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A DBSCAN algorithm shore bridge state classification method based on principal component analysis

A technology of principal component analysis and state classification, applied in computing, computer components, instruments, etc., can solve problems such as large impact on clustering results and increased time complexity of DBSCAN algorithm

Inactive Publication Date: 2019-04-26
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

However, in the DBSCAN algorithm, there are still problems that the selection of the parameter value Eps has a great influence on the clustering results, and the time complexity of the improved DBSCAN algorithm increases

Method used

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  • A DBSCAN algorithm shore bridge state classification method based on principal component analysis
  • A DBSCAN algorithm shore bridge state classification method based on principal component analysis
  • A DBSCAN algorithm shore bridge state classification method based on principal component analysis

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[0107] The experimental data comes from the vibration signal on the output end of the left hoisting motor of the quay crane, and the vibration data of three measuring points at the output end of the left hoisting motor of the quay crane were selected from 0:00 on December 28, 2009 to 23:00 on January 3, 2010 As a sample, about 8,000 pieces of data are collected every day. The principal component of the data on December 28, 2009 was used to obtain the contribution of the data points in the three dimensions, and the coordinates were transformed into two-dimensional data. Draw the k-dist diagram to determine the parameters as density threshold MinPts=4, density radius Eps=0.8, taking the data on December 28 as an example, a total of 12 clusters were divided, one of which contained 7532 data, accounting for 95.46 of the total data %, we call it the main cluster, and the other 11 clusters have a total of 68 data, accounting for 0.86%, and are collectively called the secondary clust...

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Abstract

According to the DBSCAN algorithm shore bridge state classification method based on principal component analysis, sensors are installed at all positions of a shore bridge, and data transmitted by thesensors are extracted at set time intervals; Through principal component analysis, after data centralization, a characteristic covariance matrix and characteristic values and characteristic vectors thereof are obtained, the contribution degree of each component is calculated, and a plurality of previous-order principal components are taken to carry out matrix transformation; And initializing a density threshold value and a density radius for data point clustering, realizing shore bridge state classification according to a clustering result, and realizing shore bridge state monitoring. Accurateand rapid clustering of the quay crane state is achieved, non-circular domain distribution data can be clustered, a good effect is achieved, compared with a common DBSCAN algorithm, time complexity is reduced, clustering efficiency and accuracy are improved, and abnormal data can be well recognized.

Description

technical field [0001] The invention relates to the field of port machinery, in particular to a state classification method for quay bridges with noise-based density-based spatial clustering algorithms based on principal component analysis. Background technique [0002] The use of clustering algorithms to classify the status of quay cranes can improve the efficiency and accuracy of classification, and can filter out useful information from a large amount of data, which is helpful for comprehensive and systematic understanding of equipment information, to achieve monitoring of equipment status and provide information on equipment maintenance. in accordance with. Due to the irregular and discrete characteristics of data distribution, the general distance-based clustering method is only effective for clustering data distributed in approximately circular areas. Therefore, finding an effective clustering method becomes the key to improving the effect of data clustering. [0003...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/23
Inventor 唐刚施皓正胡雄
Owner SHANGHAI MARITIME UNIVERSITY
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