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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| Veröffentlicht in: | Science progress (1916) Jg. 106; H. 4; S. 368504231212765 |
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London, England
SAGE Publications
01.10.2023
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Guoqing surname: Zhu fullname: Zhu, Guoqing – sequence: 2 givenname: Lin surname: Huang fullname: Huang, Lin – sequence: 3 givenname: Jiapeng surname: Yin fullname: Yin, Jiapeng – sequence: 4 givenname: Wen surname: Gai fullname: Gai, Wen – sequence: 5 givenname: Lijiang orcidid: 0000-0001-6464-3855 surname: Wei fullname: Wei, Lijiang email: ljwei0630@163.com |
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| CitedBy_id | crossref_primary_10_15802_stp2025_330872 crossref_primary_10_1016_j_oceaneng_2024_119545 crossref_primary_10_1109_JSEN_2025_3574410 crossref_primary_10_3390_jmse12101792 crossref_primary_10_1155_vib_6278046 crossref_primary_10_1016_j_oceaneng_2025_121319 |
| 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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| Keywords | Marine diesel engine probabilistic neural network multiple faults Levenberg Marquardt back propagation neural network Bayesian regularization back propagation neural network diagnosis neural network |
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| References | Wei, Yao, Han 2016; 95 Hu, Zhang, Liu 2020; 416 Jafarian, Mobin, Jafari 2018; 128 Jayashankara, Ganesan 2010; 51 Liu, Wen, Wang 2018; 131 Ke, Yao, Song 2021; 114 Wang, Chen, Guan 2021; 112 Huang, Xie, Huang 2014; 36 Nahim, Younes, Shraim 2016; 49 Wei, Wu, Liu 2019; 141 Li, Tse, Yang 2010; 24 Yin, Chen, Guan 2021; 45 Nawi, Khan, Rehman 2013; 11 Duddu, Pulugurtha, Mane 2020; 8 Antonić, Vukić, Kuljača 2003; 36 Castresana, Gabina, Martin 2021; 185 Puchalski 2015; 56–57 Xu, Zhao, Xu 2020; 190 Sala, Tormos, Macian 2004; 37 Cao, Hu, Xie 2020 Rubio, Vera-García, Grau 2018; 144 Hariram, Vagesh Shangar 2015; 54 Han, Hong, Meng 2020; 164 Knežević, Orović, Stazić 2020; 8 Nora, Lanzanova, Zhao 2016; 123 Wan, Yang, Sun 2020; 10 Cheng, Qiao, Jin 2020 Taghizadeh, Mandavian 2019; 143 bibr6-00368504231212765 bibr9-00368504231212765 bibr19-00368504231212765 bibr11-00368504231212765 bibr24-00368504231212765 bibr21-00368504231212765 bibr14-00368504231212765 bibr17-00368504231212765 bibr1-00368504231212765 bibr4-00368504231212765 Cao H (bibr16-00368504231212765) 2020 bibr8-00368504231212765 bibr5-00368504231212765 bibr18-00368504231212765 bibr25-00368504231212765 bibr22-00368504231212765 bibr28-00368504231212765 Duddu VR (bibr27-00368504231212765) 2020; 8 bibr15-00368504231212765 bibr7-00368504231212765 Cheng G (bibr2-00368504231212765) 2020 Yin WL (bibr10-00368504231212765) 2021; 45 bibr26-00368504231212765 bibr3-00368504231212765 Huang JL (bibr12-00368504231212765) 2014; 36 bibr23-00368504231212765 bibr13-00368504231212765 bibr20-00368504231212765 |
| References_xml | – start-page: 1 year: 2020 end-page: 17 article-title: A novel fault diagnosis method for diesel engine based on MVMD and band energy publication-title: Shock Vib – volume: 123 start-page: 71 year: 2016 end-page: 83 article-title: Effects of valve timing, valve lift and exhaust backpressure on performance and gas exchanging of a two-stroke GDI engine with overhead valves publication-title: Energy Convers Manage – volume: 131 start-page: 495 year: 2018 end-page: 501 article-title: Research on fault diagnosis method of board-level circuit based on genetic algorithm publication-title: Procedia Comput Sci – volume: 36 start-page: 48 year: 2014 end-page: 51 article-title: Research on thermal fault diagnosis of marine diesel engine based on SPA publication-title: Ship Eng – volume: 56–57 start-page: 173 year: 2015 end-page: 180 article-title: A technique for the vibration signal analysis in vehicle diagnostics publication-title: Mech Syst Signal Process – volume: 8 start-page: 1004 year: 2020 article-title: Fault tree analysis and failure diagnosis of marine diesel engine turbocharger system publication-title: J Mar Sci Eng – volume: 24 start-page: 193 year: 2010 end-page: 210 article-title: EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine publication-title: Mech Syst Signal Process – volume: 49 start-page: 570 year: 2016 end-page: 575 article-title: Modeling with fault integration of the cooling and the lubricating systems in marine diesel engine: experimental validation publication-title: IFAC-Papers Online – volume: 51 start-page: 1835 year: 2010 end-page: 1848 article-title: Effect of fuel injection timing and intake pressure on the performance of a DI diesel engine – a parametric study using CFD publication-title: Energy Convers Manage – volume: 36 start-page: 133 year: 2003 end-page: 138 article-title: Marine diesel engine faults diagnosis based on observed symptoms and expert knowledge publication-title: IFAC Proc Vol – volume: 114 start-page: 103054 year: 2021 article-title: Intelligent fault diagnosis method of common rail injector based on composite hierarchical dispersion entropy and improved least squares support vector machine publication-title: Digit Signal Process – volume: 8 start-page: 100250 year: 2020 article-title: Back-propagation neural network model to predict visibility at a road link-level publication-title: Transp