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A Particle Filtering Based Approach for Gear Prognostics
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Author(s): David He (The University of Illinois-Chicago, USA), Eric Bechhoefer (NRG Systems, USA), Jinghua Ma (The University of Illinois-Chicago, USA)and Junda Zhu (The University of Illinois-Chicago, USA)
Copyright: 2013
Pages: 10
Source title:
Diagnostics and Prognostics of Engineering Systems: Methods and Techniques
Source Author(s)/Editor(s): Seifedine Kadry (American University of the Middle East, Kuwait)
DOI: 10.4018/978-1-4666-2095-7.ch013
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Abstract
In this chapter, a particle filtering based gear prognostics method using a one-dimensional health index for spiral bevel gear subject to pitting failure mode is presented. The presented method effectively addresses the issues in applying particle filtering to mechanical component remaining useful life (RUL) prognostics by integrating a couple of new components into particle filtering: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by a whitening transform; and (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The presented prognostics method is validated using data from a spiral bevel gear case study.
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