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Ximena Alvarez
Researcher at University of Cuenca
Publications - 7
Citations - 52
Ximena Alvarez is an academic researcher from University of Cuenca. The author has contributed to research in topics: Fault (power engineering) & Sparse approximation. The author has an hindex of 3, co-authored 7 publications receiving 19 citations.
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Journal ArticleDOI
Mathematical modeling and numerical simulation of sulfamethoxazole adsorption onto sugarcane bagasse in a fixed-bed column.
TL;DR: In this paper, the authors compared analytical models and a theoretical mechanistic model for the dynamic behavior of the sulfamethoxazole adsorption on sugarcane bagasse.
Journal ArticleDOI
Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings
Mario Peña,Mariela Cerrada,Ximena Alvarez,Diana Jadan,Pablo Lucero,Barragán Milton,Rodrigo Guaman,René-Vinicio Sánchez +7 more
TL;DR: This documento propone un marco para la ingenieria de caracteristicas para identificar el conjunto de carcharacteristicas that pueden producir grupos de datos adecuados.
Journal ArticleDOI
Gearbox fault classification using dictionary sparse based representations of vibration signals
Rubén Medina,Ximena Alvarez,Diana Jadan,Jean-Carlo Macancela,René-Vinicio Sánchez,Mariela Cerrada +5 more
Proceedings ArticleDOI
ANOVA and Cluster Distance Based Contributions for Feature Empirical Analysis to Fault Diagnosis in Rotating Machinery
Mario Peña,Ximena Alvarez,Diana Jadan,Pablo Lucero,Milton Barragan,Rodrigo Guaman,Vinicio Sanchez,Mariela Cerrada +7 more
TL;DR: A general framework to analyse the feature selection oriented to identify the features that can produce clusters of data with a proper structure is proposed and aims at discovering the subset of features that are discriminating better the clusters ofData associated to several faulty conditions of the mechanical devices to build more robust fault multi-classifiers.
Proceedings ArticleDOI
Poincaré plot features from vibration signal for gearbox fault diagnosis
Rubén Medina,Ximena Alvarez,Diana Jadan,Mariela Cerrada,René-Vinicio Sánchez,Jean-Carlo Macancela +5 more
TL;DR: This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal, which shows the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.