Publisher
Springer Nature Switzerland
Reference22 articles.
1. Li, Z., Wang, K., He, Y.: Industry 4.0-potentials for predictive maintenance. In: 6th International Workshop of Advanced Manufacturing and Automation, pp. 42–46. Atlantis Press (2016)
2. Wang, K.: Intelligent predictive maintenance (IPdM) system–Industry 4.0 scenario. WIT Trans. Eng. Sci. 113, 259–268 (2016)
3. Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in Industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6. IEEE (2018)
4. Limble CMMS [WWW Document] (2021). Limble. https://limblecmms.com/predictivemaintenance/benefits-of-predictive-maintenance/. Accessed 22 Nov 2021
5. Fioravanti, R., Kumar, K., Nakata, S., Chalamala, B., Preger, Y.: Predictive-maintenance practices: for operational safety of battery energy storage systems. IEEE Power Energ. Mag. 18(6), 86–97 (2020). https://doi.org/10.1109/MPE.2020.3014542
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