Stress data classification method for pull rod in quay crane based on K-nearest neighbor algorithm

A data classification and nearest neighbor technology, applied in computing, computer components, instruments, etc., can solve problems that cannot meet engineering needs well, and achieve high precision, easy to understand and implement, and mature theoretical effects

Active Publication Date: 2018-06-29
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

[0004] The manual detection method currently used does not need to spend a lot of manpower and material resources, and requires specialized and experienced engineers to carry out the work. However, the actual results still cannot well meet the actual engineering needs.

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  • Stress data classification method for pull rod in quay crane based on K-nearest neighbor algorithm
  • Stress data classification method for pull rod in quay crane based on K-nearest neighbor algorithm
  • Stress data classification method for pull rod in quay crane based on K-nearest neighbor algorithm

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example

[0047] This paper selects the strain data of four weeks from the end of 2009 to the beginning of 2010 measured at the cross-section of the tie rod 2 meters away from the hinge in the quay bridge as a sample for classification. One data is collected every 10 seconds or so, and about 7000-8000 data can be collected in one day. Write the K-nearest neighbor algorithm with MATLAB software for data classification and analysis and drawing. The three training sets traindata1, traindata2, and traindata3 are set, and the classification categories are light load, medium load, and heavy load. See Table 1 below for the stress level. The samples in the training set correspond to several random points in the category. For the accuracy of the algorithm application, K=1 is selected, and the Euclidean distance between the test sample and the training sample is calculated. The test data belongs to the most recent category of training samples, and the classification is completed. .

[0048] Tab...

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Abstract

The invention discloses a stress data classification method for a pull rod in a quay crane based on a K-nearest neighbor algorithm. According to the method, stress value data is collected through a strain sensor on a section, 2 meters away from a hinge, of the pull rod in the quay crane, and then the data is classified through the K-nearest neighbor algorithm; and stress of a pull rod metal structure under different operation of the quay crane is analyzed by performing statistical analysis on a load state on the section, 2 meters away from the hinge, of the pull rod in the quay crane, and therefore a conclusion is obtained. The working state of the quay crane is acquired from mass engineering actual stress value data, conditions produced in actual use of the quay crane can be analyzed andcontrolled beneficially, and it is convenient to prolong the service life of the quay crane. The method based on the K-nearest neighbor algorithm has the advantages of being simple, easy to use, easyto understand, high in precision and mature in theory and having a better quay crane data classification effect.

Description

technical field [0001] The invention belongs to the field of data classification of quay cranes, in particular to a method for classifying stress data of tension rods in quay cranes based on the K-nearest neighbor algorithm. Background technique [0002] As an important loading and unloading equipment in container ports, quay cranes are widely used in container port terminals. Whether the quayside crane can work normally directly affects the production efficiency and economic benefits of the port. Therefore, the data mining and state recognition of the mechanical characteristic information of the quay crane has become the mainstream research direction. By installing sensors on the key parts of the quay crane, a large amount of data information is collected. These data are collected during the operation of the quay crane and reflect the The mechanical characteristics of the bridge under various working conditions, because the stress of the metal structure of the quay bridge ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24147
Inventor 唐刚沈佳莉胡雄顾邦平
Owner SHANGHAI MARITIME UNIVERSITY
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