FRVM – Grupos de Investigación - GECaM

Permanent URI for this communityhttp://48.217.138.120/handle/20.500.12272/1676

Browse

Filter results by year or month
Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Fault diagnosis strategy for the current source section of a field-cycling nuclear magnetic resonance instrument
    (IOP Publishing Ltd., 2025-09-12) Velez Ibarra, María Delfina; Vodanovic, Gonzalo; Laprovitta, Agustín Miguel; Peretti, Gabriela Marta; Romero, Eduardo Abel; Anoardo, Esteban
    This paper proposes a fault diagnosis strategy to address catastrophic failures in all power components of the current source of a field-cycling nuclear magnetic resonance (FC-NMR) instrument. The current source, implemented with a single power MOSFET operating in linear mode, is prone to thermal instability and degradation under high-current conditions, posing significant risks to system reliability. Due to the continuous conduction inherent in linear-mode operation, fault signatures in the MOSFET could be subtle and difficult to distinguish from normal operational variations, making diagnostic methods relying on switching transients ineffective in this context. To overcome these limitations, an active fault diagnosis framework is introduced to enhance fault detection and localization. This framework combines test signal injection with data-driven artificial intelligence classifiers. Three algorithms—ResNet, a convolutional neural network (CNN), and a nearest neighbor with dynamic time warping (NN-DTW), used as a benchmark—are evaluated using hybrid datasets derived from simulation program with integrated circuit emphasis (SPICE) simulations and experimental fault injections. The methodology employs time-domain signals measured at key circuit nodes, avoiding computationally intensive preprocessing steps. Simulation and experimental results demonstrate classification accuracies of 100% for ResNet and NN-DTW, and 95.2% for CNN, with prediction times under 20 ms for neural networks. The proposal successfully diagnoses both easy-to-detect faults, validated through simulation, and hard-to-detect faults, confirmed experimentally. The entire fault diagnosis process is completed in under 15 s, making it suitable for in-field monitoring of FC-NMR systems."
  • No Thumbnail Available
    Item
    Evaluación de daño en uniones adhesivas mediante descomposición wavelet de señales acústicas y clasificación con algoritmos de inteligencia artificial
    (Asociación Argentina de Mecánica Computacional, 2025-11-11) Tais, Carlos Esteban; Fontana, Juan Manuel; Molisani, Leonardo; O'Brien, Ronald; Ballesteros Iglesias, Maria Yolanda; del Real, Juan Carlos
    Los adhesivos estructurales son una alternativa a las uniones tradicionales, pero su integridad puede verse afectada por defectos en la aplicación o el curado. Para garantizar su fiabilidad, es esencial aplicar técnicas de evaluación no destructiva (END), donde los métodos acústico-ultrasónicos resultan especialmente útiles. Este trabajo propone un enfoque basado en la descomposición wavelet de señales acústicas para extraer características que permitan, mediante algoritmos de inteligencia artificial, la detección automática de daños en uniones adhesivas. La metodología busca mejorar la precisión en la identificación de fallas y aportar una herramienta eficiente para el monitoreo estructural.
  • No Thumbnail Available
    Item
    A test strategy for a current source designed for fast field-cycling nuclear magnetic resonance
    (IEEE, 2025-12-01) Velez Ibarra, María Delfina; Vodanovic, Gonzalo; Laprovitta, Agustín Miguel; Peretti, Gabriela Marta; Romero, Eduardo Abel; Anoardo, Esteban
    This article presents a novel structural test strategy for a single MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) source designed for Fast Field-Cycling Nuclear Mag-netic Resonance (FFC-NMR) systems. The proposed methodolo-gy enables in-field fault detection during idle intervals or before experiment initiation, a critical step to ensure the reliability and validity of the experimental outcomes. The circuit under test is divided into two sections: low-power and high-power. Each one is evaluated using tailored analog testing techniques: OBT (Oscilla-tion-Based Test) and direct current testing are applied to the low-power section, while transient analysis with DTW (Dynamic Time Warping) is used for fault detection in the high-power section. This approach achieves high fault coverage—93.7% for the low-power section and 100% for the high-power section—without requiring complex signal processing. The effectiveness of the method is validated through simulation studies complemented by experimental fault injection on a scaled-down prototype. The results demonstrate that this test strategy significantly en-hances system reliability, offering a valuable contribution to the development of more robust and maintainable FFC-NMR in-strumentation for scientific and industrial applications.