The number of extracted features for fault diagnosis in rotating machinery can grow considerably due to the large amount of available data collected from different monitored signals. Usually, feature selection or reduction are conducted through several techniques proposing a unique set of representative features for all available classes; nevertheless, in feature selection, it has been recognized that a set of individually good features do not necessarily lead to good classification. This paper proposes a general framework to analyse the feature selection oriented to identify the features that can produce clusters of data with a proper structure. In the first stage, the framework uses Analysis of Variance (ANOVA) combined with Tukey's test for ranking the significant features individually. In the second stage, a further analysis based on inter-cluster and intra-cluster distances, is accomplished to rank subsets of significant features previously identified. In this sense, our contribution aims at discovering the subset of features that are discriminating better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust fault multi-classifiers. Fault severity classification in rolling bearings is studied to test the proposed framework, with data collected from an experimental test bed under real conditions of speed and load on the rotating device.

Although the careers have a curriculum planning, in practice the student takes some additional time to complete his program. In some careers, this delay can be excessive, implying delays and the loss of resources for the student and the institution. This research was carried out with a cohort that began in March 2013 and ended in July 2018, with the aim of longitudinally analyzing the academic progress of the students, and evidencing the gap between the ideal progress and the real progress in each period, and Thus, to define optimization policies for educational management. A faculty was found where its students took 46.6% more than the ideal term; Additionally, those students, whose parents are professionals or who hold high positions, have academic progress that is statistically superior to others. Si bien las carreras cuentan con una planificación de currículo, en la práctica el estudiante demora algún tiempo adicional en cumplir su programa. En algunas carreras esa demora puede ser excesiva, implicando retrasos y la pérdida de recursos para el estudiante y la institución. Esta investigación se realizó con una cohorte que inició en marzo del 2013 y culminó en julio del 2018, con el objetivo de analizar longitudinalmente el progreso académico de los estudiantes, y evidenciar el desfase entre el avance ideal y el avance real en cada período, y así poder definir políticas de optimización de la gestión educativa. Se encontró una facultad donde sus estudiantes demoraron un 46,6% más que el plazo ideal; adicionalmente, aquellos estudiantes, cuyos padres son profesionales o que desempeñan cargos altos, tiene avances académicos estadísticamente superiores a otros.