Fast feature selection based on cluster validity index applied on data-driven bearing fault detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publication2020 IEEE ANDESCON, ANDESCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728193656
StatePublished - 13 Oct 2020
Event2020 IEEE ANDESCON, ANDESCON 2020 - Quito, Ecuador
Duration: 13 Oct 202016 Oct 2020

Publication series



Conference2020 IEEE ANDESCON, ANDESCON 2020

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This paper is supported by the Research Department of the University of Cuenca (DIUC) and by the research group GIDTEC of the Universidad Politécnica Salesiana, sede Cuenca-Ecuador.

Publisher Copyright:
© 2020 IEEE.


  • Bearings
  • Classification
  • Cluster validity index
  • Fault detection
  • Feature selection


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