DOI: https://doi.org/10.32515/2664-262X.2025.12(43).1.227-237
Status and Development Directions of Data Architecture for Intelligent Assessment of the Technical Condition of Mobile Machines of Agro-Industrial Enterprises
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, e-mail: richdad.ua@gmail.com
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Andrii Hrynkiv, Senior Researcher, PhD (Candidate of Technical Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, e-mail: AVGrinkiv@gmail.com.
Abstract
This article provides a critical survey and a practical method for assembling sensor datasets that support intelligent assessment of the technical condition of mobile agricultural machines. The survey maps available sources and shows that domain-relevant datasets are scarce, fragmented, and rarely documented with stable collection procedures. To address these gaps, the paper proposes a coherent “global to detail” pathway that links fleet decisions, field acquisition, and analytics into a reproducible blueprint.
The pathway begins with fleet-level prioritization that identifies machine classes and critical components, outlines typical failure modes and observable symptoms, and balances severity, likelihood, detectability, seasonal load, safety, and downtime costs. The result is a prioritized roadmap for sensor installation and a compact measurement plan specifying signals, installation points, sampling frequencies, and event time- stamping.
Data collection and management follow an offline-first architecture suitable for unstable connectivity in fields. Sensors feed an edge acquisition unit with unified time derived from global navigation satellite systems and the network time protocol; measurements are buffered locally and delivered with store-and-forward when links reappear, using lightweight message transports such as message queue telemetry and secure hypertext transfer. In the central environment, data land in a time-series repository or data lake governed by a metadata catalog that records dataset passports, provenance, versions, privacy and access rules, and licensing terms. A preprocessing layer performs extraction, transformation and loading, cleans artifacts, harmonizes time, and segments streams into versioned releases for model development.
Analytics follow the functional reference defined by the ISO 13374 for condition monitoring: acquisition, manipulation, state detection, health assessment, prognostics with remaining useful life estimation, and advisory generation with integration into computerized maintenance management systems. Each dataset release carries a minimal passport and acceptance gates that verify regime coverage, synchronization accuracy, limits on missing data, signal-to-noise quality, class balance, and explicit lineage with semantic versioning. The survey also found no sources providing pinned computational environments that guarantee full reproducibility; future publications should include dependency lock files or container images and concise replication guides.
Keywords
mobile machines, agricultural production, intelligent system, technical service, machine learning, predictive technical maintenance, multisensory diagnostics, diagnostic signals, anomaly detection
Full Text:
PDF
References
1. Aulin, V. V., Hrynkiv, A. V., Holovatyi, A. O., Lysenko, S. V., Holub, D. V., Kuzyk, O. V., & Tykhyi, A.A. (2020). Methodological foundations of design and operation of intelligent transportation and manufacturing systems. Lysenko V.F. [in Ukrainian].
2. Matviienko, O. O., Aulin V.V. (2025). Classification of signal types and machining methods for intelligent assessment of the technical mill of mobile machines for agro-industrial production. Central Ukrainian Scientific Bulletin. Technical Sciences, (11(42)_II), 298-312. https://doi.org/10.32515/2664- 262X.2025.11(42).2.298-312 [in Ukrainian].
