Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding
Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding
复制标题
基于多传感器的铝合金电弧焊特征提取、选择和建模实时质量监控
DOI：
10.1016/j.ymssp.2014.12.021
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发表时间：
2015-08-01
影响因子：
8.4
通讯作者：
Chen, Shanben
中科院分区：
文献类型：
--
作者：
Zhang, Zhifen;Chen, Huabin;Chen, Shanben
基于多传感器数据融合的焊接质量在线监测在智能焊接过程中受到越来越多的关注。本文主要研究铝合金钨极氩弧焊（GTAW）中典型焊接缺陷的自动检测，通过对电弧光谱、声音和电压信号的分析，实现了对典型焊接缺陷的自动检测。基于所开发的时域和频域算法，从这些信号中连续提取了41个特征参数来表征焊接过程和焊缝质量。然后，所提出的特征选择方法，即，混合的基于SVM的过滤器和包装器被成功地用于评估每个特征的灵敏度并降低特征维度。最后，选择具有19个特征的最佳特征子集以获得最高的准确度，即，94.72%使用已建立的分类模型。该研究为基于异质多传感器数据的特征提取、选择和动态建模提供了指导，以实现可靠的弧焊在线缺陷检测系统。(C)2015爱思唯尔有限公司版权所有。
Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding. (C) 2015 Elsevier Ltd. All rights reserved.