Topics: Bearings , Errors , Lubrication , Machinery , Uncertainty , Signals , Dimensions , Rolling bearings , Ablation (Vaporization technology) , Artificial neural networks

Innovative Bearing Fault Diagnosis Method: Combining Swin Transformer Deep Learning and Acoustic Emission Technology

Peng Jiang, Jinlei Xia, Wei Li, Chenqi Xu, Wenyu Sun

ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011102. doi: https://doi.org/10.1115/1.4065754

Abstracts

View articletitled, Innovative Bearing Fault Diagnosis Method: Combining Swin Transformer Deep Learning and Acoustic Emission Technology

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Topics: Bearings , Fault diagnosis , Acoustic emissions , Deep learning , Signals , Filters

Data Augmentation Based on Image Translation for Bayesian Inference-Based Damage Diagnostics of Miter Gates

Yichao Zeng, Jice Zeng, Michael D. Todd, Zhen Hu

ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011103. doi: https://doi.org/10.1115/1.4065755

Abstracts

View articletitled, Data Augmentation Based on Image Translation for Bayesian Inference-Based Damage Diagnostics of Miter Gates

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Topics: Damage , Gates (Closures) , Sensors , Generators

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

David A. Najera-Flores, Justin Jacobs, D. Dane Quinn, Anthony Garland, Michael D. Todd

ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011104. doi: https://doi.org/10.1115/1.4066054

Abstracts

View articletitled, Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

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Topics: Algorithms , Artificial neural networks , Damage , Damping , Digital twin , Dynamic response , Machine learning , Preservation , Structural health monitoring , Testing

Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

Guofeng Qian, Jice Zeng, Zhen Hu, Michael D. Todd

ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011105. doi: https://doi.org/10.1115/1.4065845

Abstracts

View articletitled, Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

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Topics: Corrosion , Simulation , Artificial neural networks

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Special Papers

A Novel Unsupervised Domain Adaptation Transformation Reconstructed Gated Recurrent Unit Framework Considering Prediction Uncertainty for Machinery Prognostics Under Variable Lubrication Conditions

Innovative Bearing Fault Diagnosis Method: Combining Swin Transformer Deep Learning and Acoustic Emission Technology

Data Augmentation Based on Image Translation for Bayesian Inference-Based Damage Diagnostics of Miter Gates

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

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Special Papers

A Novel Unsupervised Domain Adaptation Transformation Reconstructed Gated Recurrent Unit Framework Considering Prediction Uncertainty for Machinery Prognostics Under Variable Lubrication Conditions

Innovative Bearing Fault Diagnosis Method: Combining Swin Transformer Deep Learning and Acoustic Emission Technology

Data Augmentation Based on Image Translation for Bayesian Inference-Based Damage Diagnostics of Miter Gates

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

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