A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems

Muhittin Orhan*, Metin Celik

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)

Abstract

Fault detection and diagnostics (FDD) have great potential to enable safety, efficiency, and reliability measures of critical machinery systems. However, it is clear that there is a lack of systematic literature review to identify and classify the FDD studies conducted within the scope of marine engineering. This paper offers a systematic review of FDD models particular to marine machinery and systems. The numbers of 72 core articles were highlighted through a comprehensive literature review conducted in the 2002–2022 period. The studies are classified based on the mostly utilized methods such as data-driven, model-based, knowledge-based, and new generation-hybrid. In addition, new generation and hybrid methods are discussed in detail. The experimental environment (i.e. shipboard, labs, simulator) and technical details of the conducted studies are extensively discussed. While 56.94% of the examined studies are related to the main engine, 43.06% of them are related to auxiliary engines. In addition, the main and auxiliary engine studies are also divided into subject headings and examined in detail. Given the recent developments in green and smart maritime concepts, a future research agenda of the FDD studies on marine machinery systems is then pinpointed. Consequently, the study stimulates scholars interested in FDD while it enables innovative ideas for marine engineers, technology providers, ship operators, and maritime entrepreneurs.

Original languageEnglish
Pages (from-to)3-21
Number of pages19
JournalProceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment
Volume238
Issue number1
DOIs
Publication statusPublished - Feb 2024

Bibliographical note

Publisher Copyright:
© IMechE 2023.

Keywords

  • Fault detection and diagnosis
  • literature review
  • marine machinery systems

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