Preliminary communication
https://doi.org/10.31217/p.36.1.11
Evolved model for early fault detection and health tracking in marine diesel engine by means of machine learning techniques
Tolga Şahin
orcid.org/0000-0003-2158-9400
; Department of Mechanical Engineering, Faculty of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey
C. Erdem Imrak
; Department of Mechanical Engineering, Faculty of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey
Altan Cakir
orcid.org/0000-0002-8627-7689
; Physics Engineering, Faculty of Science and Letters, Istanbul Technical University, Reşitpaşa /Maslak/Sarıyer, Istanbul
Adem Candaş
orcid.org/0000-0002-9951-9122
; Department of Mechanical Engineering, Faculty of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey
Abstract
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.
Keywords
Machine learning; Multiclass classification; Marine diesel engine; Fault detection
Hrčak ID:
279528
URI
Publication date:
30.6.2022.
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