Fault location in a marine low speed two stroke diesel engine using the characteristic curves method

Nan Xu; Longbin Yang; Andrea Lazzaretto; Massimo Masi; Zhenyu Shen; YunPeng Fu; JiaMeng Wang; Nan Xu; Longbin Yang; Andrea Lazzaretto; Massimo Masi; Zhenyu Shen; YunPeng Fu; JiaMeng Wang

doi:10.3934/era.2023199

Electronic Research Archive

2023, Volume 31, Issue 7: 3915-3942. doi: 10.3934/era.2023199

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Research article

Fault location in a marine low speed two stroke diesel engine using the characteristic curves method

1.
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
2.
Department of Industrial Engineering, University of Padova, Padova 35131, Italy
3.
Department of Management and Engineering, University of Padova, Padova 35131, Italy
4.
Dalian marine diesel CO., Ltd, Dalian 116083, China

Received: 13 March 2023 Revised: 18 April 2023 Accepted: 20 April 2023 Published: 15 May 2023

When a malfunction occurs in a marine main engine system, the impact of the anomaly will propagate through the system, affecting the performance of all relevant components in the system. The phenomenon of fault propagation in the system caused by induced factors can interfere with fault localization, making the latter a difficult task to solve. This paper aims at showing how the "characteristic curves method" is able to properly locate malfunctions also when more malfunctions appear simultaneously. To this end, starting from the working principle of each component of a real marine diesel engine system, comprehensive and reasonable thermal performance parameters are chosen to describe their characteristic curves and include them in a one-dimensional thermodynamic model. In particular, the model of a low-speed two stroke MAN 6S50 MC-C8.1 diesel engine is built using the AVL Boost software and obtaining errors lower than 5% between simulated values and test bench data. The behavior of the engine is simulated considering eight multi-fault concomitant phenomena. On this basis, the fault diagnosis method proposed in this paper is verified. The results show that this diagnosis method can effectively isolate the fault propagation phenomenon in the system and quantify the additional irreversibility caused by the Induced factors. The fault diagnosis index proposed in this paper can quickly locate the abnormal components.
- marine diesel,
- fault simulation,
- fault diagnosis,
- characteristic curve
Citation: Nan Xu, Longbin Yang, Andrea Lazzaretto, Massimo Masi, Zhenyu Shen, YunPeng Fu, JiaMeng Wang. Fault location in a marine low speed two stroke diesel engine using the characteristic curves method[J]. Electronic Research Archive, 2023, 31(7): 3915-3942. doi: 10.3934/era.2023199

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Abstract

When a malfunction occurs in a marine main engine system, the impact of the anomaly will propagate through the system, affecting the performance of all relevant components in the system. The phenomenon of fault propagation in the system caused by induced factors can interfere with fault localization, making the latter a difficult task to solve. This paper aims at showing how the "characteristic curves method" is able to properly locate malfunctions also when more malfunctions appear simultaneously. To this end, starting from the working principle of each component of a real marine diesel engine system, comprehensive and reasonable thermal performance parameters are chosen to describe their characteristic curves and include them in a one-dimensional thermodynamic model. In particular, the model of a low-speed two stroke MAN 6S50 MC-C8.1 diesel engine is built using the AVL Boost software and obtaining errors lower than 5% between simulated values and test bench data. The behavior of the engine is simulated considering eight multi-fault concomitant phenomena. On this basis, the fault diagnosis method proposed in this paper is verified. The results show that this diagnosis method can effectively isolate the fault propagation phenomenon in the system and quantify the additional irreversibility caused by the Induced factors. The fault diagnosis index proposed in this paper can quickly locate the abnormal components.

