论文标题:数据校正技术的研究及应用
Research on Data Reconciliation and Its Application
论文作者 周凌柯
论文导师 褚健；苏宏业,论文学位 博士,论文专业 控制科学与工程
论文单位 浙江大学,点击次数 82,论文页数 99页File Size3290k
2005-01-01论文网 http://www.lw23.com/lunwen_107160422/ 数据校正;数据协调;显著误差检测;测量网冗余性分析
Orthogonal Frequency Division Multiplexing (OFDM); Burst Mode; Synchronization; Low- Complexity Receiver
可靠的化工过程数据是过程控制、优化操作和过程性能评估等的基础。过程实际测量数据不可避免地带有随机误差，有时甚至带有显著误差。数据校正技术利用数据的冗余性，剔除原始数据中的显著误差，对数据进行校正以降低随机误差对测量值的影响并使其满足物料或者能量平衡方程，并设法估计出未测变量，保证了数据的有效性。本论文对数据校正技术进行了较系统的研究，并在以下几个方面取得了进展： 1．迭代测量残差检验法(IMT)法尽管是一种有效的检测显著误差的方法，但是由于它利用最小二乘法求解得到的数据校正值构造检验统计量，因而容易造成显著误差误判。在本论文中，我们讨论了测量噪声存在相关性的情况，对此进行了改进，改进后的方法给出了更好的显著误差检测结果。 2．提出了一种改进的顺序识别并同步补偿法(SICC)。改进后的算法利用时间冗余性，通过加入对过程测量变量的上下限约束，避免了显著误差的误判。并且通过对显著误差进行逐步的幅度补偿，再采用测量残差检验法(MT)找出候选显著误差集，避免了投影矩阵的计算。通过加入必要的回路检测，避免了幅度补偿后矩阵奇异性的产生，仿真结果表明了改进算法的有效性。 3．基于测量网络回路，给出了变量冗余度的数学表示。为了避免在设计测量网络时对变量给出不可行的冗余度，分析了变量冗余度的上限，为测量网络的合理设计提供了理论指导。利用图论方法和整数线性规划方法相结合，建立了以费用最小为目标的最小测量网络和满足指定变量冗余度要求的冗余测量网络的传感器优化配置模型。仿真结果表明了以上方法的有效性。 4．提出了求解非线性化工过程鲁棒数据校正的新方法，此种求解方法计算上非常简便。通过使用线性化方法、罚函数法、虚拟测量方程和等价权法，鲁棒数据校正问题被转化为最小二乘估计问题。对一个非线性化工过程进行仿真研究，说明了这种方法的有效性。 5．在分析了动态数据校正中传统离群值检测法局限性的基础上，给出了改进的离群值检测法，改进后的方法能够更有效地利用正常数据的信息并且降低离群值的误判，对一个典型动态系统的仿真结果证明了其有效性。浙江大学博士学位论文 6.利用证据决策理论对显著误差进行检测，本论文中我们考虑了系统的泄漏情况，并且引入了环境节点这一虚拟节点约束方程，把此虚拟节点作为证据理论中一个证据，并对此进行了仿真研究。 7.针对安庆石化全厂物流数据校正问题，详细讨论了如何建立数据校正所需的统计模型。 最后总结了全论文的工作，并对数据校正技术在理论和应用的进一步研究方向提出了看法。
Reliable process data is the foundation of efficient process control, process operation and evaluation of process performance. However, Process measurements inevitably contain random errors and sometimes even contain gross errors. By using the redundancy in process measurements, data reconciliation can be used to eliminate measurements with gross errors, reduce the effect of random errors and make measurements comply with the conservation laws, such as the conservation laws of mass and energy balances. And therefore, the unmeasured data can be estimated. This dissertation studies the problem of data reconciliation systematically, and makes progresses in the following aspects:1. Iterative Measurement Test (IMT) method is an effective gross error detection method. But least square method is used in IMT to attain reconciled results to construct statistical value and therefore gross errors can be easily misidentified. Under the condition that measurement errors of process variable are correlated, a modified IMT method is introduced which can increase the power of correctly identifying gross error.2. A modified Serial Identification with Collective Compensation (SICC) method is introduced. By using temporal redundancy, upper and lower bounds of process measurements are added in the modified SICC method to avoid the misidentification of gross errors. To prevent the computation of project matrix, measurements are compensated by using the estimated gross error magnitudes after each gross error is identified. And after that, Measurement Test (MT) is used to find the gross error candidates. Necessary cycle detection is added to avoid the singular matrix appearing after gross error compensation,. Simulation results verify the effectiveness of modified algorithm.3. Based on the analysis of the cycles for sensor networks, mathematical representation of redundancy degree is introduced. To avoid infeasible solution, bound of redundancy degree is analyzed to give theoretical guides for the design of sensor networks. Based on a combination of concepts drawn from graph theory and Integer Linear Programming (ILP) methods, nonredundant sensor networks with minimum cost and redundant sensor networks that satisfy constraints related to redundancy degree of key variables, are established. The obtained method is verified by simulation results.4. A new method to solve robust data reconciliation problem of nonlinear chemical process is proposed. This method is very convenient in computation. Byusing several technologies including linearization method, penalty function, virtual observation equation and equivalent weights method, the robust data reconciliation problem can be transformed into a least squares estimator problem. Simulation results for a nonlinear chemical process demonstrate the efficiency of the proposed approach.5. Based on the analysis of the limitations for the existing methods, a modified approach of dynamic data reconciliation and outlier detection is presented. This method can use more information of normal data, and can efficiently decrease the effect of outliers. The simulation results on a CSTR process verify the effectiveness of the obtained algorithm.6. The application of Dempster-Shafer theory in gross error detection is discussed. The situation when leak appears in the process is considered. The environmental node is introduced as one proof in the Dempster-Shafer theory. The result is verified by simulation.7. For the problem of mass flow data reconciliation in Anqing petrochemical company, the procedure of building an efficient and user-required linear account balance model is discussed in detail.The dissertation is concluded with a summary and prospect of future data reconciliation researches.
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