An efficient simulation framework for prognostics of asymptotic processes- A case study in composite materials.

Manuel Chiachio, Juan Chiachio, Abhinav Saxena, Guillermo Rus, and Kai Goebel
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmce_14_052.pdf1.9 MBMay 26, 2014 - 12:45pm

This work presents an efficient computational framework for estimating the end of life (EOL) and remaining useful life (RUL) by combining the particle filter (PF)-based prognostics with the technique of Subset simulation. It has been named PFP-SubSim on behalf of the full denomination of the computational framework, namely, PF-based prognostics based on Subset Simulation. This scheme is especially useful when dealing with the prognostics of evolving processes with asymp- totic behaviors, as observed in practice for many degradation processes. The effectiveness and accuracy of the proposed al- gorithm is demonstrated through an example for predicting the probability density function of EOL for a carbon-fibre composite coupon subjected to an asymptotic fatigue degra- dation process. It is shown that PFP-SubSim algorithm is efficient, and at the same time, fairly accurate in obtaining the probability density function of EOL and RUL as compared to the traditional PF-based prognostic approach reported in the PHM literature

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
052
Page Count: 
13
Submission Keywords: 
Fatigue Prognosis
Subset Simulation
physics based prognostics; remaining useful life
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
  
 
 
 

follow us

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