scholarly journals An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring

2014 ◽  
Author(s):  
Shankar Sankararaman ◽  
Kai Goebel
Keyword(s):  
Uncertainty Quantification ◽  
Condition Based Monitoring

scholarly journals Uncertainty in Prognostics and Systems Health Management

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Shankar Sankararaman ◽  
Kai Goebel
Keyword(s):  
Uncertainty Quantification ◽  
Health Management ◽  
Uncertainty Propagation ◽  
Remaining Useful Life ◽  
Engineering Systems ◽  
Different Types ◽  
Future Events ◽  
Useful Life ◽  
Condition Based Monitoring ◽  
Management Issues

This paper presents an overview of various aspects of uncertainty quantification and management in prognostics and systems health management. Prognostics deals with predicting possible future failures in different types of engineering systems. It is almost practically impossible to precisely predict future events; therefore, it is necessary to account for the differentsources of uncertainty that affect prognostics, and develop a systematic framework for uncertainty quantification and management in this context. Researchers have developed computational methods for prognostics, both in the context of testing-based health management and condition-based health management. This paper explains that the interpretationof uncertainty for these two different types of situations is completely different. While both the frequentist (based on the presence of true variability) and Bayesian (based on subjectiveassessment) approaches are applicable in the context of testing-based health management, only the Bayesian approach is applicable in the context of condition-based health management. This paper illustrates that the computation of the remaining useful life is more meaningful in the context of condition-based monitoring and needs to be approachedas an uncertainty propagation problem. Further, uncertainty management issues are discussed and possible solutions are explored. Numerical examples are presented to illustrate the various concepts discussed in the paper.

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell
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