures.

03

Manage ship-to-shore communications and data.

04

Apply vibration analysis to diagnose machinery.

05

Use condition monitoring techniques.

06

Develop predictive models.

07

Implement predictive systems across fleets.

Profile

Who it's for

  • Predictive maintenance engineers
  • Maritime IoT engineers
  • Vibration analysts
  • Technical superintendents
  • Data scientists moving into the shipping sector
  • Telemetry engineers
  • Engineering or science graduates looking to enter shipping companies
Standards & tools

What you'll work with

  • ISO 20816 (mechanical vibration measurement and evaluation)
  • ISO 13374 (condition monitoring data processing and communication)
  • ISO 17359 (condition monitoring and diagnostics of machines)
  • ISO 19848 (shipboard machinery data standards)
  • IACS UR E26 (cyber resilience of ships)
  • IEC 62443 (industrial automation and control system security)
  • DNV-CG-0508 (DNV guidance on smart vessel notations)
  • Python
  • InfluxDB
  • Grafana
  • Node-RED
  • MATLAB
Syllabus

12 modules · 1900 h

  1. 1.1Temperature, pressure and flow sensors
  2. 1.2Accelerometers and vibration sensors
  3. 1.3Shaft torque and power sensors
  4. 1.4Coriolis fuel flow meters
  5. 1.5Oil quality sensors
  6. 1.6Funnel emissions sensors
  7. 1.7Structural strain gauges
  8. 1.8Wireless sensors for ships
  9. 1.9Sensor calibration and certification
  10. 1.10Case study: sensor plan for a main engine
  1. 2.1Layers of a maritime IoT architecture
  2. 2.2Edge devices and gateways
  3. 2.3Onboard industrial networks
  4. 2.4LoRaWAN and industrial Wi-Fi wireless networks
  5. 2.5MQTT, OPC UA and Modbus protocols
  6. 2.6Integration with automation systems
  7. 2.7Power supply and the marine environment
  8. 2.8Scalability of the sensor network
  9. 2.9Reference architectures
  10. 2.10Lab: IoT node with a vibration sensor
  1. 3.1VSAT communications in Ku and Ka band
  2. 3.2LEO constellations and Starlink Maritime
  3. 3.3Iridium and backup communications
  4. 3.4Bandwidth management
  5. 3.5Data compression and prioritisation
  6. 3.6Telemetry in coastal areas over 4G and 5G
  7. 3.7Transmission costs
  8. 3.8Store and forward
  9. 3.9Link security
  10. 3.10Exercise: sizing the telemetry data rate
  1. 4.1Sampling frequencies by signal type
  2. 4.2Time synchronisation using PTP and NTP
  3. 4.3Time-series databases
  4. 4.4InfluxDB and TimescaleDB
  5. 4.5Data cleaning and validation
  6. 4.6Labelling of operating states
  7. 4.7Metadata and naming standards
  8. 4.8Data retention and archiving
  9. 4.9Data governance
  10. 4.10Workshop: time-series database with InfluxDB
  1. 5.1Fundamentals of mechanical vibration
  2. 5.2FFT spectral analysis
  3. 5.3Envelope analysis for bearings
  4. 5.4Misalignment and unbalance
  5. 5.5Gear diagnostics
  6. 5.6Vibration in diesel engines
  7. 5.7Vibration in pumps and compressors
  8. 5.8ISO 10816 and ISO 20816 standards
  9. 5.9Alarm levels
  10. 5.10Lab: bearing diagnosis with FFT
  1. 6.1Oil analysis and wear particles
  2. 6.2Infrared thermography of electrical switchboards
  3. 6.3Low-frequency ultrasound
  4. 6.4Combustion pressure analysis
  5. 6.5Electric motor monitoring
  6. 6.6Motor current analysis
  7. 6.7Shaft bearing monitoring
  8. 6.8Hull and propeller monitoring
  9. 6.9Integration of techniques
  10. 6.10Exercise: analysis of an oil sample
  1. 7.1Corrective, preventive and predictive strategies
  2. 7.2Condition-based maintenance
  3. 7.3Reliability-centred maintenance
  4. 7.4FMECA failure mode analysis
  5. 7.5Maintenance indicators
  6. 7.6Maintenance management systems
  7. 7.7ISM Code requirements
  8. 7.8Class-approved planned maintenance
  9. 7.9Costs of ship maintenance
  10. 7.10Case study: FMECA of a cooling pump
  1. 8.1Anomaly detection
  2. 8.2Statistical control methods
  3. 8.3Supervised machine learning
  4. 8.4Unsupervised models
  5. 8.5Recurrent neural networks
  6. 8.6Remaining useful life models
  7. 8.7Degradation models
  8. 8.8Model validation
  9. 8.9Model explainability for crews
  10. 8.10Workshop: anomaly detection with Python
  1. 9.1Commercial monitoring platforms
  2. 9.2Open-source platforms
  3. 9.3Visualisation with Grafana
  4. 9.4Alarms and notifications
  5. 9.5Integration with the CMMS
  6. 9.6Automated equipment health reports
  7. 9.7Remote monitoring centres
  8. 9.8User experience
  9. 9.9Mobile applications for chief engineers
  10. 9.10Exercise: machinery health dashboard
  1. 10.1Monitoring class notations
  2. 10.2DNV guidelines for condition-based maintenance
  3. 10.3ABS and Lloyd's Register guidelines
  4. 10.4System approval requirements
  5. 10.5Remote surveys accepted by class
  6. 10.6Cyber security according to IACS
  7. 10.7Data for the classification society
  8. 10.8Recognition schemes
  9. 10.9Implementation experience
  10. 10.10Case study: requirements for a monitoring notation
  1. 11.1Selection of critical assets
  2. 11.2Design of a pilot plan
  3. 11.3Scaling up to the fleet
  4. 11.4Return on investment analysis
  5. 11.5Change management
  6. 11.6Crew training
  7. 11.7Suppliers and contracts
  8. 11.8Success indicators
  9. 11.9Lessons learned from implementations
  10. 11.10Lab: business case for a predictive system
  1. 12.1Definition of the system to be monitored
  2. 12.2Sensor plan
  3. 12.3Telemetry architecture
  4. 12.4Data acquisition
  5. 12.5Development of the predictive model
  6. 12.6Dashboard
  7. 12.7Return analysis
  8. 12.8Conclusions on the predictive system
  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 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The software used is Python, InfluxDB, Grafana, Node-RED and MATLAB, in free, academic or evaluation versions. Work is based on real procedures for vibration diagnosis, predictive model development and IoT architecture design. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 20816, ISO 13374, ISO 17359, ISO 19848, IACS UR E26, IEC 62443, DNV-CG-0508.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 20816, ISO 13374 and ISO 17359 applied to real cases, not as a theoretical annex.
Industry software:Python, InfluxDB, Grafana, Node-RED and MATLAB, in free, academic or evaluation versions.
Master's Thesis with defence:A complete predictive maintenance system for a vessel, from sensor plan to model and dashboard, 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.

Sensor plan
Define the system to be monitored and specify its sensors.
Telemetry architecture
Design the onboard IoT network and ship-to-shore data link.
Vibration diagnosis
Diagnose a bearing fault using FFT and envelope analysis.
Predictive model
Build an anomaly detection model in Python and a health dashboard.
Master's Thesis
A complete predictive maintenance system with business case, defended before a panel.
Career opportunities

Where this takes you

Predictive Maintenance EngineerMaritime IoT EngineerVibration AnalystTechnical SuperintendentData ScientistTelemetry Engineer
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, InfluxDB, Grafana, Node-RED and MATLAB, 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 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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