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Ship coating defect knowledge acquisition method based on PCA-rough set

A knowledge acquisition, rough set technology, applied in jetting devices, special data processing applications, instruments, etc., can solve the problems of data doping noise and interference terms

Active Publication Date: 2019-10-01
JIANGSU UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, not all information is necessary. The data is often mixed with a lot of noise and interference items. Therefore, the data needs to be preprocessed before dimensionality reduction to prevent it from adversely affecting the final data results.

Method used

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  • Ship coating defect knowledge acquisition method based on PCA-rough set
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  • Ship coating defect knowledge acquisition method based on PCA-rough set

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Embodiment Construction

[0039] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] Such as figure 1 The method for acquiring knowledge of ship coating defects based on PCA-rough sets includes the following steps:

[0041] S1: Select the target dataset:

[0042] The target data set is selected from the ship coating process database and the ship coating defect case library. The target data set includes attribute information, parameter information and environmental information. Among them, the attribute information includes painting area, painting area, surface roughness, Rust level, coating method, coating equipment, etc.; parameter information includes paint viscosity, aerodynamic force, spray distance, paint transfer rate, etc.; environmental information includes air velocity, relative humidity, air temperature, etc.

[0043] S2: Data inspection and data preprocessing:

[0044] Check and preprocess...

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Abstract

The invention discloses a ship coating defect knowledge acquisition method based on a PCA-rough set. The ship coating defect knowledge acquisition method comprises the following steps: selecting a target data set from a ship coating process database and a ship coating defect case library; checking and preprocessing the selected target data set; carrying out dimension reduction processing on the ship coating defect multi-source heterogeneous data by utilizing principal component analysis; performing knowledge acquisition on ship coating defect data by using a rough set theory; and carrying outclassification and storage of knowledge. When knowledge acquisition is carried out, various types of data such as a continuous type and a discrete type can be processed, and the problem that the datatypes are inconsistent does not need to be considered; the PCA-rough set knowledge acquisition method can acquire ship coating defect cause knowledge, the current situation that ship coating defects can only be detected later is changed, and advanced prevention and control of the coating defects are achieved.

Description

technical field [0001] The invention relates to the field of knowledge acquisition of ship coating defects, in particular to a method for acquiring knowledge of ship painting defects based on PCA-rough sets. Background technique [0002] During the construction process of ship coating, various defects will occur due to improper operation, environmental changes during drying and curing, or the quality of the coating itself. According to empirical inferences and investigations by relevant organizations, 80% of coating defects are caused by improper operation of construction personnel during the construction process. Clarifying the causes of these defects can give a strong guidance to the coating operation process and reduce the number of defects caused by irregular operations. coating defects. The traditional coating process inspects the coating quality within a certain period of time after the construction is completed, judges and records the types and levels of defects, and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/22B05B12/16B05B12/12B05B12/08B05B12/00
CPCG06F16/2465G06F16/22B05B12/16B05B12/12B05B12/084B05B12/124B05B12/00Y02P90/30
Inventor 卜赫男韩子延蔺明宇刘迪刘金锋李磊周宏根景旭文
Owner JIANGSU UNIV OF SCI & TECH
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