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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月14日

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  • Anjil Shah,
  • Vinay Vakharia,
  • Yogesh Kumar,
  • Marcin Woźniak,
  • Muhammad Fazal Ijaz

摘要

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.