Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics

By C. Li, Hyungdae Lee. 2005

262 citations recorded by the source corpus.

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A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery (2016, background): “Gear defects: (a) scuffing,(46) (b) pitting(60) and (c) crack.(32) 4 Advances in Mechanical Engineering”
An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring (2009, background): “Li and Lee (2005) proposed an approach for remaining useful life prediction of gears with a fatigue tooth crack based on a gear meshing stiffness identiﬁcation model, a gear dynamic model and a fracture mechanics model.”
A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models (2012, methodology): “Similarly, Li and Choi [16], and Li and Lee [17] proposed to use the Paris-Erdogan law coupled with a finite element model to represent the time evolution of a tooth’s crack in a gear.”
Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, methodology): “failure models [28] rely on the physics of the underlying degradation process to predict the onset of failures.”