Reliability Analysis And Optimization Design Of Aero-Engine Bearing

As a high-tech product,the research and development,manufacturing,and application of aero-engines involve multiple fields,which can reflect a country’s technological innovation capability,industrial manufacturing capability,and quality management capability.Bearings,as a key component of the normal operation of aeroengines,whose high reliability serves as a guarantee for the stable operation of aeroengines.Therefore,conducting reliability analysis on aircraft engine bearings is of great significance for improving the performance of aero-engines.In practical applications,the cost of determining the life distribution of bearings through large-scale tests is extremely high due to the characteristics of long service life of aero-engine bearings.Under such circumstances,small sample timing truncation tests are typically used to efficiently evaluate the life distribution of bearings.However,it may arise the scenarios that no failure data or only a few failure data can be collected.In this case,it is difficult to establish an accurate life prediction model for reliability analysis.In addition,the structural reliability of bearings is affected by multiple uncertain factors such as random load and material performance degradation,and it is particularly important to analyze the structural reliability of bearings.Additionally,in order to control the manufacturing cost of bearings and improve economic benefits,it is necessary to optimize the design on the premise of meeting the specified reliability.Based on the above questions,this dissertation studies the reliability analysis and design optimization method of aero-engine bearings.The specific research contents are summarized as follows:(1)Reliability analysis of bearings based on small sample timing truncated tests.By analyzing a variety of failure probability estimation methods,the disadvantages of traditional methods that the failure probability estimation results of each method will fluctuate considerably when the censoring time,test grouping,and sample size are different,are concluded.Based on the Expected Bayes(E-Bayes)method,the upper limit of the failure probability at the final censoring time is introduced,and combined with the characteristics of the Weibull distribution curve,the upper limit of the failure probability at each censoring time is derived.Taking the lower limit of the failure probability at the first time as zero,the lower limit of the corrected failure probability at each time is derived,and an improved E-Bayes estimation method is proposed.At the same time,reliability analysis of aero-engine bearings under the conditions of zero failure data and very few failure data is conduced,and the results are compared with the existing failure probability estimation methods to verify the effectiveness.(2)Structural reliability analysis of bearing based on Kriging model.Aiming at the problems of low efficiency,a high number of function calls,and strict stopping conditions of traditional structural reliability analysis methods based on surrogate model,this dissertation proposes a maximum prediction error stopping criterion by analyzing the shortcomings of traditional methods.The criterion transforms the local stopping criterion into global stopping criterion,which can ensure the accuracy of the solution and reduce the calculation number of performance function.In order to cooperate with the proposed stopping criterion,a multi-feature parallel adding point strategy is proposed to further improve the solution efficiency.At the same time,a typical bearing used in aero-engines is selected as an example,and the limit state function of the bearing is established based on the Hertz contact theory.The improved method is further implemented in the structural reliability analysis of the aero-engine bearing to verify the effectiveness of the proposed method.(3)Reliability based design optimization method of bearings based on PSO and PMA.Aiming at the geometric structure of aero-engine bearings,the size parameters affecting the reliability of bearing structure are analyzed and optimized as design variables.Taking the total mass of the bearing as the objective function,the mathematical model of reliability optimization is established by calculus.This dissertation analyzes the principles of the standard particle swarm optimization(PSO)algorithm and the dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm and then proposes an improved DMS-PSO algorithm with adaptive learning factors and a chasing update strategy.The effectiveness of the improved DMS-PSO algorithm is demonstrated through a set of experiments on four classic test functions.It is further applied to the reliability optimization design of aero-engine bearings.Uses the improved DMS-PSO algorithm to find the optimal solution,and uses the performance measure approach(PMA)to solve the reliability constraint.