Name
Abstract | ||
|---|---|---|
This research proposes the use of Artificial Neural Networks to diagnose industrial networks communication via Profibus DP Protocol. These diagnostics are based on information provided by the Physical Layer from the Profibus DP Protocol. In order to analyze the physical layer, an Artificial Neural Network first analyzes signal samples transmitted through the industrial network. In case these signals show some deformation, the Artificial Neural Network indicates a possible cause for the problem, after all, problems from Profibus networks generate specific and distinctive standards imprinted on the digital signal wave formats. Before the Artificial Neural Network analysis, the signal was pre-processed through a clipper methodology. The project was validated by data obtained from concrete Profibus networks created in laboratory. The results were satisfactory, proving the great strength and versatility that intelligent computer systems have when applied to the purposes outlined in this work. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/INDIN.2014.6945515 | INDIN |
Keywords | Field | DocType |
signal processing,profibus,topology,neural nets,protocols,artificial neural networks,wave form | Profibus,Digital signal,Real-time computing,Physical layer,Time delay neural network,Engineering,Industrial network,Artificial neural network,Clipper (electronics) | Conference |
ISSN | Citations | PageRank |
1935-4576 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guilherme Serpa Sestito | 1 | 4 | 1.11 |
| Paulo Henrique Toledo de Oliveira e Souza | 2 | 0 | 0.34 |
| Eduardo A. Mossin | 3 | 0 | 0.34 |
| Dennis Brandão | 4 | 23 | 5.57 |
| Andre Luis Dias | 5 | 4 | 1.11 |