SavedAdd your journal

Journals / Journal of Eta Maritime Science / 2020 / Cilt: 8 - Sayı: 2

Prediction of Ship Main Engine Failures by Artificial Neural Networks

Burak GOKSU
Kadir Emrah ERGINER
Pages
98–113
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, the history of failure in the critical nine main engine related subsystems have been analyzed by artificial neural network method, which is consistent with condition-based maintenance applications and subsequently helps to bring out the potential breakdowns in the recorded history of failure.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:Operational capacity is ensured by adopting a systematic and proper maintenance policy that increases effectiveness and efficiency by reducing downtime.
💡
Conclusion & Impact:In this study, the history of failure in the critical nine main engine related subsystems have been analyzed by artificial neural network method, which is consistent with condition-based maintenance applications and subsequently helps to bring out the potential breakdowns in the recorded history of failure.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

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. This study aims to provide an additional feature for predictive maintenance software for the analysis of the upcoming conditions of the main engine systems. In this study, the history of failure in the critical nine main engine related subsystems have been analyzed by artificial neural network method, which is consistent with condition-based maintenance applications and subsequently helps to bring out the potential breakdowns in the recorded history of failure.

🔖

Cite This Article

GOKSU, B., ERGINER, K. E. (2020). Prediction of Ship Main Engine Failures by Artificial Neural Networks. Journal of Eta Maritime Science, Cilt: 8 - Sayı: 2, 98–113. https://ajindex.com/articles/165190

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Göksu, B. (2017). Gemi Dizel Makinelerinde Planlı Bakım Sistemi Uygulamaları ve Arıza Modlarının Analizi (MSc Thesis). Dokuz Eylül Üniversitesi
  2. IMO. (2018). International Safety Management Code, IMO Publishing
  3. Lazakis, I., Turan, O., & Aksu, S. (2010). Increasing Ship Operational Reliability Through the Implementation of a Holistic Maintenance Management Strategy. Ships Offshore Struct. 5, 337– 357
  4. Liu, L., Logan, K.P., Cartes D.A., & Srivastava, S.K. (2007). Fault Detection, Diagnostics, and Prognostics: Software Agent Solutions. IEEE Trans. Veh. Technol. 56, 1613–1622
  5. Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., & Figari, M. (2016). Machine Learning Approaches for Improving Condition-Based Maintenance of Naval Propulsion Plants. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 230, 136–153
  6. Macal, C., & North, M. (2015). Introductory Tutorial: Agent-Based Modeling and Simulation. Proc. - Winter Simul. Conf. 2015–January, 6–20
  7. Sharma, A., Yadava, G.S., & Deshmukh, S.G. (2011). A Literature Review and Future Perspectives on Maintenance Optimization. J. Qual. Maint. Eng. 17, 5–25
  8. Swanson, L. (2001). Linking Maintenance Strategies to Performance. Int. J. Prod. Econ. 70, 237–244
  9. Dekker, R. (1996). Applications of Maintenance Optimization Models: A Review and Analysis. Reliab. Eng. Syst. Saf. 51, 229–240
  10. Spires, C. (1996). Asset and Maintenance Management – Becoming A Boardroom Issue. Manag. Serv. Qual. An Int. J. 6, 13–15
  11. Muchiri, P., Pintelon, L., Gelders, L., & Martin, H. (2011). Development of Maintenance Function Performance Measurement Framework and Indicators. Int. J. Prod. Econ. 131, 295– 302
  12. Moubray, J. (1999). Reliability-Centered Maintenance, 2nd ed. Butterworth- Heinemann
  13. Márquez, A.C. (2007). The Maintenance Management Framework: Models and Methods for Complex Systems Maintenance, Springer Science & Business Media
  14. Akyuz, E., & Celik, M. (2017). Using of A’WOT to Design an Enhanced Planned Maintenance System (E-PMS) On- Board Ship. Brodogradnja 68, 61–75
  15. Hetherington, C., Flin, R., & Mearns, K. (2006). Safety in Shipping: The Human Element. J. Safety Res. 37, 401–411
  16. Turan, O., Olcer, A.I., Lazakis, I., Rigo, P., & Caprace, J.D. (2009). Maintenance/ Repair and Production-Oriented Life Cycle Cost/Earning Model for Ship Structural Optimisation During Conceptual Design Stage. J. Econ. Behav. Organ. 4, 107–125
  17. Lazakis, I., Turan, O., Alkaner, &