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.
Prior knowledge check
These are the skills this programme gives you. Tick the ones you already master and we will tell you which part of the syllabus is for you.
You are starting from scratch in this subject. The programme is designed precisely for that.
What is the Course in Predictive Maintenance with AI and Onboard IoT Sensors?
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.
Syllabus of the Course in Predictive Maintenance with AI and Onboard IoT Sensors
5 modules. Select a module to see its content.
Maintenance Strategies on Ships
- 1.1. Corrective and preventive maintenance
- 1.2. Condition-based maintenance
- 1.3. Predictive maintenance
- 1.4. Planned maintenance management systems
- 1.5. Classification society requirements
IoT Sensors and Data Acquisition
- 2.1. Vibration sensors
- 2.2. Temperature, pressure and flow
- 2.3. Online oil analysis
- 2.4. Onboard sensor networks
- 2.5. Ship-to-shore data transmission
Signal Analysis and Diagnostics
- 3.1. Vibration analysis
- 3.2. Thermography
- 3.3. Oil analysis
- 3.4. Engine performance
- 3.5. Diagnosis of typical faults
Artificial Intelligence for Prediction
- 4.1. Anomaly detection
- 4.2. Remaining useful life prediction
- 4.3. Models with historical data
- 4.4. Digital twins of machinery
- 4.5. Model validation
Implementation and Case Study
- 5.1. Selection of critical equipment
- 5.2. System architecture
- 5.3. Organisation and procedures
- 5.4. Return on investment
- 5.5. Case study on a ship
Duration and format of the Course in Predictive Maintenance with AI and Onboard IoT Sensors
It is 300 hours. Tell us how many you can study each week and we will tell you when you will really finish.
This is the pace at which most students finish. It fits well with the instalment plan.
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programmes.
Dra. Marina AlcoverAcademic Director and Head of Quality Assurance
Ing. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Cap. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dra. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Ing. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Ing. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Ing. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration CoordinatorObjectives of the Course in Predictive Maintenance with AI and Onboard IoT Sensors
Career opportunities after the Course in Predictive Maintenance with AI and Onboard IoT Sensors
Questions about the Course in Predictive Maintenance with AI and Onboard IoT Sensors
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.
Classification 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 has a workload of 300 hours. You can progress at your own pace within the schedule we give you when you enrol.
Each programme has its own start date, which appears on this page and is confirmed by the admissions team when you request information.
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Admission process
No waiting for a call back. If your documents are in order, you can enrol in the same session.
- 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.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrolmentChoose full payment or instalments and reserve your place.
Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.
Ask us before you enrol
Leave your details and admissions will write to you to answer any questions. If you are already sure, you can apply and pay in the same session.