Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

Abstract

Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10−2 m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10−2 m and 1.69 × 10−2 m and the standard variances of 6.77 × 10−4 m and 4.08 × 10−4 m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.

Contribution Rate Evaluation Method of Equipment System of Systems Based on Fault Tree

“…By analyzing various factors that cause system failure, the inverted tree logic diagram is established. Based on the logic diagram, the combination of factors causing system failure is established by qualitative analysis, and the main factors causing system failure are established by quantitative analysis, so as to find out the solutions to improve system reliability [13,14].…”

Structural dynamic reliability analysis: review and prospects

“…The neural network method is combined with extremum RSM to analyze the structural dynamic reliability (Song et al, 2017(Song et al, , 2018. Zhao et al (2020) extended the application of extremum neural networks in dynamic reliability analysis of flexible mechanisms. Lu et al (2018a, b) developed the Kriging with extremum RSM for structural dynamic reliability.…”