Fault Analysis of Ship Machinery Using Machine Learning Techniques

Abstract

Maintenance and repair of ship systems and prediction of fault probability have become an important issue for dynamic ship systems recently. Increasing the usability of systems by detecting the fault analysis is one of the current work areas. In recent years, the rapid development of information technologies and the machine learning approaches developing accordingly have made it possible to integrate machine learning techniques into ship systems. The use of machine learning in the studies has enabled this method to be tested in the areas of maintenance, repair, and fault analysis. To be able to predict the fault that may occur in the ship machinery systems and prevent the fault accordingly, can increase the lifespan of ship machinery's. In this study, the data obtained from an LM-2500 type ship engine were analyzed by regression and Artificial Neural Networks (ANN) algorithms to predict the fault of ship machinery. The results were compared for linear regression, decision tree regression, k nearest neighbours’ regression, random forest regression, bayesian ridge regression, extra tree regression, linear SVR regression and ANN algorithms. As a result of the analysis, it was revealed that the ANN method gave better results in machine fault prediction compared to the regression methods.