数据协调与过失误差侦破的鲁棒估计同步方法

Simultaneous gross error detection and data reconciliation based on the robust estimation principle

为获得较准确的满足物料平衡和热量平衡的数据 ,传统的测量数据检验法 ( MIMT)计算复杂 ,而已有的数据协调和过失误差侦破的同步方法存在着不能准确识别过失误差等问题。基于鲁棒估计原理以及影响函数提出了一种新的鲁棒估计方法 ,用于稳态情况下的数据协调和过失误差侦破 ,并对计算过程中遇到的变量相关性问题进行了分析。计算结果表明 ,这种方法对线性和非线性问题都具有良好的效果 ,在侦破出过失误差的同时 。

Data acqvisition subject to both mass and energy balances using the conventional Modified Iterative Measurement Test (MIMT) is very complex, but other simultaneous methods for gross error detection and data reconciliation do not work well. The robust estimation principle and influence function were used to propose a new robust approach which can be used with steady state systems for simultaneous gross error detection and data reconciliation. The calculation results show that the approach provides good reconciled data, and at same time, can correctly identify gross errors for both linear and nonlinear systems.