References

[1]	V. T. Lamaris, D. T. Hountalas, A general purpose diagnostic technique for marine diesel engines–Application on the main propulsion and auxiliary diesel units of a marine vessel, Energy Convers. Manage., 51 (2010), 740–753. https://doi.org/10.1016/j.enconman.2009.10.031 doi: 10.1016/j.enconman.2009.10.031
[2]	D. W. Wang, L. Shi, S. P. Zhu, B. Liu, Y. H. Qian, K. Y. Deng, Numerical and thermodynamic study on effects of high and low pressure exhaust gas recirculation on turbocharged marine low-speed engine, Appl. Energy, 261 (2020), 114346. https://doi.org/10.1016/j.apenergy.2019.114346 doi: 10.1016/j.apenergy.2019.114346
[3]	J. Carlton, J. Aldwinkle, J. Anderson, Future Ship Powering Options: Exploring Alternative Methods of Ship Propulsion, London: Royal Academy of Engineering, 2013.
[4]	Brent, Haight, 2011 marine propulsion order survey: a review of mechanical drive, auxiliary and diesel-electric marine propulsion orders in 2010, Diesel & Gas Turbine Worldwide, 43 (2011), 30–30.
[5]	A. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Syst. Signal Process., 20 (2006), 1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012 doi: 10.1016/j.ymssp.2005.09.012
[6]	J. Galindo, J. R. Serrano, F. Vera, C. Cervello, M. Lejeune, Relevance of valve overlap for meeting Euro 5 soot emissions requirements during load transient process in heavy duty diesel engines, Int. J. Veh. Des., 41 (2006), 343–367, https://doi.org/10.1504/IJVD.2006.009675 doi: 10.1504/IJVD.2006.009675
[7]	G. Vera, P. Rubio, H. Grau, Improvements of a failure database for marine diesel engines using the RCM and simulations, Energies, 13 (2019), 104. https://doi.org/10.3390/en13010104 doi: 10.3390/en13010104
[8]	A. Jose, V. G. Francisco, H. G. Jose, M. C. Jose, A. H. Daniel, Marine diesel engine failure simulator based on thermodynamic model, Appl. Therm. Eng., 144 (2018), 982–995. https://doi.org/10.1016/j.applthermaleng.2018.08.096 doi: 10.1016/j.applthermaleng.2018.08.096
[9]	R. Pawletko, S. Polanowski, Evaluation of current developments and trends in the diagnosis of marine diesel engines based on the indicator diagrams analysis, J. KONES, 21(2014), 389–396. https://doi.org/10.5604/12314005.1130492 doi: 10.5604/12314005.1130492
[10]	A. J. Bayba, D. N. Siegel, K. Tom, Application of Autoassociative Neural Networks to Health Monitoring of the CAT 7 Diesel Engine, Army Research Laboratory, 2012.
[11]	D. T. Hountalas, Prediction of marine diesel engine performance under fault conditions, Appl. Therm. Eng., 20 (2000), 1753–1783. https://doi.org/10.1016/S1359-4311(00)00006-5 doi: 10.1016/S1359-4311(00)00006-5
[12]	J. Rubio, F. Vera-García, Sistema de Diagnóstico de Motor Diesel Marino Basado en Modelo Termodinámico y de Inteligencia Artificial, Ph.D thesis, Universidad Politécnica de Cartagena, 2017. https://doi.org/10.13140/RG.2.2.32226.63688
[13]	X. W. Li, Study on fault diagnosis of narine diesel engine, Comput. Simul., 12 (2012), 21–32. https://doi.org/10.1108/eb052507 doi: 10.1108/eb052507
[14]	Z. Jian, Simulation research on EGR reducing No_x emission of diesel engine, Int. J. Energy Power Eng., 4 (2015), 275–279. https://doi.org/10.11648/j.ijepe.20150405.16 doi: 10.11648/j.ijepe.20150405.16
[15]	C. Iclodean, N. Burnete, Computer simulation of ci engines fuelled with biofuels by modelling injection iRate law, Res. J. Agric. Sci., 44 (2012), 249–257.
