Condition monitoring method for marine engine room equipment based on machine learning

WANG Ruihan; CHEN Hui; GUAN Cong

doi:10.19693/j.issn.1673-3185.02150

Volume 16 Issue 1

Feb. 2021

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Chinese Journal of Ship Research > 2021 > 16(1): 158-166, 192. > DOI: 10.19693/j.issn.1673-3185.02150 CSTR: 32390.14.j.issn.1673-3185.02150

WANG R H, CHEN H, GUAN C. Condition monitoring method for marine engine room equipment based on machine learning[J]. Chinese Journal of Ship Research, 2021, 16(1): 158–166, 192. DOI: 10.19693/j.issn.1673-3185.02150

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Condition monitoring method for marine engine room equipment based on machine learning

WANG Ruihan^{1, 2,},
CHEN Hui^{1, 2, ,},
GUAN Cong^{1, 2,}

1.
School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
2.
Key Laboratory of High Performance Ship Technology of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China

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Received Date: October 19, 2020
Revised Date: November 15, 2020
Available Online: December 23, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Graphical Abstract

Abstract

Abstract

Objectives In order to realize the intelligent condition monitoring of marine engine room equipment, machine learning algorithms are introduced and a condition monitoring method based on manifold learning and an isolation forest is proposed.
Methods As condition-monitoring data is multi-dimensional, the proposed method extracts useful features through manifold learning, thereby reducing the dimensions and complexity of the raw data. An isolation forest algrithm is introduced to utilize the normal condition data to train and construct multiple sub forest detectors, realizing the fault monitoring of the target equipment. To validate the proposed scheme, a two-stroke marine diesel engine was developed in Matlab/Simulink to simulate reliable normal and fault condition datasets.
Results Comparisons of the simulated datasets of the different fault monitoring schemes demonstrate that the proposed method has a highest fault detection rate of 98.5% and lowest false alarm rate of 3%.
Conclusions The method proposed in this study improves the fault monitoring performance of marine equipment.
- marine diesel engine,
- fault monitoring,
- manifold learning,
- isolation forest

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References (17)

References

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Condition monitoring method for marine engine room equipment based on machine learning