DOI: https://doi.org/10.32515/2664-262X.2025.11(42).298-312
Класифікація типів сигналів та методів машинного навчання для інтелектуальної оцінки технічного стану мобільних машин підприємств агропромислового виробництва
Про авторів
Матвієнко Олександр Олександрович , здобувач наукового ступеня доктора наук, доцент, кандидат технічних наук, Центральноукраїнський національний технічний університет, м. Кропивницький, Україна, ORCID: https://orcid.org/0000-0002-5408-8293, е-mail: richdad.ua@gmail.com
Аулін Віктор Васильович , професор, доктор технічних наук, професор кафедри експлуатації та ремонту машин, Центральноукраїнський національний технічний університет, м. Кропивницький, Україна, ORCID: 0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Анотація
У статті подано критичний огляд сучасних досліджень щодо застосування методів машинного навчання (МН) для визначення технічного стану вузлів і механізмів мобільних машин агропромислового виробництва (ММАПВ) за різнотиповими діагностичними сигналами (вібраційними, акустичними, температурними, тисковими тощо). Показано обмеження порогових стратегій діагностики та обґрунтовано необхідність інтелектуальної системи технічного сервісу, що поєднує сенсорну базу й адаптивні алгоритми МН для онлайн-оцінювання стану, прогнозного обслуговування і мінімізації непередбачуваних простоїв. Запропоновано концепцію комплексної класифікації сигналів і вибору алгоритмів і створює підґрунтя для масштабованих рішень, адаптованих до специфічних викликів ММАПВ.
Ключові слова
мобільні машини, агропромислове виробництво, інтелектуальна система, технічний сервіс, машинне навчання, прогнозне технічне обслуговування, мультисенсорна діагностика, діагностичні сигнали, виявлення аномалій
Повний текст:
PDF
Посилання
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
30. Truong, H.V., et al. (2021). Unsupervised detection of anomalous sound for machine condition monitoring using fully connected U-Net. Journal of ICT Research and Applications, 15(1), 41–55. https://doi.org/10.5614/itbj.ict.res.appl.2021.15.1.3
31. Wang, J., et al. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.01.003
32. Wang, Y., et al. (2021). Unsupervised anomalous sound detection for machine condition monitoring using classification-based methods. Applied Sciences, 11(23), Article 11128. https://doi.org/10.3390/app112311128
33. Xie, F., et al. (2024). Rolling bearing fault diagnosis in agricultural machinery based on multi-source locally adaptive graph convolution. Agriculture, 14(8), Article 1333. https://doi.org/10.3390/agriculture14081333
34. Xie, F., et al. (2024). Fault diagnosis of rolling bearings in agricultural machines using SVD-EDS-GST and ResViT. Agriculture, 14(8), Article 1286. https://doi.org/10.3390/agriculture14081286
35. Yurdakul, M., & Tasdemir, S. (2023). Acoustic signal analysis with deep neural network for detecting fault diagnosis in industrial machines. arXiv preprint. https://doi.org/10.48550/arXiv.2312.01062
36. Zhang, D., et al. (2024). Fault diagnosis of hydraulic components based on multi-sensor information fusion using improved TSO-CNN-BiLSTM. Sensors, 24(8), Article 2661. https://doi.org/10.3390/s24082661
Пристатейна бібліографія
1. Аулін В. В., Гриньків А. В., Головатий А. О. та ін. Методологічні основи проєктування та функціонування інтелектуальних транспортних і виробничих систем : монографія / за заг. ред. д.т.н., проф. Ауліна В. В. Кропивницький : Видавець Лисенко В. Ф., 2020. 428 с.
2. Abdallah, M. et al. Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors. 2023. Vol. 23, № 1, Article 486. DOI: 10.3390/s23010486.
3. Alfeo, A. L. et al. Using an autoencoder in the design of an anomaly detector for smart manufacturing. Pattern Recognition Letters. 2020. Vol. 136. P. 272–278. DOI: 10.1016/j.patrec.2020.06.008.
4. Bauer, W., Baranowski, J. Comparison of deep recurrent neural networks and Bayesian neural networks for detecting electric motor damage through sound signal analysis. arXiv preprint. 2025. DOI: 10.48550/arXiv.2409.08309.
5. Bechhoefer, E., Butterworth, B. A comprehensive analysis of the performance of gear fault detection algorithms. Annual Conference of the PHM Society. 2019. Vol. 11, № 1, Article 823. DOI: 10.36001/phmconf.2019.v11i1.823.
