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  • 【 Cover Article 】 Deng Qingtian's team from Chang'an University: Study on the crack expansion low of laminated porous structure
  • MODELING AND CARRYING CAPACITY ANALYSIS OF CIRCULAR ARC TOOTH PROFILE CIRCULAR ARC TOOTH LINE CYLINDRICAL GEAR
  • RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK

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Journal of Mechanical Strength

Journal of Mechanical Strength Journal of Mechanical Strength
  • ISSN: 1001-9669
  • CN: 41-1134/TH
  • Governed by: China Machinery Industry Federation
  • Sponsored by: China Academy of Machinery Zhengzhou Research Institute of Mechanical Engineering Co., Ltd. ；Chinese Mechanical Engineering Society
  • Frequency：Monthly
  • Tel：0371-67710821
  • Address: No.149, Kexue, Avenue, Zhengzhou,Henan, China
  • E-mail：jxqd1975@foxmail.com
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Volume 48 期 3，2026 2026年第48卷第3期

  • ·Optimization·Reliability·

    LIU Hanru, ZHI Pengpeng, HE Ruiheng, ZHANG Junfu, ZHAO Yadong

    DOI：10.16579/j.issn.1001.9669.2026.03.001
    摘要：ObjectiveAiming at the problems of difficult characterization of performance functions and low efficiency in reliability optimization design of complex mechanical structures, an adaptive reliability optimization design method based on the improved artificial bee colony (IABC) algorithm and parallel infilling Kriging model was proposed.MethodsFirstly, a parametric structural performance function model was established, and the relation between design variables and performance functions was characterized by the Kriging model. Secondly, a parallel infilling strategy combining adaptive learning functions H and B was proposed to improve the fitting accuracy. Then, the IABC algorithm was developed by improving the adaptive step size adjustment, global optimal nectar source tracking, and roulette mechanism to enhance efficiency. Finally, the Joint Committee on Structural Safety combined method (JC), and IABC algorithm were integrated to obtain the optimal solution under reliability constraints.ResultsNumerical examples demonstrate that the proposed method balances the accuracy and efficiency of structural reliability optimization. Compared with traditional methods, the number of performance function calls is significantly reduced, the effective calculation methods for the robust design of complex structures are provided.  
    关键词：Kriging model;Reliability optimization design;Artificial bee colony algorithm;Learning function;Parallel infilling strategy   
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    WEI Xin, TANG Jiayin, SONG Qingbiao, LI Wenxue

    DOI：10.16579/j.issn.1001.9669.2026.03.002
    摘要：ObjectiveIn the field of engineering technology, the degradation of systems is affected by various factors such as user operations, manufacturing processes, and working environments. The correlation between the initial state and the degradation rate (CISDR) and dynamic covariates are key factors to be considered in degradation models; however, there is little literature that takes both types of randomness into account simultaneously. To address the above issue, a reliability model based on the Gamma degradation process, which considers dynamic covariates and the correlation between the initial degradation state of the system and the degradation rate, was established for research and analysis.MethodsFirstly, a residual life prediction model was established. Based on the statistical analysis of condition monitoring data during system operation, the residual life distribution of the product was further inferred. Secondly, in view of the differences in operation and operating environment during the system degradation cycle, the Ornstein-Uhlenbeck (OU) process was used to characterize the changes in dynamic covariates, and a Gamma degradation model considering dynamic covariates was constructed. Thirdly, the relation between dynamic covariates and degradation rate was established through an exponential effect model. Finally, a bivariate normal distribution was employed to build a correlation model between the initial degradation state of the system and the degradation rate, and the system reliability function and the probability density of residual life were derived.ResultsThe results show that both the simulation examples and application cases have verified that the established model can significantly improve the accuracy of residual life prediction. Meanwhile, the residual life prediction that takes two types of random effects into account is more objective.  
    关键词：Random initial state;Dynamic covariate;OU process;Residual useful life prediction;Gamma process   
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    LI Wenxue, TANG Jiayin, SONG Qingbiao

    DOI：10.16579/j.issn.1001.9669.2026.03.003
    摘要：ObjectiveAiming at the problem that complex systems such as gear transmission have the ability to recover from performance degradation damage after external random shocks, and existing models are difficult to accurately evaluate reliability, a flexible reliability assessment model was constructed from the perspective of competitive failure to realize precise reliability analysis of such systems.MethodsFirstly, a nonlinear variable-rate Wiener process was adopted to characterize performance degradation, considering random factors and individual differences to provide a basis for describing dynamic degradation characteristics. Secondly, a Poisson process was coordinated to characterize random shocks, clarifying the influence mechanism of fatal and non-fatal shocks. Then, a self-recovery function was used to describe the complete or partial recovery process of degradation damage, and a flexible competitive failure reliability model was constructed. Finally, a multi-stage parameter estimation method was proposed based on the quasi-Newton iterative algorithm and cubic spline interpolation to ensure the accuracy of model parameter solution.ResultsExample verification shows that after the system operates for about 1 100 hours, ignoring the self-recovery effect will underestimate the reliability level. The reliability difference between the established model and the simulation results does not exceed 0.015 7, and the model is applicable to linear/constant degradation rate systems and classic systems without self-recovery, providing a reference for the reliability assessment of self-recovery systems such as gear transmission.  
    关键词：Nonlinear Wiener process;Self-recovery;Random shock;Competitive failure;Flexible model   
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    LIU Cen, YANG Fan, WU Senlin, LIN Hui, HONG Kai, LIU Xiaoning

    DOI：10.16579/j.issn.1001.9669.2026.03.004
    摘要：ObjectiveTo address the reliability design challenges of steel spherical tanks when static strength and load distribution parameters are interval values, a set of reliability design criteria for spherical tanks based on the combination of probabilistic and non-probabilistic theories was established.MethodsFirstly, by introducing set theory, a reliability index model was established when the strength distribution parameters were interval values. Then, various conditions, including hydraulic, gas pressure, and gas-liquid combination tests under both extreme and general requirements, as well as normal operation, were systematically analyzed. Finally, the reliable indicators, stress limiting coefficients and safety factors for yield and burst strength were derived based on the median diameter formula.ResultsThe range of values for yield and burst strength coefficients that meet reliability requirements under various test and operational conditions are determined, and corresponding condition factors are given. The research results offer a quantitative reliability reference scheme for the static strength design of spherical tanks, enhancing the safety and rationality of the design.  
    关键词：Spherical tank;Static strength;Reliability;Safety factor;Working condition coefficient;Test stress limiting coefficient   
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