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Belarmino Pulido
Researcher at University of Valladolid
Publications - 53
Citations - 763
Belarmino Pulido is an academic researcher from University of Valladolid. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 15, co-authored 52 publications receiving 702 citations. Previous affiliations of Belarmino Pulido include Vanderbilt University.
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Possible conflicts: a compilation technique for consistency-based diagnosis
TL;DR: The possible conflict concept is proposed as a compilation technique for consistency-based diagnosis and its relation to conflicts in the general diagnosis engine (GDE) framework is analyzed and compared with other compilation techniques.
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Minimal Structurally Overdetermined sets for residual generation: A comparison of alternative approaches
Joaquim Armengol,Anibal Bregon,Teresa Escobet,Esteban R. Gelso,Mattias Krysander,Mattias Nyberg,Xavier Olive,Belarmino Pulido,Louise Travé-Massuyès +8 more
TL;DR: In this article, the issue of residual generation using structural analysis has been studied by several authors, and four recently proposed algorithms that solve this problem are presented and compared, and compared with each other.
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Diagnosability Analysis Considering Causal Interpretations for Differential Constraints
TL;DR: A model characterization and corresponding algorithms are developed for studying system diagnosability using a structural decomposition that avoids generating the full set of system analytical redundancy relations.
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An event-based distributed diagnosis framework using structural model decomposition
Anibal Bregon,Matthew Daigle,Indranil Roychoudhury,Gautam Biswas,Xenofon Koutsoukos,Belarmino Pulido +5 more
TL;DR: Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, a distributed diagnoser design algorithm is developed to build local event-based diagnosers that are constructed based on global diagnosability analysis of the system.
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State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems
TL;DR: It is proved that the structure of the Minimal Evaluable Model for a Possible Conflict can be used in real-world industrial systems to guide the design of the state space model of the neural network, reducing its complexity and avoiding the process of multiple unknown parameter estimation in the first principles models.