# A Survey on Data-Driven Fault Diagnostic Techniques for Marine Diesel Engines

arXiv: https://arxiv.org/abs/2404.10363 · HTML: https://mlsanity.com/paper/2404.10363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 34 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning](https://mlsanity.com/paper/2609.06257/) (2026)

## Abstract (arXiv)

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