Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms

Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely invol...

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Published in:	Science progress (1916) Vol. 106; no. 4; p. 368504231212765
Main Authors:	Zhu, Guoqing, Huang, Lin, Yin, Jiapeng, Gai, Wen, Wei, Lijiang
Format:	Journal Article
Language:	English
Published:	London, England SAGE Publications 01.10.2023 Sage Publications Ltd
Subjects:	Accuracy Algorithms Back propagation Back propagation networks Bayesian analysis Diagnostic systems Diesel Diesel engines Engineering & Technology Engines Fault diagnosis Faults Internal combustion engines Marine engines Marine technology Neural networks Parametric analysis Regularization Marine diesel engine probabilistic neural network multiple faults Levenberg Marquardt back propagation neural network Bayesian regularization back propagation neural network diagnosis neural network
ISSN:	0036-8504, 2047-7163, 2047-7163
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Abstract	Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.
AbstractList	Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines. Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.
Author	Wei, Lijiang Yin, Jiapeng Huang, Lin Gai, Wen Zhu, Guoqing
AuthorAffiliation	2 Simulation Training Center, Naval University of Engineering , Wuhan, China 4 ALFA LAVAL Technology Company, Shanghai, China 3 Merchant Marine College, 12477 Shanghai Maritime University , Shanghai, China 5 College of Information Engineering, 12477 Shanghai Maritime University , Shanghai, China 1 Research Institute of Equipment Simulation Technology, Navy University of Engineering, Wuhan, China
AuthorAffiliation_xml	– name: 3 Merchant Marine College, 12477 Shanghai Maritime University , Shanghai, China – name: 1 Research Institute of Equipment Simulation Technology, Navy University of Engineering, Wuhan, China – name: 2 Simulation Training Center, Naval University of Engineering , Wuhan, China – name: 5 College of Information Engineering, 12477 Shanghai Maritime University , Shanghai, China – name: 4 ALFA LAVAL Technology Company, Shanghai, China
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Cites_doi	10.1016/j.energy.2015.12.020 10.1016/j.protcy.2013.12.157 10.1016/j.neucom.2019.03.092 10.1016/j.applthermaleng.2018.08.096 10.1016/j.aej.2015.06.007 10.1016/S1474-6670(17)37796-0 10.1016/j.ymssp.2009.06.012 10.1016/j.apor.2021.102681 10.1016/S1474-6670(17)30874-1 10.1016/j.measurement.2020.108019 10.3390/jmse8121004 10.1016/j.measurement.2018.04.062 10.1016/j.procs.2018.04.241 10.1016/j.apacoust.2018.09.002 10.1016/j.ymssp.2014.11.007 10.1016/j.dsp.2021.103054 10.1016/j.knosys.2019.105324 10.1016/j.applthermaleng.2020.116343 10.3390/app10155199 10.1016/j.enconman.2009.11.006 10.1115/1.4043138 10.1016/j.ifacol.2016.08.083 10.1016/j.enconman.2016.05.059
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SubjectTerms	Accuracy Algorithms Back propagation Back propagation networks Bayesian analysis Diagnostic systems Diesel Diesel engines Engineering & Technology Engines Fault diagnosis Faults Internal combustion engines Marine engines Marine technology Neural networks Parametric analysis Regularization
Title	Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms
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