Rolling Ball-Bearing Fault Classification Using Variational Mode Decomposition and Footprint of Hilbert Transform

  • Rahul Dubey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)


Vibration mark is a standout among the best approach for analyzing metal ball blame. Different looks into have demonstrated the empirical mode decomposition as one of the base strategies for this reason. Analysis of metal ball blame is one of the testing errands of repair and support division in the ventures. Support vector machine (SVM), K-implies grouping, extreme learning machine (ELM) are couple of late created approaches for the characterization of metal roller blame. In this paper, another approach has been introduced to characterize the moving metal roller blame utilizing variational mode decomposition (VMD) and the impression of the Hilbert changes. In this approach, the crude flag is disintegrated utilizing VMD system, and in the second stage, the impression investigation of Hilbert change alongside the manufactured neural system has been improved the situation and the motivation behind the classification of crude information. The consequence of the proposed approach has additionally been contrasted and the past accessible strategies. The outcome demonstrates that the proposed calculation as better characterization exactness with diminished level of multifaceted nature.


Bearing fault Footprint Hilbert transform Neural network VMD 



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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rahul Dubey
    • 1
  1. 1.Department of ElectronicsMadhav Institute of Technology and ScienceGwaliorIndia

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