with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article-en

Authors

Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Monitoring the status of machinery is a crucial aspect of production and service units to uphold operational efficiency. Timely changes in engine lubricant significantly contribute to enhanced performance and extended engine lifespan. However, determining the precise replacement time remains a challenge. Oil spectral analysis, while effective, is both expensive and time-intensive. This study aims to introduce an alternative method to engine lubricant spectral analysis. The investigation involves analyzing the results of spectral analysis and dielectric coefficients of 17 engine lubricant samples through statistical methods. The primary objective is to develop models for predicting oil contaminants based on dielectric properties, offering a substitute for spectral analysis. To achieve this, several intermediate goals are pursued. Multilayer perceptron artificial neural networks (MLP-ANN) and support vector machine (SVM) methods are employed for modeling. The performance of the two models is assessed using indicators such as Root Mean Square Error (RMSE), model efficiency, and R-squared (R2). The results indicate that the SVM model consistently demonstrates an efficiency exceeding 0.95 for all predicted indices (Fe, Pb, Cu, Al, Mo, Na, Si, and Vis@100). Consequently, dielectric spectroscopy of lubricant emerges as a viable alternative to traditional oil spectral analysis.

Keywords

Main Subjects

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

  1. Altıntaş, O., Aksoy, M., Ünal, E., Akgöl, O., & Karaaslan, M. (2019). Artificial neural network approach for locomotive maintenance by monitoring dielectric properties of engine lubricant. Measurement, 145, 678-686. https://doi.org/10.1016/j.measurement.2019.05.087
  2. Ashtiani, S.-H. M., Rohani, A., & Aghkhani, M. H. (2020). Soft computing-based method for estimation of almond kernel mass from its shell features. Scientia Horticulturae, 262, 109071. https://doi.org/10.1016/j.scienta.2019.109071
  3. Bhattacharya, A., & Dan, P. K. (2014). Recent trend in condition monitoring for equipment fault diagnosis. International Journal of System Assurance Engineering and Management, 5, 230-244. https://doi.org/10.1007/s13198-013-0151-z
  4. Cardoso, D., & Ferreira, L. (2020). Application of predictive maintenance concepts using artificial intelligence tools. Applied Sciences, 11(1), 18. https://doi.org/10.3390/app11010018
  5. Chamkalani, A., Mohammadi, A. H., Eslamimanesh, A., Gharagheizi, F., & Richon, D. (2012). Diagnosis of asphaltene stability in crude oil through “two parameters” SVM model. Chemical Engineering Science, 81, 202-208. https://doi.org/10.1016/j.ces.2012.06.060
  6. Chaudhry, A. A., Buchwald, J., & Nagel, T. (2021). Local and global spatio-temporal sensitivity analysis of thermal consolidation around a point heat source. International Journal of Rock Mechanics and Mining Sciences, 139, 104662. https://doi.org/10.1016/j.ijrmms.2021.104662
  7. Chun, S.-M. (2006). Study on Mutual Relation between the Level of Deterioration Influenced by the Changes of Chemical and Physical Properties and the Change of Dielectric Constant for Engine Oil-Diesel Engine Oil. Tribology and Lubricants, 22(5), 290-300. https://doi.org/10.9725/kstle.2006.22.5.290
  8. Duchowski, J. K., & Mannebach, H. (2006). A novel approach to predictive maintenance: a portable, multi-component MEMS sensor for on-line monitoring of fluid condition in hydraulic and lubricating systems. Tribology Transactions, 49(4), 545-553. https://doi.org/10.1080/10402000600885183
  9. Eslamimanesh, A., Gharagheizi, F., Illbeigi, M., Mohammadi, A. H., Fazlali, A., & Richon, D. (2012). Phase equilibrium modeling of clathrate hydrates of methane, carbon dioxide, nitrogen, and hydrogen+ water soluble organic promoters using Support Vector Machine algorithm. Fluid Phase Equilibria, 316, 34-45. https://doi.org/10.1016/j.fluid.2011.11.029
  10. Fayazi, A., Arabloo, M., Shokrollahi, A., Zargari, M. H., & Ghazanfari, M. H. (2014). State-of-the-art least square support vector machine application for accurate determination of natural gas viscosity. Industrial & Engineering Chemistry Research, 53(2), 945-958. https://doi.org/10.1021/ie402829p
  11. Gerhardt, R. A. (2022). What is Impedance and Dielectric Spectroscopy? IEEE Instrumentation & Measurement Magazine, 25(4), 14-20. https://doi.org/10.1109/MIM.2022.9777776
  12. Glagolev, M. (2012). Sensitivity analysis of the model. Environmental Dynamics and Global Climate Change, 3(3), 31-53. https://doi.org/10.17816/edgcc3331-53
