CN110162853B - Mechanical experiment data preprocessing method based on mechanical model - Google Patents
Mechanical experiment data preprocessing method based on mechanical model Download PDFInfo
- Publication number
- CN110162853B CN110162853B CN201910383017.2A CN201910383017A CN110162853B CN 110162853 B CN110162853 B CN 110162853B CN 201910383017 A CN201910383017 A CN 201910383017A CN 110162853 B CN110162853 B CN 110162853B
- Authority
- CN
- China
- Prior art keywords
- data
- experimental data
- group
- filtering
- mechanical model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a mechanical experiment data preprocessing method based on a mechanical model, which comprises the following steps of A, receiving and storing 1-N groups of experiment data; B. presetting an invalid influence parameter R0 based on the material fatigue limit, and identifying and storing approximate inflection points in the data filtering path; C. when N is 1, taking the group of experimental data points as an initial point and an initial filtering circle center point; D. selecting a next group of experimental data points, determining an invalid influence parameter R and filtering data; E. determining the next filtering direction through the kink, the identification of the reversal point and the optimization of Chebyshev first-order linear approximation; F. and D, filtering along the data path, and repeating the step C and the step D to obtain filtered data and storing the filtered data. The invention has the following advantages: the method for processing and analyzing the experimental data of the mechanical model is effectively enhanced, the analysis speed of the experimental data is improved, and the problem that a large amount of redundant experimental data influences the calculation efficiency of important parameters in the mechanical model at present is solved.
Description
Technical Field
The invention belongs to the field of mechanical experiment data preprocessing, and particularly relates to a mechanical experiment data preprocessing method based on a mechanical model.
Background
In a mechanical experiment test aiming at material performance, different test methods are required to be implemented on various characteristics of a material, relevant experiment data are obtained, and when the quantity of the relevant data is large, the data needs to be preprocessed, so that simpler but accurate experiment data are obtained, and reasonable material performance analysis is carried out at a later stage.
However, in the process of acquiring the existing experimental data, a large amount of redundant experimental data affects the data processing efficiency, such as acquiring small-amplitude data such as noise, and meanwhile, the material experimental data obtained through the experiment has a large data volume due to different loading conditions, and the mechanical experimental data has extremely high requirements on computing resources and low large data processing capacity due to the overlarge data volume. Through retrieval, the method for processing the fatigue life test data of the material disclosed in patent No. CN102798568A, the method for processing the fatigue test data of the metal structure disclosed in patent No. CN107967400A and analyzing the reliability of the fatigue test data of the metal structure disclosed in patent No. 109001779A, and the method and the device for filtering the trajectory data of the set filtering interval do not solve the defects.
Disclosure of Invention
The invention aims to provide a mechanical experimental data preprocessing method based on a mechanical model aiming at the defects of the prior art, which effectively enhances an experimental data processing and analyzing method aiming at the mechanical model under the condition of ensuring the accuracy of experimental data through preprocessing the experimental data, improves the analysis speed of the experimental data and solves the problem that a great amount of redundant experimental data influences the calculation efficiency of important parameters in the mechanical model at present.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a mechanics experimental data preprocessing method based on mechanics model comprises the following steps,
A. receiving and storing 1-N groups of experimental data; the method comprises the following specific steps:
a1, receiving 1-N groups of experimental data acquisition requests sent by a client, wherein the experimental data acquisition requests comprise tension-torsion fatigue experimental data of a Crossland model, axial and torsion load data corresponding to specific parameters in a mechanical model and load and displacement acquired in the experimental process, and solving positive stress and shear stress;
a2, determining a model parameter solving process according to a model concrete form for a concrete mechanical model, judging experimental data selected for the mechanical model, and determining the solving process of the experimental data according to the mechanical model contained in a request after a server receives a data request sent by a client;
a3, selecting the Nth group of experimental data, wherein the data are axial force and shearing force obtained by tension-torsion fatigue experiment measurement, N is set to be 1-N to represent the processing sequence of the experimental data, and N is gradually increased along with the reading of the experimental data;
B. presetting an invalid influence parameter R0 based on the material fatigue limit, and identifying and storing approximate inflection points in the data filtering path; the method comprises the following specific steps:
b1, presetting an ineffective influence parameter R0 based on the fatigue limit of the material;
b2, keeping the invalid influence parameter R unchanged, and then always keeping the invalid influence parameter R to be R0, and performing the steps C, D, E and F;
b3, obtaining the filtered data in the previous step, namely the approximate inflection point, and storing the data.
