Name
Abstract | ||
|---|---|---|
Industrial manufacturing environments are often characterized as being stochastic, dynamic and chaotic, being crucial the implementation of proper maintenance strategies to ensure the production efficiency, since the machines' breakdown leads to a degradation of the system performance, causing the loss of productivity and business opportunities. In this context, the use of emergent ICT technologies, such as Internet of Things (IoT), machine learning and augmented reality, allows to develop smart and predictive maintenance systems, contributing for the reduction of unplanned machines' downtime by predicting possible failures and recovering faster when they occur. This paper describes the deployment of a smart and predictive maintenance system in an industrial case study, that considers IoT and machine learning technologies to support the online and real-time data collection and analysis for the earlier detection of machine failures, allowing the visualization, monitoring and schedule of maintenance interventions to mitigate the occurrence of such failures. The deployed system also integrates machine learning and augmented reality technologies to support the technicians during the execution of maintenance interventions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISIE45063.2020.9152441 | 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE) |
Keywords | DocType | ISSN |
Industrial maintenance,Predictive maintenance,Intelligent Decision Support,Augmented reality | Conference | 2163-5137 |
ISBN | Citations | PageRank |
978-1-7281-5636-1 | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filipe Alves | 1 | 1 | 0.35 |
| Hasmik Badikyan | 2 | 1 | 0.35 |
| António H. J. Moreira | 3 | 9 | 5.55 |
| João Azevedo | 4 | 1 | 0.35 |
| Pedro Miguel Moreira | 5 | 1 | 0.35 |
| Luis F. Romero | 6 | 32 | 8.89 |
| Paulo Leitao | 7 | 102 | 16.48 |