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Permutation entropy-based 2D feature extraction for bearing fault diagnosis

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Abstract

Bearing fault diagnosis based on the classification of patterns of permutation entropy is presented in this paper. Patterns of permutation entropy are constructed by using non-uniform embedding of the vibration signal into a delay coordinate space with variable time lags. These patterns are interpreted, processed and classified by employing deep learning techniques based on convolutional neural networks. Computational experiments are used to compare the accuracy of classification with other methods and to demonstrate the efficacy of the presented early defect detection and classification method.

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Acknowledgements

This research was supported by the Research, Development and Innovation Fund of Kaunas University of Technology (project acronym DDetect). This research is also partially supported by International Science and technology cooperation project (Grant No.: BZ2018022).

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Authors and Affiliations

  1. Center for Nonlinear Systems, Kaunas University of Technology, Studentu 50-146, Kaunas, LT, 51368, Lithuania

    Mantas Landauskas & Minvydas Ragulskis

  2. Department of Engineering Mechanics, Hohai University, Hohai, 210098, China

    Maosen Cao

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  1. Mantas Landauskas
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Correspondence to Minvydas Ragulskis.

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Landauskas, M., Cao, M. & Ragulskis, M. Permutation entropy-based 2D feature extraction for bearing fault diagnosis. Nonlinear Dyn 102, 1717–1731 (2020). https://doi.org/10.1007/s11071-020-06014-6

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