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CourseNaval, Offshore & Sports Nautical Applications open

Marine IoT Course for Predictive Maintenance

Online 300-hour course in marine IoT for predictive maintenance. 6 modules covering ISO 17359 Condition monitoring and diagnostics of machines. General guidelines, ISO 13374 Condition monitoring and diagnostics of machines. Data processing, and Node-RED. Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Compare maintenance strategies applied to vessels.

02

Select suitable sensors for critical equipment.

03

Design onboard IoT architectures with satellite connectivity.

04

Analyse vibration signals and other condition parameters.

05

Apply anomaly detection and remaining life models.

06

Plan the implementation of predictive maintenance across a fleet.

07

Develop a predictive maintenance system for a specific piece of equipment.

Profile

Who it's for

  • Predictive maintenance engineer who wants to specialise in marine IoT for predictive maintenance.
  • Technical superintendent who wants to specialise in marine IoT for predictive maintenance.
  • Chief engineer who wants to specialise in marine IoT for predictive maintenance.
  • Industrial IoT engineer who wants to specialise in marine IoT for predictive maintenance.
  • Reliability analyst who wants to specialise in marine IoT for predictive maintenance.
  • Condition monitoring technician who wants to specialise in marine IoT for predictive maintenance.
  • Engineering or science graduates seeking to enter shipping companies.
Standards & tools

What you'll work with

  • ISO 17359 Condition monitoring and diagnostics of machines. General guidelines
  • ISO 13374 Condition monitoring and diagnostics of machines. Data processing
  • ISO 13381-1 Prognostics of machines
  • ISO 20816 Mechanical vibration. Measurement and evaluation
  • ISO 19848 Ships and marine technology. Standard data for machinery and equipment
  • International Safety Management Code (ISM Code)
  • IACS Unified Requirements E27
Syllabus

6 modules · 300 h

  1. 1.1Corrective, preventive and predictive strategies
  2. 1.2Condition-based maintenance
  3. 1.3ISM Code requirements on maintenance
  4. 1.4Planned maintenance management systems
  5. 1.5Critical ship equipment
  6. 1.6Failure mode and effects analysis
  7. 1.7Maintenance cost and availability
  8. 1.8Class society acceptance of predictive maintenance
  9. 1.9Examples of failures in marine machinery
  10. 1.10Case study: FMEA of a ballast pump
  1. 2.1Vibration with accelerometers
  2. 2.2Thermography and temperature
  3. 2.3Online oil analysis
  4. 2.4Pressure, flow and electrical current
  5. 2.5Acoustic emission and ultrasonics
  6. 2.6Wireless sensors and power supply
  7. 2.7Installation in the marine environment
  8. 2.8Data acquisition and sampling frequency
  9. 2.9Integration with the automation system
  10. 2.10Laboratory: vibration measurement on an auxiliary engine
  1. 3.1Gateways and edge computing
  2. 3.2MQTT and OPC UA protocols
  3. 3.3Onboard data servers
  4. 3.4Satellite transmission and compression
  5. 3.5Cloud IoT platforms
  6. 3.6Time-series databases
  7. 3.7Visualisation dashboards
  8. 3.8IoT device security
  9. 3.9Remote device management
  10. 3.10Workshop: data flow with Node-RED and Grafana
  1. 4.1Vibration signal processing
  2. 4.2Spectral and envelope analysis
  3. 4.3Vibration severity standards
  4. 4.4Statistical anomaly detection
  5. 4.5Machine learning models
  6. 4.6Remaining useful life estimation
  7. 4.7Expert diagnostic rules
  8. 4.8Validation against failure history
  9. 4.9False alarms and their management
  10. 4.10Practical: anomaly detection in a compressor
  1. 5.1Selection of pilot equipment
  2. 5.2Integration with the maintenance system
  3. 5.3Organisation onboard and ashore
  4. 5.4Crew training
  5. 5.5Return on investment
  6. 5.6Contracts with technology suppliers
  7. 5.7Cybersecurity requirements
  8. 5.8Scaling across the fleet
  9. 5.9Programme success indicators
  10. 5.10Case study: predictive maintenance pilot on a bulk carrier
  1. 6.1Study equipment and vessel
  2. 6.2Failure mode analysis
  3. 6.3Sensor selection
  4. 6.4Onboard IoT architecture
  5. 6.5Diagnostic model
  6. 6.6Monitoring dashboard
  7. 6.7Implementation plan
  8. 6.8Pilot return on investment
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
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: students progress at their own pace within the intake, which makes it possible to combine the programme with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Node-RED, InfluxDB, Grafana, AWS IoT Core, Python, in free, academic or evaluation versions. Tests are studied using real measurement and analysis procedures for vibration in auxiliary machinery, deployment of an onboard IoT architecture, and anomaly detection with machine learning. Assessment is continuous by module, plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven Master's Thesis:safety case, test plan, compliance dossier and operating limits.
Industry-mentored:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, cyber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Study equipment and vessel

Compare maintenance strategies applied to vessels.

Failure mode analysis

Select suitable sensors for critical equipment.

Sensor selection

Design onboard IoT architectures with satellite connectivity.

Onboard IoT architecture

Analyse vibration signals and other condition parameters.

Diagnostic model

Apply anomaly detection and remaining life models.

Career opportunities

Where this takes you

Predictive maintenance engineerTechnical superintendentChief engineerIndustrial IoT engineerReliability analystCondition monitoring technician
Admissions & scholarships

Requirements

  • No prior qualification required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient command of Spanish to follow technical regulatory reading.

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 comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

The work is carried out with Node-RED, InfluxDB, Grafana, AWS IoT Core, Python. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

SEIUM's own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.

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