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

Creative Commons License

Sahin T., İmrak C. E., Cakir A., Candaş A.

POMORSTVO-SCIENTIFIC JOURNAL OF MARITIME RESEARCH, cilt.36, sa.1, ss.95-104, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.31217/p.36.1.11
  • Dergi Adı: POMORSTVO-SCIENTIFIC JOURNAL OF MARITIME RESEARCH
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), Central & Eastern European Academic Source (CEEAS), Geobase, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.95-104
  • Anahtar Kelimeler: Machine learning, Multiclass classification, Marine diesel engine, Fault detection, DECISION-SUPPORT-SYSTEM, CLASSIFICATION
  • İstanbul Teknik Üniversitesi Adresli: Evet

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.