  13. Gomółka, L., & Augustynowicz, A. (2019). Evaluation of applicability of dielectric constant in monitoring aging processes in engine oils. Eksploatacja i Niezawodność, 21(2), 177-185. https://doi.org/10.17531/ein.2019.2.1
  14. Guan, L., Feng, X., Xiong, G., & Xie, J. (2011). Application of dielectric spectroscopy for engine lubricating oil degradation monitoring. Sensors and Actuators A: Physical, 168(1), 22-29. https://doi.org/10.1016/j.sna.2011.03.033
  15. Heidari, P., Rezaei, M., & Rohani, A. (2020). Soft computing-based approach on prediction promising pistachio seedling base on leaf characteristics. Scientia Horticulturae, 274, 109647. https://doi.org/10.1016/j.scienta.2020.109647
  16. Heredia-Cancino, J., Ramezani, M., & Álvarez-Ramos, M. (2018). Effect of degradation on tribological performance of engine lubricants at elevated temperatures. Tribology International, 124, 230-237. https://doi.org/10.1016/j.triboint.2018.04.015
  17. Hong, S.-H., & Jeon, H.-G. (2022). Monitoring the conditions of hydraulic oil with integrated oil sensors in construction equipment. Lubricants, 10(11), 278. https://doi.org/10.3390/lubricants10110278
  18. Iooss, B., & Lemaître, P. (2015). A review on global sensitivity analysis methods. Uncertainty management in simulation-optimization of complex systems: algorithms and applications, 101-122. https://doi.org/10.1007/978-1-4899-7547-8_5
  19. Kim, H.-J., Seo, K.-J., Kang, K. H., & Kim, D.-E. (2016). Nano-lubrication: A review. International Journal of Precision Engineering and Manufacturing, 17, 829-841. https://doi.org/10.1007/s12541-016-0102-0
  20. Król, A., Gocman, K., & Giemza, B. (2015). Neural networks as a tool to characterise oil state after porous bearings prolonged tests. Materials Science, 21(3), 466-472. https://doi.org/10.5755/j01.ms.21.3.7506
  21. Lazakis, I., Raptodimos, Y., & Varelas, T. (2018). Predicting ship machinery system condition through analytical reliability tools and artificial neural networks. Ocean Engineering, 152, 404-415. https://doi.org/10.1016/j.oceaneng.2017.11.017
  22. Li, L., Chang, W., Zhou, S., & Xiao, Y. (2017). An identification and prediction model of wear-out fault based on oil monitoring data using PSO-SVM method. Paper presented at the 2017 Annual Reliability and Maintainability Symposium (RAMS). https://doi.org/10.1109/RAM.2017.7889670
  23. Li, Z., Fei, F., & Zhang, G. (2022). Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors. Sensors, 22(15), 5901. https://doi.org/10.3390/s22155901
  24. Lillicrap, T. P., Cownden, D., Tweed, D. B., & Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature communications, 7(1), 13276. https://doi.org/10.1038/ncomms13276
  25. Macián, V., Tormos, B., Olmeda, P., & Montoro, L. (2003). Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis. Tribology International, 36(10), 771-776. https://doi.org/10.1016/S0301-679X(03)00060-4
  26. Mondelin, A., Claudin, C., Rech, J., & Dumont, F. (2011). Effects of lubrication mode on friction and heat partition coefficients at the tool–work material interface in machining. Tribology Transactions, 54(2), 247-255. https://doi.org/10.1080/10402004.2010.538489
  27. Mosher, P. (2007). Predicting failure–condition monitoring in action. World Pumps, 2007(484), 24-28. https://doi.org/10.1016/S0262-1762(06)71208-1
  28. Mumby, S. J. (1989). An overview of laminate materials with enhanced dielectric properties. Journal of Electronic Materials, 18(2), 241-250. https://doi.org/10.1007/BF02657415
  29. Newell, G. E. (1999). Oil analysis cost‐effective machine condition monitoring technique. Industrial Lubrication and tribology, 51(3), 119-124. https://doi.org/10.1108/00368799910268066
  30. Nüchter, M., Ondruschka, B., Bonrath, W., & Gum, A. (2004). Microwave assisted synthesis–a critical technology overview. Green Chemistry, 6(3), 128-141. https://doi.org/10.1039/B310502D
  31. Pourramezan, E., Omidvar, M., Motavalizadehkakhky, A., Zhiani, R., & Darzi, H. H. (2024). Enhanced adsorptive removal of methylene blue using ternary nanometal oxides in an aqueous solution. Biomass Conversion and Biorefinery, 1-13. https://doi.org/10.1007/s13399-023-05225-2
  32. Pourramezan, M.-R., & Rohani, A. (2024). Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques. SAE International Journal of Fuels and Lubricants, 18(04-18-01-0005). https://doi.org/10.4271/04-18-01-0005
  33. Pourramezan, M.-R., Rohani, A., & Abbaspour-Fard, M. H. (2023a). Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring. ACS omega. https://doi.org/10.1021/acsomega.3c07780
  34. Pourramezan, M.-R., Rohani, A., & Abbaspour-Fard, M. H. (2023b). Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy. Lubricants, 11(9), 382. https://doi.org/10.3390/lubricants11090382
