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Issues
March 2017
ISSN 2332-9017
EISSN 2332-9025
In this Issue
Research Papers
Single-Machine Infinite-Bus Power System Excitation Control Design With Resilient Extended Kalman Filter
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011001.
doi: https://doi.org/10.1115/1.4034018
Topics:
Design
,
Errors
,
Excitation
,
Kalman filters
,
Power systems (Machinery)
,
Failure
,
Machinery
,
Sensors
How to Take Into Account Model Inaccuracy When Estimating the Uncertainty of the Result of Data Processing
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011002.
doi: https://doi.org/10.1115/1.4034450
Topics:
Algorithms
,
Uncertainty
Probabilistic Modeling of Pitting Corrosion in Insulated Components Operating in Offshore Facilities
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011003.
doi: https://doi.org/10.1115/1.4034603
Topics:
Corrosion
,
Density
,
Insulation
,
Modeling
,
Ocean engineering
,
Failure
,
Inspection
Plastic Collapse Behaviors of Tubulars with Recess Patterns—Application in Hollow Carrier Perforating Guns
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011004.
doi: https://doi.org/10.1115/1.4034660
Topics:
Collapse
,
Finite element analysis
Resilient and Robust Control of Time-Delay Wind Energy Conversion Systems
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011005.
doi: https://doi.org/10.1115/1.4034661
Topics:
Actuators
,
Delays
,
Design
,
Disks
,
Generators
,
Robust control
,
Uncertainty
,
Wind energy
,
Wind turbines
,
Stability
Imprecise Probabilities in Fatigue Reliability Assessment of Hydraulic Turbines
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011006.
doi: https://doi.org/10.1115/1.4034690
Topics:
Probability
,
Reliability
Time-Dependent Reliability Analysis Using a Vine-ARMA Load Model
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 011007.
doi: https://doi.org/10.1115/1.4034805
Topics:
Event history analysis
,
Modeling
,
Stochastic processes
,
Stress
,
Simulation
,
Rivers
Technical Brief
Reliability Analysis of Repairable System With Multiple-Input and Multi-Function Component Based on Goal-Oriented Methodology
ASME J. Risk Uncertainty Part B. March 2017, 3(1): 014501.
doi: https://doi.org/10.1115/1.4034744
Topics:
Event history analysis
,
Signals
,
Probability
Select Articles from Part A: Civil Engineering
Technical Papers
Development of Flood Inundation Maps and Quantification of Flood Risk in an Urban Catchment of Brahmaputra River
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2017, 3(1): A4015001.
doi: https://doi.org/10.1061/AJRUA6.0000822
Probabilistic Assessment of Debris Flow Peak Discharge by Monte Carlo Simulation
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2017, 3(1): A4015002.
doi: https://doi.org/10.1061/AJRUA6.0000855
Topics:
Flow (Dynamics)
,
Simulation
Patterns in Climate-Related Parameters as Proxy for Rainfall Deficiency and Aridity: Application to Burkina Faso
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2017, 3(1): A4016001.
doi: https://doi.org/10.1061/AJRUA6.0000860
Topics:
Climate
Probabilistic Flood Hazard Mapping Using Bivariate Analysis Based on Copulas
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2017, 3(1): A4016002.
doi: https://doi.org/10.1061/AJRUA6.0000883
Robust Prioritization Framework for Transport Infrastructure Adaptation Investments under Uncertainty of Climate Change
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. March 2017, 3(1): E4015001.
doi: https://doi.org/10.1061/AJRUA6.0000852
Topics:
Climate change
,
Uncertainty
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Daily Engine Performance Trending Using Common Flight Regime Identification
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On the Consistent Classification and Treatment of Uncertainties in Structural Health Monitoring Applications
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Special Section on Uncertainty-Aware Diagnostics and Prognostics for Health Monitoring and Risk Management of Engineered Systems
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On the efficacy of sparse representation approaches for determining nonlinear structural system equations of motion
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Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems
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