C. When N is 1, taking the group of experimental data points as an initial point and an initial filtering circle center point;
D. selecting a next group of experimental data points, determining an invalid influence parameter R and filtering data; the method comprises the following specific steps:
d1, selecting the Nth group of experimental data;
d2, determining an Nth invalid influence parameter range RN according to the corresponding Nth set of mechanical model parameters, wherein the first invalid influence parameter range R1 is determined by material properties according to the corresponding mechanical model;
d3, selecting the Nth group of experimental data, and comparing the first invalid influence parameter range R1 in the step d2 to judge whether the data is reserved, wherein the judgment specifically comprises the following steps:
the Crossland model is formulated as follows.
Wherein α c and β c are material constants, σ max is a maximum hydrostatic pressure value, Δ τ Mises is a shear stress amplitude of Von Mises, an invalid influence parameter range RN is set to Δ τ Mises/2 according to a crossbar mechanical model, an RN value can be determined by material properties according to a corresponding mechanical model, the relation between the Nth group of experimental data and RN is compared, if the influence range of the Nth group of experimental data is outside the Nth invalid influence parameter range RN of the corresponding mechanical model, the step E is carried out, if the influence range of the Nth group of experimental data is within the Nth invalid influence parameter range RN of the corresponding mechanical model, the data is not retained, the center of a filtering circle does not move, the R value is redefined, and the steps d1 to d3 are recycled;
E. determining the next filtering direction through the kink, the identification of the reversal point and the optimization of Chebyshev first-order linear approximation; the method comprises the following specific steps:
e1, judging whether the current group of data points are the kinking and inverting points of the filtering path; if yes, reserving the group of data, storing the group of data in the Mth point of the new experiment data file, recalculating the filtering direction N, and performing the step C, wherein M represents the sequence of the new experiment data, and the M is gradually increased along with the storage of the new experiment data, wherein M is less than or equal to N; if not, the group of data is not reserved, the M value of the new experimental data file is not changed, and the next step is carried out simultaneously;
e2, determining whether the current data point N ═ N' is near the approximate inflection point obtained in step B, specifically, N is a positive integer and N >2 among the first N points of the approximate inflection point, which is given by the user; if yes, optimizing the filtering direction without using Chebyshev first-order linear approximation; if not, carrying out the next step;
e3, selecting N groups of data, namely N ', N ' +1, N ' +2, and N ' + N, as (Xi, Yi), i ═ 0,1,2, and N, as the interleaving point group needed by fitting the straight line by using chebyshev's best linear approximation;
e4, setting a fitting straight line as Y-b 1-X + c1, selecting residual values e-Yi- (b-Xi + c) |, selecting (X0, Y0), (Xn, Yn), (Xm, Ym) as an initial fitting interleaving point set by using a Remidz algorithm of an interleaving point set, substituting the initial fitting interleaving point set into a residual value equation to obtain an algebraic equation set, and determining initial undetermined coefficients b1, c1 and e1, wherein m is a positive integer, and m is (n +1)/2 or n/2;
e5, calibrating the data points (Xi, Yi), if (Xk, Yk) exists in the data points (Xi, Yi), so that e ═ Yk- (b1 × Xk + c1) | ═ max | Yi- (k1 × Xi + c1) | > e1, replacing any one of (X0, Y0), (Xn, Yn), (Xm, Ym) with (Xk, Yk), and enabling new data point groups to be distributed on two sides of a straight line in a staggered mode in sequence according to the initial data sequence, and then carrying out the next step; otherwise, skipping the next step e6, and performing step e 7;
e6, substituting the new data point group obtained in e5 for the old point group, substituting the new data point group into a residual value equation in e4, calculating new values of b1, c1 and e1, if the obtained value of e1 is within the allowable error range and is approximately equal to the value of e1 obtained last time, stopping searching, otherwise, repeating the step of e 5;
e7, obtaining a best fit straight line expression Y-b 1X + c1, and determining a next data filtering direction n 1;
e8, moving the center of the filtering circle along the direction n 1;
F. and D, filtering along the data path, and repeating the step C and the step D to obtain filtered data and storing the filtered data.
Further, the experimental data obtaining request in step a1 further includes an experimental data set, and data exchange can be performed at one time.
Further, the acquired requested experimental data is data in a user-defined data file.
Further, the acquired experimental data is a certain type of data set in the user-defined data files.
The invention has the following beneficial effects:
1. the mechanical experiment data preprocessing method is combined with a mechanical model and a theoretical method, and helps development and application of the mechanical model, including analysis of influence of initial experiment data on the mechanical model; giving continuous experimental data, and determining the change rule of the subsequent judgment standard along with the experimental data; adjusting data processing judgment standards according to parameters in the specific mechanical model; quantifying the superiority and inferiority of the parameters of the mechanical model to the experimental data processing; and optimizing the experimental data preprocessing method according to the experimental data and the mechanical model. According to the invention, through the pretreatment of the experimental data, under the condition of guaranteeing the accuracy of the experimental data, the experimental data processing and analyzing method aiming at the mechanical model is effectively enhanced, the analysis speed of the experimental data is improved, the problem that the calculation efficiency of important parameters in the mechanical model is influenced by a large amount of redundant experimental data at present is solved, the big data processing capacity of the experimental data aiming at the specific mechanical model is improved under the condition of limited resources, and the user experience is improved.
