Publicação
Multi-agent system for diagnosing defects on a car assembly line
| Resumo: | Traditional approaches to diagnosing geometric defects in automotive assembly lines are based on isolated methods, which have limitations in terms of robustness and early detection of anomalies. This dissertation presents a hierarchical multi-agent architecture for collaborative defect diagnosis, organized into three layers: Point Agents perform local analysis by applying multiple diagnostic algorithms; Station Agents coordinate groups of agents within each station; Inter-Station Agent provides a systemic view by identifying correlations between stations. Coordination uses correlation-based clustering and leader election, enabling efficient aggregation of diagnostics. Communication flows hierarchically and laterally between correlated agents. This organization provides scalability, modularity, and robustness by confining local failures. Experimental validation demonstrates that the collaborative architecture achieves superior accuracy compared to isolated methods, showing that the complementarity between distributed algorithms provides more robust diagnostics and early warning capabilities. |
|---|---|
| Autores principais: | Izidorio, Felipe Merenda |
| Assunto: | Multi-agent systems Distributed architecture Industrial diagnosis Machine learning Industry 4.0 |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | Traditional approaches to diagnosing geometric defects in automotive assembly lines are based on isolated methods, which have limitations in terms of robustness and early detection of anomalies. This dissertation presents a hierarchical multi-agent architecture for collaborative defect diagnosis, organized into three layers: Point Agents perform local analysis by applying multiple diagnostic algorithms; Station Agents coordinate groups of agents within each station; Inter-Station Agent provides a systemic view by identifying correlations between stations. Coordination uses correlation-based clustering and leader election, enabling efficient aggregation of diagnostics. Communication flows hierarchically and laterally between correlated agents. This organization provides scalability, modularity, and robustness by confining local failures. Experimental validation demonstrates that the collaborative architecture achieves superior accuracy compared to isolated methods, showing that the complementarity between distributed algorithms provides more robust diagnostics and early warning capabilities. |
|---|