http://repositorio.lnec.pt:8080/jspui/handle/123456789/69 2026-10-26T10:07:26Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019981 Fault Detection Algorithm Implementation for Distributed Sensor Networks Nunes, W.; Ferreira, P.; Reis, J. P. NDT.net Abstract This work presents the development of a data-driven digital twin at sensor level for structural health monitoring of a metallic suspension bridge in Portugal, with a focus on local validation of strain sensors using long-term monitoring data acquired between 2014 and 2017. The central challenge lies in the automated distinction between normal environmental variations and the onset of critical sensor faults. Using the Prophet library, the proposed model effectively captured the dominant 12-hour and 24-hour cycles governing bridge behavior. The results demonstrate that integrating external temperature and solar position as predictive variables results in a substantial improvement compared to models based solely on solar radiation, reducing the Mean Absolute Percentage Error to 2.5%. The anomaly detection system, based on a Binomial Hypothesis Test, exhibited high sensitivity, successfully identifying debonding faults as small as 5%. It is concluded that the reliability of the digital twin depends on empirically calibrated control limits, as the theoretical uncertainty intervals provided by the software proved unreliable for this application. Overall, this work establishes a robust methodology for long-term sensor self- validation in critical infrastructure. 2026-05-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019868 Towards the operational demonstration of a horizontal axis radial magnetic levitation bearing with zero-field cooled high temperature superconducting bulks Costa, A.; Fernandes, J.; Branco, P.; Zheng, J. Publicado pela Elsevier B.V. em nome da Universidade Jiaotong de Xangai 2026-03-07T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019842 Synopsis of Special Issue on Superconductors and Magnetic Materials Costa, A.; Fernandes, J.; Rodriguez, E. Abstract This editorial consists of a synopsis of the research in the Special Issue on “Superconductors and Magnetic Materials”, specifying the studies and highlighting main results and conclusions. This collection of research (1) demonstrates the possibility of notably decreasing AC losses by replacing the copper encapsulation of rare Earth barium copper oxide tapes with strong magnetic encapsulation; (2) predicts typical gains expected from soft-magnet and superconductor flux concentrators for low magnetic field sensing; (3) reveals that the n-value surfaces of high-Tc tapes can be estimated with a high accuracy using feed-forward deep neural network learning; (4) predicts the detection of a monopole plasma phase in high-Tc iron-based superconductors with a Tc above 70 K; and (5) proposes an analytical model to accurately predict the gap-to-Tc ratio for yttrium hydrides at high pressures. 2026-03-09T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019773 Melt Pool Monitoring as a Defect Detection Method for Quality Control of AlSi10Mg Parts Produced by PBF-LB/M Ferreira, C.; Ferreira, P.; Marques, J.; Cardoso, P.; Batalha, R.; Valido, A.; Garcês, A.; Reis, L.; Cláudio, R. Abstract Additive Manufacturing (AM), particularly Laser Powder Bed Fusion of Metals (PBF-LB/M), has become a key technology in the aeronautical industry to produce lightweight, high-performance components with complex geometries. However, the quality and reliability of AM parts remain critical concerns due to the potential formation of process-induced defects such as lack of fusion, keyhole, and gas porosity. Ensuring structural integrity requires robust, efficient, and non-destructive quality control methods. This study investigates the use of a Melt Pool Monitoring (MPM) software as a non-destructive defect detection method for AlSi10Mg components manufactured via PBF-LB/M. By analysing thermal emission data captured during the build process, histogram-based profiles were generated and correlated with defects identified through high-resolution X-Ray computed microtomography (µCT). The correlation between both methods validates the MPM capability to detect and identify different forms of defects. Samples were manufactured under both optimal and intentionally varied process parameters to induce different types of defects. The resulting MPM histograms revealed distinct patterns associated with specific morphologies of defects, enabling rapid classification of part quality. This approach demonstrates the potential of MPM as a pragmatic and versatile solution for in situ quality assurance in AM, reducing time-consuming post-production inspections. The findings support the integration of MPM into quality control workflows for aeronautical AlSi10Mg components, contributing to improved process monitoring, defect mitigation, and certification readiness. Furthermore, this methodology lays the groundwork for future implementation of artificial intelligence (AI) models trained on MPM data. By training machine learning (ML) models on MPM derived histogram data and corresponding µCT validation, it is possible to automate the classification of parts as acceptable or defective in real time, streamlining quality control in production. 2026-01-01T00:00:00Z