3. Wang, Y., Zheng, Y., Zhang, Y., Xie, Y., Xu, S., Hu, Y., & He, L. (2021). Unsupervised anomalous sound detection for machine condition monitoring using classification-based methods. Applied Sciences, 11(23), 11128. https://doi.org/10.3390/app112311128
4. Cheng, T., & Guo, F. (2024). Machine anomalous sound detection based on audio synthesis generative adversarial network. Journal of Physics: Conference Series, 2816(1), 012041. https://doi.org/10.1088/1742-6596/2816/1/012041
5. Khorram, A., Khalooei, M., & Rezghi, M. (2021). End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Applied Intelligence, 51(2), 736–751. https://doi.org/10.1007/s10489-020-01859-1
6. Chegini, S. N., Manjili, M. J. H., & Bagheri, A. (2020). New fault diagnosis approaches for detecting the bearing slight degradation. Meccanica, 55(1), 261–286. https://doi.org/10.1007/s11012-019-01116-x
7. Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2021). Fault detection of bearing: An unsupervised machine learning approach exploiting feature extraction and dimensionality reduction. Informatics, 8(4), 85. https://doi.org/10.3390/informatics8040085
8. Attaran, B., Ghanbarzadeh, A., & Moradi, S. (2020). A novel intelligent fault diagnosis approach for critical rotating machinery in the time-frequency domain. International Journal of Engineering, Transactions C: Aspects, 33(4), 668–675. https://doi.org/10.5829/ije.2020.33.04a.18
9. Bouaouiche, K., Menasria, Y., & Khalfa, D. (2023). Diagnosis of rotating machine defects by vibration analysis. Acta IMEKO, 12(1), 1–6. https://doi.org/10.21014/actaimeko.v12i1.1438
10. Singh, M. T. (2025). Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration. arXiv. https://arxiv.org/abs/2504.20756
11. Matania, O., Bachar, L., Bechhoefer, E., & Bortman, J. (2024). Signal processing for the condition-based maintenance of rotating machines via vibration analysis: A tutorial. Sensors, 24(2), 454. https://doi.org/10.3390/s24020454
12. Zhang, B., Zhou, C., Li, W., Ji, S., Li, H., Tong, Z., & Ng, S.-K. (2022). Intelligent bearing fault diagnosis based on open set convolutional neural network. Mathematics, 10(21), 3953. https://doi.org/10.3390/math10213953
13. Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In Proceedings of the European Conference of the Prognostics and Health Management Society 2016 (PHME 2016), Bilbao, Spain. PHM Society. https://papers.phmsociety.org/index.php/phme/article/view/1577
14. Saad, A., Usman, A., Arif, S., Liwicki, M., & Almqvist, A. (2023, April 29–30). Bearing fault detection scheme using machine learning for condition monitoring applications. In Proceedings of the International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023), Dubai, United Arab Emirates. https://www.diva-portal.org/smash/get/diva2:1793876/FULLTEXT01.pdf
15. Ignjatovska, A., Shishkovski, D., & Pecioski, D. (2023, October 20–21). 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
16. Zhang, D., Zheng, K., Liu, F., & Li, B. (2024). Fault diagnosis of hydraulic components based on multi-sensor information fusion using improved TSO-CNN-BiLSTM. Sensors, 24(8), 2661. https://doi.org/10.3390/s24082661
17. Abdallah, M., Joung, B.-G., Lee, W. J., Mousoulis, C., Raghunathan, N., Shakouri, A., Sutherland, J. W., & Bagchi, S. (2023). Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors, 23(1), 486. https://doi.org/10.3390/s23010486
18. Ciaburro, G., & Iannace, G. (2022). Machine-learning-based methods for acoustic emission testing: A review. Applied Sciences, 12(20), 10476. https://doi.org/10.3390/app122010476
19. Rahman, A., Hoque, M. E., Rashid, F., Alam, F., & Ahmed, M. M. (2022). Health condition monitoring and control of vibrations of a rotating system through vibration analysis. Journal of Sensors, 2022, Article 4281596. https://doi.org/10.1155/2022/4281596
20. 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. Journal of Agricultural Machinery Science, 10(2), 101–106. https://dergipark.org.tr/tr/download/article-file/558529
21. Elsamanty, M. M., Salman, W. S., & Ibrahim, A. A. (2021). Fault diagnosis of rotary machines based on vibration signature and machine learning algorithm. Engineering Research Journal (Shoubra), 50(1), 41–47. https://doi.org/10.21608/erjsh.2021.225704.
22. Oulmane, A., Lakis, A. A., & Mureithi, N. W. (2014). Application of Fourier descriptors and artificial neural network to bearing vibration signals for fault detection and classification. Universal Journal of Aeronautical & Aerospace Sciences, 2, 37–54. www.papersciences.com/Oulmane-Univ-J-Aer-Aerosp- Scien-Vol2-2014-3.pdf.
23. Koizumi, Y., Kawaguchi, Y., Imoto, K., Nakamura, T., Nikaido, Y., Tanabe, R., Purohit, H., Suefusa, K., Endo, T., Yasuda, M., & Harada, N. (2020, March 1). DCASE 2020 Challenge Task 2 Development Dataset (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.3678171.
24. Kawaguchi, Y., Imoto, K., Koizumi, Y., Harada, N., Niizumi, D., Dohi, K., Tanabe, R., Purohit, H., & Endo, T. (2021, March 1). DCASE 2021 Challenge Task 2 Development Dataset (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.4562016.
25. National Aeronautics and Space Administration. (2023, November 2). IMS Bearings [Dataset]. NASA Open Data Portal. https://data.nasa.gov/dataset/ims-bearings.
26. Case Western Reserve University. (n.d.). Bearing Data Center. Case School of Engineering. https://engineering.case.edu/bearingdatacenter.
