Assistance system for an automated log-quality and assortment estimation based on data-driven approaches using hydraulic signals of forestry machines

Geiger, Chris; Maier, Niklas; Kalinke, Florian; Geimer, Marcus

doi:10.25368/2020.97

Cited by 2 publications

(1 citation statement)

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“…The ANN model is used to estimate a payload mass for a hydraulic forestry crane based on the hydraulic pressure of the inner boom cylinder and the grapple position [20,21]. The vision system and convolutional neural network are reported to recognize a wood log weight for a forestry crane [22]. The quay crane load identification method based on the signals from an accelerometer and strain gauge sensors installed on the crane's structure parts is reported in [23].…”

Section: Introductionmentioning

confidence: 99%

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

Kusznir

Smoczek

2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%