A Prognostic Approach Based on Particle Filtering and Optimized Tuning Kernel Smoothing



Published Jul 8, 2014
Yang Hu Piero Baraldi Francesco Di Maio Enrico Zio


This paper proposes a novel approach based on a Particle Filtering technique and an Optimized Tuning Kernel Smoothing method for the prediction on the Remaining Useful Life (RUL) of a degrading component. We consider a case in which a model describing the degradation process is available, but the exact values of the model parameters are unknown and observations of historical degradation trajectories in similar components are unavailable. A numerical application concerning the prediction of the RUL of degrading Lithium-ion batteries is considered. The obtained results show that the proposed method can provide a satisfactory RUL prediction as well as the parameters estimation.

How to Cite

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2014). A Prognostic Approach Based on Particle Filtering and Optimized Tuning Kernel Smoothing. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1501
Abstract 269 | PDF Downloads 104



Model-based Prognostics, Remaining Useful Life, Parameter Estimation, Particle Filtering, Optimized Tuning Kernel Smoothing, Battery

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