Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

Diego Cabrera, Fernando Sancho, Chuan Li, Mariela Cerrada, René Vinicio Sánchez, Fannia Pacheco, José Valente de Oliveira

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

52 Citas (Scopus)

Resumen

Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods.

Idioma originalInglés
Páginas (desde-hasta)53-64
Número de páginas12
PublicaciónApplied Soft Computing Journal
Volumen58
DOI
EstadoPublicada - 1 sep. 2017

Nota bibliográfica

Publisher Copyright:
© 2017 Elsevier B.V.

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