多新息理论优化卡尔曼滤波焊缝在线识别

发布时间：2018-03-08 23:35

本文选题：多新息理论　切入点：卡尔曼滤波　出处：《焊接学报》2017年03期 　论文类型：期刊论文

【摘要】：针对间隙小于0.05 mm的低碳钢对接焊缝,用磁光传感方法获取焊缝位置信息,研究多新息理论优化卡尔曼滤波在焊缝识别及跟踪中的应用.在获取磁光图像及提取焊缝位置的过程中存在较多干扰,而传统卡尔曼滤波受噪声的影响较大,难以对焊缝偏差进行最优估计.为此,结合多新息理论,提出一种焊缝位置检测的卡尔曼滤波改进算法,在对当前时刻进行预测时,充分考虑之前多个时刻的运动状态,综合历史数据估计出焊缝位置信息,对不同新息值进行试验比较并考虑计算量和滤波精度,发现选用两个新息值优化卡尔曼滤波算法可得到较好的效果.结果表明,多信息理论优化卡尔曼滤波算法可有效提高焊缝位置检测精度.
[Abstract]:For the butt weld of low carbon steel with clearance less than 0.05 mm, the position information of weld is obtained by magneto-optic sensing method. This paper studies the application of multi-innovation theory optimization Kalman filter in weld seam identification and tracking. There is more interference in the process of obtaining magneto-optic image and extracting weld position, but the traditional Kalman filter is greatly affected by noise. It is difficult to estimate the welding seam deviation optimally. In this paper, an improved Kalman filter algorithm for welding seam position detection is proposed based on the theory of multi-innovation. When predicting the current moment, the motion state of several previous moments is fully taken into account. By synthesizing the historical data to estimate the position information of the weld, the different innovation values are tested and compared, and the computational complexity and filtering accuracy are considered. It is found that the Kalman filtering algorithm with two innovative values can be used to optimize the performance of the proposed algorithm. The results show that the proposed algorithm can achieve good results. Optimization of Kalman filter algorithm based on multi-information theory can effectively improve the accuracy of weld position detection.
【作者单位】：广东工业大学广东省计算机集成制造重点实验室;佛山职业技术学院;
【基金】：国家自然科学基金资助项目(51675104) 广东省科技发展专项资金资助项目(2016A010102015) 广州市科技计划资助项目(201510010089) 广东省计算机集成制造重点实验室开放基金资助项目(CIMSOF2016008) 佛山市科技创新专项基金资助项目(2014AG10015)
【分类号】：TG441.7

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