Anova and cluster distance based contributions for feature empirical analysis to fault diagnosis in rotating machinery

Mario Pena, Ximena Alvarez, Diana Jadan, Pablo Lucero, Milton Barragan, Rodrigo Guaman, Vinicio Sanchez, Mariela Cerrada

Resultado de la investigación: Contribución a una conferenciaDocumento

8 Citas (Scopus)

Resumen

© 2017 IEEE. 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.
Idioma originalInglés
Páginas69-74
Número de páginas6
DOI
EstadoPublicada - 9 dic. 2017
Evento2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 - Shanghai, China
Duración: 16 ago. 201718 ago. 2017

Conferencia

Conferencia2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017
Título abreviadoSDPC 2017
País/TerritorioChina
CiudadShanghai
Período16/08/1718/08/17

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