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- W4386702905 abstract "Rolling bearing failures can lead to machinery breakdowns and loss of control, posing significant operational and safety risks. Traditional algorithms based on the empirical wavelet transform (EWT) face challenges in accurately extracting valid components under variable conditions. Additionally, conventional maxpooling methods often overlook critical features, while fully connected layers tend to overfitting due to excessive parameterization. To address these challenges, an enhanced algorithm is proposed, defined as the improved EWT-improved ResNet (IEWT-IResNet) method. Firstly, the vibration signals are preprocessed to enhance resilience under variable conditions. Secondly, an improved EWT (IEWT) is proposed with an optimal ridge-curve extraction strategy. And it incorporates an efficient weighted energy entropy (EWEE) as a sensitive indicator for signal reconstruction. Additionally, the 1D angular-serial signals are transformed into 2D image matrices using the generalized S-transform. Finally, an optimized residual network model is presented, incorporating singular value decomposition (SVD) pooling and global SVD pooling (GSP) strategies in the pooling and full connection layers. These enhancements benefits the optimal feature extraction while mitigating overfitting. The experimental results demonstrate the superior feature extraction capabilities of the proposed method when processing noisy and nonstationary signals. Furthermore, the effectiveness of the approach is investigated and validated using real-world data from rolling bearings." @default.
- W4386702905 created "2023-09-14" @default.
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- W4386702905 date "2023-01-01" @default.
- W4386702905 modified "2023-10-18" @default.
- W4386702905 title "Intelligent fault diagnosis of rolling bearings based on an improved empirical wavelet transform and ResNet under variable conditions" @default.
- W4386702905 doi "https://doi.org/10.1109/jsen.2023.3313582" @default.
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