.png){kind=icon}
.png){kind=icon}
Return
AIMGC-PSO: A fusion optimization algorithm for enhancing three-parameter Weibull parameter estimation performance in small-sample fatigue failure data processing
.png){kind=icon}
PRE
AI
J
J
Y
Z
H
DOI：10.1016/j.probengmech.2026.103978.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• AIMGC-PSO fusion algorithm for small-sample fatigue failure data processing. • Enhancing three-parameter Weibull parameter estimation performance. • Incorporating several innovative mechanisms within PSO optimization framework. • Outperforming other methods in 450,000 Monte Carlo simulations. • Superior applicability validated on two real-world small-sample failure datasets.
Journal
IF:
3.5
Papers:
1.7K
Citations:
4.1K
Organization
Cited Papers