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Galbadrakh Sandag Naranbaatar Erdenesuren Ariunbayar Samdantsoodol

Abstract

Used oil serves as a repository of wear elements, reflective of the friction generated among various engine components. The accumulation of these elements offers vital insights for understanding wear and tear in diesel engine parts. Accurate prediction of such wear is imperative for strategic maintenance planning and cost efficiency.


This paper presents an innovative methodology that leverages artificial neural network modeling to forecast the degradation of locomotive diesel engine components based on the concentrations of wear elements in used oil and the corresponding vehicle mileage. The proposed supervised artificial neural network comprises two hierarchical architectures: one linking diesel locomotive operation and wear element concentration, and another associating diesel locomotive operation with diesel engine component wear. Through experimental validation, the results highlight the efficacy of the developed neural network model in precisely estimating the wear of diesel engine components. This predictive capability empowers informed maintenance decision-making to bolster engine reliability and performance.

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Keywords

wear elements, neural networks, wear of diesel engine parts, Lubrication Condition monitoring (LCM)

References
[1] J. M. Wakiru, L. Pintelon, P. N. Muchiri, and P. K. Chemweno, “A review on lubricant condition monitoring information analysis for maintenance decision support,” Mech. Syst. Signal Process., vol. 118, pp. 108–132, Mar. 2019, https://doi.org/10.1016/j.ymssp.2018.08.039
[2] J. Wakiru, L. Pintelon, P. N. Muchiri, P. K. Chemweno, and S. Mburu, “Toward an innovative lubricant condition monitoring strategy for maintenance of aging multiunit systems.,” Reliab. Eng. Syst. Saf., vol. 204, p. 107200, Dec. 2020, https://doi.org/10.1016/j.ress.2020.107200
[3] Fan, B. Li, S. Feng, J. Mao, and Y.-B. Xie, “Modeling and experimental investigations on the relationship between wear debris concentration and wear rate in lubrication systems,” Tribol. Int., vol. 109, pp. 114–123, May 2017, https://doi.org/10.1016/j.triboint.2016.12.015
[4] S. Yan, B. Ma, X. Wang, J. Chen, and C. Zheng, “Maintenance policy for oil-lubricated systems with oil analysis data,” Eksploat. Niezawodn. – Maint. Reliab., vol. 22, no. 3, pp. 455–464, Sep. 2020, https://doi.org/10.17531/ein.2020.3.8
[5] D. D. J. Passoni, M. T. T. Pacheco, and L. Silveira, “Raman spectroscopy for the identification of differences in the composition of automobile lubricant oils related to SAE specifications and additives,” Instrum. Sci. Technol., vol. 49, no. 2, pp. 164–181, Mar. 2021, https://doi.org/10.1080/10739149.2020.1807356
[6] M. Sejkorová, M. Kučera, I. Hurtová, and O. Voltr, “Application of FTIR-ATR Spectrometry in Conjunction with Multivariate Regression Methods for Viscosity Prediction of Worn-Out Motor Oils,” Appl. Sci., vol. 11, no. 9, p. 3842, Apr. 2021, https://doi.org/10.3390/app11093842
[7] S. Zzeyani, M. Mikou, J. Naja, and A. Elachhab, “Spectroscopic analysis of synthetic lubricating oil,” Tribol. Int., vol. 114, pp. 27–32, Oct. 2017, https://doi.org/10.1016/j.triboint.2017.04.011
[8] F. Zhou, K. Yang, D. Li, and X. Shi, “Acid Number Prediction Model of Lubricating Oil Based on Mid-Infrared Spectroscopy,” Lubricants, vol. 10, no. 9, p. 205, Aug. 2022, https://doi.org/10.3390/lubricants10090205
[9] D02 Committee, “Test Method for Determination of Wear Metals and Contaminants in Used Lubricating Oils or Used Hydraulic Fluids by Rotating Disc Electrode Atomic Emission Spectrometry,” ASTM International. DOI: 10.1520/D6595-22.
