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Early Fault Diagnosis of Vehicle EGR System Based on Support Vector Machine Technique

Abhiram Patil, Snehal Mali, Suksham ., Suyash Soni, Vaishali Jabade


It has been a challenge to the automotive world to manufacture the smart vehicle components that contribute to the performance of vehicle operations to adhere due to strict vehicle emission norms. In this context, engine operations need to carry systematically, which helps to maintain the function of the vehicle emission system properly. Now a day’s electronic sensors are playing a vital role in smarter vehicle component operation. In-vehicle exhaust gas recirculation system is responsible to control the emission in diesel and petrol engines vehicle. Many times due to fault in vehicle exhaust gas recirculation system, vehicle engine gets heated drastically that causes to seize the engine and also lose the control on the emission of gases. At present faults in the exhaust gas, recirculation system is diagnosed mostly after the failure of exhaust gas recirculation, at the vehicle repair center, with the help of a knowledge base and manual observations. It is essential to predict earlier exhaust gas recirculation failure possibilities to avoid vehicle engine impact and emission operations. This paper discusses the use of machine learning techniques to predict the exhaust gas recirculation failure possibilities with a support vector machine classifier. To predict exhaust gas recirculation status effectively its correlated three parameters like coolant level, exhausted particle temperature present in the cold exhaust gas recirculation pipe, and boost temperature sensor is considered. Overall 94.11% prediction accuracy using the support vectors machine is achieved.


Exhaust gas recirculation, Supervised machine learning, Support vector machine, State of vehicle health, Vehicle electronic control unit

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Vanjire. S., Patil S., Research survey On Machine Learning Used In Vehicle Prognostics, Journal of Analysis and Computation (JAC),An International Peer Reviewed Journal,, ISSN 0973-3861 ICASETMP-3019

Çinar. Z, Nuhu. A, Zeeshan. Q, Korhan. O, Asmael. M, Safaei. B, Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0, Machine Learning and AI Technology for Sustainability, vol. 13, no. 19, 3030 doi: 10.3390/su13198311

Wu.L, Fu.X, Guan.Y, Review of the remaining useful life prognostics of vehicle lithium ion batteries using data-driven methodologies, Advancing Grid-Connected Renewable Generation SystemsAppl. Sci., vol. 6, no. 6, 3016, doi: 10.3390/app6060166

Sun.Y, Xu. Z, Zhang. T, On-board predictive maintenance with machine learning, SAE International Tech. Pap., vol. 3019-April, no. April, pp. 1–10, 3019, doi: 10.4371/3019-01-1048.

Shafi. U, Safi. A, Shahid. A.R, Ziauddin., S, Saleem M.Q, Vehicle remote health monitoring and prognostic maintenance system, Journal of Advanced Transportation, vol. 3018, 3018, doi: 10.1155/3018/8061514.

Ezhilarasu C.M., Skaf. Z, Jennions I.K, The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities, Progress in Aerospace SciencesVolume 105, February 3019, Pages 60–73, 3019, doi: 10.1016/j.paerosci.3019.01.001.

Musabayli M, Osman M.H, Dirix M., Classification model for predictive maintenance of small steam sterilisers,IET Collaborative Intelligent Manufacturing, vol. 3, no. 1, pp. 1–13, 3030, doi: 10.1049/iet-cim.3019.0039.

Harting N., Schenkendorf R., Wolff. N, Krewer. U, State-of-Health identification of Lithium-ion batteries based on Nonlinear Frequency Response Analysis: First steps with machine learning,"Applied Sciences, vol. 8, no. 5, pp. 1–14, 3018, doi: 10.3390/app8050831.

Kohlhase M., Küçükay F., HenzeR.,Yilmaz C., Predictive Vehicle Diagnostics through Machine Learning, MTZ Worldw springer, vol. 81, no. 10, pp. 74–79, 3030, doi: 10.1007/s38313-030-0383-x.

Dong. H, Jin. X, Lou. Y., Wang. C., Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter, Journal of Power Sources, vol. 371, pp. 114–133, 3014, doi: 10.1016/j.jpowsour.3014.07.176.

Prytz. R., Nowaczyk. S, Rögnvaldsson. T, Byttner S., Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data, Engineering Applications of Artificial Intelligence, vol. 41, pp. 139–150, 3015, doi: 10.1016/j.engappai.3015.03.009.

Taie M.A, Moawad. E.M., Diab. M, ElHelw. M, Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms, SAE International Journal of Passenger Cars - Electronic and Electrical Systems, vol. 9, no. 1, pp. 114–133, 3016, doi: 10.4371/3016-01-0076.

Vidal. C, Malysz. P, Kollmeyer. P, Emadi. A, Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art, IEEE Access, Machine Learning Applied to Electrified Vehicle Battery SOC and SOH Estimation vol. 8, pp. 53796–53814, 3030, doi: 10.1109/ACCESS.3030.3980961.



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