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

Yang Hu, Piero Baraldi, Francesco Di Maio, and Enrico Zio
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Politecnico di Milano
AttachmentSizeTimestamp
phmce_14_035.pdf501.18 KBMay 23, 2014 - 3:27pm

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.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
035
Page Count: 
9
Submission Keywords: 
Model-based Prognostics; Remaining Useful Life; Parameter Estimation; Particle Filtering; Optimized Tuning Kernel Smoothing; Battery
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Uncertainty Quantification and Management in PHM
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