An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring

Abstract

Internal combustion (IC) engines are widely employed in power systems such as marine ships, small power stations and vehicles. However, due to its complex working conditions and sophisticated degradation mechanisms, IC engines commonly suffer various types of malfunctioning and faults, which affects their performance in power delivery. Therefore, it is important to monitor the condition of IC engines and detect faults occurred in time. In this paper, two unsupervised data-driven models using machine learning techniques are employed and investigated for the purpose of online condition monitoring and fault isolation of IC engines. A misfire and a lubrication system filter blocking faults are experimentally studied on a purposely built marine engine test rig. The performance of the two models and their contribution maps are discussed, which provides guidance for using such unsupervised models for the condition monitoring and fault detection of IC engines.