A Conceptual Implementation Process for Smart Maintenance Technologies
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-52391-5_3
Reference61 articles.
1. Abidi, M. H., Mohammed, M. K., & Alkhalefah, H. (2022). Predictive maintenance planning for industry 4.0 using machine learning for sustainable manufacturing. Sustainability, 14(6), 3387. https://doi.org/10.3390/su14063387
2. Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081. https://doi.org/10.3390/app12168081
3. Ahmad, R., & Kamaruddin, S. (2012a). An overview of time-based and condition-based maintenance industrial application. Computers & Industrial Engineering, 63, 135–149. https://doi.org/10.1016/j.cie.2012.02.002
4. Ahmad, R., & Kamaruddin, S. (2012b). A review of condition-based maintenance decision-making. European J. Industrial Engineering, 6, 519–541. https://doi.org/10.1504/EJIE.2012.048854
5. Ahmer, M., Marklund, P., Gustafsson, M., & Berglund, K. (2022). An implementation framework for condition-based maintenance in a bearing ring grinder. Procedia CIRP, 107, 746–751. https://doi.org/10.1016/j.procir.2022.05.056
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