Prediction of Ship Main Engine Failures by Artificial Neural Networks


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Göksu B., Erginer K. E.

JOURNAL OF ETA MARITIME SCIENCE, vol.8, no.2, pp.98-113, 2020 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.5505/jems.2020.90377
  • Journal Name: JOURNAL OF ETA MARITIME SCIENCE
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.98-113
  • Keywords: Neural Networks, Planned maintenance, Ship engine failures, MAINTENANCE MANAGEMENT-SYSTEM, DESIGN, OPTIMIZATION, RELIABILITY, SELECTION
  • Dokuz Eylül University Affiliated: Yes

Abstract

Maintenance practices are considered as the means of providing safety and security to environment and quality service, and despite increasing the costs for companies with certain increments, they contribute to their reputation and reliability. Maintenance planning of ships consists of setting priorities and planning the efficient use of the sources. One of the main objectives of this study is to bring up more profits from commercial activities by optimizing the availability of vessels. Operational capacity is ensured by adopting a systematic and proper maintenance policy that increases effectiveness and efficiency by reducing downtime. To reach at such a target, recent failure data is analyzed and through this analysis certain procedures are developed for spare parts availability and these procedures are utilized in maintenance applications.