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<p>The efficacy of vibration-based fault diagnosis using CNN models could be improved through efficient image extraction from the vibration signals. This study proposed a time-frequency domain vibration image extraction framework based on spectral kurtogram to enhance the classification performance of a Bayesian-tuned CNN model in wind turbine gearbox fault diagnostics. Time-frequency domain kurtograms extracted from vibration signals of a wind turbine gearbox are employed to train a CNN model optimized by Bayesian optimization. Findings of the study show that the kurtogram-based CNN model performed better (recording 171 fewer alarms) than a comparable model trained on images extracted from the time domain vibration signals, highlighting the efficacy of the kurtogram method. In a similar fashion, the Bayesian-optimized CNN model with a classification accuracy of 99.1% recorded 79 fewer false alarms than its standalone counterpart which had an accuracy of 93.2%. It can be concluded that the optimized CNN model trained on Kurtograms is a potential tool for reliable vibration-based condition monitoring of the wind turbine gearbox, minimizing the incidence of false alarms.</p>

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Spectral kurtograms for performance enhancement of Bayesian-tuned CNN in wind turbine gearbox fault diagnostics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月24日

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  • Samuel M. Gbashi,
  • Obafemi O. Olatunji,
  • Paul A. Adedeji,
  • Nkosinathi Madushele

摘要

The efficacy of vibration-based fault diagnosis using CNN models could be improved through efficient image extraction from the vibration signals. This study proposed a time-frequency domain vibration image extraction framework based on spectral kurtogram to enhance the classification performance of a Bayesian-tuned CNN model in wind turbine gearbox fault diagnostics. Time-frequency domain kurtograms extracted from vibration signals of a wind turbine gearbox are employed to train a CNN model optimized by Bayesian optimization. Findings of the study show that the kurtogram-based CNN model performed better (recording 171 fewer alarms) than a comparable model trained on images extracted from the time domain vibration signals, highlighting the efficacy of the kurtogram method. In a similar fashion, the Bayesian-optimized CNN model with a classification accuracy of 99.1% recorded 79 fewer false alarms than its standalone counterpart which had an accuracy of 93.2%. It can be concluded that the optimized CNN model trained on Kurtograms is a potential tool for reliable vibration-based condition monitoring of the wind turbine gearbox, minimizing the incidence of false alarms.