Feature engineering based on ANOVA, cluster validity assessment and KNN for fault diagnosis in bearings

Mario Peña, Mariela Cerrada, Ximena Alvarez, Diana Jadán, Pablo Lucero, Barragán Milton, Rodrigo Guamán, René Vinicio Sánchez

Research output: Contribution to conferencePaper

15 Scopus citations


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.

Original languageEnglish
Number of pages12
StatePublished - 1 Jan 2018
EventJournal of Intelligent and Fuzzy Systems - , Netherlands
Duration: 1 Jan 1996 → …


ConferenceJournal of Intelligent and Fuzzy Systems
Period1/01/96 → …


  • bearings
  • cluster validity assessment
  • fault diagnosis
  • Feature engineering
  • KNN


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