Soft-Computing-Based Estimation of a Static Load for an Overhead Crane

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@Article{kusznir:2023:Sensors,

• author = "Tom Kusznir and Jaroslaw Smoczek",
• title = "Soft-Computing-Based Estimation of a Static Load for an Overhead Crane",
• journal = "Sensors",
• year = "2023",
• volume = "23",
• number = "13",
• pages = "Article No. 5842",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1424-8220",
• URL = "https://www.mdpi.com/1424-8220/23/13/5842",
• DOI = "doi:10.3390/s23135842",
• abstract = "Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an alternative solution is to derive information about the load weight indirectly from other sensors. In this paper, a static payload weight is estimated based on the local strain of a crane's girder and the current position of the trolley. Soft-computing-based techniques are used to derive a nonlinear input-output relationship between the measured signals and the estimated payload mass. Data-driven identification is performed using a novel variant of genetic programming named grammar-guided genetic programming with sparse regression, multi-gene genetic programming, and subtractive fuzzy clustering method combined with the least squares algorithm on experimental data obtained from a laboratory overhead crane. A comparative analysis of the methods showed that multi-gene genetic programming and grammar-guided genetic programming with sparse regression performed similarly in terms of accuracy and both performed better than subtractive fuzzy clustering. The novel approach was able to find a more parsimonious model with its direct implantation having a lower execution time.",
• notes = "also known as \cite{s23135842}",

}

Genetic Programming entries for Tom Kusznir Jaroslaw Smoczek

Citations