基于电信号复合多尺度熵的CMT焊缝熔宽分类识别

Classification and recognition of weld width in CMT based on refined composite multiscale entropy of electrical signal

【目的】焊接电信号复杂性的定量表征是监测和评估焊接质量的重要方法,近似熵、样本熵仅能从单一尺度刻画焊接电信号的复杂性,无法表征电信号在多尺度上的复杂性。【方法】该文引入复合多尺度熵(Refined composite multiscale entropy,RCMSE)刻画电信号在多尺度上的复杂性,结合特征评价和支持向量机模式识别模型,提出了一种基于复合多尺度熵与支持向量机的熔宽分类识别方法。首先,将电流信号进行数据归一化,利用RCMSE提取信号多尺度复杂性特征;然后,利用特征评价方法—序列前向选择(Sequential forward selection,SFS)评估RCMSE中每个尺度的熵值对熔宽的可分性,筛选出关联度最高的熵值,构成特征向量;最后,将特征向量输入到支持向量机(Support vector machine,SVM)的熔宽多分类器中进行训练和识别。【结果】将RCMSE应用于电弧电流信号复杂性特征提取,采用序列前向特征选择对RCMSE特征值进行选择,结合SVM提出了一种焊缝熔宽分类方法。应用于堆焊试验数据分析,预测准确率可达94.4%。【结论】RCMSE能够有效地提取电信号的多尺度复杂性特征,获得较高的焊缝熔宽分类识别率,为度量熔焊过程电流信号的多尺度复杂性提供了一种方法。

[Objective]Quantitative characterization of complexity of welding electrical signals is an important method to monitor and evaluate welding quality,approximate entropy and sample entropy can only describe complexity of welding electrical signals in a single scale,but cannot represent complexity in multiscale.[Methods]In this study,refined composite multiscale entropy(RCMSE)was introduced to describe complexity of electrical signals in multiple scales.Feature selection and support vector machine pattern recognition model was combined,then a weld width classification method based on composite multiscale entropy and support vector machine was proposed.Firstly,current signal was normalized and the multiscale complexity features were extracted by RCMSE.Then,separability of entropy value of each scale to weld width was evaluated by feature selection method,that was sequential forward selection(SFS),the highest correlation entropy value was obtained to form feature vector.Finally,feature vector was input into weld width multi-classifier of support vector machine(SVM)for training and recognition.[Results]RCMSE was applied to complexity feature extraction of arc current signal,sequential forward feature selection was use to select RCMSE feature values,and a method of weld width classification was proposed based on SVM.[Conclusion]RCMSE can effectively extract multiscale complexity feature of electrical signals and obtain a high recognition rate of weld width classification,which provided a method to measure multiscale complexity of signal in welding process.