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Research Article | Open Access |

Bearing Fault Classification based on the Adaptive Orthogonal Transform Method

Author 1: Mohamed Azergui Author 2: Abdenbi Abenaou Author 3: Hassane Bouzahir
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 9, No. 1 · Published 2018

DOI: https://doi.org/10.14569/IJACSA.2018.090151

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Abstract

In this work, we propose an approach based on building an adaptive base which permits to make accurate decisions for diagnosis. The orthogonal adaptive transformation consists of calculating the adaptive operator and the standard spectrum for every state, using two sets of vibration signal records for each type of fault. To classify a new signal, we calculate the spectral vector of this signal in each base. Then, the similarity between this vector and other standard spectra is computed. The experimental results show that the proposed method is very useful for improving the fault detection.

Keywords

Condition monitoring vibration analysis adaptive orthogonal transformation bearing fault

How to Cite this Article

Azergui, M., Abenaou, A., & Bouzahir, H. (2018). Bearing Fault Classification based on the Adaptive Orthogonal Transform Method. International Journal of Advanced Computer Science and Applications, 9(1). https://doi.org/10.14569/IJACSA.2018.090151

Azergui, Mohamed, et al.. "Bearing Fault Classification based on the Adaptive Orthogonal Transform Method." International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, 2018, https://doi.org/10.14569/IJACSA.2018.090151.

@article{Azergui2018,
  title     = {Bearing Fault Classification based on the Adaptive Orthogonal Transform Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Azergui and Abdenbi Abenaou and Hassane Bouzahir},
  doi       = {10.14569/IJACSA.2018.090151},
  url       = {https://doi.org/10.14569/IJACSA.2018.090151}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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