  35. Pourramezan, M.-R., Rohani, A., & Abbaspour-Fard, M. H. (2024). Machine Learning-Based Predictions of Metal and Non-Metal Elements in Engine Oil Using Electrical Properties. Lubricants, 12(12), 411. https://doi.org/10.3390/lubricants12120411
  36. Pourramezan, M.-R., Rohani, A., & Abbaspour-Fard, M. H. (2025). Comprehensive Review of Dielectric, Impedance, and Soft Computing Techniques for Lubricant Condition Monitoring and Predictive Maintenance in Diesel Engines. Lubricants, 13(8), 328. https://doi.org/10.3390/lubricants13080328
  37. Pourramezan, M.-R., Rohani, A., Keramat Siavash, N., & Zarein, M. (2022). Evaluation of lubricant condition and engine health based on soft computing methods. Neural Computing and Applications, 1-13. https://doi.org/10.1007/s00521-021-06688-y
  38. Raadnui, S., & Kleesuwan, S. (2005). Low-cost condition monitoring sensor for used oil analysis. Wear, 259(7-12), 1502-1506. https://doi.org/10.1016/j.wear.2004.11.009
  39. Rahimi, M., Pourramezan, M.-R., & Rohani, A. (2022). Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach. Expert Systems with Applications, 203, 117494. https://doi.org/10.1016/j.eswa.2022.117494
  40. Rezaei, M., Rohani, A., Heidari, P., & Lawson, S. (2021). Using soft computing and leaf dimensions to determine sex in immature Pistacia vera genotypes. Measurement, 174, 108988. https://doi.org/10.1016/j.measurement.2021.108988
  41. Rohani, A., Abbaspour-Fard, M. H., & Abdolahpour, S. (2011). Prediction of tractor repair and maintenance costs using Artificial Neural Network. Expert Systems with Applications, 38(7), 8999-9007. https://doi.org/10.1016/j.eswa.2011.01.118
  42. Sangha, M. S., Gomm, J. B., & Yu, D. (2008). Neural network fault classification of transient data in an automotive engine air path. International Journal of Modelling, Identification and Control, 3(2), 148-155. https://doi.org/10.1504/IJMIC.2008.019352
  43. Sapotta, B., Schwotzer, M., Wöll, C., & Franzreb, M. (2022). On the Integration of Dielectrometry into Electrochemical Impedance Spectroscopy to Obtain Characteristic Properties of a Dielectric Thin Film. Electroanalysis, 34(3), 512-522. https://doi.org/10.1002/elan.202100484
  44. Shi, Y., Song, X., & Song, G. (2021). Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network. Applied Energy, 282, 116046. https://doi.org/10.1016/j.apenergy.2020.116046
  45. Siavash, N. K., Ghobadian, B., Najafi, G., Rohani, A., Tavakoli, T., Mahmoodi, E., & Mamat, R. (2021). Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression. Environmental Research, 196, 110434. https://doi.org/10.1016/j.envres.2020.110434
  46. Soltanali, H., Rohani, A., Abbaspour-Fard, M. H., & Farinha, J. T. (2021). A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing. Applied Soft Computing, 98, 106738. https://doi.org/10.1016/j.asoc.2020.106738
  47. Woodley, B. (1978). Failure prediction by condition monitoring (part 1). International Journal of Materials in Engineering Applications, 1(1), 19-26. https://doi.org/10.1016/0141-5530(78)90004-3
  48. You, M., Liu, F., & Meng, G. (2011). Benefits from condition monitoring techniques: a case study on maintenance scheduling of ball grid array solder joints. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 225(3), 205-215. https://doi.org/10.1177/2041300910393426
  49. Yu, S., Zhao, D., Chen, W., & Hou, H. (2016). Oil-immersed power transformer internal fault diagnosis research based on probabilistic neural network. Procedia Computer Science, 83, 1327-1331. https://doi.org/10.1016/j.procs.2016.04.276
  50. Zarein, M., Khoshtaghaza, M. H., & Ameri Mahabadi, H. (2019). Dielectric Properties of Castor-based Biodiesel Using Microwave. Fuel and Combustion, 12(1), 1-12. https://doi.org/10.22034/jfnc.2019.87991
  51. Zeng, Y., Zhang, H., Zhang, H., & Hu, Z. (2010). Effective permittivity calculation of composites with interpenetrating phases. Journal of Electronic Materials, 39, 1351-1357. https://doi.org/10.1007/s11664-010-1229-x
  52. Zhu, B., Wang, X., Luo, L., Zhang, N., & Liu, X. (2022). Influence of lubricant supply on thermal and efficient performances of a gear reducer for electric vehicles. Journal of Tribology, 144(1), 011202. https://doi.org/10.1115/1.4052681
  53. Zhu, X., Zhong, C., & Zhe, J. (2017). Lubricating oil conditioning sensors for online machine health monitoring–A review. Tribology International, 109, 473-484. https://doi.org/10.1016/j.triboint.2017.01.015
  54. Zzeyani, S., Mikou, M., & Naja, J. (2018). Physicochemical Characterization of the Synthetic Lubricating Oils Degradation under the Effect of Vehicle Engine Operation. Eurasian Journal of Analytical Chemistry, 13(4). https://doi.org/10.29333/ejac/90761
CAPTCHA Image