Fault diagnosis of Marine diesel engine based on deep belief network

Guo Qiang Zhong; Huai Yu Wang; Kun Yang Zhang; Bao Zhu Jia

doi:10.1109/CAC48633.2019.8997060

Fault diagnosis of Marine diesel engine based on deep belief network

Guo Qiang Zhong
, Huai Yu Wang
, Kun Yang Zhang
, Bao Zhu Jia

Dalian Maritime University
Guangdong Ocean University

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

14 Citations (Scopus)

Abstract

In order to improve the accuracy of intelligent fault diagnosis of Marine diesel engine, deep learning is introduced into the fault diagnosis of Marine diesel engine, and an intelligent fault diagnosis method of Marine diesel engine based on correlation analysis and Deep Belief Network (DBN) is proposed. In this method, the method of correlation analysis is used to reduce the attributes of samples and remove the features with low correlation. Then deep belief network is used to study the samples after dimension reduction and a fault diagnosis model of Marine diesel engine is established. Through analyzing the data obtained from experiments with a fault simulation model for Marine diesel engines built on AVL BOOST, the proposed method has higher fault identification accuracy and better generalization performance than BP Neural Network (BPNN) and Support Vector Machine (SVM). This method can be used for the fault diagnosis of Marine diesel engine.

Original language	English
Title of host publication	Proceedings - 2019 Chinese Automation Congress, CAC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3415-3419
Number of pages	5
ISBN (Electronic)	9781728140940
DOIs	https://doi.org/10.1109/CAC48633.2019.8997060
Publication status	Published - Nov 2019
Externally published	Yes
Event	2019 Chinese Automation Congress, CAC 2019 - Hangzhou, China Duration: 22 Nov 2019 → 24 Nov 2019

Publication series

Name	Proceedings - 2019 Chinese Automation Congress, CAC 2019

Conference

Conference	2019 Chinese Automation Congress, CAC 2019
Country/Territory	China
City	Hangzhou
Period	22/11/19 → 24/11/19

UN SDGs

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

SDG 14 Life Below Water

Keywords

correlation analysis
deep belief network
fault diagnosis
marine diesel engine

Access to Document

10.1109/CAC48633.2019.8997060

Cite this

Zhong, G. Q., Wang, H. Y., Zhang, K. Y., & Jia, B. Z. (2019). Fault diagnosis of Marine diesel engine based on deep belief network. In Proceedings - 2019 Chinese Automation Congress, CAC 2019 (pp. 3415-3419). Article 8997060 (Proceedings - 2019 Chinese Automation Congress, CAC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC48633.2019.8997060

@inproceedings{2c588cd17e9047c68e35ef92098b3456,

title = "Fault diagnosis of Marine diesel engine based on deep belief network",

abstract = "In order to improve the accuracy of intelligent fault diagnosis of Marine diesel engine, deep learning is introduced into the fault diagnosis of Marine diesel engine, and an intelligent fault diagnosis method of Marine diesel engine based on correlation analysis and Deep Belief Network (DBN) is proposed. In this method, the method of correlation analysis is used to reduce the attributes of samples and remove the features with low correlation. Then deep belief network is used to study the samples after dimension reduction and a fault diagnosis model of Marine diesel engine is established. Through analyzing the data obtained from experiments with a fault simulation model for Marine diesel engines built on AVL BOOST, the proposed method has higher fault identification accuracy and better generalization performance than BP Neural Network (BPNN) and Support Vector Machine (SVM). This method can be used for the fault diagnosis of Marine diesel engine.",

keywords = "correlation analysis, deep belief network, fault diagnosis, marine diesel engine",

author = "Zhong, \{Guo Qiang\} and Wang, \{Huai Yu\} and Zhang, \{Kun Yang\} and Jia, \{Bao Zhu\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Chinese Automation Congress, CAC 2019 ; Conference date: 22-11-2019 Through 24-11-2019",

year = "2019",

month = nov,

doi = "10.1109/CAC48633.2019.8997060",

language = "English",

series = "Proceedings - 2019 Chinese Automation Congress, CAC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3415--3419",

booktitle = "Proceedings - 2019 Chinese Automation Congress, CAC 2019",

address = "United States",

}

Zhong, GQ, Wang, HY, Zhang, KY & Jia, BZ 2019, Fault diagnosis of Marine diesel engine based on deep belief network. in Proceedings - 2019 Chinese Automation Congress, CAC 2019., 8997060, Proceedings - 2019 Chinese Automation Congress, CAC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 3415-3419, 2019 Chinese Automation Congress, CAC 2019, Hangzhou, China, 22/11/19. https://doi.org/10.1109/CAC48633.2019.8997060

Fault diagnosis of Marine diesel engine based on deep belief network. / Zhong, Guo Qiang; Wang, Huai Yu; Zhang, Kun Yang et al.
Proceedings - 2019 Chinese Automation Congress, CAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3415-3419 8997060 (Proceedings - 2019 Chinese Automation Congress, CAC 2019).

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

TY - GEN

T1 - Fault diagnosis of Marine diesel engine based on deep belief network

AU - Zhong, Guo Qiang

AU - Wang, Huai Yu

AU - Zhang, Kun Yang

AU - Jia, Bao Zhu

PY - 2019/11

Y1 - 2019/11

N2 - In order to improve the accuracy of intelligent fault diagnosis of Marine diesel engine, deep learning is introduced into the fault diagnosis of Marine diesel engine, and an intelligent fault diagnosis method of Marine diesel engine based on correlation analysis and Deep Belief Network (DBN) is proposed. In this method, the method of correlation analysis is used to reduce the attributes of samples and remove the features with low correlation. Then deep belief network is used to study the samples after dimension reduction and a fault diagnosis model of Marine diesel engine is established. Through analyzing the data obtained from experiments with a fault simulation model for Marine diesel engines built on AVL BOOST, the proposed method has higher fault identification accuracy and better generalization performance than BP Neural Network (BPNN) and Support Vector Machine (SVM). This method can be used for the fault diagnosis of Marine diesel engine.

AB - In order to improve the accuracy of intelligent fault diagnosis of Marine diesel engine, deep learning is introduced into the fault diagnosis of Marine diesel engine, and an intelligent fault diagnosis method of Marine diesel engine based on correlation analysis and Deep Belief Network (DBN) is proposed. In this method, the method of correlation analysis is used to reduce the attributes of samples and remove the features with low correlation. Then deep belief network is used to study the samples after dimension reduction and a fault diagnosis model of Marine diesel engine is established. Through analyzing the data obtained from experiments with a fault simulation model for Marine diesel engines built on AVL BOOST, the proposed method has higher fault identification accuracy and better generalization performance than BP Neural Network (BPNN) and Support Vector Machine (SVM). This method can be used for the fault diagnosis of Marine diesel engine.

KW - correlation analysis

KW - deep belief network

KW - fault diagnosis

KW - marine diesel engine

UR - https://www.scopus.com/pages/publications/85080094599

U2 - 10.1109/CAC48633.2019.8997060

DO - 10.1109/CAC48633.2019.8997060

M3 - Conference contribution

AN - SCOPUS:85080094599

T3 - Proceedings - 2019 Chinese Automation Congress, CAC 2019

SP - 3415

EP - 3419

BT - Proceedings - 2019 Chinese Automation Congress, CAC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Chinese Automation Congress, CAC 2019

Y2 - 22 November 2019 through 24 November 2019

ER -

Fault diagnosis of Marine diesel engine based on deep belief network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this