1.中交第二航务工程局有限公司 武汉 430040; 2.长大桥梁建设施工技术交通行业重点实验室 武汉 430040; 3.交通运输行业交通基础设置智能制造技术研发中心 武汉 430040; 4.中交公路长大桥建设国家工程研究中心有限公司 武汉 430040
TU641
Cheng Xuecong
1.CCCC Second Harbor Engineering Company LTD，Wuhan 430040, China；2.Key Laboratory of LargeSpan Bridge Construction Technology，Wuhan 430040, China； 3.Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure，Wuhan 430040, China；4.CCCC Highway Bridge National Engineering Research Centre Co., Ltd.，Wuhan 430040, ChinaZhang Yipeng
1.CCCC Second Harbor Engineering Company LTD，Wuhan 430040, China；2.Key Laboratory of LargeSpan Bridge Construction Technology，Wuhan 430040, China； 3.Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure，Wuhan 430040, China；4.CCCC Highway Bridge National Engineering Research Centre Co., Ltd.，Wuhan 430040, ChinaDong Qifeng
1.CCCC Second Harbor Engineering Company LTD，Wuhan 430040, China；2.Key Laboratory of LargeSpan Bridge Construction Technology，Wuhan 430040, China； 3.Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure，Wuhan 430040, China；4.CCCC Highway Bridge National Engineering Research Centre Co., Ltd.，Wuhan 430040, ChinaJi Xiaoyu
1.CCCC Second Harbor Engineering Company LTD，Wuhan 430040, China；2.Key Laboratory of LargeSpan Bridge Construction Technology，Wuhan 430040, China； 3.Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure，Wuhan 430040, China；4.CCCC Highway Bridge National Engineering Research Centre Co., Ltd.，Wuhan 430040, China1.CCCC Second Harbor Engineering Company LTD，Wuhan 430040, China;2.Key Laboratory of LargeSpan Bridge Construction Technology，Wuhan 430040, China; 3.Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure，Wuhan 430040, China;4.CCCC Highway Bridge National Engineering Research Centre Co., Ltd.，Wuhan 430040, China
随着基础设施建设工程走向海洋，船载式混凝土搅拌站得到了大量的应用，但其在波浪激励下会产生升沉、横摇、纵摇、横荡、纵荡的复合运动，从而会导致计量系统产生偏差。基于此给出一种基于周期滑动平均卡尔曼滤波的船载式称重传感器误差抑制算法。首先，通过传统卡尔曼滤波对原始数据进行处理，消除其中的随机误差；然后，通过短时傅里叶变换对数据进行频谱分析，获取周期性误差的频率特征；最后通过滑动窗口均值滤波来消除系统中的周期性误差。通过6自由度实验平台模拟存在波浪激励时船舶的运动情况并通过三点秤进行计量称重，分别记录不同算法处理后的称重数据。实验结果表明，原始称重数据最大误差为96%；卡尔曼滤波处理的称重数据最大误差为21%；本文给出算法处理的称重数据最大误差为03%，该算法能够有效消除由于周期性波浪激励所造成的周期性误差和传感器本身所产生的随机误差，提高船载式混凝土搅拌站的计量精度。
With the infrastructure construction project to the sea, the shipborne concrete batching plant has been widely used, but its compound movement of rising and sinking, horizontal rocking, horizontal and vertical under wave excitation will occur, which will lead to deviation of the metering system. Based on this, an error suppression algorithm of shipborne load cell based on periodic sliding average Kalman filtering is proposed. Firstly, the original data is processed by traditional Kalman filtering to eliminate random errors. Then, the spectrum analysis of the data is carried out by shorttime Fourier transform to obtain the frequency characteristics of periodic error. Finally, periodic errors in the system are eliminated by sliding window mean filtering. Through the sixdegreeoffreedom experimental platform, the movement of the ship in the presence of wave excitation is simulated and weighed by measuring and weighing through the threepoint scale, and the weighing data processed by different algorithms are recorded separately. The experimental results show that the maximum error of the original weighing data is 96%. The maximum error of the weighing data processed by the Kalman filter is 21%. In this paper, the maximum error of the weighing data processed by the algorithm is 03%, which can effectively eliminate the periodic error caused by periodic wave excitation and the random error generated by the sensor itself, and improve the measurement accuracy of the shipborne concrete batching plant.
程雪聪,张益鹏,董奇峰,纪晓宇.基于周期滑动平均卡尔曼滤波的船载式称重传感器误差抑制算法[J].电子测量技术,2023,46(17):149-154
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