陈星燎,刘 通,卢 姗,房雪键.高精度电子分析天平参数估计与滤波[J].电子测量与仪器学报,2023,37(10):65-73
高精度电子分析天平参数估计与滤波
High-precision electronic analytical balance parameter estimation and filtering
  
DOI:
中文关键词:  电子分析天平  AR 参数估计  混合滤波
英文关键词:electronic analytical balance  AR parameter estimation  hybrid filtering
基金项目:天津自然基金面上项目(21JCYBJC00670)资助
作者单位
陈星燎 1. 天津中德应用技术大学智能制造学院 
刘 通 1. 天津中德应用技术大学智能制造学院 
卢 姗 2. 天津市科学技术发展战略研究院 
房雪键 1. 天津中德应用技术大学智能制造学院 
AuthorInstitution
Chen Xingliao 1. Intelligent Manufacturing College, Tianjin Sino-German University of Applied Sciences 
Liu Tong 1. Intelligent Manufacturing College, Tianjin Sino-German University of Applied Sciences 
Lu Shan 2. Tianjin Academy of Science and Technology for Development 
Fang Xuejian 1. Intelligent Manufacturing College, Tianjin Sino-German University of Applied Sciences 
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中文摘要:
      电子分析天平的传感器结构和测量电路比较复杂,不能够精确求出系统的传递函数。 为了估计出系统的传递函数并提 高测量数据的信噪比,先通过拉氏变换的方法估计了系统传递函数的阶数,推导了稳态下系统的状态方程,通过自回归方法估 计系统参数并结合卡尔曼滤波的方法对测量结果滤波。 试验通过离线数据估计出了方程参数和噪声强度,并验证了测量过程 数据的平稳性,显著性水平低至 0. 001。 参数估计加卡尔曼滤波混合方法与普遍采用的滑动窗口滤波法作了比较,新方法的平 滑性和稳定性均有显著提高,测量标准差可达原有方法的 30%,线性度可达 6. 7×10 -5 ,响应时间较原方法低至 10%。 4 个样品 的试验数据验证了该方法的可行性和有效性。
英文摘要:
      The sensor structure and measurement circuit of electronic analytical balances are complex and cannot accurately calculate the transfer function of the system. In order to estimate the transfer function of the system and improve the signal-to-noise ratio of the measurement data, the order of the transfer function of the system is estimated by the method of Laplace transform, the equation of state of the system under steady state is derived, and the method of estimating the system parameters by autoregressive method and combining with Kalman filtering is proposed to filter the measurement data. Through the experimental estimation of equation parameters and noise intensity from offline data, the filtered data verify the smoothness of the process, and the significant level is as low as 0. 001. Compared with the commonly used sliding window filtering method, the smoothness and stability of the new method are significantly improved, the measurement standard deviation is 30% of the original method, the linearity can reach 6. 7×10 -5 , and the response time is as low as 10%. The experimental data of four samples verify the feasibility and effectiveness of the proposed method.
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