scholarly journals Uncertainty Quantification in Remaining Useful Life of Aerospace Components using State Space Models and Inverse FORM

2013 ◽  
Author(s):  
Shankar Sankararaman ◽  
Kai Goebel
Keyword(s):  
Uncertainty Quantification ◽  
State Space ◽  
Remaining Useful Life ◽  
State Space Models ◽  
Inverse Form ◽  
Useful Life

Failure Prognosis Based on Adaptive State Space Models

2016 ◽  
Author(s):  
Guangxing Bai ◽  
Amirmahyar Abdolsamadi ◽  
Pingfeng Wang
Keyword(s):  
State Space ◽  
Environmental Changes ◽  
Remaining Useful Life ◽  
State Space Models ◽  
Data Driven ◽  
Operational Conditions ◽  
Useful Life ◽  
Failure Prognosis ◽  
Model Recognition ◽  
Life Predictions

This paper presents a generic data-driven failure prognosis method based on adaptive state space models for engineering systems, which integrates adaptive model recognition with a dynamic system model for remaining useful life prediction. The developed approach employs a statistical learning framework for adaptively learning of time-series degradation performance, and then a Bayesian technique for self-updating of data-driven models to adapt the operational or environmental changes. With the developed approach, the prognosis technique can eliminate the dependence to system specific models and be adaptive to system performance changes due to degradation or variation of system operational conditions, thereby yielding accurate remaining useful life predictions. The developed methodology is demonstrated by an engineering case study.

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.

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