A Survey on Data-Driven Fault Diagnostic Techniques for Marine Diesel Engines
Ayah Youssef, Hassan Noura, Abderrahim El Amrani, El Mostafa El Adel, Mustapha Ouladsine
TL;DR
This survey addresses fault diagnosis in marine diesel engines with a data-driven lens, organizing techniques around engine subsystems (fuel injection, intake/exhaust, lubrication/cooling) and tracing the evolution from traditional to AI-enabled methods. It collates methods such as feature extraction, dimensionality reduction, supervised/unsupervised learning, and domain adaptation, reporting notable accuracies up to the ~95% range in targeted fault scenarios. A key finding is that generalization across entire subsystems remains limited by data scarcity and fault diversity, with most studies focusing on specific faults rather than holistic condition monitoring. The authors advocate for broader, subsystem-wide datasets and fault-isolation analyses to enhance reliability, safety, and regulatory compliance in marine diesel operation, and plan to extend the work into a journal with deeper fault-isolation discussion.
Abstract
Fault diagnosis in marine diesel engines is vital for maritime safety and operational efficiency.These engines are integral to marine vessels, and their reliable performance is crucial for safenavigation. Swift identification and resolution of faults are essential to prevent breakdowns,enhance safety, and reduce the risk of catastrophic failures at sea. Proactive fault diagnosisfacilitates timely maintenance, minimizes downtime, and ensures the overall reliability andlongevity of marine diesel engines. This paper explores the importance of fault diagnosis,emphasizing subsystems, common faults, and recent advancements in data-driven approachesfor effective marine diesel engine maintenance