A Decision-Theoretic Approach for Prioritizing Maintenance Activities in Organic Computing Systems

Author:

Görlich-Bucher Markus,Heider Michael,Ciemala Tobias,Hähner Jörg

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Aussel, N., Jaulin, S., Gandon, G., Petetin, Y., Fazli, E., Chabridon, S.: Predictive models of hard drive failures based on operational data. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 619–625. IEEE (2017)

2. Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcalá, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)

3. Görlich, M., Stein, A., Hähner, J.: Towards physical disturbance robustness in organic computing systems using MOMDPs. In: ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems, pp. 1–4. VDE (2019)

4. Görlich-Bucher, M.: Dealing with hardware-related disturbances in organic computing systems. In: INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik-Informatik für Gesellschaft (Workshop-Beiträge). Gesellschaft für Informatik eV (2019)

5. Hardt, F., Kotyrba, M., Volna, E., Jarusek, R.: Innovative approach to preventive maintenance of production equipment based on a modified TPM methodology for industry 4.0. Appl. Sci. 11(15), 6953 (2021)

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