27. Cathy Siyu. (n.d.). Mechanical-datasets [Dataset]. GitHub. github.com/cathysiyu/Mechanical-datasets.
28. Paderborn University, Chair of Design and Drive Technology (KAt). (n.d.). Bearing Datacenter: Data sets and download [Dataset]. mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets- and-download
29. Clark Gable Wang. (n.d.). JNU-Bearing-Dataset [Dataset]. GitHub. github.com/ClarkGableWang/JNU- Bearing-Dataset.
30. Prognostics and Health Management Society. (n.d.). Public data sets. PHM Society. phmsociety.org/public-data-sets/.
31. Society for Machinery Failure Prevention Technology. (n.d.). Fault data sets. MFPT. www.mfpt.org/fault- data-sets/.
32. josh101. (n.d.). Machinery Fault Database — Induction Motor Fault [Dataset]. Kaggle. www.kaggle.com/ datasets/josh101/machinery-fault-database-induction-motor-fault.
33. Purdue University. (n.d.). Purdue Motor dataset [Dataset]. Google Drive. https://drive.google.com/drive/u/2/folders/1QX3chnSTKO3PsEhi5kBdf9WwMBmOriJ8.
34. UCI Machine Learning Repository. (n.d.). Condition monitoring of hydraulic systems [Dataset]. University of California, Irvine. https://archive.ics.uci.edu/dataset/447/condition+monitoring+of+hydraulic+systems.
Citations
1. Аулін В. В., Гриньків А. В., Головатий А. О., Лисенко С. В., Голуб Д. В., Кузик О.В., Тихий А. А. Методологічні основи проектування та функціонування інтелектуальних транспортних і виробничих систем: монографія під заг. ред. д.т.н., проф. Ауліна В.В. Кропивницький: Видавець Лисенко В.Ф., 2020. 428с.
2. Матвієнко О. О., Аулін В. В. Класифікація типів сигналів та методів машинного навчання для інтелектуальної оцінки технічного стану мобільних машин підприємств агропромислового виробництва. Збірник наукових праць. Науковий вісник. Технічні науки. 2025. № 11(42)_ІІ. С. 298–312. DOI: 10.32515/2664-262X.2025.11(42).2.298-312.
3. 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, No.23. 11128. DOI: 10.3390/app112311128.
4. Cheng T., Guo F. Machine anomalous sound detection based on audio synthesis generative adversarial network. Journal of Physics: Conference Series. 2024. Vol. 2816, No. 1. 012041. DOI: 10.1088/1742-6596/2816/1/012041.
5. Khorram A., Khalooei M., Rezghi M. End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Applied Intelligence. 2021. Vol. 51, No. 2. P. 736–751. DOI: 10.1007/s10489-020-01859-1.
6. Chegini S. N., Manjili M. J. H., Bagheri A. New fault diagnosis approaches for detecting the bearing slight degradation. Meccanica. 2020. Vol. 55, No. 1. P. 261–286. DOI: 10.1007/s11012-019-01116-x.
7. Brito L. C., Susto G. A., Brito J. N., Duarte M. A. V. Fault detection of bearing: An unsupervised machine learning approach exploiting feature extraction and dimensionality reduction. Informatics. 2021. Vol. 8, No. 4. 85. DOI: 10.3390/informatics8040085.
8. Attaran B., Ghanbarzadeh A., Moradi S. A novel intelligent fault diagnosis approach for critical rotating machinery in the time-frequency domain. International Journal of Engineering. Transactions C: Aspects. 2020. Vol. 33, No. 4. P. 668–675. DOI: 10.5829/ije.2020.33.04a.18.
9. Bouaouiche K., Menasria Y., Khalfa D. Diagnosis of rotating machine defects by vibration analysis. Acta IMEKO. 2023. Vol. 12, No. 1. P. 1–6. DOI: 10.21014/actaimeko.v12i1.1438.
10. Singh M. T. Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration. arXiv preprint. 2025. arXiv:2504.20756. URL: https://arxiv.org/abs/2504.20756.
11. Matania O., Bachar L., Bechhoefer E., Bortman J. Signal processing for the condition-based maintenance of rotating machines via vibration analysis: A tutorial. Sensors. 2024. Vol. 24, No. 2. 454. DOI: 10.3390/s24020454.
