Abstract
The reliability analysis of complex mechanisms involves time-varying, high-nonlinearity, and multiparameters. The traditional way is to employ Monte Carlo (MC) simulation to achieve the reliability level, but this method consumes too much computing resources and is even computationally intractable. To improve the efficiency and accuracy of dynamic probabilistic analysis of complex mechanisms, an intelligent extremum surrogate modeling framework (IESMF, short for) is proposed based on extremum response surface method (ERSM), combined with artificial neural network (ANN) method and an improved optimize particle swarm optimization (PSO) method. Hereinto, the ERSM is used to simplify the dynamic process of output response to the extremum value of transient analysis; ANN is applied to establish a mathematical model between input variables and response, and the improved PSO method is utilized in search of initial weights and thresholds of the model. The effectiveness of the IESMF is demonstrated to perform the Rack-and-pinion steering mechanism (RPSM) reliability analysis. The results show that when the allowable value of gear root stress is equal to 850 MPa, the RPSM has a reliability degree of 0.9971. Through the validation process, it is illustrated that IESMF is accurate and efficient in dynamic probabilistic analysis of complex mechanisms, and its comprehensive performance is better than the MC method and ERSM. The research effort offers new ideas for the reliability estimation of a complex mechanism, thus enriching the method and theory of mechanical reliability design.
Highlights
For complex mechanisms, e.g., the steering mechanism of the aircraft nose wheel, its reliability level is seriously affected by many time-varying factors, such as speed, acceleration, load, and so on [1,2,3]. e limit state equation of complex mechanisms in probabilistic analysis has the characteristics of being highly nonlinear and multivariable. is leads to some problems in the process of reliability analysis, a large amount of calculation, and difficulty in ensuring the accuracy of calculation, which leads to the difficulty of mechanism reliability analysis [4,5,6]. erefore, to improve the performance of the mechanical system, the randomness of input parameters must be considered in mechanism reliability analysis
According to the excellent characteristics of artificial neural network (ANN), we propose a dynamic probability analysis method (ANNERSM), which combines extreme response surface methods (ERSM) and ANN. e outstanding simplified computing ability of ERSM and the powerful nonlinear mapping function of ANN are integrated into this method
E object of this study is to propose an intelligent extremum surrogate modeling framework (IESMF) based on ERSM, combined with an ANN model and an improved optimize particle swarm optimization (PSO) method. e improved PSO is used to search the initial thresholds and weights of the model. e feasibility and effectiveness of IESMF are analysed by the rack-and-pinion steering mechanism of the nose landing gear
Summary
E.g., the steering mechanism of the aircraft nose wheel, its reliability level is seriously affected by many time-varying factors, such as speed, acceleration, load, and so on [1,2,3]. e limit state equation of complex mechanisms in probabilistic analysis has the characteristics of being highly nonlinear and multivariable. is leads to some problems in the process of reliability analysis, a large amount of calculation, and difficulty in ensuring the accuracy of calculation, which leads to the difficulty of mechanism reliability analysis [4,5,6]. erefore, to improve the performance of the mechanical system, the randomness of input parameters must be considered in mechanism reliability analysis. E probabilistic analysis involves random factors such as material parameters and physical field loads It has acceptable accuracy in describing the failure response and is a feasible alternative method [10,11,12]. To improve the approximation ability and computational efficiency of ERSM, a feasible method is to establish a high-precision extremum response surface function based on the extreme value surrogate model. In the process of fitting high-nonlinearity, multiparameters, and time-varying limit state functions, there are always local optimization and overfitting problems in the training process, which affect the prediction accuracy, and its further application in the probability design of complex mechanisms is limited. E object of this study is to propose an intelligent extremum surrogate modeling framework (IESMF) based on ERSM, combined with an ANN model and an improved optimize particle swarm optimization (PSO) method.
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