2020
DOI: 10.1111/ffe.13167
|View full text |Cite
|
Sign up to set email alerts
|

Fatigue residual life estimation of jib structure based on improved v‐SVR algorithm obtaining equivalent load spectrum

Abstract: Fatigue tests of truck crane jib structure have great difficulty, long testing cycle, and high cost. Therefore, with the portfolio strategy for "collection, prediction, measurement and simulation" being introduced, an improved support vector regression (v-SVR) algorithm is proposed to predict the equivalent load spectrum based on the small measured load spectrum and the fatigue residual life evaluation model is built. First, the v-SVR algorithm corrected in kernel function, decision function, and parameter opt… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 12 publications
(2 citation statements)
references
References 15 publications
(16 reference statements)
0
2
0
Order By: Relevance
“…18 It has been successfully used as an extremely powerful tool to optimize the chemical compositions of materials to achieve desired properties, 19,20 for instance, developing high strength and high ductility Al alloy 21 and high strength with high conductivity Cu alloy. [22][23][24][25] Additionally, in mechanical science, machine learning method is used to predict fatigue life under uniaxial or multiaxial loading, [26][27][28][29][30][31] fatigue limit, 32,33 and short fatigue crack propagation behavior. 34,35 The established model through machine learning algorithms for predicting fatigue life mostly focuses on only one material in the aforementioned publishments.…”
Section: Introductionmentioning
confidence: 99%
“…18 It has been successfully used as an extremely powerful tool to optimize the chemical compositions of materials to achieve desired properties, 19,20 for instance, developing high strength and high ductility Al alloy 21 and high strength with high conductivity Cu alloy. [22][23][24][25] Additionally, in mechanical science, machine learning method is used to predict fatigue life under uniaxial or multiaxial loading, [26][27][28][29][30][31] fatigue limit, 32,33 and short fatigue crack propagation behavior. 34,35 The established model through machine learning algorithms for predicting fatigue life mostly focuses on only one material in the aforementioned publishments.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, it is crucial that the influence of different working conditions on the load spectrum is fully considered in the process of programming the load spectrum [27][28][29]. Compiling a program load spectrum suitable for hybrid electric vehicles can improve the reliability and accuracy of the simulation test of the fatigue life of the reducer [30][31][32]. Xu L et al compiled a load spectrum for the impeller for the simulation analysis of fatigue life-influencing factors and fatigue fracture regions.…”
Section: Introductionmentioning
confidence: 99%