Open AccessMACHINE LEARNING METHODS IN MONITORING OPERATING BEHAVIOUR OF MARINE TWO-STROKE DIESEL ENGINE
Author(s) -
Aleksandar Vorkapić,
Radoslav Radonja,
Karlo Babić,
Sanda Martinčić-Ipšić
Publication year - 2020
Publication title -
transport
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 31
eISSN - 1648-4142
pISSN - 1648-3480
DOI - 10.3846/transport.2020.14038
Subject(s) - support vector machine , mean squared error , multilayer perceptron , anomaly detection , propulsion , diesel engine , engineering , fuel efficiency , random forest , machine learning , computer science , artificial intelligence , artificial neural network , automotive engineering , mathematics , statistics , aerospace engineering
The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.