Browsing by Author "Cerrada, Mariela"

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Fast feature selection based on cluster validity index applied on data-driven bearing fault detection
(Institute of Electrical and Electronics Engineers Inc., 2020) Cabrera, Diego; Sánchez, René Vinicio; Peña Ortega, Mario Patricio; Cerrada, Mariela
The Prognostics and Health Management (PHM) approach aims to reduce potential failures or machine downtime by determining the system state through the identification of the signals changes produced by the system's faults. Machine learning (ML) approaches for fault diagnosis usually have high-dimensional feature space that can be obtained from signal processing. Nevertheless, as more features are included in the ML algorithms the processing time increases, there is a tendency for overfitting, and the performance may even decrease. Feature selection has multiple goals including building more simple and comprehensible models, improving the performance on ML algorithms, and preparing clean and understandable data. This paper proposes a methodological framework based on a cluster validity index (CVI) and Sequential Forward Search (SFS) to select the best subset of features applied on the problem of fault severity classification in rolling bearing. The results show that a perfect classification can be obtained with KNN with at least six selected features.
Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings
(2018) Peña Ortega, Mario Patricio; Cerrada, Mariela; Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Lucero, Pablo M; Barragán Landy, Milton Francisco; Guamán Guachichullca, Noé Rodrigo; Sánchez, René Vinicio
The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of data. First, the framework uses ANOVA combined with Tukey's test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device. © 2018 - IOS Press and the authors. All rights reserved.
Methods for transforming observable to latent variables of adolescent eating behavior using mathematical tools and social behavior criteria
(Springer, Cham, 2022) Ochoa Avilés, Angélica María; Siguencia, Julio Fernando; Cerrada, Mariela; Cabrera, Diego; Sánchez, René Vinicio
Social factors such as intelligence, attitude, self-esteem (latent variables) are variables that provide relevant information to facilitate the generation of behavioral patterns or models for experts in the social area. However, such information cannot be measured directly, since they are not quantifiable. There are underlying characteristics (observable variables) to the social factors which can be measured directly to the subject of study. For this reason, a methodology based on the transformation of variables using mathematical tools is proposed. The proposed objective is to transform observable variables into hidden variables by three methods; using the arithmetic mean, Euclidean distance and exponential function. Finally, a metric based on EMD distances is applied to evaluate the similarity of the concept resulting from the three transformation methods. The EMD metric allows to evaluate the cost paid for taking one form of distribution to another, in this case the arithmetic mean and exponential function methods generate the lowest cost, that is, there is greater similarity between the distributions of the observable variables and the distribution of the resulting construct.