Extension Research On The Theory And Application Of Dynamic Uncertainty

Compared with static measurement systems, dynamic measurement systems are more widely used. But under most circumstance, the measurement uncertainty evaluations of dynamic measurement systems are staticize as static measurement systems. This kind of method is more and more inapplicable for the dynamic measurement systems whose need more and more dynamic and real time performances. That is because the dynamic uncertainty has a lot of deficiencies in the theory and application methods. This thesis aims to do researches in this area, including dynamic uncertainty evaluation, outlier data elimination, true value evaluation, multi-observers and multi-dimensions processing method, etc.The characters of dynamic measurement data are discussed first, and then some models are selected to describe the components of dynamic measurement data and evaluate the dynamic measurement uncertainty. Based on the evaluation result, a confidence distance is defined to describe the consistency of the data from different observers. After that consistency matrix can be got, and the outlier data can be detected by solving the matrix. A spatial redundancy model and its data processing method are set up to resolve the reliability problem of the dynamic measurement system whose reliability usually cannot be improved by time repeat measurement. To evaluate the measurement uncertainty of multi-dimension measurement results, the multi-dimension measurement theory are also studied, including multi-dimension measurement transfer rule, geometric meaning of multi-dimension measurement result, the comparison method of different multi-dimension result, and the true value evaluation method of multi multi-dimension observers.To provide more processing methods for dynamic measurement uncertainty, the applicable algorithms of every theory in this thesis are also provided in every part. Especially, some key technique, such as picking up method of the maximum consistency measurement data set, and reduction method of rounding off errors, are discussed detailedly. Some algorithms, such as searching maximum consistency measurement data set by graph theory and logical AND operation, and hierarchical combine method are set up to solve the problem. As the measurement uncertainty theory and its evaluating are usually not treated as a necessary step, a method using measurement uncertainty as a key parameter to build systems and algorithms are discussed in this thesis. Based on this method, a set of algorithms, such as filtering method, surface enhanced Raman scattering signal processing algorithm, sensor failure detective method, data fusion algorithm, and multi-platform locating method, are set up. After simulation and using in real system, these methods are proved to have good performances. The using methods of measurement uncertainty also supply a new way to spread measurement uncertainty theory.