Prediction of Ship Main Engine Failures by Artificial Neural Networks

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.