Load Weight Classification of The Quayside Container Crane Based On K-Means Clustering Algorithm

2nd International Conference on Design, Materials, and Manufacturing

材料科学;机械制造

Zhang, Bingqian^1 ; Hu, Xiong^2 ; Tang, Gang^2 ; Wang, Yide^3

Logistics Research Center, Shanghai Maritime University, China^1

Logistics Engineering College, Shanghai Maritime University, China^2

IETR CNRS UMR6164, University of Nantes, France^3

关键词: Axial direction;    Correlation analysis;    K-Means clustering algorithm;    Load classification;    Load weights;    Quayside container cranes;    Radial direction;    Vibration intensity;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/220/1/012034/pdf
DOI  :  10.1088/1757-899X/220/1/012034

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The precise knowledge of the load weight of each operation of the quayside container crane is important for accurately assessing the service life of the crane. The load weight is directly related to the vibration intensity. Through the study on the vibration of the hoist motor of the crane in radial and axial directions, we can classify the load using K-means clustering algorithm and quantitative statistical analysis. Vibration in radial direction is significantly and positively correlated with that in axial direction by correlation analysis, which means that we can use the data only in one of the directions to carry out the study improving then the efficiency without degrading the accuracy of load classification. The proposed method can well represent the real-time working condition of the crane.