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SA-ConSinGAN and reservoir computing fusion for accurate bearing fault classification and severity identification using GAF-based techniques
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  • Published: 14 February 2026

SA-ConSinGAN and reservoir computing fusion for accurate bearing fault classification and severity identification using GAF-based techniques

  • Anjil Shah1,
  • Vinay Vakharia1,
  • Yogesh Kumar2,
  • Marcin Woźniak3 &
  • …
  • Muhammad Fazal Ijaz4 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

In the present study, five reservoir computing models are compared and analyzed for bearing fault classification and severity level identification. Three Gramian Angular Field (GAF) methodologies, such as Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Robust Gramian Angular Summation Field (RGASF) images, were applied to generate various faulty condition images, and Deep Echo State Network (Deep-ESN), Liquid State Machine (LSM), Neuron-Astrocyte Liquid State Machine (NALSM), Random Vector Functional Link (RVFL), and Echo State Network (ESN) were trained and cross-validated. Due to the limited availability of the fault dataset, SA-ConSinGAN was employed to increase the number of transformed images of different bearing fault conditions. Ten-fold cross-validation was used to evaluate the performance of the reservoir models with correct identifications of bearing faults and severity levels. It is noticed that the combination of RVFL+GADF gave 100% accuracy in fault classifications, and fault severity level S3 achieved 100% accuracy with RVFL+GADF combinations. Also, RVFL+GASF and RVFL+RGASF combinations reached 99.96% fault classification accuracy. The results brought out the importance of GAF-based image processing techniques and SA-ConSinGAN along with reservoir computing models in predictive maintenance systems for fault diagnosis.

Data availability

Authors have used a publicly available bearing dataset from Case Western Reserve University. Link: https://engineering.case.edu/bearingdatacenter.

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Acknowledgements

The authors would like to acknowledge the, support given by PDEU Gandhinagar for providing the necessary experimental facilities for conducting research.

Funding

Authors would like to acknowledge contribution to this research from the Rector of the Silesian University of Technology, Gliwice, Poland under proquality grant no.09/020/RGJ26/0053.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, School of Technology, PDEU Gandhinagar, Gandhinagar, Gujarat, 382426, India

    Anjil Shah & Vinay Vakharia

  2. Department of Computer Science and Engineering, School of Technology, PDEU Gandhinagar, Gandhinagar, Gujarat, 382426, India

    Yogesh Kumar

  3. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland

    Marcin Woźniak

  4. School of Technology, Business and Hospitality Faculty, Torrens University Australia, Campus Flinders, Melbourne, VIC, 3000, Australia

    Muhammad Fazal Ijaz

Authors
  1. Anjil Shah
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  2. Vinay Vakharia
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Contributions

Conceptualization, A.S., V.V., Y.K.,; Methodology, A.S., V.V., Y.K., W.K and M.F.I.; software, A.S., V.V., Y.K., W.K., and M.F.I.; Validation Y.K., M.F.I, and W.K., Formal analysis A.S., V.V.,; Investigation, W.K., and M.F.I.; Resources, W.K., M.F.I., Data curation, A.S., V.V., Y.K., writing—original draft preparation, A.S., V.V., Y.K.,; writing—review and editing, A.S., V.V., Y.K., W.K., and M.F.I.; visualization, W.K., and M.F.I.;., Supervision V.V., M.F.I., and Y.K., and W.K.; Project administration, V.V., and W.K, and M.F.I; Funding acquisition, W.K., M.F.I.; All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Vinay Vakharia, Marcin Woźniak or Muhammad Fazal Ijaz.

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The authors declare no competing interests.

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Shah, A., Vakharia, V., Kumar, Y. et al. SA-ConSinGAN and reservoir computing fusion for accurate bearing fault classification and severity identification using GAF-based techniques. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39807-7

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  • Received: 06 December 2025

  • Accepted: 09 February 2026

  • Published: 14 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39807-7

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Keywords

  • Reservoir computing
  • Random vector functional link
  • SA-ConSinGAN
  • Robust gramian angular summation field
  • Fault diagnosis
  • Fault severity classification
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