12. Zhang B., Zhou C., Li W., Ji S., Li H., Tong Z., Ng S.-K. Intelligent bearing fault diagnosis based on open set convolutional neural network. Mathematics. 2022. Vol. 10, No. 21. 3953. DOI: 10.3390/math10213953.
13. Lessmeier C., Kimotho J. K., Zimmer D., Sextro W. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In: Proceedings of the European Conference of the Prognostics and Health Management Society 2016 (PHME 2016), 5–8 July 2016, Bilbao, Spain. PHM Society, 2016. URL: https://papers.phmsociety.org/index.php/phme/article/view/1577.
14. Saad A., Usman A., Arif S., Liwicki M., Almqvist A. Bearing fault detection scheme using machine learning for condition monitoring applications. In: Proceedings of the International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023), 29–30 April 2023, Dubai, UAE. 2023. URL: https://www.diva-portal.org/smash/get/diva2:1793876/FULLTEXT01.pdf.
15. 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.
16. 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, No. 8. 2661. DOI: 10.3390/s24082661.
17. Abdallah M., Joung B.-G., Lee W. J., Mousoulis C., Raghunathan N., Shakouri A., Sutherland J. W., Bagchi S. Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors. 2023. Vol. 23, No. 1. 486. DOI: 10.3390/s23010486.
18. Ciaburro G., Iannace G. Machine-learning-based methods for acoustic emission testing: A review. Applied Sciences. 2022. Vol. 12, No. 20. 10476. DOI: 10.3390/app122010476.
19. Rahman A., Hoque M. E., Rashid F., Alam F., Ahmed M. M. Health condition monitoring and control of vibrations of a rotating system through vibration analysis. Journal of Sensors. 2022. 2022. Article ID 4281596. DOI: 10.1155/2022/4281596.
20. 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. Journal of Agricultural Machinery Science. 2014. Vol. 10, No. 2. P. 101–106. URL: https://dergipark.org.tr/tr/download/article-file/558529.
21. Elsamanty M. M., Salman W. S., Ibrahim A. A. Fault diagnosis of rotary machines based on vibration signature and machine learning algorithm. Engineering Research Journal (Shoubra). 2021. Vol. 50, No. 1.P. 41–47. DOI: 10.21608/erjsh.2021.225704.
22. Oulmane A., Lakis A. A., Mureithi N. W. Application of Fourier descriptors and artificial neural network to bearing vibration signals for fault detection and classification. Universal Journal of Aeronautical & Aerospace Sciences. 2014. Vol. 2. P. 37–54. URL: https://www.papersciences.com/Oulmane-Univ-J-Aer- Aerosp-Scien-Vol2-2014-3.pdf.
23. Koizumi Y., Kawaguchi Y., Imoto K., Nakamura T., Nikaido Y., Tanabe R., Purohit H., Suefusa K., Endo T., Yasuda M., Harada N. DCASE 2020 Challenge Task 2 Development Dataset. Version 1.0. [Dataset]. Zenodo. 2020. DOI: 10.5281/zenodo.3678171.
24. Kawaguchi Y., Imoto K., Koizumi Y., Harada N., Niizumi D., Dohi K., Tanabe R., Purohit H., Endo T. DCASE 2021 Challenge Task 2 Development Dataset. Version 1.0. [Dataset]. Zenodo. 2021. DOI: 10.5281/zenodo.4562016.
25. National Aeronautics and Space Administration (NASA). IMS Bearings. [Dataset]. NASA Open Data Portal. 2023. URL: https://data.nasa.gov/dataset/ims-bearings (дата звернення: 1.09.2025).
26. Case Western Reserve University Bearing Data Center : веб-сайт. URL: https://engineering.case.edu/bearingdatacenter (дата звернення: 1.09.2025).
27. Cathy Siyu. Mechanical-datasets. [Dataset]. GitHub. URL: https://github.com/cathysiyu/Mechanical- datasets (дата звернення: 1.09.2025).
28. Paderborn University, Chair of Design and Drive Technology (KAt). Bearing Datacenter: Data sets and download. [Dataset]. URL: https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing- datacenter/data-sets-and-download (дата звернення: 1.09.2025).
29. Clark Gable Wang. JNU-Bearing-Dataset. [Dataset]. GitHub. URL: https://github.com/ClarkGableWang/JNU-Bearing-Dataset (дата звернення: 1.09.2025).
30. Prognostics and Health Management Society (PHM Society). Public data sets : веб‑сторінка. URL: https://phmsociety.org/public-data-sets/ (дата звернення: 1.09.2025).
