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This study develops new reliability models for components facing non-stationary random loads. The research introduces methods to evaluate dynamic reliability, considering load-strength correlations and strength degradation for improved component assessment.

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Area of Science:

  • Mechanical Engineering
  • Reliability Engineering
  • Materials Science

Background:

  • Conventional load-strength interference models are inadequate for assessing component reliability under dynamic, non-stationary random loads.
  • Existing static reliability calculations fail to capture the time-varying nature of component performance.
  • Dynamic changes in reliability necessitate advanced modeling approaches beyond traditional methods.

Purpose of the Study:

  • To develop novel reliability models for components subjected to non-stationary random loads.
  • To enable the evaluation of dynamic reliability by accounting for load and strength variations over time.
  • To investigate the influence of key parameters on component reliability and hazard rates.

Main Methods:

  • Developed an approach to convert non-stationary random loads into equivalent stationary loads for reliability calculation.
  • Derived reliability models based on longitudinal and transverse distributions of loads.
  • Incorporated strength degradation and Poisson process for random load occurrences into dynamic reliability models.
  • Accounted for the correlation between random load and component strength.

Main Results:

  • Introduced novel reliability models capable of evaluating dynamic reliability under non-stationary random loads.
  • Demonstrated the derivation of reliability models based on longitudinal and transverse load distributions.
  • Established dynamic reliability models considering strength degradation and Poisson load occurrences.

Conclusions:

  • The dispersion of initial strength significantly impacts component reliability and hazard rates.
  • Variations in the transverse standard load coefficient also exert a substantial influence on reliability outcomes.
  • The developed models provide a more accurate assessment of component reliability under complex loading conditions.