Prethodno priopćenje

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 ; İstanbul Teknik Üniversitesi, Makina Fakültesi, Istanbul, Türkiye
C. Erdem Imrak ; İstanbul Teknik Üniversitesi, Makina Fakültesi, Istanbul, Türkiye
Altan Cakir orcid.org/0000-0002-8627-7689 ; İstanbul Teknik Üniversitesi, Fen-Edebiyat Fakültesi
Adem Candaş orcid.org/0000-0002-9951-9122 ; İstanbul Teknik Üniversitesi, Makina Fakültesi, Istanbul, Türkiye, Istanbul, Türkiye

Sažetak

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.

Ključne riječi

Machine learning; Multiclass classification; Marine diesel engine; Fault detection

Hrčak ID:

279528

URI

https://hrcak.srce.hr/279528

Datum izdavanja:

30.6.2022.

Posjeta: 1.402 *