Machine Learning approach for Predictive Maintenance in Industry 4.0
Abstract
Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpected motor failures and greatly improves the system reliability. This paper describes a Machine Learning architecture for Predictive Maintenance, based on Random Forest approach. The system was tested on a real industry example, by developing the data collection and data system analysis, applying the Machine Learning approach and comparing it to the simulation tool analysis. Data has been collected by various sensors, machine PLCs and communication protocols and made available to Data Analysis Tool on the Azure Cloud architecture. Preliminary results show a proper behavior of the approach on predicting different machine states with high accuracy.
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The effectiveness of PdM systems relies heavily on the analytical capabilities provided by various data mining and machine learning techniques.
“Methodologically, the effectiveness of PdM systems relies heavily on the analytical capabilities provided by various data mining and machine learning techniques.”
From A Robust Conformal Framework for IoT-Based Predictive Maintenance (2025) · cites this paper as Paolanti et al. (2018)
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Ahmed, I., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3146552 · Cited by 853 · see citing works
Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., Paolanti, M., Toscano, G., Ciandrini, G., Carrotta, A., et al. (2020) · Information · doi:10.3390/info11040202 · Cited by 158 · see citing works
Self-citationDesigning a feature selection method based on explainable artificial intelligence
Zacharias, J., von Zahn, M., Chen, J., & Hinz, O. (2022) · Electronic Markets · doi:10.1007/s12525-022-00608-1 · Cited by 109 · see citing works
End-to-End Industrial IoT Platform for Actionable Predictive Maintenance
Christou, I. T., Kefalakis, N., Zalonis, A., Soldatos, J., & Bröchler, R. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.11.028 · Cited by 41 · see citing works
A Robust Conformal Framework for IoT-Based Predictive Maintenance
Moccardi, A., Conte, C., Chandra Ghosh, R., & Moscato, F. (2025) · Future Internet · doi:10.3390/fi17060244 · Cited by 10 · see citing works
Edge AI System Using a Thermal Camera for Industrial Anomaly Detection
Oliveira, V. M., & Moreira, A. H. J. (2022) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06371-8_12 · Cited by 10 · see citing works
- Authors
- Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J.
- Affiliations
- Marche Polytechnic University (IT)
- Uppsala University (SE)
- Venue
- 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-6
- OpenAlex
- W2888844977
- Cited by
- 296
- References
- 0
- Citations by year
- 2026: 26 · 2025: 47 · 2024: 66 · 2023: 57 · 2022: 40 · 2021: 30 · 2020: 17 · 2019: 13
- FWCI
- 22.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion)
- U-PAD Unimc - Open Digital Publications (University of Macerata) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- predictive maintenance, condition monitoring, electric motors, random forest, machine learning, Industry 4.0, sensor data acquisition, PLC, communication protocols, cloud data analysis, Microsoft Azure, motor failure
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