DOI: https://doi.org/10.32515/2664-262X.2025.11(42).298-312
Classification of Signal Types and Machining Methods for Intelligent Assessment of the Technical Mill of Mobile Machines for Agro-Industrial Production
About the Authors
Oleksandr Matviienko, Doctoral Student, Associate Professor, Candidate of Technical Sciences, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-5408-8293, е-mail: richdad.ua@gmail.com
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Operation and Repair of Machines, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Abstract
The article provides a critical review of current research on the use of machine learning (ML) methods for identifying the technical state of units and mechanisms of agro-industrial production machines for different types of diagnostic signals (vibration, acoustic, temperature, vice, etc.). The exchange of threshold diagnostic strategies is shown and the need for an intelligent technical service system is highlighted, which integrates a sensor base and adaptive MN algorithms for online assessment of the plant, predictive maintenance and minimization of untransferable downtime. The concept of complex signal classification and selection of algorithms is proposed and creates a framework for scaling solutions adapted to specific agro-industrial production machines calls.
Keywords
mobile machines, agricultural production, intelligent system, technical service, machine learning, predictive technical maintenance, multisensory diagnostics, diagnostic signals, anomaly detection
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References
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28. Tao, H., Jia, P., Wang, X., & Wang, L. (2024). Real-time fault diagnosis for hydraulic system based on multi-sensor convolutional neural network. Sensors, 24(2), Article 353. https://doi.org/10.3390/s24020353
29. Tran, H., et al. (2017). Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier. High Speed Machining, 3(1), 23–41. https://doi.org/10.1515/HSM-2017-0003
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Citations
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Copyright (c) 2025 Oleksandr Matviienko, Viktor Aulin
Classification of Signal Types and Machining Methods for Intelligent Assessment of the Technical Mill of Mobile Machines for Agro-Industrial Production
About the Authors
Oleksandr Matviienko, Doctoral Student, Associate Professor, Candidate of Technical Sciences, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-5408-8293, е-mail: richdad.ua@gmail.com
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Operation and Repair of Machines, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Abstract
Keywords
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PDFReferences
1. Aulin, V.V., Hrynʹkiv, A.V., Holovatyi, A.O., et al. (2020). Metodolohichni osnovy proiektuvannia ta funktsionuvannia intelektualʹnykh transportnykh i vyrobnychykh system: monohrafiia. V.V. Aulin (Ed.). Kropyvnytskyi: Vydavetsʹ Lysenko V.F. [in Ukrainian].
2. Abdallah, M., et al. (2023). Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors, 23(1), Article 486. https://doi.org/10.3390/s23010486
3. Alfeo, A.L., et al. (2020). Using an autoencoder in the design of an anomaly detector for smart manufacturing. Pattern Recognition Letters, 136, 272–278. https://doi.org/10.1016/j.patrec.2020.06.008
4. Bauer, W., & Baranowski, J. (2025). Comparison of deep recurrent neural networks and Bayesian neural networks for detecting electric motor damage through sound signal analysis. arXiv preprint. https://doi.org/10.48550/arXiv.2409.08309
5. Bechhoefer, E., & Butterworth, B. (2019). A comprehensive analysis of the performance of gear fault detection algorithms. Annual Conference of the PHM Society, 11(1), Article 823. https://doi.org/10.36001/phmconf.2019.v11i1.823
6. Brito, L.C., et al. (2022). An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mechanical Systems and Signal Processing, 163, Article 108105. https://doi.org/10.1016/j.ymssp.2021.108105
7. Cheng, T., & Guo, F. (2024). Machine anomalous sound detection based on audio synthesis generative adversarial network. Journal of Physics: Conference Series, 2816(1), Article 012041. https://doi.org/10.1088/1742-6596/2816/1/012041
8. Ciaburro, G., & Iannace, G. (2022). Machine-learning-based methods for acoustic emission testing: A review. Applied Sciences, 12(20), Article 10476. https://doi.org/10.3390/app122010476
