AI powered fault detection for marine engines operating with B30 biodiesel.

The project aims to address challenges in implementing B30 biodiesel engines in
Indonesian maritime transportation due to fuel-related faults, leading to higher
maintenance costs and hindering widespread adoption. It proposes developing a
fault estimation model using machine learning techniques to predict and assess
common biodiesel engine faults, facilitating timely detection and cost-effective
maintenance. The project involves customising thermodynamic models, simulating
healthy and faulty operations, training ML models, and verifying them with real-time
data. Additionally, the project includes organising a workshop on AI in the
Indonesian maritime industry to discuss relevant challenges and solutions. The
project contributes to sustainable development by promoting eco-friendly practices,
reducing operational costs, and fostering economic growth of Indonesia. Key
performance indicators will measure success, including the development of fault
diagnosis models, skill development, dissemination of research outputs, and
awareness creation. The impacts of the project span academic, social, economic,
environmental, and cultural domains, benefiting stakeholders and promoting
research in maritime transportation across Indonesia and south-east Asia.