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

Abstract 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.