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
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

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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

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