Model Adaptation for Prognostics in a Particle Filtering Framework

Bhaskar Saha and Kai Goebel
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
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ijPHM_11_006.pdf507.06 KBJuly 29, 2011 - 2:09pm

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Publication Year: 
2011
Publication Volume: 
2
Publication Control Number: 
006
Page Count: 
10
Submission Keywords: 
model-based prognostics
particle filters
model adaptation
sensitivity analysis
Submission Topic Areas: 
Component-level PHM
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

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