On the Use of Particle Flow to Enhance the Computational Performance of Particle-Filtering-based Prognostics

Javier A. Oliva and Torsten Bertram
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Full Paper
phmc_14_034.pdf505.99 KBSeptember 16, 2014 - 1:48pm

Prognostic approaches based on particle filtering employ physical based models in order to estimate the remaining useful life (RUL) of systems. To this aim a set of particles is used to first estimate the degradation state of the system and then to predict the distribution of the RUL through simulation. The computational complexity of this approach is a function of the number of particles used in the state estimation and of the time each particle needs to simulate the RUL. It is therefore clear that enhancing the computational performance of this approach requires reducing the number of particles. In this paper we employ a novel approach for reducing the number of particles needed in the particle filter by introducing a deterministic particle flow, in which the particles are progressively migrated without randomly sampling from any distribution. The estimation of the remaining driving range (RDR) of an electric vehicle is used as the case study to illustrate the improvement in computational performance of the proposed approach.

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Submission Keywords: 
Model-based Prognostics; Particle Filtering; electric vehicles
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
Uncertainty Quantification and Management in PHM

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