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.
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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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