An Hybrid Model-Based/Data-Driven Approach for Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks

Piero Baraldi, Enrico Zio, Michele Compare, and Sergio Sauco
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Full Paper
phmce_12_012.pdf152.92 KBJune 7, 2012 - 6:41am

Particle Filtering (PF) is a model-driven approach widely used in prognostics, which requires models of both the degradation process and the measurement acquisition system. In many practical cases, analytical models are not available, but a dataset containing a number of pairs component state - corresponding measurement may be available.
In this work, a data-driven approach based on a bagged ensemble of Artificial Neural Networks (ANNs) is adopted to build an empirical measurement model of a Particle Filter for the prediction of the Residual Useful Life (RUL) of a structure whose degradation process is described by a stochastic fatigue crack growth model of literature. The work focuses on the investigation of the capability of the proposed approach to cope with the uncertainty affecting the RUL prediction.

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Submission Keywords: 
particle filtering
Ensemble of ANNs
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Model-based methods for fault detection, diagnostics, and prognosis
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