Real-Time Marine Diesel Engine Simulation for Fault Diagnosis

Abstract

Automated monitoring systems are now the standard on most large vessels; however, few are equipped with diagnostic systems. This paper presents new developments in the area of fault diagnosis based on intelligent software agents. The research objective was to design an agent capable of continuous real-time machine learning by using an artificial neural network known as the cerebellar model articulation controller (CMAC). An engine simulator that can model both normal and faulty engine operations was used to develop the learning system controller in a flexible and cost-efficient manner. This paper provides a description of the selected CMAC, a brief overview of the real-time engine simulator and its integration with the learning system as well as a few results.