Prognosis of gear health using stochastic dynamical models with online parameter estimation

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Published Mar 26, 2021
Matej Gasˇperin Pavle Bosˇkoski Dani Juricˇicˇ

Abstract

In this paper we present a statistical approach to the estimation of the time in which an operating gear will achieve the critical stage. The approach relies on measured vibration signals. From these signals features are extracted first and then their evolution over time is predicted. This is done owing to the dynamic model that relates hidden degradation phenomena with measured outputs. The Expectation-Maximization algorithm is used to estimate the parameters of the underlying state- space model on-line. Time to reach safety alarm threshold is determined by making the prediction using the estimated linear model. The results obtained on a pilot testbed are presented.

How to Cite

Gasˇperin, M., Bosˇkoski, P. ., & Juricˇicˇ, D. . (2021). Prognosis of gear health using stochastic dynamical models with online parameter estimation. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1634
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Keywords

damage detection, damage modeling, gears, materials damage prognostics, prognostics, remaining useful life (RUL)

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Section
Technical Papers