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

doi:10.1007/978-3-030-99075-6_38

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 proceeding › Conference contribution › peer-review

1 Scopus citations

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 language	English
Title of host publication	Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering
Editors	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
Publisher	Springer Science and Business Media B.V.
Pages	463-475
Number of pages	13
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_38
State	Published - 2023
Externally published	Yes
Event	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Tianjin, China Duration: 20 Oct 2021 → 23 Oct 2021

Publication series

Name	Mechanisms and Machine Science
Volume	117
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Country/Territory	China
City	Tianjin
Period	20/10/21 → 23/10/21

Keywords

Fault detection
IC engine
Lubrication system filter blocking
Misfire
Unsupervised machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-99075-6_38

Cite this

Liang, X., Fu, C., Sun, X., Duan, F., Mba, D., Gu, F., & Ball, A. D. (2023). An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring. In H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (Eds.), Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering (pp. 463-475). (Mechanisms and Machine Science; Vol. 117). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-030-99075-6_38

Liang, Xiaoxia ; Fu, Chao ; Sun, Xiuquan et al. / An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring. Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. editor / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Springer Science and Business Media B.V., 2023. pp. 463-475 (Mechanisms and Machine Science).

@inproceedings{d0860c0cbbc84b8cae48789776d6dc93,

title = "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.",

keywords = "Fault detection, IC engine, Lubrication system filter blocking, Misfire, Unsupervised machine learning",

author = "Xiaoxia Liang and Chao Fu and Xiuquan Sun and Fang Duan and David Mba and Fengshou Gu and Ball, {Andrew D.}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 ; Conference date: 20-10-2021 Through 23-10-2021",

year = "2023",

doi = "10.1007/978-3-030-99075-6_38",

language = "英语",

isbn = "9783030990749",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

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editor = "Hao Zhang and Guojin Feng and Hongjun Wang and Fengshou Gu and Sinha, {Jyoti K.}",

booktitle = "Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering",

}

Liang, X, Fu, C, Sun, X, Duan, F, Mba, D, Gu, F & Ball, AD 2023, An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. Mechanisms and Machine Science, vol. 117, Springer Science and Business Media B.V., pp. 463-475, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, China, 20/10/21. https://doi.org/10.1007/978-3-030-99075-6_38

An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring. / Liang, Xiaoxia; Fu, Chao; Sun, Xiuquan et al.
Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. ed. / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Springer Science and Business Media B.V., 2023. p. 463-475 (Mechanisms and Machine Science; Vol. 117).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring

AU - Liang, Xiaoxia

AU - Fu, Chao

AU - Sun, Xiuquan

AU - Duan, Fang

AU - Mba, David

AU - Gu, Fengshou

AU - Ball, Andrew D.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Fault detection

KW - IC engine

KW - Lubrication system filter blocking

KW - Misfire

KW - Unsupervised machine learning

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M3 - 会议稿件

AN - SCOPUS:85138795038

SN - 9783030990749

T3 - Mechanisms and Machine Science

SP - 463

EP - 475

BT - Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering

A2 - Zhang, Hao

A2 - Feng, Guojin

A2 - Wang, Hongjun

A2 - Gu, Fengshou

A2 - Sinha, Jyoti K.

PB - Springer Science and Business Media B.V.

T2 - 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021

Y2 - 20 October 2021 through 23 October 2021

ER -

Liang X, Fu C, Sun X, Duan F, Mba D, Gu F et al. An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring. In Zhang H, Feng G, Wang H, Gu F, Sinha JK, editors, Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. Springer Science and Business Media B.V. 2023. p. 463-475. (Mechanisms and Machine Science). doi: 10.1007/978-3-030-99075-6_38

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

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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