Browsing by Author "Cerrada Lozada, Mariela"

Now showing 1 - 3 of 3

Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery
(Institute of Electrical and Electronics Engineers, 2017) Peña Ortega, Mario Patricio; Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Lucero, Pablo; Barragán Landy, Milton Francisco; Guamán Guachichullca, Noé Rodrigo; Cerrada Lozada, Mariela; Peña Ortega, Mario Patricio
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. © 2017 IEEE.
Gearbox fault classification using dictionary sparse based representations of vibration signals
(2018) Medina, Rubén; Jadán Avilés, Diana Carolina; Álvarez Palomeque, Lourdes Ximena; Macancela Poveda, Jean Carlo; Sánchez Loja, René Vinicio; Cerrada Lozada, Mariela
Fault detection in rotating machinery is important for optimizing maintenance chores and avoiding severe damages to other parts. Signal processing based fault detection is usually performed by considering classical techniques for alternative representation of significant signals in time domain, frequency domain or time-frequency domain. An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed. A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed. A dictionary is learned by using a training set of signals from the normal case. This dictionary is used for obtaining the sparse representation of signals in the test set and the norm metric is used to measure the residual from the sparse representation. The extracted features are useful for machine learning based fault detection. The analysis is performed considering different load conditions. ANOVA statistical analysis shows that there are significant differences between features in the normal case and each of the faulty classes, and best ranked features form well separated clusters. An experiment of fault classification is developed using a support vector machine for multi-class classification of faults. The accuracy obtained is 95.1% in the cross-validation testing.
Poincaré plot features from vibration signal for gearbox fault diagnosis
(Institute of Electrical and Electronics Engineers Inc., 2018) Álvarez Palomeque, Lourdes Ximena; Jadán Avilés, Diana Carolina; Cerrada Lozada, Mariela; Sánchez, René Vinicio; Macancela Poveda, Jean Carlo
This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.