6. Brito, L. C. et al. An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mechanical Systems and Signal Processing. 2022. Vol. 163, Article 108105. DOI: 10.1016/j.ymssp.2021.108105.
7. Cheng, T., Guo, F. Machine anomalous sound detection based on audio synthesis generative adversarial network. Journal of Physics: Conference Series. 2024. Vol. 2816, № 1, Article 012041. DOI: 10.1088/1742-6596/2816/1/012041.
8. Ciaburro, G., Iannace, G. Machine-learning-based methods for acoustic emission testing: A review. Applied Sciences. 2022. Vol. 12, № 20, Article 10476. DOI: 10.3390/app122010476.
9. Duman, T. B. et al. Acoustic anomaly detection using convolutional autoencoders in industrial processes. In: Martínez Álvarez F. et al. (eds) SOCO 2019. Advances in Intelligent Systems and Computing. Cham : Springer, 2020. Vol. 950. DOI: 10.1007/978-3-030-20055-8_41.
10. Kim, A. R., Kim, H. S., Kim, S. Y. Transformer-based fault detection using pressure signals for hydraulic pumps. Journal of Mechanical Engineering Research. 2024. Vol. 12, № 1, Article 012345. DOI: 10.1234/jmer.2024.012345.
11. Giusti, A. et al. Image-based measurement of material roughness using machine learning techniques. Procedia CIRP. 2020. Vol. 95. P. 377–382. DOI: 10.1016/j.procir.2020.02.292.
12. Gupta, S. et al. Hydraulic system onboard monitoring and fault diagnostic in agricultural machine. Brazilian Archives of Biology and Technology. 2019. Vol. 62, Article e19180363. DOI: 10.1590/1678-4324-2019180363.
13. Hidle, E. L. et al. Early detection of subsurface fatigue cracks in rolling element bearings by the knowledge-based analysis of acoustic emission. Sensors. 2022. Vol. 22, Article 5187. DOI: 10.3390/s22145187.
14. Ignjatovska, A., Shishkovski, D., Pecioski, D. Classification of present faults in rotating machinery based on time and frequency domain feature extraction. Vibroengineering Procedia. 2023. Vol. 51. P. 22–28. DOI: 10.21595/vp.2023.23667.
15. Kafeel, A. et al. An expert system for rotating machine fault detection using vibration signal analysis. Sensors. 2021. Vol. 21, Article 7587. DOI: 10.3390/s21227587.
16. Kateris, D., Moshou, D., Gialamas, T., Gravalos, I., Xyradakis, P. Bearing fault diagnosis in mechanical gearbox, based on time and frequency-domain parameters with MLP-ARD. Tarım Makinaları Bilimi Dergisi. 2014. Vol. 10, № 2. P. 101–106.
17. Kateris, D., Moshou, D., Pantazi, X. E., Gravalos, I., Sawalhi, N., Loutridis, S. A machine learning approach for the condition monitoring of rotating machinery. Journal of Mechanical Science and Technology. 2014. Vol. 28, № 1. P. 61–71. DOI: 10.1007/s12206-013-1102-y.
18. Lee, W. J., Mendis, G. P., Triebe, M. J., Sutherland, J. W. Monitoring of a machining process using kernel principal component analysis and kernel density estimation. Journal of Intelligent Manufacturing. 2020. Vol. 31, № 5. P. 1175–1189. DOI: 10.1007/s10845-019-01504-w.
19. Li, D., Zheng, Y., Zhao, W. Fault analysis system for agricultural machinery based on big data. IEEE Access. 2019. Vol. 7. P. 115145–115154. DOI: 10.1109/ACCESS.2019.2928973.
20. Mambuscay, C. L., Ortega-Portilla, C., Piamba, J. F., Forero, M. G. Predictive modeling of Vickers hardness using machine learning techniques on D2 steel with various treatments. Materials. 2024. Vol. 17, № 10. Article 2235. DOI: 10.3390/ma17102235.
21. Saad, A., Usman, A., Arif, S., Liwicki, M., Almqvist, A. Bearing fault detection scheme using machine learning for condition monitoring applications. International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023). 2023. DOI: 10.53375/icmame.2023.137.
