Diagnostic on Car Internal Combustion Engine through Noise
DOI:
10.33395/sinkron.v8i2.12392Keywords:
Noise; Internal Combustion Engine; Diagnostics; Car SoundsAbstract
Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.
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Alam, M. M., Aktar, M. A., Idris, N. D. M., & Al-Amin, A. Q. (2023). World energy economics and geopolitics amid COVID-19 and post-COVID-19 policy direction. World Development Sustainability, 2(July 2022), 100048. https://doi.org/10.1016/j.wds.2023.100048
Carpinteiro, C., Lopes, J., Abelha, A., & Santos, M. F. (2023). ScienceDirect ScienceDirect A Comparative Study of Classification Algorithms for Early A Comparative Study of Classification Algorithms for Early Detection of Diabetes Detection of Diabetes. Procedia Computer Science, 220, 868–873. https://doi.org/10.1016/j.procs.2023.03.117
Dwyer, T., Cordeil, M., Czauderna, T., Delir Haghighi, P., Ens, B., Goodwin, S., Jenny, B., Marriott, K., & Wybrow, M. (2020). The Data Visualisation and Immersive Analytics Research Lab at Monash University. Visual Informatics, 4(4), 41–49. https://doi.org/10.1016/j.visinf.2020.11.001
Hindarto, D. (2022). Perbandingan Kinerja Akurasi Klasifikasi K-NN, NB dan DT pada APK Android. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(1), 486–503. https://doi.org/10.35957/jatisi.v9i1.1542
Hindarto, D., & Handri Santoso. (2021). Android APK Identification using Non Neural Network and Neural Network Classifier. Journal of Computer Science and Informatics Engineering (J-Cosine), 5(2), 149–157. https://doi.org/10.29303/jcosine.v5i2.420
Hindarto, D., Indrajit, R. E., & Dazki, E. (2021). Sustainability of Implementing Enterprise Architecture in the Solar Power Generation Manufacturing Industry. Sinkron, 6(1), 13–24. https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11115
Hindarto, D., & Santoso, H. (2022). PERFORMANCE COMPARISON OF SUPERVISED LEARNING USING NON-NEURAL NETWORK AND NEURAL NETWORK. Janapati, 11, 49–62.
Huang, A., Xu, R., Chen, Y., & Guo, M. (2023). Research on multi-label user classification of social media based on ML-KNN algorithm. Technological Forecasting and Social Change, 188(August 2022), 122271. https://doi.org/10.1016/j.techfore.2022.122271
Hyodo, S., Yoshii, T., Satoshi, M., & Hirotoshi, S. (2017). An analysis of the impact of driving time on the driver’s behavior using probe car data. Transportation Research Procedia, 21, 169–179. https://doi.org/10.1016/j.trpro.2017.03.086
Ivanov, E., Khoroshavin, A., & Karsakov, A. (2020). Visual programming environment based on data visualization grammar specification. Procedia Computer Science, 178(2019), 434–439. https://doi.org/10.1016/j.procs.2020.11.045
Li, L., Lin, J., Wu, N., Xie, S., Meng, C., Zheng, Y., Wang, X., & Zhao, Y. (2022). Review and outlook on the international renewable energy development. Energy and Built Environment, 3(2), 139–157. https://doi.org/10.1016/j.enbenv.2020.12.002
Liu, J., Li, X., Zhang, X., Xu, S., & Dong, L. (2011). Misfire diagnosis of diesel engine based on rough set and Neural Network. Procedia Engineering, 16, 224–229. https://doi.org/10.1016/j.proeng.2011.08.1076
Permai, S. D., & Tanty, H. (2018). Linear regression model using bayesian approach for energy performance of residential building. Procedia Computer Science, 135, 671–677. https://doi.org/10.1016/j.procs.2018.08.219
Traivivatana, S., Wangjiraniran, W., Junlakarn, S., & Wansophark, N. (2017). Impact of Transportation Restructuring on Thailand Energy Outlook. Energy Procedia, 138, 393–398. https://doi.org/10.1016/j.egypro.2017.10.178
Vidal, T. Y. G., Claude Valery, N. A., Emmanuel, A. N., Nelson, I. B. J., Moïse, L. N., Cyrille, M., & Ruben, M. (2021). Performance map of a LPG-diesel dual-fuel engine based on experimental and non-linear least squares determined wiebe function. Scientific African, 13. https://doi.org/10.1016/j.sciaf.2021.e00900
Wedha, B. Y. (2022). Enterprise Architecture untuk Industri Truk Logistik di Indonesia. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 1137–1150. https://doi.org/10.35957/jatisi.v9i2.1255
Wedha, B. Y., Karjadi, D. A., Wedha, A. E. P. B., & Santoso, H. (2022). Style Transfer Generator for Dataset Testing Classification. SinkrOn, 7(2), 448–454. https://doi.org/10.33395/sinkron.v7i2.11375
Wolak, A., Molenda, J., Zając, G., & Janocha, P. (2021). Identifying and modelling changes in chemical properties of engine oils by use of infrared spectroscopy. Measurement: Journal of the International Measurement Confederation, 186. https://doi.org/10.1016/j.measurement.2021.110141
Zheng, T., Zhang, Y., Li, Y., & Shi, L. (2019). Real-time combustion torque estimation and dynamic misfire fault diagnosis in gasoline engine. Mechanical Systems and Signal Processing, 126, 521–535. https://doi.org/10.1016/j.ymssp.2019.02.048
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Copyright (c) 2023 William Surya Sjah, Ben Rahman, Djarot Hindarto, Alessandro Benito Putra Bayu Wedha
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