基于改进NSGA3的焊接缺陷评估特征选择

doi:10.12305/j.issn.1001-506X.2022.07.17

Abstract

Abstract:

Feature selection plays an important role in welding defect assessment based on multi-source information fusion because of improved detection accuracy and speed. The multi-source feature set consists of arc voltage features and arc sound features and is characterized by the presence of redundant and complementary features in the feature set. Therefore, this paper proposes a multi-objective feature selection method based on the improved non-dominated sorting genetic algorithm-Ⅲ (NSGA3), aiming to find the optimal feature subset in a multi-source feature set. Firstly, the method analyses the feature set for correlation, redundancy, and complementary characteristics, and then builds a multi-objective feature selection optimization model with the objective of minimizing redundancy and maximizing correlation and complementarity. Then a new mutation operator is proposed to guide the mutation process based on redundancy and complementary evaluation functions to reduce the influence of invalid features and improve convergence efficiency. Support vector machine is used as learners to verify the learning effect in the experiments. The results show that the proposed method can achieve better performance in terms of feature subset dimension and prediction accuracy compared with the other three methods.

Key words: improved non-dominated sorting genetic algorithm-Ⅲ (NSGA3), redundancy, complementarity, feature selection, mutation operator

CLC Number:

TP391

Bo LI, Jiahao ZHOU, Minmin LIU, Pinchao ZHU. Feature selection for welding defect assessment based on improved NSGA3[J]. Systems Engineering and Electronics, 2022, 44(7): 2211-2218.

Figures/Tables 11

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References 28

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编号	名称	编号	名称
1	均方根值	7	脉冲因子
2	整流平均值	8	功率谱熵^[25]
3	样本方差	9	能量熵^[26]
4	峰-峰值	10	奇异谱熵^[27]
5	波形因子	11	频率标准差
6	峰值因子	-	-

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Feature selection for welding defect assessment based on improved NSGA3

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