31. Society for Machinery Failure Prevention Technology (MFPT). Fault data sets : веб‑сторінка. URL: https://www.mfpt.org/fault-data-sets/ (дата звернення: 1.09.2025).
32. josh101. Machinery Fault Database — Induction Motor Fault. [Dataset]. Kaggle. URL: www.kaggle.com/datasets/josh101/machinery-fault-database-induction-motor-fault (дата звернення: 1.09.2025).
33. Purdue University. Purdue Motor dataset. [Dataset]. Google Drive. URL: drive.google.com/drive/u/2/folders/1QX3chnSTKO3PsEhi5kBdf9WwMBmOriJ8 (дата звернення: 1.09.2025).
34. UCI Machine Learning Repository. Condition monitoring of hydraulic systems. [Dataset]. University of California, Irvine. URL: archive.ics.uci.edu/dataset/447/condition+monitoring+of+hydraulic+systems (дата звернення: 1.09.2025).
Copyright (©) 2025, Oleksandr Matviienko, Viktor Aulin, Andriy Grynkiv
Status and Development Directions of Data Architecture for Intelligent Assessment of the Technical Condition of Mobile Machines of Agro-Industrial Enterprises
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, e-mail: richdad.ua@gmail.com
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Andrii Hrynkiv, Senior Researcher, PhD (Candidate of Technical Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, e-mail: AVGrinkiv@gmail.com.
Abstract
Keywords
Full Text:
PDFReferences
1. Aulin, V. V., Hrynkiv, A. V., Holovatyi, A. O., Lysenko, S. V., Holub, D. V., Kuzyk, O. V., & Tykhyi, A.A. (2020). Methodological foundations of design and operation of intelligent transportation and manufacturing systems. Lysenko V.F. [in Ukrainian].
2. Matviienko, O. O., Aulin V.V. (2025). Classification of signal types and machining methods for intelligent assessment of the technical mill of mobile machines for agro-industrial production. Central Ukrainian Scientific Bulletin. Technical Sciences, (11(42)_II), 298-312. https://doi.org/10.32515/2664- 262X.2025.11(42).2.298-312 [in Ukrainian].
3. Wang, Y., Zheng, Y., Zhang, Y., Xie, Y., Xu, S., Hu, Y., & He, L. (2021). Unsupervised anomalous sound detection for machine condition monitoring using classification-based methods. Applied Sciences, 11(23), 11128. https://doi.org/10.3390/app112311128
4. Cheng, T., & Guo, F. (2024). Machine anomalous sound detection based on audio synthesis generative adversarial network. Journal of Physics: Conference Series, 2816(1), 012041. https://doi.org/10.1088/1742-6596/2816/1/012041
5. Khorram, A., Khalooei, M., & Rezghi, M. (2021). End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Applied Intelligence, 51(2), 736–751. https://doi.org/10.1007/s10489-020-01859-1
6. Chegini, S. N., Manjili, M. J. H., & Bagheri, A. (2020). New fault diagnosis approaches for detecting the bearing slight degradation. Meccanica, 55(1), 261–286. https://doi.org/10.1007/s11012-019-01116-x
7. Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2021). Fault detection of bearing: An unsupervised machine learning approach exploiting feature extraction and dimensionality reduction. Informatics, 8(4), 85. https://doi.org/10.3390/informatics8040085
8. Attaran, B., Ghanbarzadeh, A., & Moradi, S. (2020). A novel intelligent fault diagnosis approach for critical rotating machinery in the time-frequency domain. International Journal of Engineering, Transactions C: Aspects, 33(4), 668–675. https://doi.org/10.5829/ije.2020.33.04a.18
9. Bouaouiche, K., Menasria, Y., & Khalfa, D. (2023). Diagnosis of rotating machine defects by vibration analysis. Acta IMEKO, 12(1), 1–6. https://doi.org/10.21014/actaimeko.v12i1.1438
10. Singh, M. T. (2025). Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration. arXiv. https://arxiv.org/abs/2504.20756
11. Matania, O., Bachar, L., Bechhoefer, E., & Bortman, J. (2024). Signal processing for the condition-based maintenance of rotating machines via vibration analysis: A tutorial. Sensors, 24(2), 454. https://doi.org/10.3390/s24020454
12. Zhang, B., Zhou, C., Li, W., Ji, S., Li, H., Tong, Z., & Ng, S.-K. (2022). Intelligent bearing fault diagnosis based on open set convolutional neural network. Mathematics, 10(21), 3953. https://doi.org/10.3390/math10213953
13. Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In Proceedings of the European Conference of the Prognostics and Health Management Society 2016 (PHME 2016), Bilbao, Spain. PHM Society. https://papers.phmsociety.org/index.php/phme/article/view/1577
14. Saad, A., Usman, A., Arif, S., Liwicki, M., & Almqvist, A. (2023, April 29–30). Bearing fault detection scheme using machine learning for condition monitoring applications. In Proceedings of the International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023), Dubai, United Arab Emirates. https://www.diva-portal.org/smash/get/diva2:1793876/FULLTEXT01.pdf
15. Ignjatovska, A., Shishkovski, D., & Pecioski, D. (2023, October 20–21). 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
16. Zhang, D., Zheng, K., Liu, F., & Li, B. (2024). Fault diagnosis of hydraulic components based on multi-sensor information fusion using improved TSO-CNN-BiLSTM. Sensors, 24(8), 2661. https://doi.org/10.3390/s24082661
17. Abdallah, M., Joung, B.-G., Lee, W. J., Mousoulis, C., Raghunathan, N., Shakouri, A., Sutherland, J. W., & Bagchi, S. (2023). Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors, 23(1), 486. https://doi.org/10.3390/s23010486
18. Ciaburro, G., & Iannace, G. (2022). Machine-learning-based methods for acoustic emission testing: A review. Applied Sciences, 12(20), 10476. https://doi.org/10.3390/app122010476
19. Rahman, A., Hoque, M. E., Rashid, F., Alam, F., & Ahmed, M. M. (2022). Health condition monitoring and control of vibrations of a rotating system through vibration analysis. Journal of Sensors, 2022, Article 4281596. https://doi.org/10.1155/2022/4281596
20. 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. Journal of Agricultural Machinery Science, 10(2), 101–106. https://dergipark.org.tr/tr/download/article-file/558529
21. Elsamanty, M. M., Salman, W. S., & Ibrahim, A. A. (2021). Fault diagnosis of rotary machines based on vibration signature and machine learning algorithm. Engineering Research Journal (Shoubra), 50(1), 41–47. https://doi.org/10.21608/erjsh.2021.225704.
22. Oulmane, A., Lakis, A. A., & Mureithi, N. W. (2014). Application of Fourier descriptors and artificial neural network to bearing vibration signals for fault detection and classification. Universal Journal of Aeronautical & Aerospace Sciences, 2, 37–54. www.papersciences.com/Oulmane-Univ-J-Aer-Aerosp- Scien-Vol2-2014-3.pdf.
23. Koizumi, Y., Kawaguchi, Y., Imoto, K., Nakamura, T., Nikaido, Y., Tanabe, R., Purohit, H., Suefusa, K., Endo, T., Yasuda, M., & Harada, N. (2020, March 1). DCASE 2020 Challenge Task 2 Development Dataset (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.3678171.
24. Kawaguchi, Y., Imoto, K., Koizumi, Y., Harada, N., Niizumi, D., Dohi, K., Tanabe, R., Purohit, H., & Endo, T. (2021, March 1). DCASE 2021 Challenge Task 2 Development Dataset (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.4562016.
25. National Aeronautics and Space Administration. (2023, November 2). IMS Bearings [Dataset]. NASA Open Data Portal. https://data.nasa.gov/dataset/ims-bearings.
26. Case Western Reserve University. (n.d.). Bearing Data Center. Case School of Engineering. https://engineering.case.edu/bearingdatacenter.
27. Cathy Siyu. (n.d.). Mechanical-datasets [Dataset]. GitHub. github.com/cathysiyu/Mechanical-datasets.
28. Paderborn University, Chair of Design and Drive Technology (KAt). (n.d.). Bearing Datacenter: Data sets and download [Dataset]. mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets- and-download
29. Clark Gable Wang. (n.d.). JNU-Bearing-Dataset [Dataset]. GitHub. github.com/ClarkGableWang/JNU- Bearing-Dataset.
30. Prognostics and Health Management Society. (n.d.). Public data sets. PHM Society. phmsociety.org/public-data-sets/.
31. Society for Machinery Failure Prevention Technology. (n.d.). Fault data sets. MFPT. www.mfpt.org/fault- data-sets/.
32. josh101. (n.d.). Machinery Fault Database — Induction Motor Fault [Dataset]. Kaggle. www.kaggle.com/ datasets/josh101/machinery-fault-database-induction-motor-fault.
