Condition monitoring and fault diagnosis of a marine diesel engine with machine learning techniques

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Koçak G., Gokcek V., Genc Y.

Pomorstvo, vol.37, no.1, pp.32-46, 2023 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.31217/p.37.1.4
  • Journal Name: Pomorstvo
  • Journal Indexes: 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
  • Page Numbers: pp.32-46
  • Keywords: Condition monitoring, Fault diagnosis, Machine learning, Ship engine room
  • Istanbul Technical University Affiliated: Yes


A marine engine room is a complex system in which many different subsystems are interacting with each other. At the center of this system is the main diesel engine which produces the propulsion force. Many other components such as compressed air, cooling, heating, lubricating oil, fuel, and pumping systems act as auxiliary machines to the main engine. Automation of many functions in the engine room is starting to play an important role in new generation ships to provide better control using sensors monitoring the engine and its environment. Sensors exist in the current generation ships, but engineers evaluate the sensor data for the presence of any problems. Maintenance actions are taken based on these manual analyses or regular maintenance is carried out at times determined by manufacturers, whether such actions are needed or not. With machine learning, it is possible to develop an algorithm using past evaluations made by engineers. Recent studies show that highly accurate results can be obtained using machine learning methods when there is sufficient data. In this study, we develop new learning-based algorithms and evaluate them on data obtained from a realistic ship engine room simulator. Data for a predetermined set of parameters of a high-power diesel engine were collected and analyzed for their role in a set of fault situations. These fault conditions and the associated sensor data are used to train a set of classifiers achieving fault detection up to 99% accuracy. These are promising results in preventing future damage to the engine or its supporting components by predicting failures before they occur.