Missing data imputation and fault classification using generative and transfer learning techniques for a wind turbine gearbox with sensor malfunctions

Abstract

Wind turbine gearbox failures significantly contribute to operational downtime and elevated maintenance expenses, necessitating advanced integrated condition monitoring techniques. In practical scenarios, sensor malfunction frequently leads to incomplete acoustic and vibration signals, impeding accurate fault diagnosis. To address this critical challenge, this study proposes an innovative data imputation strategy utilizing generative adversarial imputation networks (GAINs) to effectively reconstruct missing signal data. Acoustic and vibration data were collected from a scaled gearbox setup, replicating multicomponent fault conditions and varying sensor malfunction scenarios with missing data rates from 10 to 50%. Continuous wavelet transform was applied to convert imputed signals from one-dimensional time-series data into two-dimensional spectrograms, enhancing distinct fault feature visualization. Subsequently, fault classification was conducted using pretrained deep learning models, specifically SqueezeNet and DenseNet-201. The proposed GAIN showed a high coefficient of determination (0.98077–0.998253) with Pearson’s correlation coefficients exceeding 0.99 underscoring GAIN’s exceptional capability to preserve original signal variance and maintain the physical relevance of imputed signals. DenseNet-201 consistently outperformed SqueezeNet, achieving a maximum accuracy of 95.205% at 10% missing data and maintaining high accuracy of 92.343% even at 50% data loss. Conversely, SqueezeNet demonstrated accuracy ranging from 91.512 to 87.509% under similar conditions. The proposed methodology effectively integrates data imputation technique with transfer learning-based classification, demonstrating superior robustness, accuracy, and practical applicability in gearbox condition monitoring under real-world sensor malfunction conditions.