9. Duman, T.B., et al. (2020). Acoustic anomaly detection using convolutional autoencoders in industrial processes. In Á. Martínez Álvarez et al. (Eds.), SOCO 2019. Advances in Intelligent Systems and Computing (Vol. 950). Springer. https://doi.org/10.1007/978-3-030-20055-8_41
10. Kim, A.R., Kim, H.S., & Kim, S.Y. (2024). Transformer-based fault detection using pressure signals for hydraulic pumps. Journal of Mechanical Engineering Research, 12(1), Article 012345. https://doi.org/10.1234/jmer.2024.012345
11. Giusti, A., et al. (2020). Image-based measurement of material roughness using machine learning techniques. Procedia CIRP, 95, 377–382. https://doi.org/10.1016/j.procir.2020.02.292
12. Gupta, S., et al. (2019). Hydraulic system onboard monitoring and fault diagnostic in agricultural machine. Brazilian Archives of Biology and Technology, 62, Article e19180363. https://doi.org/10.1590/1678-4324-2019180363
13. Hidle, E.L., et al. (2022). Early detection of subsurface fatigue cracks in rolling element bearings by the knowledge-based analysis of acoustic emission. Sensors, 22, Article 5187. https://doi.org/10.3390/s22145187
14. Ignjatovska, A., Shishkovski, D., & Pecioski, D. (2023). Classification of present faults in rotating machinery based on time and frequency domain feature extraction. Vibroengineering Procedia, 51, 22–28. https://doi.org/10.21595/vp.2023.23667
15. Kafeel, A., et al. (2021). An expert system for rotating machine fault detection using vibration signal analysis. Sensors, 21, Article 7587. https://doi.org/10.3390/s21227587
16. Kateris, D., Moshou, D., Gialamas, T., Gravalos, I., & Xyradakis, P. (2014). Bearing fault diagnosis in mechanical gearbox, based on time and frequency-domain parameters with MLP-ARD. Tarım Makinaları Bilimi Dergisi, 10(2), 101–106.
17. Kateris, D., Moshou, D., Pantazi, X.E., Gravalos, I., Sawalhi, N., & Loutridis, S. (2014). A machine learning approach for the condition monitoring of rotating machinery. Journal of Mechanical Science and Technology, 28(1), 61–71. https://doi.org/10.1007/s12206-013-1102-y
18. Lee, W.J., Mendis, G.P., Triebe, M.J., & Sutherland, J.W. (2020). Monitoring of a machining process using kernel principal component analysis and kernel density estimation. Journal of Intelligent Manufacturing, 31(5), 1175–1189. https://doi.org/10.1007/s10845-019-01504-w
19. Li, D., Zheng, Y., & Zhao, W. (2019). Fault analysis system for agricultural machinery based on big data. IEEE Access, 7, 115145–115154. https://doi.org/10.1109/ACCESS.2019.2928973
20. Mambuscay, C.L., Ortega-Portilla, C., Piamba, J.F., & Forero, M.G. (2024). Predictive modeling of Vickers hardness using machine learning techniques on D2 steel with various treatments. Materials, 17(10), Article 2235. https://doi.org/10.3390/ma17102235
21. Saad, A., Usman, A., Arif, S., Liwicki, M., & Almqvist, A. (2023). Bearing fault detection scheme using machine learning for condition monitoring applications. ICMAME 2023. https://doi.org/10.53375/icmame.2023.137
22. Salawu, E.Y., et al. (2023). Condition monitoring of farm machinery, a maintenance strategy for a sustainable livestock production: A review. E3S Web of Conferences, 430, Article 01227. https://doi.org/10.1051/e3sconf/202343001227
23. Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 18(3), 625–644. https://doi.org/10.1016/S0888-3270(03)00020-7
24. Scanlon, P., & Bergin, S. (2008). Using support vector machines and acoustic noise signal for degradation analysis of rotating machinery. Artificial Intelligence Review, 28, 1–15. https://doi.org/10.1007/s10462-008-9081-6
25. Shan, J., Cai, D., Fang, F., Khan, Z., & Fan, P. (2024). Unsupervised multivariate time series data anomaly detection in industrial IoT: A confidence adversarial autoencoder network. IEEE Open Journal of the Communications Society, 5, 7752–7766. https://doi.org/10.1109/OJCOMS.2024.3511951
26. Singh, M.T. (2025). Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration. arXiv preprint. https://doi.org/10.48550/arXiv.2504.20756
27. Sreevidya, N., et al. (2024). Classification of anomalies in industrial machines utilizing machine sounds and deep learning. In Proc. of the 19th IEEE Conf. on Industrial Electronics and Applications (ICIEA). https://doi.org/10.1109/ICIEA61579.2024.10665175
28. Tao, H., Jia, P., Wang, X., & Wang, L. (2024). Real-time fault diagnosis for hydraulic system based on multi-sensor convolutional neural network. Sensors, 24(2), Article 353. https://doi.org/10.3390/s24020353
29. Tran, H., et al. (2017). Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier. High Speed Machining, 3(1), 23–41. https://doi.org/10.1515/HSM-2017-0003
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