Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation

Jose R. Celaya, Abhinav Saxena, and Kai Goebel
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA
AttachmentSizeTimestamp
phmc_12_061.pdf359.21 KBSeptember 24, 2012 - 5:49am

This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it re- lates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Publication Year: 
2012
Publication Volume: 
3
Publication Control Number: 
061
Page Count: 
10
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
PHM for electronics
Verification and validation
Submitted by: 
  
 
 
 

follow us

PHM Society on Facebook Follow PHM Society on Twitter PHM Society on LinkedIn PHM Society RSS News Feed