Fatigue Reliability Analysis of Aviation Bearings Based on ANN

doi:10.12068/j.issn.1005-3026.2018.06.018

Abstract

Abstract: An intelligent method is proposed to complete the contact fatigue reliability analysis of aviation bearings. The temperature field is approximated using quadratic polynomial with intercrossing term， and the stress model under thermal elastohydrodynamic lubrication (EHL) is set up. Considering the randomness of the thermal EHL， material properties and fatigue strength correction factors， the probabilistic reliability analysis model is established using artificial neural network (ANN)， and the reliability sensitivity analysis is completed based on the advanced first order second moment (AFOSM). The numerical example shows that the established probabilistic reliability analysis model could correctly reflect the effect of the thermal EHL on contact fatigue of aviation bearings， and the proposed intelligent method has an excellent global search capability as well as a highly efficient computing performance compared with the traditional Monte Carlo method， and then the results are verified with the fatigue life test of rolling bearings considering the non-interacting variance analysis.

Key words: contact fatigue, thermal elastohydrodynamic lubrication (EHL), aviation bearing, reliability, artificial neural network (ANN), genetic algorithm (GA)

CLC Number:

TH132
V215

JIN Yan， LIU Shao-jun. Fatigue Reliability Analysis of Aviation Bearings Based on ANN[J]. Journal of Northeastern University Natural Science, 2018, 39(6): 850-855.

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