1. Thoben, K.-D., Ait-Alla, A., Franke, M., Hribernik, K., Lütjen, M., Freitag, M.: Real-time predictive maintenance based on complex event processing. In: Enterprise Interoperability, pp. 291–296 (2018). 10.1002/9781119564034.ch36
2. Cachada, A., Barbosa, J., Leitno, P., Gcraldcs, C. A. S., Deusdado, L., Costa, J., Romero, L.: Maintenance 4.0: intelligent and predictive maintenance system architecture. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) (2018).
https://doi.org/10.1109/etfa.2018.8502489
3. Bousdekis, A., Lepenioti, K., Ntalaperas, D., Vergeti, D., Apostolou, D., Boursinos, V.: A RAMI 4.0 view of predictive maintenance: software architecture, platform and case study in steel industry. In: Proper, H., Stirna, J. (eds.) Advanced Information Systems Engineering Workshops. CAiSE 2019. Lecture Notes in Business Information Processing, vol. 349. Springer, Cham (2019)
4. Bousdekis, A., Mentzas, G.: Condition-based predictive maintenance in the frame of industry 4.0. In: Lödding, H., Riedel, R., Thoben, K.D., von Cieminski, G., Kiritsis, D. (eds.) Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. APMS 2017. IFIP Advances in Information and Communication Technology, vol. 513. Springer, Cham (2017)
5. Al-Najjar, B., Algabroun, H., Jonsson, H.: Smart maintenance model using cyber physical system. In: International Conference on “Role of Industrial Engineering in Industry 4.0 Paradigm” (ICIEIND), Bhubaneswar, India, September 27–30, pp. 1–6 (2018)