33. Purdue University. (n.d.). Purdue Motor dataset [Dataset]. Google Drive. https://drive.google.com/drive/u/2/folders/1QX3chnSTKO3PsEhi5kBdf9WwMBmOriJ8.
34. UCI Machine Learning Repository. (n.d.). Condition monitoring of hydraulic systems [Dataset]. University of California, Irvine. https://archive.ics.uci.edu/dataset/447/condition+monitoring+of+hydraulic+systems.
Citations
1. Аулін В. В., Гриньків А. В., Головатий А. О., Лисенко С. В., Голуб Д. В., Кузик О.В., Тихий А. А. Методологічні основи проектування та функціонування інтелектуальних транспортних і виробничих систем: монографія під заг. ред. д.т.н., проф. Ауліна В.В. Кропивницький: Видавець Лисенко В.Ф., 2020. 428с.
2. Матвієнко О. О., Аулін В. В. Класифікація типів сигналів та методів машинного навчання для інтелектуальної оцінки технічного стану мобільних машин підприємств агропромислового виробництва. Збірник наукових праць. Науковий вісник. Технічні науки. 2025. № 11(42)_ІІ. С. 298–312. DOI: 10.32515/2664-262X.2025.11(42).2.298-312.
3. 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, No.23. 11128. DOI: 10.3390/app112311128.
4. Cheng T., Guo F. Machine anomalous sound detection based on audio synthesis generative adversarial network. Journal of Physics: Conference Series. 2024. Vol. 2816, No. 1. 012041. DOI: 10.1088/1742-6596/2816/1/012041.
5. Khorram A., Khalooei M., Rezghi M. End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis. Applied Intelligence. 2021. Vol. 51, No. 2. P. 736–751. DOI: 10.1007/s10489-020-01859-1.
6. Chegini S. N., Manjili M. J. H., Bagheri A. New fault diagnosis approaches for detecting the bearing slight degradation. Meccanica. 2020. Vol. 55, No. 1. P. 261–286. DOI: 10.1007/s11012-019-01116-x.
7. Brito L. C., Susto G. A., Brito J. N., Duarte M. A. V. Fault detection of bearing: An unsupervised machine learning approach exploiting feature extraction and dimensionality reduction. Informatics. 2021. Vol. 8, No. 4. 85. DOI: 10.3390/informatics8040085.
8. Attaran B., Ghanbarzadeh A., Moradi S. A novel intelligent fault diagnosis approach for critical rotating machinery in the time-frequency domain. International Journal of Engineering. Transactions C: Aspects. 2020. Vol. 33, No. 4. P. 668–675. DOI: 10.5829/ije.2020.33.04a.18.
9. Bouaouiche K., Menasria Y., Khalfa D. Diagnosis of rotating machine defects by vibration analysis. Acta IMEKO. 2023. Vol. 12, No. 1. P. 1–6. DOI: 10.21014/actaimeko.v12i1.1438.
10. Singh M. T. Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration. arXiv preprint. 2025. arXiv:2504.20756. URL: https://arxiv.org/abs/2504.20756.
11. Matania O., Bachar L., Bechhoefer E., Bortman J. Signal processing for the condition-based maintenance of rotating machines via vibration analysis: A tutorial. Sensors. 2024. Vol. 24, No. 2. 454. DOI: 10.3390/s24020454.
12. Zhang B., Zhou C., Li W., Ji S., Li H., Tong Z., Ng S.-K. Intelligent bearing fault diagnosis based on open set convolutional neural network. Mathematics. 2022. Vol. 10, No. 21. 3953. DOI: 10.3390/math10213953.
13. Lessmeier C., Kimotho J. K., Zimmer D., Sextro W. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In: Proceedings of the European Conference of the Prognostics and Health Management Society 2016 (PHME 2016), 5–8 July 2016, Bilbao, Spain. PHM Society, 2016. URL: https://papers.phmsociety.org/index.php/phme/article/view/1577.
14. Saad A., Usman A., Arif S., Liwicki M., Almqvist A. Bearing fault detection scheme using machine learning for condition monitoring applications. In: Proceedings of the International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023), 29–30 April 2023, Dubai, UAE. 2023. URL: https://www.diva-portal.org/smash/get/diva2:1793876/FULLTEXT01.pdf.
15. 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.
16. 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, No. 8. 2661. DOI: 10.3390/s24082661.