[16]	T. Firsa, AVL Boost simulation of engine performance and emission for compressed natural gas direct injection engine, J. Energy Environ., 6 (2014).
[17]	O. C. Basurko, Z. Uriondo, Condition-Based maintenance for medium speed diesel engines used in vessels in operation, Appl. Therm. Eng., 80 (2015), 404–412. https://doi.org/10.1016/j.applthermaleng.2015.01.075 doi: 10.1016/j.applthermaleng.2015.01.075
[18]	G. Qi, Z. Zhu, K. Erqinhu, Y. Chen, Y. Chai, J. Sun, Fault-diagnosis for reciprocating compressors using big data and machine learning, Simul. Modell. Pract. Theory, 80 (2018), 104–127. https://doi.org/10.1016/j.simpat.2017.10.005 doi: 10.1016/j.simpat.2017.10.005
[19]	Z. Zhu, Y. Lei, G. Qi, Y. Chen, Y. Chai, Y. An, et al., A review of the application of deep learning in intelligent fault diagnosis of rotating machinery, Measurement, 206 (2022), 112346. https://doi.org/10.1016/j.measurement.2022.112346 doi: 10.1016/j.measurement.2022.112346
[20]	X. Huang, G. Qi, N. Mazur, Y. Chai, Deep residual networks-based intelligent fault diagnosis method of planetary gearboxes in cloud environments, Simul. Modell. Pract. Theory, 116 (2022), 102469. https://doi.org/10.1016/j.simpat.2021.102469 doi: 10.1016/j.simpat.2021.102469
[21]	V. Kneevi, J. Orovi, L. Stazi, J. Ulin, Fault tree analysis and failure diagnosis of marine diesel engine turbocharger system, J. Mar. Sci. Eng., 8 (2020), 1004. https://doi.org/10.3390/jmse8121004 doi: 10.3390/jmse8121004
[22]	Z. Jiang, D. Wei, L. Wang, Z. Zhao, J. Zhang, Fault diagnosis of diesel engines based on a classification and regression tree (CART) decision tree in Chinese, J. Beijing Univ. Chem. Technol., 45 (2018), 71–75. https://doi.org/10.13543/j.bhxbzr.2018.04.013 doi: 10.13543/j.bhxbzr.2018.04.013
[23]	F. Elamin, Y. Fan, F. Gu, A. Ball, Diesel engine valve clearance detection using acoustic emission, Advances in Mechanical Engineering, 2 (2010), 495741. https://doi.org/10.1155/2010/495741 doi: 10.1155/2010/495741
[24]	K. Chen, Z. Mao, H. Zhao, J. Zhang, A variational stacked autoencoder with harmony search optimizer for valve train fault diagnosis of diesel engine, Sensors, 20 (2019), 223. https://doi.org/10.3390/s20010223 doi: 10.3390/s20010223
[25]	A. Valero, C. Torres, L. Serra, A general theory of thermoeconomics. Part Ⅰ: Structural analysis, in 1992 International Symposium ECOS, ASME, New York, USA, (1992), 137–145.
[26]	A. Valero, C. Torres, F. Lerch, Structural theory and thermoeconomic diagnosis. Part Ⅲ: intrinsic and induced malfunctions, in 1999 International Symposium ECOS, ASME, New York, USA, (1999), 8–10.
[27]	A. Valero, C. Torres, L. Serra, Structural theory and thermoeconomic diagnosis. Part Ⅰ: On malfunction and dysfunction analysis, Energy Convers. Manage., 43 (2002), 1503–1518. https://doi.org/10.1016/S0196-8904(02)00032-8 doi: 10.1016/S0196-8904(02)00032-8
[28]	A. Valero, M. A. Lozano, J. L. Bartolomé, On-line monitoring of power-plant performance, using exergetic cost techniques, Appl. Therm. Eng., 16 (1996), 933–948. https://doi.org/10.1016/1359-4311(95)00092-5 doi: 10.1016/1359-4311(95)00092-5
[29]	A. Valero, F. Lerch, L. Serra, Structural theory and thermoeconomic diagnosis. Part Ⅱ: Application to an actual power plant, Energy Convers. Manage., 43 (2002), 1519–1535. https://doi.org/10.1016/S0196-8904(02)00033-X doi: 10.1016/S0196-8904(02)00033-X
