A hybrid prognosis approach for life prediction of gears subjected to progressive pitting failure mode

Pradeep Kundu (), Makarand S.Kulkarni and Ashish K.Darpe
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Pradeep Kundu: University of Strathclyde
Makarand S.Kulkarni: IIT Bombay
Ashish K.Darpe: IIT Delhi

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 3, No 21, 1325-1346

Abstract: Abstract The surface/subsurface-initiated pitting is one of the most common failure modes in a gear. Although models for predicting the initiation of pitting are available, a model for predicting the growth of an existing pitting is not yet available. The present work proposes a hybrid prognosis approach for estimating the growth of pitting on the gear tooth surface, utilizing both empirical model (i.e., modified Paris law) and measured pitting area. The proposed approach is generic as the pitting growth rate is modelled as a function of gear material properties (such as Poisson’s ratio and modulus of elasticity), oil property (such as dynamic viscosity), gear geometrical parameters (such as pitch circle diameter, module, pressure angle and face width) and operating conditions (such as applied load and speed). The pitting evolution process is likely to be different for similar gear pairs due to inherent variations in material properties, manufacturing process, etc. Hence for a more accurate life prediction, the pit growth model parameters need to be updated, which is done using Bayesian inference.

Keywords: Gear; Pitting; Paris law; Bayesian inference; Hybrid prognosis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01852-6

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