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



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%.

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Correspondence to Alexandre L. A. Mesquita.

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Lobato, T.H.G., da Silva, R.R., da Costa, E.S. et al. An Integrated Approach to Rotating Machinery Fault Diagnosis Using, EEMD, SVM, and Augmented Data. J. Vib. Eng. Technol. 8, 403–408 (2020).

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  • Vibration
  • EEMD
  • SVM
  • Fault diagnosis
  • Augmented data