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

London South Bank University
Hebei University of Technology
University of Huddersfield
De Montfort University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering
编辑	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
出版商	Springer Science and Business Media B.V.
页	463-475
页数	13
ISBN（印刷版）	9783030990749
DOI	https://doi.org/10.1007/978-3-030-99075-6_38
出版状态	已出版 - 2023
已对外发布	是
活动	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Tianjin, 中国期限: 20 10月 2021 → 23 10月 2021

丛书

姓名	Mechanisms and Machine Science
卷	117
ISSN（印刷版）	2211-0984
ISSN（电子版）	2211-0992

会议

会议	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
国家/地区	中国
市	Tianjin
时期	20/10/21 → 23/10/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 14 水下生物

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (版本), Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering (页码 463-475). (Mechanisms and Machine Science; 卷 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 等. / 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. 编辑 / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Springer Science and Business Media B.V., 2023. 页码 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.",

pages = "463--475",

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. 在 H Zhang, G Feng, H Wang, F Gu & JK Sinha (版本), Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. Mechanisms and Machine Science, 卷 117, Springer Science and Business Media B.V., 页码 463-475, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, 中国, 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 等.
Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. 编辑 / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Springer Science and Business Media B.V., 2023. 页码 463-475 (Mechanisms and Machine Science; 卷 117).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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

UR - https://www.scopus.com/pages/publications/85138795038

U2 - 10.1007/978-3-030-99075-6_38

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

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 等. An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring. 在 Zhang H, Feng G, Wang H, Gu F, Sinha JK, 编辑, Proceedings of IncoME-VI and TEPEN 2021 - Performance Engineering and Maintenance Engineering. Springer Science and Business Media B.V. 2023. 页码 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

摘要

丛书

会议

联合国可持续发展目标

访问文件

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学术指纹

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