Res Interdiscip Perspect – volume: 54 start-page: 807 year: 2015 end-page: 814 article-title: Influence of compression ratio on combustion and performance characteristics of direct injection compression ignition engine publication-title: Alexandria Eng J – volume: 164 start-page: 108019 year: 2020 article-title: Temperature drift modeling and compensation of capacitive accelerometer based on AGA-BP neural network publication-title: Measurement (Mahwah NJ) – volume: 141 start-page: 081006 year: 2019 article-title: Nitrogen oxides reduction effect and the influence mechanism of exhaust valve lift on a two-stroke marine diesel engine publication-title: J Eng Gas Turb Power Trans ASME – volume: 144 start-page: 982 year: 2018 end-page: 995 article-title: Marine diesel engine failure simulator based on thermodynamic model publication-title: Appl Therm Eng – volume: 112 start-page: 102681 year: 2021 article-title: Research on the fault monitoring method of marine diesel engines based on the manifold learning and isolation forest publication-title: Appl Ocean Res – start-page: 73 year: 2020 end-page: 80 article-title: Diesel engine fault diagnosis method based on multi-source information fusion publication-title: Marine Technology – volume: 185 start-page: 116343 year: 2021 article-title: Comparative performance and emissions assessments of a single-cylinder diesel engine using artificial neural network and thermodynamic simulation publication-title: Appl Therm Eng – volume: 190 start-page: 105324 year: 2020 article-title: Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models publication-title: Knowl Based Syst – volume: 11 start-page: 18 year: 2013 end-page: 23 article-title: A new Levenberg Marquardt based back propagation algorithm trained with cuckoo search publication-title: Proc Technol – volume: 128 start-page: 527 year: 2018 end-page: 536 article-title: Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring publication-title: Measurement (Mahwah NJ) – volume: 416 start-page: 47 year: 2020 end-page: 58 article-title: Charging stations expansion planning under government policy driven based on Bayesian regularization backpropagation learning publication-title: Neurocomputing – volume: 95 start-page: 223 year: 2016 end-page: 232 article-title: Effects of methanol to diesel ratio and diesel injection timing on combustion, performance and emissions of a methanol port premixed diesel engine publication-title: Energy – volume: 10 start-page: 5199 year: 2020 article-title: A method for monitoring lubrication conditions of journal bearings in a diesel engine based on contact potential publication-title: Appl Sci – volume: 143 start-page: 48 year: 2019 end-page: 58 article-title: Fault detection of injectors in diesel engines using vibration time–frequency analysis publication-title: Appl Acoust – volume: 37 start-page: 199 year: 2004 end-page: 204 article-title: A fuzzy diagnosis module for oil analysis in industrial diesel engines publication-title: IFAC Proc Vol – volume: 45 start-page: 270 year: 2021 end-page: 275 article-title: Research on fault identification method of diesel engine based on PCA-PNN publication-title: J Wuhan Univ Technol Transp Sci Eng – start-page: 73 year: 2020 ident: bibr16-00368504231212765 publication-title: Marine Technology – ident: bibr22-00368504231212765 doi: 10.1016/j.energy.2015.12.020 – ident: bibr18-00368504231212765 doi: 10.1016/j.protcy.2013.12.157 – ident: bibr17-00368504231212765 doi: 10.1016/j.neucom.2019.03.092 – ident: bibr20-00368504231212765 doi: 10.1016/j.applthermaleng.2018.08.096 – ident: bibr21-00368504231212765 doi: 10.1016/j.aej.2015.06.007 – ident: bibr9-00368504231212765 doi: 10.1016/S1474-6670(17)37796-0 – ident: bibr8-00368504231212765 doi: 10.1016/j.ymssp.2009.06.012 – ident: bibr11-00368504231212765 doi: 10.1016/j.apor.2021.102681 – volume: 8 start-page: 100250 year: 2020 ident: bibr27-00368504231212765 publication-title: Transp Res Interdiscip Perspect – volume: 45 start-page: 270 year: 2021 ident: bibr10-00368504231212765 publication-title: J Wuhan Univ Technol Transp Sci Eng – ident: bibr4-00368504231212765 doi: 10.1016/S1474-6670(17)30874-1 – ident: bibr28-00368504231212765 doi: 10.1016/j.measurement.2020.108019 – ident: bibr3-00368504231212765 doi: 10.3390/jmse8121004 – ident: bibr14-00368504231212765 doi: 10.1016/j.measurement.2018.04.062 – ident: bibr26-00368504231212765 doi: 10.1016/j.procs.2018.04.241 – ident: bibr6-00368504231212765 doi: 10.1016/j.apacoust.2018.09.002 – volume: 36 start-page: 48 year: 2014 ident: bibr12-00368504231212765 publication-title: Ship Eng – ident: bibr5-00368504231212765 doi: 10.1016/j.ymssp.2014.11.007 – ident: bibr7-00368504231212765 doi: 10.1016/j.dsp.2021.103054 – ident: bibr13-00368504231212765 doi: 10.1016/j.knosys.2019.105324 – ident: bibr19-00368504231212765 doi: 10.1016/j.applthermaleng.2020.116343 – ident: bibr1-00368504231212765 doi: 10.3390/app10155199 – ident: bibr24-00368504231212765 doi: 10.1016/j.enconman.2009.11.006 – ident: bibr25-00368504231212765 doi: 10.1115/1.4043138 – start-page: 1 year: 2020 ident: bibr2-00368504231212765 publication-title: Shock Vib – ident: bibr15-00368504231212765 doi: 10.1016/j.ifacol.2016.08.083 – ident: bibr23-00368504231212765 doi: 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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