Dr. Marina AlcoverAcademic director focused on measurable results and operational excellence in maritime education. Integrates IMO/STCW regulations and ISO 21001/9001 f
Course · 300 h · 5 modules
Course in Predictive Maintenance with AI and IoT Sensors on Board
Teaches you to move from planned to predictive maintenance on ships. You will learn to monitor the condition of engines, pumps and equipment with IoT sensors, analyse vibration, temperature and oil data, and use artificial…
Duration300 h
FormatOnline
Starts21 December
Places20 / 20
Prior knowledge
Prior Knowledge Assessment
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Programme overview
What is the Course in Predictive Maintenance with AI and IoT Sensors on Board?
The Course in Predictive Maintenance with AI and IoT Sensors on Board
Teaches you to move from planned to predictive maintenance on ships. You will learn to monitor the condition of engines, pumps and equipment with IoT sensors, analyse vibration, temperature and oil data, and use artificial intelligence to anticipate failures before they happen.
Key advantages
- Fewer breakdowns: Anticipating failures in critical equipment.
- Real data: Exercises with marine machinery data.
- Sensors and connectivity: Complete ship-to-shore architecture.
- Applied AI: Anomaly detection models.
- Class recognition: Condition-based maintenance schemes.
Programme type
Course
Study mode
Online
Duration
300 hours
Modules
5
Programme highlights
- Monitor equipment: Use vibration, temperature and pressure sensors.
- Analyse data: Interpret trends and anomalies.
- Apply AI: Build simple predictive models.
- Optimise maintenance: Cut costs and downtime.
- Advance your career: Combine marine engineering and data analysis.
- Stay ahead of breakdowns with predictive maintenance.
Curriculum
Programme Structure
The programme is structured in 5 modules, listed below in order of study.
01
Maintenance Strategies on Ships
- Corrective and preventive maintenance
- Condition-based maintenance
- Predictive maintenance
- Planned maintenance systems
- Classification society requirements
02
IoT Sensors and Data Acquisition
- Vibration sensors
- Temperature, pressure and flow
- Online oil analysis
- On-board sensor networks
- Data transfer to shore
03
Signal Analysis and Diagnosis
- Vibration analysis
- Thermography
- Oil analysis
- Engine performance
- Diagnosing typical faults
04
Artificial Intelligence for Prediction
- Anomaly detection
- Predicting remaining useful life
- Models built on historical data
- Digital twins of machinery
- Model validation
05
Implementation and Case Study
- Selecting critical equipment
- System architecture
- Organisation and procedures
- Return on investment
- Case study on a ship
Workload
Study Time and Workload
300 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
Engineer officers and chief engineers: who want to modernise maintenance Technical superintendents: responsible for fleet reliability Maintenance engineers: at shipyards and repair yards Data specialists: interested in marine industrial applications Equipment manufacturers: offering monitoring services
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Understand maintenance strategies
- 02Select sensors
- 03Analyse signals
- 04Apply AI
- 05Manage data
- 06Implement a programme
Careers
Career Opportunities
01Reliability engineer: At shipowners and technical managers.
02Machinery condition analyst: Shore-based monitoring centres.
03Technical superintendent: Modern maintenance management.
04Maritime IoT specialist: Manufacturers and technology firms.
Questions
Entry Requirements
It is not compulsory. Accessible tools are used and the logic of the models is explained.
Main and auxiliary engines, pumps, compressors, separators and rotating equipment in general.
Class societies have condition-based maintenance schemes, and the course explains how to fit into them.
Yes. The exercises use marine machinery data.
Yes. You design the predictive maintenance programme for a ship.
100% online, with a virtual campus available 24/7, live sessions that are recorded, and tutorials with the teaching staff.
The programme involves 300 hours of study. You can work at your own pace within the timetable we give you when you enrol.
Each programme has its own start date, shown on this page and confirmed by our admissions team when you enquire.
On completing the programme you receive the Course in Predictive Maintenance with AI and IoT Sensors on Board qualification awarded by Navalis Magna Institute.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
Entry profile: A technical, marine engineering or engineering background, or experience in industrial or naval maintenance. Language: A good working level of English to follow classes and materials. Technical requirements: A computer with a stable internet connection, an up-to-date browser and a webcam for live sessions and project presentations. Admissions process: Online application, profile review and a short interview with the academic team. We confirm your start date when you enquire.
Questions?
Frequently Asked Questions
Leave your details and admissions will write back. If you already know this is the one, go straight to the application and pay in the same session.