Editorial: Uncertainty in PHM

Shankar Sankararaman, Sankaran Mahadevan, and Marcos E. Orchard
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Uncertainty in PHM
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_15_000.pdf341 KBFebruary 17, 2017 - 5:31pm

Prognostics, the science of prediction, is inherently affected by several sources of uncertainty (natural variability, data uncertainty, and model uncertainty). It is important to rigorously account for these sources of uncertainty while predicting the behavior of engineering systems, and compute the overall uncertainty in the remaining useful life prediction. Uncertainties that exhibit complex, non-linear interactions need to be aggregated using computational methods. If there is a large uncertainty associated with the remaining useful life prediction, then such information may not be useful for meaningful decision-making. Therefore, recent research efforts have focused on developing methods to characterize, interpret, incorporate, and quantify uncertainty in prognostics, quantify the risk associated with system operation decisions, and eventually facilitate risk-informed decision-making activities such as fault mitigation, mission re-planning, etc. This special issue on Uncertainty in PHM focuses on computational methods and practical applications dealing with the representation, interpretation, quantification, and management of uncertainty in prognostics and health management.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
000
Page Count: 
4
Submission Keywords: 
editorial
Submission Topic Areas: 
Uncertainty Quantification and Management in PHM
Submitted by: 
  
 
 
 

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