A Pre-processing Method of Mechanics Experiment Data Based on Mechanics Model

Abstract

The invention discloses a method for preprocessing mechanical experiment data based on a mechanical model. A. Receive and store 1-N groups of experimental data; B. Predetermine invalid influence parameter R0 based on material fatigue limit, and identify data filtering path and storage; C, N=1, take this group of experimental data points as the initial point and the initial filter circle center point; D, select the next group of experimental data points, determine the invalid influence parameter R and perform data filtering; E 1. Determine the next filtering direction by identifying kinks and inversion points and optimizing Chebyshev's first-order linear approximation; F. Filtering along the data path, repeating steps C and D to obtain and store filtered data. The invention has the following advantages: effectively enhance the experimental data processing and analysis method for the mechanical model, improve the speed of experimental data analysis, and solve the problem that a large amount of redundant experimental data affects the calculation efficiency of important parameters in the mechanical model.

Description

technical field [0001] The invention belongs to the field of preprocessing of mechanical experiment data, and in particular relates to a preprocessing method of mechanical experimental data based on a mechanical model. Background technique [0002] In the mechanical experiment test for material properties, it is necessary to implement different test methods for each characteristic of the material, and obtain relevant experimental data. When the relevant data volume is large, it is necessary to pre-process the data, so as to obtain a relatively simple but accurate experiment. Data for reasonable material performance analysis later. [0003] However, there is a large amount of redundant experimental data in the collection process of the existing experimental data, which affects the data processing efficiency, such as collecting small-amplitude data such as noise. Excessive amount of experimental data will require extremely high computing resources, and the processing capacity...

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Problems solved by technology

[0003] However, there is a large amount of redundant experimental data in the collection process of the existing experimental data, which affects the data processing efficiency, such as collecting small-amplitude data such as noise. The large amount of experimental data will require extremely high computing resources, and the processing capacity of big data is low

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