Abstract
Aiming at overcoming the problem that the mechanism function of the unlocking trigger device is difficult to obtain and the corresponding reliability analysis cannot be performed, a motion reliability analysis method based on the CPSO-BR-BP neural network proxy model is proposed. Firstly, the particle swarm algorithm is optimized through the chaotic sequence, and the back-propagation (BP) neural network is optimized using Chaos Particle Swarm Optimization (CPSO) and Bayesian Regularization (BR) algorithm. The CPSO-BR-BP neural network proxy model is established, and the reliability of shape memory alloys (SMA) wire unlocking based on the structural function is calculated. Moreover, according to the structural function of the separation process, the motion reliability based on the proxy model and the improved membership function is calculated. Finally, a series reliability model is established based on the unlocking process and the separation process to calculate the reliability of the whole machine. The reliability of the unlocking trigger device is analyzed by the proposed method. Results show that the proposed method is computationally efficient with the calculated reliability of 0.9987.
Highlights
To reduce the separation impact of the device, ensure the safety of connection and separation, and avoid space pollution, researchers have applied shape memory alloys (SMA) to achieve the unlocking function [8,9,10,11,12,13]
Yan et al [23] proposed an artificial neural network model based on genetic algorithm optimization to analyze the reliability of aviation bearing fatigue and overcomed the problem of artificial neural network local optimization and premature convergence problems. e above studies mostly optimize the characteristics of BP neural network models and do not analyze the accuracy and efficiency of proxy models
They are difficult to solve the reliability issues of actual engineering cases. To this end, considering working principle of unlocking trigger device and uncertain parameters during movement, a new reliability analysis method based on CPSO-BR-BP neural network (Chaos Particle Swarm OptimizationBayesian Regularization-BP neural network) is proposed, and the unlocking trigger device is used as the research object. e influence of each uncertainty parameter on the device performance is clarified, and the motion reliability of the SMA wire unlocking and separation process is analyzed under different coefficients to verify its motion reliability under uncertain parameters. e research method provides a reliable theoretical reference for further improving the structural performance of the unlocking trigger device
Summary
In the process of calculating motion reliability, a reasonable membership function is a prerequisite for the quantification of parameter uncertainty. E membership function based on the 6σ principle is improved to reduce the deviation between the uncertainty parameter and the actual manufacturing, and it can improve the accuracy of the motion reliability analysis of the separation process. According to the overall separation time index of the unlocking trigger device, the threshold of the response time of the separation process is determined, and the movement reliability function is constructed. E unlocking trigger device consists of the SMA wire unlocking process and the separation device separation process according to the working principle, and the movement process conforms to the characteristics of the series reliability model.
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