Reliability optimization design for product key structure based on integration dimension-reduction considering high-dimensional heterogeneous uncertainties

被引:0
|
作者
Hong, Zhaoxi [1 ,2 ]
Tan, Jianrong [1 ,2 ]
He, Lili [3 ]
Hu, Bingtao [2 ]
Zhang, Zhifeng [2 ]
Song, Xiuju [2 ]
Feng, Yixiong [2 ,4 ]
机构
[1] Ningbo Innovation Center, Zhejiang University, Ningbo,315100, China
[2] State Key Laboratory of Fluid Power and Mechatronic Systems, Zhcjiang University, Hangzhou,310027, China
[3] School of Computer Science and Technology, Zhcjiang Sci-Tech University, Hangzhou,310018, China
[4] State Key Laboratory of Public Big Data, Guizhou University, Guiyang,550025, China
关键词
Structural optimization;
D O I
10.13196/j.cims.2024.0573
中图分类号
学科分类号
摘要
Reliability optimization design of key structure for complex products focuses on searching the optimal values of the design variables on the premise of meeting the reliability requirements, which is an important way to ensure product performance and improve human experience. It is worth noting that uncertainty factors arc ubiquitous and diverse in the process of structural design, especially interval uncertainty and random uncertainty often exist at the same time and have high parameter dimensions, making traditional design methods no longer applicable. Therefore, a novel method of reliability optimization design for product key structure based on integration dimension-reduction was put forward that taken the high-dimensional heterogeneous uncertainties including random factors and interval factors into consideration. With the Kriging approximation modeling and the multi-objective particle swarm intelligence algorithm, the efficient double-layer nesting computing framework of reliability optimization for high-dimensional heterogeneous uncertainties product key structure was established. The inner layer was reliability analysis with integration dimension-reduction, where the random variables and interval variables in performance function for key structure were analyzed according to univariate dimension-reduction and Taylor expansion respectively. The upper and lower limits of performance function for key structure were obtained by the superposition of the low-dimensional integrations which were converted with the Gaussian integration to calculate the reliability of the key structure quickly. The outer layer was the iterative optimization with a multi-objective particle swarm optimization algorithm based on the reliability analysis results in inner layer, and the design vectors that could meet the reliability requirements were optimized by objective functions to obtain the optimal design vector. The calculation cost of reliability optimization design for product key structures with high-dimensional heterogeneous uncertainties could be reduced in this way. The rationality and superiority of the proposed method were verified by a case study of beam design for a large hydraulic press. © 2024 CIMS. All rights reserved.
引用
收藏
页码:4152 / 4167
相关论文
共 50 条
  • [1] Convolutional Dimension-Reduction With Knowledge Reasoning for Reliability Approximations of Structures Under High-Dimensional Spatial Uncertainties
    Shi, Luojie
    Zhou, Kai
    Wang, Zequn
    JOURNAL OF MECHANICAL DESIGN, 2024, 146 (07)
  • [2] A recursive dimension-reduction method for high-dimensional reliability analysis with rare failure event
    Jiang, Zhong-ming
    Feng, De-Cheng
    Zhou, Hao
    Tao, Wei-Feng
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 213
  • [3] An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets
    Li, Ying
    Wang, Gang
    Chen, Huiling
    Shi, Lian
    Qin, Lei
    JOURNAL OF BIONIC ENGINEERING, 2013, 10 (02) : 231 - 241
  • [4] An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets
    Ying Li
    Gang Wang
    Huiling Chen
    Lian Shi
    Lei Qin
    Journal of Bionic Engineering, 2013, 10 : 231 - 241
  • [5] Reliability Assessment of Flight Vehicle Stage Separation Considering High-Dimensional Uncertainties
    Nie Z.-W.
    Wang H.
    Qin M.
    Zhang H.-R.
    Yuhang Xuebao/Journal of Astronautics, 2021, 42 (12): : 1525 - 1531
  • [6] Approximate optimization of systems with high-dimensional uncertainties and multiple reliability constraints
    Ching, Jianye
    Hsu, Wei-Chih
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2008, 198 (01) : 52 - 71
  • [7] High-dimensional Data Dimension Reduction Based on KECA
    Hu, Yongde
    Pan, Jingchang
    Tan, Xin
    SENSORS, MEASUREMENT AND INTELLIGENT MATERIALS, PTS 1-4, 2013, 303-306 : 1101 - 1104
  • [9] An Optimization Method of Flexible Manufacturing System Reliability Allocation Based on Two Dimension-Reduction Strategies
    Xu, Jingjing
    Tao, Long
    Pei, Yanhu
    Liu, Zhifeng
    Yan, Qiaobin
    Cheng, Qiang
    MACHINES, 2024, 12 (01)
  • [10] Re-examining Supervised Dimension Reduction for High-Dimensional Bayesian Optimization
    Chen, Quanlin
    Huo, Jing
    Chen, Yiyu
    Ding, Tianyu
    Gao, Yang
    Li, Dong
    He, Xu
    PARALLEL PROBLEM SOLVING FROM NATURE-PPSN XVIII, PT II, PPSN 2024, 2024, 15149 : 356 - 373