Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM

Rubén Medina, Jean Carlo Macancela, Pablo Lucero, Diego Cabrera, René Vinicio Sánchez, Mariela Cerrada

Research output: Contribution to journalArticlepeer-review

Abstract

This paper describes two algorithms for feature extraction from the Poincaré plot which is constructed with the vibration signals measured in roller bearings and gearboxes. The extracted features are used for classifying 10 types of fault conditions in a gearbox and 7 types of fault conditions a roller bearings. Both vibration signal datasets were acquired at different loads and speeds. The feature extraction using Algorithm 1 performs the feature calculation from the Poincaré plot constructed with the raw vibration signals. In contrast, the Algorithm 2 requires an additional stage where the vibration signal is pre-processed for identifying the peaks of the signal. This peak sequence is equivalent to a non-uniform sub-sampling of the vibration signal that retains relevant information useful for fault classification. The fault classification is attained by using a multi-class Support Vector Machine. The proposed method is tested using the tenfold cross-validation. Results show that both algorithms could attain classification accuracies as high as 99.3% for the gearbox dataset and 100% for the roller bearings. The results are compared to other classification approaches performed on the same datasets by using other different features. The comparison shows that the approach in this paper has a performance as good as obtained by using well-known statistical features.

Original languageEnglish
JournalJournal of Intelligent Manufacturing
DOIs
StateAccepted/In press - 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Bearing fault diagnosis
  • Gearbox fault diagnosis
  • Poincaré plot
  • Rotating machinery
  • Support vector machine
  • Vibration signals

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