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Application of Machine Learning Techniques and Spectrum Images of Vibration Orbits for Fault Classification of Rotating Machines

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Abstract

A comparative analysis of machine learning techniques for fault diagnosis of rotating machines based on images of vibration spectra is presented. The feature extraction of different types of faults, including unbalance, misalignment, shaft crack, rotor–stator rubbing, and hydrodynamic instability, is performed by processing spectral images of vibration orbits acquired during the machine run-up. The classifiers are trained with simulated data and tested with both simulated and experimental data. The latter are obtained from laboratory measurements performed on an rotor-disc system supported on hydrodynamic bearings. To generate the simulated data, a numerical model is developed using the finite element method. Deep learning, ensemble and traditional classification methods are evaluated. The ability of the methods to generalize the image classification is evaluated based on their performance in classifying experimental test patterns that were not used during training. The results of this research indicate that, despite considerable computational cost, the method based on convolutional neural networks presents the best performance.

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Acknowledgements

An early version of paper was presented at XXIII Congresso Brasileiro de Automática (CBA 2020). The authors express their appreciation to the Petrobras company for making the experimental test bench available for this research. D. A. Rade also gratefully acknowledges the funding provided by Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP (grant #2015/20363-6).

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Authors and Affiliations

  1. Petróleo Brasileiro S.A. (Petrobras), São José dos Campos, SP, Brazil

    Clayton Eduardo Rodrigues

  2. Division of Electronic Engineering, Instituto Tecnológico de Aeronáutica (ITA), São José Campos, SP, Brazil

    Cairo Lúcio Nascimento Júnior

  3. Division of Mechanical Engineering, Instituto Tecnológico de Aeronáutica (ITA), São José Campos, SP, Brazil

    Domingos Alves Rade

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  1. Clayton Eduardo Rodrigues
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  2. Cairo Lúcio Nascimento Júnior
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  3. Domingos Alves Rade
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Correspondence to Clayton Eduardo Rodrigues.

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Rodrigues, C.E., Júnior, C.L.N. & Rade, D.A. Application of Machine Learning Techniques and Spectrum Images of Vibration Orbits for Fault Classification of Rotating Machines. J Control Autom Electr Syst 33, 333–344 (2022). https://doi.org/10.1007/s40313-021-00805-x

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  • DOI: https://doi.org/10.1007/s40313-021-00805-x

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