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Issues
March 2025
In Progress
ISSN 2332-9017
EISSN 2332-9025
In this Issue
Special Papers
A Novel Unsupervised Domain Adaptation Transformation Reconstructed Gated Recurrent Unit Framework Considering Prediction Uncertainty for Machinery Prognostics Under Variable Lubrication Conditions
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011101.
doi: https://doi.org/10.1115/1.4065753
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
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011102.
doi: https://doi.org/10.1115/1.4065754
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
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011103.
doi: https://doi.org/10.1115/1.4065755
Topics:
Damage
,
Gates (Closures)
,
Sensors
,
Generators
Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011104.
doi: https://doi.org/10.1115/1.4066054
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
ASME J. Risk Uncertainty Part B. March 2025, 11(1): 011105.
doi: https://doi.org/10.1115/1.4065845
Topics:
Corrosion
,
Simulation
,
Artificial neural networks
Select Articles from Part A: Civil Engineering
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The ASME Ayyub-Wiechel Risk Analysis Award
ASME J. Risk Uncertainty Part B (December 2024)
Uncertainty Quantification In The Prediction of Remaining Useful Life Considering Multiple Failure Modes
ASME J. Risk Uncertainty Part B
A data driven black box approach for the inverse quantification of set-theoretical uncertainty
ASME J. Risk Uncertainty Part B
Identification of crashworthy designs combining active learning and the solution space methodology
ASME J. Risk Uncertainty Part B
