An Integrated Approach to Rotating Machinery Fault Diagnosis Using, EEMD, SVM, and Augmented Data

  • Thiago H. G. Lobato
  • Roger R. da Silva
  • Ednelson S. da Costa
  • Alexandre L. A. MesquitaEmail author
Original Paper



Since reliability and extended service life of rotating machinery are the industries´ major concerns, fault diagnosis systems are constantly being improved, especially by artificial intelligence methods. Current paper proposes a diagnostic method integrating stationary and non-stationary signal processing techniques, selection of multiple attributes, and classification by machine-learning algorithm. The technique was applied to a small number of measured signals.


The integrated method uses the ensemble empirical mode decomposition (EEMD) (which handles nonlinear and non-stationary data) for signal processing, and the support vector machine (SVM) for the classification of the machinery condition with a small number of signals. Augmented data and feature selection with a genetic algorithm are used to improve the accuracy of the analysis.

Results and Conclusions

Evaluation was obtained by vibration signals from a rotor test rig with different types of faults. Experimental results showed that the proposed method successfully identifies the rotor´s faults with accuracy of 95.19%.


Vibration EEMD SVM Fault diagnosis Augmented data 



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Copyright information

© Krishtel eMaging Solutions Private Limited 2019

Authors and Affiliations

  1. 1.Federal University of Pará, Institute of TechnologyBelém-PABrazil

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