[10] J. J. Gertler, “Fault Detection and Diagnosis,” in Encyclopedia of Quantitative Risk Analysis and Assessment, 1st ed., E. L. Melnick and B. S. Everitt, Eds., Wiley, 2008. DOI: 10.1002/9780470061596.risk0506.
[11] J. Z. Sikorska, M. Hodkiewicz, and L. Ma, “Prognostic modeling options for remaining useful life estimation by industry,” Mech. Syst. Signal Process., vol. 25, no. 5, Art. no. 5, Jul. 2011, https://doi.org/10.1016/j.ymssp.2010.11.018
[12] “Technical diagnostics and prediction of the residual life of the method of spectral analysis of oil” GOST20 759 Moscow, 1991.
[13] V. Manieniyan, G. Vinodhini, R. Senthilkumar, and S. Sivaprakasam, “Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation,” Energy, vol. 114, pp. 603–612, Nov. 2016, https://doi.org/10.1016/j.enpol.2016.08.040
[14] H. Zheng et al., “Modeling and prediction for diesel performance based on deep neural network combined with virtual sample,” Sci. Rep., vol. 11, no. 1, p. 16709, Aug. 2021, https://doi.org/10.1038/s41598-021-96259-x
[15] S. Mohanty, S. Hazra, and S. Paul, “Intelligent prediction of engine failure through computational image analysis of wear particle,” Eng. Fail. Anal., vol. 116, p. 104731, Oct. 2020, https://doi.org/10.1016/j.engfailanal.2020.104731
[16] M. Rahimi, M.-R. Pourramezan, and A. Rohani, “Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach,” Expert Syst. Appl., vol. 203, p. 117494, Oct. 2022, https://doi.org/10.1016/j.eswa.2022.117494
[17] J. Kang, Y. Lu, H. Luo, J. Li, Y. Hou, and Y. Zhang, “Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty,” Mech. Ind., vol. 22, p. 29, 2021, https://doi.org/10.1051/meca/2021028
[18] Ö. Can, T. Baklacioglu, E. Özturk, and O. Turan, “Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel,” Energy, vol. 247, p. 123473, May 2022, https://doi.org/10.1016/j.energy.2022.123473
[19] A. V. Prabhu, A. Alagumalai, and A. Jodat, “Artificial neural networks to predict the performance and emission parameters of a compression ignition engine fuelled with diesel and preheated biogas–air mixture,” J. Therm. Anal. Calorim., vol. 145, no. 4, pp. 1935–1948, Aug. 2021, https://doi.org/10.1007/s10973-021-10683-9
[20] Ovecharenko, S.M, Modeling the process of accumulation of wear products in diesel engine oil, Vestnik, RGUPS, 2005 (in Russian, Овчаренко, С. М, Моделирование процесса накопления продуктов износа в моторном масле дизеля. in №1. Вестник РГУПС, 2005)
[21] P. Baranitharan, K. Ramesh, and R. Sakthivel, “Measurement of performance and emission distinctiveness of Aegle marmelos seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine using ANN and RSM,” Measurement, vol. 144, pp. 366–380, Oct. 2019, https://doi.org/10.1016/j.measurement.2019.05.037
[22] Gotov, B.-E., Tserendondog, T., Choimaa, L., & Amar, B. (2022). Quadcopter Stabilization using Neural Network Model from Collected Data of PID Controller . ICT Focus, 1(1), 10–21. https://doi.org/10.58873/sict.v1i1.28
[23] S.A. Billings. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatiotemporal Domains," Wiley, ISBN 978-1-1199-4359-4, 2013.
[24] Francisco Blasques, Siem Jan Koopman & André Lucas (2020) Nonlinear autoregressive models with optimality properties, Econometric Reviews, 39:6, 559-578, https://doi.org/10.1080/07474938.2019.1701807
[25] I. J. Leontaritis and S. A. Billings. "Input-output parametric models of nonlinear systems. Part I: Deterministic nonlinear systems." Int'l J of Control 41:303-328, 1985.
Citation Format
How to Cite
Sandag, G., Erdenesuren, N., & Samdantsoodol, A. (2023). Predictive Neural Network Modeling for Diesel Engine Part Wear Assessment via Analysis of Wear Element Concentration in Used Oil. ICT Focus, 2(1), 25–40. https://doi.org/10.58873/sict.v2i1.45
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