8
REFERENCES
[1] Mine.io website, https://mineio-horizon.eu/ (last
accessed 05.11.2024).
[2] “D2.3 Functional and Non-Functional Requirements
in the Digitisation of the Mining Sector – Version 2,”
Mine.io Project Deliverable (2024), will be published
after project review at https://mineio-horizon.eu/.
[3] Sharma, N. R., Mishra, A. K., &Jain, S. (2022).
OEE improvement of mining shovels by survival
analysis and linear optimisation as per sustainable
development goals. International Journal of Mining,
Reclamation and Environment, 36(5), 323–355.
[4] Kruczek, P., Gomolla, N., Hebda-Sobkowicz,
J., Michalak, A., Śliwiński, P., Wodecki, J., ...&
Zimroz, R. (2019). Predictive maintenance of mining
machines using advanced data analysis system based
on the cloud technology. In Proceedings of the 27th
International Symposium on Mine Planning and
Equipment Selection-MPES 2018 (pp. 459–470).
Springer International Publishing.
[5] Dayo-Olupona, O., Genc, B., Celik, T., &Bada, S.
(2023). Adoptable approaches to predictive mainte-
nance in mining industry: An overview. Resources
Policy, 86, 104291.
[6] Bousdekis, A., Apostolou, D., and Mentzas, G.
(2020). Predictive Maintenance in the 4th Industrial
Revolution: Benefits, Business Opportunities,
and Managerial Implications. IEEE Engineering
Management Review, 48:1, 57–62.
[7] Lepenioti, K., Fikardos, M., Bousdekis, A., Apostolou,
D., and Mentzas, G. (2024). Autonomous Analytics
as a Service: From Vision to Action. Enterprise
Interoperability X: Enterprise Interoperability
through Data, AI, and Robotics, Proceedings of
I-ESA 2024. (in press).
[8] Ferreira, L., Pilastri, A., Sousa, V., Romano, F., &
Cortez, P. (2021). Prediction of maintenance equip-
ment failures using automated machine learning.
In International Conference on Intelligent Data
Engineering and Automated Learning (pp. 259–
267). Cham: Springer International Publishing.
[9] Barnewold, L., Lottermoser, B. G. (2020).
Identification of digital technologies and digitalisa-
tion trends in the mining industry. In International
Journal of Mining Science and Technology Volume
30, Issue 6 (pp. 747 – 757). https://www.science
direct.com/sci-ence/article/pii/S20952686
20300744. (last accessed 13.11.2024).
[10] Agboola, O., Babatunde, D. E., Fayomi, O. S.
I., Sadiku, E. R., Popoola, P., Moropeng, L., ...&
Mamudu, O. A. (2020). A review on the impact of
mining operation: Monitoring, assessment and man-
agement. Results in Engineering, Volme 8, 100181.
https://www.sciencedirect.com/science/article/pii
/S259012302030089X (last accessed 13.11.2024).
[11] Frederick, B., (2023), Mining innovation:
Underground mining visibility, locating assets and
protecting people. https://blogs.cisco.com/industrial
-iot/mining-innovation-underground-mining
-visibility-locating-assets-and-protecting-people (last
accessed 16.08.2024).
[12] Schunnesson, H., (1998). Rock characterisation
using percussive drilling: Int J Rock Mech Min Sc
35:6 pp. 711–725
REFERENCES
[1] Mine.io website, https://mineio-horizon.eu/ (last
accessed 05.11.2024).
[2] “D2.3 Functional and Non-Functional Requirements
in the Digitisation of the Mining Sector – Version 2,”
Mine.io Project Deliverable (2024), will be published
after project review at https://mineio-horizon.eu/.
[3] Sharma, N. R., Mishra, A. K., &Jain, S. (2022).
OEE improvement of mining shovels by survival
analysis and linear optimisation as per sustainable
development goals. International Journal of Mining,
Reclamation and Environment, 36(5), 323–355.
[4] Kruczek, P., Gomolla, N., Hebda-Sobkowicz,
J., Michalak, A., Śliwiński, P., Wodecki, J., ...&
Zimroz, R. (2019). Predictive maintenance of mining
machines using advanced data analysis system based
on the cloud technology. In Proceedings of the 27th
International Symposium on Mine Planning and
Equipment Selection-MPES 2018 (pp. 459–470).
Springer International Publishing.
[5] Dayo-Olupona, O., Genc, B., Celik, T., &Bada, S.
(2023). Adoptable approaches to predictive mainte-
nance in mining industry: An overview. Resources
Policy, 86, 104291.
[6] Bousdekis, A., Apostolou, D., and Mentzas, G.
(2020). Predictive Maintenance in the 4th Industrial
Revolution: Benefits, Business Opportunities,
and Managerial Implications. IEEE Engineering
Management Review, 48:1, 57–62.
[7] Lepenioti, K., Fikardos, M., Bousdekis, A., Apostolou,
D., and Mentzas, G. (2024). Autonomous Analytics
as a Service: From Vision to Action. Enterprise
Interoperability X: Enterprise Interoperability
through Data, AI, and Robotics, Proceedings of
I-ESA 2024. (in press).
[8] Ferreira, L., Pilastri, A., Sousa, V., Romano, F., &
Cortez, P. (2021). Prediction of maintenance equip-
ment failures using automated machine learning.
In International Conference on Intelligent Data
Engineering and Automated Learning (pp. 259–
267). Cham: Springer International Publishing.
[9] Barnewold, L., Lottermoser, B. G. (2020).
Identification of digital technologies and digitalisa-
tion trends in the mining industry. In International
Journal of Mining Science and Technology Volume
30, Issue 6 (pp. 747 – 757). https://www.science
direct.com/sci-ence/article/pii/S20952686
20300744. (last accessed 13.11.2024).
[10] Agboola, O., Babatunde, D. E., Fayomi, O. S.
I., Sadiku, E. R., Popoola, P., Moropeng, L., ...&
Mamudu, O. A. (2020). A review on the impact of
mining operation: Monitoring, assessment and man-
agement. Results in Engineering, Volme 8, 100181.
https://www.sciencedirect.com/science/article/pii
/S259012302030089X (last accessed 13.11.2024).
[11] Frederick, B., (2023), Mining innovation:
Underground mining visibility, locating assets and
protecting people. https://blogs.cisco.com/industrial
-iot/mining-innovation-underground-mining
-visibility-locating-assets-and-protecting-people (last
accessed 16.08.2024).
[12] Schunnesson, H., (1998). Rock characterisation
using percussive drilling: Int J Rock Mech Min Sc
35:6 pp. 711–725