基于VMD能量熵和GA-SVM的焊接冷裂纹声发射信号分类方法研究

Research on classification method of welding cold crack’s AE signals based on VMD energy entropy and GA-SVM

针对低合金高强钢焊接过程中存在焊接工艺不规范导致的延迟冷裂纹,提出一种监测与分类识别方法。首先利用声发射(AE)技术对焊后试件进行持续监测,之后根据出现冷裂纹试件的监测数据选取冷裂纹声发射信号中的起裂信号和氢聚信号,并对两者进行基于变分模态分解(VMD)的能量熵特征提取,最后利用遗传算法(GA)将传统支持向量机(SVM)进行改进后对信号进行分类识别,并结合传统支持向量机(SVM)的识别分类结果进行对比。同时为了验证VMD能量熵相较于其他能量熵在特征提取上的精准性,将提取冷裂纹声发射信号的EMD能量熵和CEEMDAN能量熵进行分类识别效果对比。结果表明,利用VMD能量熵作为焊接冷裂纹声发射信号的识别特征相较于其他能量熵特征识别精度更高,且随着支持向量机的优化识别精度会进一步提高到95%。

Aiming at the delayed cold crack caused by non-standard welding process in the welding process of low-alloy high-strength steel,a monitoring and classification method is proposed.First,acoustic emission(AE)technology is used to continuously monitor the post-welding specimen.Then,crack initiation signal and hydrogen aggregation signal in the AE signal of cold crack are selected according to the monitoring data of the specimen with cold crack,and energy entropy feature extraction based on variational mode decomposition(VMD)is carried out for both.Finally,genetic algorithm(GA)is used to improve the traditional support vector machine(SVM)to classify and recognize the signal,and the recognition and classification results of the traditional SVM are compared.At the same time,in order to verify the accuracy of VMD energy entropy in feature extraction,the EMD energy entropy and CEEMDAN energy entropy of cold crack acoustic emission signal extraction were compared for classification and recognition effect.The results show that when the VMD energy entropy is used as the identification feature of the acoustic emission signal of welding cold crack,the recognition accuracy is higher than other energy entropy features,and the recognition accuracy will further increase to 95%with the optimization of support vector machine.