Fatigue reliability of high speed bearing based on genetic algorithm optimized artificial neural network

To complete the fatigue reliability analysis of aviation bearing under thermal elastohydrodynamic lubrication (EHL) efficiently and accurately, an artificial intelligent method was proposed. The heat stress from temperature was approximated using quadratic polynomial with intercrossing term and then mapped into the Hertz contact zone. The contact stress model, which included the thermal EHL, was established. Considering the randomness of the thermal EHL, the material properties and fatigue strength correction factors, the probabilistic reliability analysis model was established using artificial neural network (ANN). Genetic algorithm(GA) was employed to search the minimum reliability index and the design point by introducing an adjusting factor in penalty function. Reliability sensitivity analysis was completed based on the advanced first order second moment (AFOSM). Numerical example shows that the established probabilistic reliability analysis model could correctly reflect the effect of thermal EHL on contact fatigue of aviation bearing. Compared with the traditional Monte Carlo method, the proposed method presents a difference in failure probability of 2.0×10-4, a relative error of 23.8% and a relative time consumption of only 0.15%, which means that the proposed method has an excellent global search capability and a highly efficiency.