Dynamic system gain estimation method based on historical data steady-state value

Abstract

The invention provides a dynamic system gain estimation method based on a historical data steady-state value, and the method comprises the steps: firstly, dividing an input time sequence and an outputtime sequence into short data segments through employing a linear segmentation representation method, and finding out data segments with input and output being in a steady state at the same time; calculating input and output steady-state values from the data segment under the steady-state condition; elements, in the steady-state values, associated with the same steady-state gain in statistical significance are found, the elements are divided into one group, the steady-state gain of each group is estimated, and interval estimation of estimation parameters is given. According to the method, theinput and output steady-state values can be automatically found in the historical data sample, a plurality of steady-state gains under different working conditions can be accurately and effectively estimated, verification is carried out through a visualization method, and the problems that the time consumption for searching the steady-state values is long, the steady-state values are easily influenced by nonlinearity and the steady-state gain change is difficult to detect are solved.

Description

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AI Technical Summary

Benefits of technology

This technology helps make sure that there's no problem finding an accurate estimate of gains or other physical quantities during operation without having too much effort on your part by providing a way to see how well it works compared against previous methods such as looking at just one point instead of all points along its length.

Problems solved by technology

This patents discusses various ways how stable or unstable states may affect physical processes such as dynamics and performance. These changes require accurate estimation during the time it takes to operate these machines accurately. Current estimates involve either destructive testing (such as measuring resistance) or estimating static parameters with specific test procedures. Additionally, current approaches only provide limited results due to factors like environmental effects caused by temperature variations or other variables.

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