S
Sebastian Tornil-Sin
Researcher at Spanish National Research Council
Publications - 30
Citations - 601
Sebastian Tornil-Sin is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Fault detection and isolation & Bayesian probability. The author has an hindex of 11, co-authored 30 publications receiving 445 citations. Previous affiliations of Sebastian Tornil-Sin include Polytechnic University of Catalonia.
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Leak localization in water distribution networks using Bayesian classifiers
TL;DR: This paper presents a method for leak localization in water distribution networks (WDNs) based on Bayesian classifiers, which is applied on-line to the computed residuals to determine the location of leaks in the WDN.
Journal ArticleDOI
Leak localization in water distribution networks using a mixed model-based/data-driven approach
Adrià Soldevila,Joaquim Blesa,Sebastian Tornil-Sin,Eric Duviella,Rosa M. Fernandez-Canti,Vicenç Puig +5 more
TL;DR: In this paper, a new method for leak localization in water distribution networks (WDNs) is proposed, where residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model, and a classifier is applied to the residuals with the aim of determining the leak location.
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Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach
Damiano Rotondo,Rosa M. Fernandez-Canti,Sebastian Tornil-Sin,Sebastian Tornil-Sin,Joaquim Blesa,Joaquim Blesa,Vicenç Puig,Vicenç Puig +7 more
TL;DR: In this article, a fault detection test is based on checking the consistency between the measurements and the output estimations provided by the Takagi-Sugeno (TS) interval observers.
Journal ArticleDOI
Leak Localization in Water Distribution Networks using Pressure Residuals and Classifiers
Lise Ferrandez-Gamot,Pierre Busson,Joaquim Blesa,Sebastian Tornil-Sin,Vicenç Puig,Eric Duviella,Adrià Soldevila +6 more
TL;DR: A data-driven approach based on the use of statistical classifiers working in the residual space is proposed for leak localization, trained using leak data scenarios in all the nodes of the network considering uncertainty in demand distribution, additive noise in sensors and leak magnitude.
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Robust Fault Diagnosis of Nonlinear Systems Using Interval Constraint Satisfaction and Analytical Redundancy Relations
TL;DR: A robust fault diagnosis problem for nonlinear systems considering both bounded parametric modeling errors and noise is addressed using parity-equation-based analytical redundancy relations (ARR) and interval constraint satisfaction techniques.