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Short course · Innovation and startups

Course in Predictive Maintenance with AI and Onboard IoT Sensors

This course teaches you to move from planned maintenance to predictive maintenance on ships. You will learn which IoT sensors make sense on board and how AI models use their data to anticipate failures, along with data quality and cost-effectiveness.

100% online300 hours5 modulesTaught in EnglishEnrol any time

What is this programme?

This short course shows you how to move from planned maintenance to predictive maintenance on ships. You will learn to monitor the condition of engines, pumps and other equipment with IoT sensors, to analyse vibration, temperature and oil data, and to use artificial intelligence to anticipate failures before they occur.

What sets the course apart: fewer breakdowns, by anticipating failures in critical equipment; real data, with exercises based on marine machinery data; sensors and connectivity, covering the full architecture from ship to shore; applied AI, with anomaly-detection models; and condition-based maintenance programmes aligned with classification society requirements.

Programme highlights

  • Monitor equipment with vibration, temperature and pressure sensors.
  • Analyse data to interpret trends and anomalies.
  • Build simple predictive models with AI.
  • Optimise maintenance to reduce costs and downtime.
  • Combine marine engineering with data analysis.
  • Stay ahead of breakdowns.

Study plan

01Maintenance Strategies on Ships60 h
Corrective and preventive maintenance. Condition-based maintenance. Predictive maintenance. Planned maintenance management systems. Classification society requirements.
02IoT Sensors and Data Acquisition60 h
Vibration sensors. Temperature, pressure and flow. Online oil analysis. Onboard sensor networks. Data transmission to shore.
03Signal Analysis and Diagnostics60 h
Vibration analysis. Thermography. Oil analysis. Engine performance. Diagnosis of typical faults.
04Artificial Intelligence for Prediction60 h
Anomaly detection. Remaining useful life prediction. Models built on historical data. Digital twins of machinery. Model validation.
05Implementation and Case Study60 h
Selection of critical equipment. System architecture. Organisation and procedures. Return on investment. Case study on a ship.

Duration and format

300hours in total
5modules of 60 hours
100%online, at your own pace
Openenrolment all year round

At 10 hours a week you would finish in about 30 weeks; at 20 hours a week, in about 15.

What you will learn

  • Understand maintenance strategies.
  • Select sensors.
  • Analyse signals.
  • Apply AI.
  • Manage data.
  • Implement a predictive maintenance programme.

Career outcomes

  • Reliability engineer in shipping companies and technical managers.
  • Machinery condition analyst in shore-based monitoring centres.
  • Technical superintendent with a modern approach to maintenance management.
  • Maritime IoT specialist with manufacturers and technology companies.

Entry requirements

  • A technical, engineering or marine engine-room background, or experience in industrial or marine maintenance.
  • A sufficient command of the language of instruction to follow classes and materials; basic technical English is recommended.
  • A computer with a stable internet connection, an up-to-date browser and a camera for live sessions and project presentations.
  • Admission process: online application, profile review and a short interview.

How to enrol

Enrol online

Complete your enrolment on this page, or apply through admissions if you prefer to talk to us first.

Receive your access

We send you the details to enter the virtual campus and start the programme.

Study at your pace

Work through the modules when it suits you and complete the programme from anywhere in the world.

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