DEFESA DE DISSERTAÇÃO DE MESTRADO Nº 319
Aluno: Hugo Nascimento Leite
Título: “Early Fault Detection and Diagnosis in Industrial Machines. An Approach Using Digital Twins"
Orientador: Diego José Rátiva Millán
Examinador Externo: Rodrigo de Paula Monteiro (UPE)
Examinador Interno: João Fausto Lorenzato de Oliveira
Data-hora: 19 de março de 2025, às 10h.
Local: Formato presencial - Sala de Atos.
Resumo:
"The emergence of Industry 4.0 has catalyzed the integration of advanced technologies to enhance manufacturing efficiency, reliability, and competitiveness. Fault Detection and Diagnosis (FDD) systems are critical for minimizing downtime and ensuring operational continuity. This research investigates the integration of Digital Twin (DT) technology with Machine Learning (ML) models for real-time fault detection and diagnosis (RT-FDD) in discrete manufacturing machines. Two industrial systems—a Pick-and-Place machine and a Furnace—were modeled using linear and non-linear models to develop Digital Twins. By combining DT-generated features with conventional real-time process data, the proposed approach improved F1 scores by up to 11% and demonstrated enhanced robustness in both inter-cycle and intra-cycle fault detection tasks. Notably, for the Furnace machine, the method enabled fault detection 40% earlier in the cycle while maintaining the same F1 Score of 94%, and provided reliable diagnosis with an F1 Score of 80% at only 15% of cycle completion. A comprehensive evaluation of 16 ML algorithms highlighted the effectiveness of DT features in boosting predictive performance. The results underscore the potential of DT-enhanced ML models for predictive maintenance, reducing inefficiencies, and advancing smart manufacturing systems."
O Programa