17. Abdallah M., Joung B.-G., Lee W. J., Mousoulis C., Raghunathan N., Shakouri A., Sutherland J. W., Bagchi S. Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors. 2023. Vol. 23, No. 1. 486. DOI: 10.3390/s23010486.
18. Ciaburro G., Iannace G. Machine-learning-based methods for acoustic emission testing: A review. Applied Sciences. 2022. Vol. 12, No. 20. 10476. DOI: 10.3390/app122010476.
19. Rahman A., Hoque M. E., Rashid F., Alam F., Ahmed M. M. Health condition monitoring and control of vibrations of a rotating system through vibration analysis. Journal of Sensors. 2022. 2022. Article ID 4281596. DOI: 10.1155/2022/4281596.
20. 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. Journal of Agricultural Machinery Science. 2014. Vol. 10, No. 2. P. 101–106. URL: https://dergipark.org.tr/tr/download/article-file/558529.
21. Elsamanty M. M., Salman W. S., Ibrahim A. A. Fault diagnosis of rotary machines based on vibration signature and machine learning algorithm. Engineering Research Journal (Shoubra). 2021. Vol. 50, No. 1.P. 41–47. DOI: 10.21608/erjsh.2021.225704.
22. Oulmane A., Lakis A. A., Mureithi N. W. Application of Fourier descriptors and artificial neural network to bearing vibration signals for fault detection and classification. Universal Journal of Aeronautical & Aerospace Sciences. 2014. Vol. 2. P. 37–54. URL: https://www.papersciences.com/Oulmane-Univ-J-Aer- Aerosp-Scien-Vol2-2014-3.pdf.
23. Koizumi Y., Kawaguchi Y., Imoto K., Nakamura T., Nikaido Y., Tanabe R., Purohit H., Suefusa K., Endo T., Yasuda M., Harada N. DCASE 2020 Challenge Task 2 Development Dataset. Version 1.0. [Dataset]. Zenodo. 2020. DOI: 10.5281/zenodo.3678171.
24. Kawaguchi Y., Imoto K., Koizumi Y., Harada N., Niizumi D., Dohi K., Tanabe R., Purohit H., Endo T. DCASE 2021 Challenge Task 2 Development Dataset. Version 1.0. [Dataset]. Zenodo. 2021. DOI: 10.5281/zenodo.4562016.
25. National Aeronautics and Space Administration (NASA). IMS Bearings. [Dataset]. NASA Open Data Portal. 2023. URL: https://data.nasa.gov/dataset/ims-bearings (дата звернення: 1.09.2025).
26. Case Western Reserve University Bearing Data Center : веб-сайт. URL: https://engineering.case.edu/bearingdatacenter (дата звернення: 1.09.2025).
27. Cathy Siyu. Mechanical-datasets. [Dataset]. GitHub. URL: https://github.com/cathysiyu/Mechanical- datasets (дата звернення: 1.09.2025).
28. Paderborn University, Chair of Design and Drive Technology (KAt). Bearing Datacenter: Data sets and download. [Dataset]. URL: https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing- datacenter/data-sets-and-download (дата звернення: 1.09.2025).
29. Clark Gable Wang. JNU-Bearing-Dataset. [Dataset]. GitHub. URL: https://github.com/ClarkGableWang/JNU-Bearing-Dataset (дата звернення: 1.09.2025).
30. Prognostics and Health Management Society (PHM Society). Public data sets : веб‑сторінка. URL: https://phmsociety.org/public-data-sets/ (дата звернення: 1.09.2025).
31. Society for Machinery Failure Prevention Technology (MFPT). Fault data sets : веб‑сторінка. URL: https://www.mfpt.org/fault-data-sets/ (дата звернення: 1.09.2025).
32. josh101. Machinery Fault Database — Induction Motor Fault. [Dataset]. Kaggle. URL: www.kaggle.com/datasets/josh101/machinery-fault-database-induction-motor-fault (дата звернення: 1.09.2025).
33. Purdue University. Purdue Motor dataset. [Dataset]. Google Drive. URL: drive.google.com/drive/u/2/folders/1QX3chnSTKO3PsEhi5kBdf9WwMBmOriJ8 (дата звернення: 1.09.2025).
34. UCI Machine Learning Repository. Condition monitoring of hydraulic systems. [Dataset]. University of California, Irvine. URL: archive.ics.uci.edu/dataset/447/condition+monitoring+of+hydraulic+systems (дата звернення: 1.09.2025).
Copyright (©) 2025, Oleksandr Matviienko, Viktor Aulin, Andriy Grynkiv