Hybrid Data-Driven and Knowledge-Based Predictive Maintenance Framework in the Context of Industry 4.0-Reference-Cited by-同舟云学术

Hybrid Data-Driven and Knowledge-Based Predictive Maintenance Framework in the Context of Industry 4.0

Published:2023-12-22 Issue: Volume: Page:319-337
ISSN:0302-9743
Container-title:Model and Data Engineering
language:
Short-container-title:

Author:

Abdelillah Fidma Mohamed^ORCID,Nora Hamour^ORCID,Samir Ouchani^ORCID,Sidi-Mohammed Benslimane^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-49333-1_23

Reference26 articles.

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2. Cao, Q., et al.: Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach. Cybern. Syst. 50, 1–15 (2019)

3. Cao, Q., et al.: Combining evidential clustering and ontology reasoning for failure prediction in predictive maintenance. In: ICAART (2), pp. 618–625 (2020)

4. Cao, Q., Samet, A., Zanni-Merk, C., de Bertrand de Beuvron, F., Reich, C.: An ontology-based approach for failure classification in predictive maintenance using fuzzy c-means and SWRL rules. Procedia Comput. Sci. 159, 630–639 (2019)

5. Dalenogare, L.S., Benitez, G.B., Ayala, N.F., Frank, A.G.: The expected contribution of industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 204, 383–394 (2018). ISSN 0925–5273

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