Evolved model for early fault detection and health tracking in marine diesel engine by means of machine learning techniques

Tolga Şahin*, C. Erdem Imrak, Altan Cakir, Adem Candaş

*Bu çalışma için yazışmadan sorumlu yazar

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1 Atıf (Scopus)

Özet

The Coast Guard Command, which has a wide range of duties as saving human lives, protecting natural resources, preventing marine pollution and battle against smuggling, uses diesel main engines in its ships, as in other military and commercial ships. It is critical that the main engines operate smoothly at all times so that they can respond quickly while performing their duties, thus enabling fast and early detection of faults and preventing failures that are costly or take longer to repair. The aim of this study is to create and to develop a model based on current data, to select machine learning algorithms and ensemble methods, to develop and explain the most appropriate model for fast and accurate detection of malfunctions that may occur in 4-stroke high-speed diesel engines. Thus, it is aimed to be an exemplary study for a data-based decision support mechanism.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)95-104
Sayfa sayısı10
DergiPomorstvo
Hacim36
Basın numarası1
DOI'lar
Yayın durumuYayınlandı - Haz 2022

Bibliyografik not

Publisher Copyright:
© Faculty of Maritime Studies Rijeka, 2022.

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