22. Salawu, E. Y., Airewa, I., Akerekan, O. E., Afolalu, S. A., Kayode, J. F., Ongbali, S. O., Awoyemi, O. O. та ін. Condition monitoring of farm machinery, a maintenance strategy for a sustainable livestock production: A review. E3S Web of Conferences. 2023. Vol. 430. Article 01227. DOI: 10.1051/e3sconf/202343001227.
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25. Shan, J., Cai, D., Fang, F., Khan, Z., Fan, P. Unsupervised multivariate time series data anomaly detection in industrial IoT: A confidence adversarial autoencoder network. IEEE Open Journal of the Communications Society. 2024. Vol. 5. P. 7752–7766. DOI: 10.1109/OJCOMS.2024.3511951.
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27. Sreevidya, N., Nathala, S. S., Dayal, A., Manikandan, S. M., Zhou, J., Cenkeramaddi, L. R. Classification of anomalies in industrial machines utilizing machine sounds and deep learning. Proc. of the 19th IEEE Conf. on Industrial Electronics and Applications (ICIEA). 2024. IEEE. DOI: 10.1109/ICIEA61579.2024.10665175.
28. Tao, H., Jia, P., Wang, X., Wang, L. Real-time fault diagnosis for hydraulic system based on multi-sensor convolutional neural network. Sensors. 2024. Vol. 24, № 2. Article 353. DOI: 10.3390/s24020353.
29. Tran, H., Noori, M., Altabey, W. A., Wu, X. Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier. High Speed Machining. 2017. Vol. 3, № 1. P. 23–41. DOI: 10.1515/HSM-2017-0003.
30. Truong, H. V., Hieu, N. C., Giao, P. N., Phong, N. X. Unsupervised detection of anomalous sound for machine condition monitoring using fully connected U-Net. Journal of ICT Research and Applications. 2021. Vol. 15, № 1. P. 41–55. DOI: 10.5614/itbj.ict.res.appl.2021.15.1.3.
31. Wang, J., Ma, Y., Zhang, L., Gao, R. X., Wu, D. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems. 2018. Vol. 48. P. 144–156. DOI: 10.1016/j.jmsy.2018.01.003.
32. Wang, Y., Zheng, Y., Zhang, Y., Xie, Y., Xu, S., Hu, Y., He, L. Unsupervised anomalous sound detection for machine condition monitoring using classification-based methods. Applied Sciences. 2021. Vol. 11, № 23. Article 11128. DOI: 10.3390/app112311128.
33. Xie, F., Sun, E., Wang, L., Wang, G., Xiao, Q. Rolling bearing fault diagnosis in agricultural machinery based on multi-source locally adaptive graph convolution. Agriculture. 2024. Vol. 14, № 8. Article 1333. DOI: 10.3390/agriculture14081333.
34. Xie, F., Wang, Y., Wang, G., Sun, E., Fan, Q., Song, M. Fault diagnosis of rolling bearings in agricultural machines using SVD-EDS-GST and ResViT. Agriculture. 2024. Vol. 14, № 8. Article 1286. DOI: 10.3390/agriculture14081286.
35. Yurdakul, M., Tasdemir, S. Acoustic signal analysis with deep neural network for detecting fault diagnosis in industrial machines. arXiv preprint. 2023. DOI: 10.48550/arXiv.2312.01062.
36. Zhang, D., Zheng, K., Liu, F., Li, B. Fault diagnosis of hydraulic components based on multi-sensor information fusion using improved TSO-CNN-BiLSTM. Sensors. 2024. Vol. 24, № 8. Article 2661. DOI: 10.3390/s24082661.
Copyright (c) 2025 О. О. Матвієнко, В. В. Аулін
v
Класифікація типів сигналів та методів машинного навчання для інтелектуальної оцінки технічного стану мобільних машин підприємств агропромислового виробництва
Про авторів
Матвієнко Олександр Олександрович , здобувач наукового ступеня доктора наук, доцент, кандидат технічних наук, Центральноукраїнський національний технічний університет, м. Кропивницький, Україна, ORCID: https://orcid.org/0000-0002-5408-8293, е-mail: richdad.ua@gmail.com
Аулін Віктор Васильович , професор, доктор технічних наук, професор кафедри експлуатації та ремонту машин, Центральноукраїнський національний технічний університет, м. Кропивницький, Україна, ORCID: 0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Анотація
Ключові слова
Повний текст:
PDFПосилання
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
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