Abstract
Industry suffers from many machine-related problems, such as breakdown, failures, personnel safety, and management cost. Predictive maintenance is an industrial and research area that is permeating goods and services production systems, aimed at preventing critical issues in machinery and workplaces, and reducing the costs in terms of resources, time and money caused by incoming risk events that can slow or even stop the production. This paper presents TD4 a Big Data IoT architecture able to: (i) acquire huge amounts of data from real-time sensor streams, (ii) analyze and prepare the data, scaling over a network of distributed working nodes, (iii) perform real-time fault prediction. Experiments on well-known benchmarks show the applicability of the proposed architecture on different real scenarios.
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The execution environment is a single computer node with 12 Intel Xeon Gold 6136 CPUs and 32 GB of RAM memory.
References
Alfeo, A.L., Cimino, M.G., Manco, G., Ritacco, E., Vaglini, G.: Using an autoencoder in the design of an anomaly detector for smart manufacturing. Pattern Recogn. Lett. 136, 272–278 (2020)
Groba, C., Cech, S., Rosenthal, F., Gossling, A.: Architecture of a predictive maintenance framework. In: 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2007), pp. 59–64 (2007). https://doi.org/10.1109/CISIM.2007.14
Gustafson, J.L.: Gustafson’s Law, pp. 819–825. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-09766-4_78
Killeen, P., Ding, B., Kiringa, I., Yeap, T.: IoT-based predictive maintenance for fleet management. Procedia Comput. Sci. 151, 607–613 (2019). https://doi.org/10.1016/j.procs.2019.04.184, www.sciencedirect.com/science/article/pii/S1877050919306519, the 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019)/The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019)/Affiliated Workshops
Lee, J., Qiu, J., Yu, G., Lin, J.: Rexnord technical services: bearing data set (2007). https://ti.arc.nasa.gov/project/prognostic-data-repository, NASA Ames Prognostics Data Repository
Motaghare, O., Pillai, A.S., Ramachandran, K.: Predictive maintenance architecture. In: 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4 (2018). https://doi.org/10.1109/ICCIC.2018.8782406
Sahba, R., Radfar, R., Rajabzadeh Ghatari, A., Pour Ebrahimi, A.: Development of industry 4.0 predictive maintenance architecture for broadcasting chain. Adv. Eng. Inform. 49(C) (2021). https://doi.org/10.1016/j.aei.2021.101324
Salierno, G., Morvillo, S., Leonardi, L., Cabri, G.: An architecture for predictive maintenance of railway points based on big data analytics. In: Dupuy-Chessa, S., Proper, H.A. (eds.) CAiSE 2020. LNBIP, vol. 382, pp. 29–40. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49165-9_3
Saxena, A., Goebel, K.: Turbofan engine degradation simulation data set (2008). https://ti.arc.nasa.gov/project/prognostic-data-repository, NASA Ames Prognostics Data Repository
Çoban, S., Gökalp, M.O., Gökalp, E., Eren, P.E., Koçyiğit, A.: Predictive maintenance in healthcare services with big data technologies. In: 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), pp. 93–98 (2018). https://doi.org/10.1109/SOCA.2018.00021
Acknowledgement
This paper has been partially supported by the project “True Detective 4.0: Strumenti e Servizi Intelligenti di Monitoraggio in Tempo Reale per la Manutenzione Predittiva di apparati, per l’Ottimizzazione dei Processi Produttivi e di Automazione Industriale e per la Gestione della Sicurezza Fisica in Ambito Aziendale" funded by the Ministry of Economic Development (MISE), project code number F/190105/01-03/X44. Terms and conditions enforced by the project regulation do not allow us to make public the source code of the software platform.
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Argento, L., De Francesco, E., Lambardi, P., Piantedosi, P., Romeo, C. (2022). TrueDetective 4.0: A Big Data Architecture for Real Time Anomaly Detection. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_43
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