Methodology for Thermal Analysis of Combustion Engines, Applied to Automobiles

Abstract:

The combustion engines are widely used in the daily life of people, in cars, we find them with greater emphasis, and currently hybrid engines and electric motors are being used. In this analysis, one of the important factors for the failure of combustion engines is the temperature, for which cars have cooling systems, through the use of radiators and coolant-based systems. In this work we present a methodology for the analysis of the state of the components of the combustion engine system in cars, through the use and analysis of thermal images, for which it is necessary to identify the zones or control points depending on the model and type of engine and car, The analysis procedure requires a thermal camera, the results presented are related to the comparison of an optical and thermal image, in order to locate the control point, the method can be applied by mechanics, maintenance personnel and car drivers themselves, to analyze the condition of their car.

• Periodical: Advances in Science and Technology (Volume 151)
• Pages: 93-100
• DOI: https://doi.org/10.4028/p-MbC0S8
• Online since: June 2024
• Authors: Wilver Auccahuasi*, Kitty Urbano, Edwin Flores, Christian Ovalle, Medalith Campos -Sobrino, Humberto Quispe-Peña, Julia Sobrino-Mesias, Nicanor Benites, Juan Marin-Huapaya, Rosalynn Ornella Flores-Castañeda, Sandro Olaya-Cotera
• Keywords: Analysis, Engines, Imaging, Prevention, Temperature, Thermal Imaging

References

[1] Scott, S.; Chen, W.-Y.; Heifetz, A. Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals. Sensors 2023, 23, 8462

DOI: 10.3390/s23208462

[2] Zhu, D.; Li, J.; Wang, F.; Gong, X.; Cong, W.; Wang, P.; Liu, Y. A Method for Extracting Contours of Building Facade Hollowing Defects Using Polarization Thermal Images Based on Improved Canny Algorithm. Buildings 2023, 13, 2563. https://doi.org/10.3390/ buildings13102563

DOI: 10.3390/buildings13102563

[3] Wang, F.; Wang, Z.; Chen, Z.; Zhu, D.; Gong, X.; Cong, W. An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm. Appl. Sci. 2023, 13, 11031

DOI: 10.3390/app131911031

[4] Biswanath, M.K.; Hoegner, L.; Stilla, U. Thermal Mapping from Point Clouds to 3D Building Model Facades. Remote Sens. 2023, 15, 4830

DOI: 10.3390/rs15194830

[5] Korolev, S.; Urmanov, I.; Sorokin, A.; Girina, O. Detecting Volcano Thermal Activity in Night Images Using Machine Learning and Computer Vision. Remote Sens. 2023, 15, 4815

DOI: 10.3390/rs15194815

[6] Liu, J.; Zhou, X.; Wan, Z.; Yang, X.; He, W.; He, R.; Lin, Y. Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE. Sensors 2023, 23, 8101

DOI: 10.3390/s23198101

[7] Li, S.; Wang, G.; Zhang, H.; Zou, Y. SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion. Remote Sens. 2023, 15, 4467

DOI: 10.3390/rs15184467

[8] Maśko, M.; Borowska, M.; Sikorska, U.; Ciesielska, A.; Zdrojkowski, Ł.; Domino, M. Quantification of the Area of the Highest Temperature in Equine Infrared Images. Appl. Sci. 2023, 13, 11006

DOI: 10.3390/app131911006

[9] Zhao, K.; Duan, Y.; Chen, J.; Li, Q.; Hong, X.; Zhang, R.; Wang, M. Detection of Respiratory Rate of Dairy Cows Based on Infrared Thermography and Deep Learning. Agriculture 2023, 13, 1939

DOI: 10.3390/agriculture13101939

[10] A Silva, W.C.d.; Silva, J.A.R.d.; Silva, É.B.R.d.; Barbosa, A.V.C.; Sousa, C.E.L.; Carvalho, K.C.d.; Santos, M.R.P.d.; Neves, K.A.L.; Martorano, L.G.; Camargo Júnior, R.N.C.; et al. Characterization of Thermal Patterns Using Infrared Thermography and Thermolytic Responses of Cattle Reared in Three Different Systems during the Transition Period in the Eastern Amazon, Brazil. Animals 2023, 13, 2735

DOI: 10.3390/ani13172735

[11] De Meneck, F.; Santana, V.; Brioschi, G.C.; Haddad, D.S.; Neves, E.B.; Franco, M.d.C.; Brioschi, M.L. Infrared Imaging of the Brain-Eyelid Thermal Tunnel: A Promising Method for Measuring Body Temperature in Afebrile Children. Int. J. Environ. Res. Public Health 2023, 20, 6867

DOI: 10.3390/ijerph20196867

[12] Angrisani, L.; De Benedetto, E.; Duraccio, L.; Lo Regio, F.; Ruggiero, R.; Tedesco, A. Infrared Thermography for Real-Time Assessment of the Effectiveness of Scoliosis Braces. Sensors 2023, 23, 8037

DOI: 10.3390/s23198037

[13] Li, L.; Tang, W.; Yang, H.; Xue, C. Classification of User Emotional Experiences on B2C Websites Utilizing Infrared Thermal Imaging. Sensors 2023, 23, 7991. https://doi.org/

DOI: 10.3390/s23187991

[14] Drahanský, M.; Charvát, M.; Macek, I.; Mohelníková, J. Thermal Imaging Detection System: A Case Study for Indoor Environments. Sensors 2023, 23, 7822. https://doi.org/10.3390/ s23187822

DOI: 10.3390/s23187822

[15] Ovalle, C., Auccahuasi, W., Meza, S., Rojas, K., Cosme, M., Inciso-Rojas, M., ... & Auccahuasi, A. (2023). Muscle temperature analysis, using thermal imaging, applied to the treatment of muscle recovery. Procedia Computer Science, 218, 1247-1256.

DOI: 10.1016/j.procs.2023.01.103