[30]	V. Vittorio, L. M. Serra, V. Antonio, Effects of the productive structure on the results of the thermoeconomic diagnosis of energy systems, Int. J. Thermodyn., 5 (2002), 127–137. https://doi.org/10.5541/ijot.95 doi: 10.5541/ijot.95
[31]	V. Vittorio, L. M. Serra, A. Valero, Zooming procedure for the thermoeconomic diagnosis of highly complex energy systems, Int. J. Thermodyn., 5 (2002), 75–83. https://doi.org/10.5541/ijot.90 doi: 10.5541/ijot.90
[32]	V. Vittorio, L. M. Serra, V. Antonio, The effects of the control system on the thermoeconomic diagnosis of a power plant, Energy, 29 (2004), 331–359. https://doi.org/10.1016/j.energy.2003.10.003 doi: 10.1016/j.energy.2003.10.003
[33]	A. Stoppato, A. Lazzaretto, Exergetic analysis for energy system diagnosis, in 1996 Biennial Joint Conference on Engineering Systems Design and Analysis, ASME, New York, USA, (1996), 191–198.
[34]	A. Lazzaretto, A. Toffolo, A critical review of the thermoeconomic diagnosis methodologies for the location of causes of malfunctions in energy systems, J. Energy Res. Technol., 128 (2003), 345–354. https://doi.org/10.1115/1.2358148 doi: 10.1115/1.2358148
[35]	A. Stoppato, C. Carraretto, A. Mirandola, A diagnosis procedure for energy conversion plants: Part Ⅰ—Description of the method, in ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, New York, USA, (2001), 493–500. https://doi.org/10.1115/IMECE2001/AES-23658
[36]	A. Stoppato, C. Carraretto, A. Mirandola, A diagnosis procedure for energy conversion plants: Part Ⅱ—Application and results, in ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, New York, USA, (2001), 501–508. https://doi.org/10.1115/IMECE2001/AES-23659
[37]	A. Toffolo, A. Lazzaretto, A new thermoeconomic method for the location of causes of malfunctions in energy systems, J. Energy Res. Technol., 129 (2007), 1–9. https://doi.org/10.1115/1.2424960 doi: 10.1115/1.2424960
[38]	A. Lazzaretto, A. Toffolo, R. Passuello, The characteristic curve method in energy systems diagnosis: analysis of uncertainties in a real plant, in ASME International Mechanical Engineering Congress and Exposition, ASME, New York, USA, (2005), 379–390.
[39]	AVL BOOST Theory Reference, v2020. Available from: https://www.avl.com/en/search.
[40]	H. M. Nahim, R. Younes, H. Shraim, M. Ouladsine, Oriented review to potential simulator for faults modeling in diesel engine, J. Mar. Sci. Technol., 21 (2016), 533–551. https://doi.org/10.1007/s00773-015-0358-6 doi: 10.1007/s00773-015-0358-6
[41]	G. Radica, Expert system for diagnosis and optimisation of marine diesel engines, Strojarstvo, 50 (2008), 105–116.
[42]	P. Karpiński, K. Pietrykowski, L. Grabowski, Turbocharging the aircraft two-stroke diesel engine, Combust. Engines, 178 (2019). https://doi.org/10.19206/CE-2019-319 doi: 10.19206/CE-2019-319
[43]	G. Cong, T. Gerasimos, C. Hui, Analysis of two stroke marine diesel engine operation including turbocharger cut-out by using a zero-dimensional model, Energies, 8 (2015), 5738–5764. https://doi.org/10.3390/en8065738 doi: 10.3390/en8065738
[44]	L. Wang, P. Fu, N. Wang, T. Morosuk, Y. Yang, G. Tsatsaronis, Malfunction diagnosis of thermal power plants based on advanced exergy analysis: The case with multiple malfunctions occurring simultaneously, Energy Convers. & Manage., 148 (2017), 1453–1467. https://doi.org/10.1016/j.enconman.2017.06.086 doi: 10.1016/j.enconman.2017.06.086

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