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

Xiaoxia Liang, Chao Fu, Xiuquan Sun, Fang Duan, David Mba, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of IncoME-VI and TEPEN 2021
Subtitle of host publicationPerformance Engineering and Maintenance Engineering
EditorsHao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
PublisherSpringer, Cham
Pages463-475
Number of pages13
Volume117
ISBN (Electronic)9783030990756
ISBN (Print)9783030990749
DOIs
Publication statusPublished - 18 Sep 2022
Event6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Hebei University of Technology, Tianjin, China
Duration: 20 Oct 202123 Oct 2021
Conference number: 6
https://tepen.net/conference/tepen2021/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume117
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated titleTEPEN-2021 and IncoME-VI
Country/TerritoryChina
CityTianjin
Period20/10/2123/10/21
Internet address

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