Published on: March 2026 2026
A MACHINE LEARNING APPROACH TO THE CLASSIFICATION OF ENGINE HEALTH
Sriram Sridhar Prem Kumar
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Abstract
Engine quality is the fulcrum of effective transportation and determines the health of the car. However, there are several parameters that need to be taken into consideration while estimating engine health which makes it cumbersome and tedious to always do it manually using techniques including but not limited to compression tests, leak down tests etc. This is where machine learning can step in. Machine learning uses historical data for prediction or classification tasks and can be extremely useful for this task of classifying the engine quality as normal, or, one with faults.
This can be an extremely useful tool to aid engine quality assessment either during testing phases or can be incorporated into the car for onboard diagnostics and early-stage detection and repairs making life comfortable for the user and increases the reputation of the automobile brand. In this paper, we therefore apply several machine learning techniques for classification of engine quality namely Logistic Regression, Artificial Neural Networks, Support Vector Machines, Decision Trees and Random Forest using several input features. We evaluate model performance using metrics such as training time, accuracy, precision, recall and model stability. We recommend the best model for this task of engine health classification and show that this is the way forward for intelligent maintenance services offered to the car owners in this digital age.
How to Cite this Paper
Sridhar, S. & Kumar, P. (2026). A Machine Learning Approach to the Classification of Engine Health. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(03). https://doi.org/10.55041/ijcope.v2i3.051
Sridhar, Sriram, and Prem Kumar. "A Machine Learning Approach to the Classification of Engine Health." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i3.051.
Sridhar, Sriram, and Prem Kumar. "A Machine Learning Approach to the Classification of Engine Health." International Journal of Creative and Open Research in Engineering and Management 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i3.051.
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References
- Seda Şahin, Comparison of machine learning algorithms for predicting diesel/biodiesel/iso-pentanol blend engine performance and emissions, Heliyon, Volume 9, Issue 11,2023, e21365, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2023.e21365.
- Benjamín Pla, Joaquín De la Morena, Pau Bares, Alexandra Aramburu, A supervised machine learning technique for combustion diagnosis using a vibration sensor signal, Fuel, Volume 343, 2023, 127869,ISSN 0016-2361, https://doi.org/10.1016/j.fuel.2023.127869.
- Radhika Sitaraman, Sadaf Batool, Hoseinali Borhan, Javad Mohammadpour Velni, Jeffrey D. Naber, Mahdi Shahbakhti, Machine Learning-based Classification of Combustion Events in an RCCI Engine Using Heat Release Rate Shapes, IFAC-PapersOnLine, Volume 55, Issue 37, 2022, Pages 601-607, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2022.11.248.
- Fengnian Zhao, David L.S. Hung, Applications of machine learning to the analysis of engine in-cylinder flow and thermal process: A review and outlook, Applied Thermal Engineering, Volume 220, 2023, 119633, ISSN 1359-4311, https://doi.org/10.1016/j.applthermaleng.2022.119633.
- Karunamurthy, K., Janvekar, A.A., Palaniappan, P.L. et al.Prediction of IC engine performance and emission parameters using machine learning: A review. J Therm Anal Calorim 148, 3155–3177 (2023). https://doi.org/10.1007/s10973-022-11896-2.
- Adham Chaibi, Ahmed Stitou, Bousmaki Labiba, David Tchoffa, Driss Serrou, Abderrahman El Mhamedi, Ismail Lagrat, Classification of Engine Health States: Approaches to Predict Degradation,IFAC-PapersOnLine, Volume 59, Issue 10, 2025, Pages 1856-1861, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2025.09.312.
- Jin-sol Jung, Changmin Son, Andrew Rimell,Rory J. Clarkson, Alexander H. Karl, Impact of Data Quality on Predictive Engine Health Model using Machine Learning, https://doi.org/10.2514/6.2024-1131
- S. V, H. J. G, T. Nandakumar, P. P, S. V, "Predictive Modeling of Engine Health using Machine Learning Techniques," KSII Transactions on Internet and Information Systems, vol. 19, no. 10, pp. 3345-3371, 2025. DOI: 10.3837/tiis.2025.10.005.
- https://www.kaggle.com/datasets/parvmodi/automotive-vehicles-engine-health-dataset
- Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew,Extreme learning machine: Theory and applications, Neurocomputing,Volume 70, Issues 1–3, 2006, Pages 489-501, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2005.12.126.
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- •Published on: Mar 13 2026
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