2. According to the invention, a proper invalid influence parameter range R value is selected, the processing capacity of the mechanical experiment data is improved on the premise of ensuring the mechanical theory calculation result, and the computing capacity of the mechanical model for the actual data is greatly improved.
3. Step E, redefining a filtering direction under the condition of dynamic filtering, namely fitting a straight line to enable the maximum distance from each point to the straight line to be minimum, namely performing Chebyshev first-order linear approximation on the straight line, and accordingly obtaining a more accurate filtering path result.
Description of the drawings:
the following examples are presented to enable one of ordinary skill in the art to more fully understand the present invention and are not intended to limit the scope of the embodiments described herein.
Fig. 1 is a system flow chart of a mechanical experiment data preprocessing method based on a mechanical model.
Fig. 2 is a system flow diagram of a mechanical model based on the mechanical model mechanical experiment data preprocessing method of the present invention.
Fig. 3 is a schematic flow diagram of optimal linear approximation optimal filtering direction of chebyshev based on mechanical model mechanics experiment data preprocessing method of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Fig. 1 and fig. 2 show a mechanical experiment data preprocessing method based on a mechanical model, which includes the following steps,
A. receiving and storing 1-N groups of experimental data;
B. presetting an invalid influence parameter R0 based on the material fatigue limit, and identifying and storing approximate inflection points in the data filtering path;
C. when N is 1, taking the group of experimental data points as an initial point and an initial filtering circle center point;
D. selecting a next group of experimental data points, determining an invalid influence parameter R and filtering data;
E. determining the next filtering direction through the kink, the identification of the reversal point and the optimization of Chebyshev first-order linear approximation;
F. and D, filtering along the data path, and repeating the step C and the step D to obtain filtered data and storing the filtered data.
On the basis of this embodiment, the specific steps of step a are:
a1, receiving 1-N groups of experimental data acquisition requests sent by a client, wherein the experimental data acquisition requests comprise tension-torsion fatigue experimental data of a Crossland model, axial and torsion load data corresponding to specific parameters in a mechanical model and load and displacement acquired in the experimental process, and solving positive stress and shear stress;
a2, determining a model parameter solving process according to a model concrete form for a concrete mechanical model, judging experimental data selected for the mechanical model, and determining the solving process of the experimental data according to the mechanical model contained in a request after a server receives a data request sent by a client;
a3, selecting the Nth group of experimental data, wherein the data are axial force and shearing force measured by a tension-torsion fatigue experiment, setting N to be 1-N to represent the processing sequence of the experimental data, and N is gradually increased along with the reading of the experimental data.
On the basis of this embodiment, the specific steps of step B are:
b1, presetting an ineffective influence parameter R0 based on the fatigue limit of the material;
b2, keeping the invalid influence parameter R unchanged, and then always keeping the invalid influence parameter R to be R0, and performing the steps C, D, E and F;
b3, obtaining the filtered data in the previous step, namely the approximate inflection point, and storing the data.
On the basis of this embodiment, the specific steps of step D are:
d1, selecting the Nth group of experimental data;
d2, determining an Nth invalid influence parameter range RN according to the corresponding Nth set of mechanical model parameters, wherein the first invalid influence parameter range R1 is determined by material properties according to the corresponding mechanical model;
d3, selecting the Nth group of experimental data, and comparing the first invalid influence parameter range R1 in the step d2 to judge whether the data is reserved, wherein the judgment specifically comprises the following steps:
the Crossland model is the following equation:
and E, if the influence range of the Nth group of experimental data is outside the Nth invalid influence parameter range RN of the corresponding mechanical model, the step E is carried out, and if the influence range of the Nth group of experimental data is within the Nth invalid influence parameter range RN of the corresponding mechanical model, the data is not retained, and the circle center of the filtering circle does not move.
In the step, as the R value is reduced, the amount of residual experimental data is large, parameters in the Crossland model are greatly influenced by the experimental data, the calculation result is closer to the actual theoretical result, but the data analysis amount is large, and particularly, the operation efficiency is seriously influenced when the amount of the experimental data is large; with the increase of the R value, the residual experimental data amount is less, the influence of the experimental data on the parameters in the Crossland model is less, the calculation result is more deviated from the actual theoretical result, the data analysis amount is less, particularly, the calculation efficiency is greatly improved when the experimental data amount is larger, the updated experimental data is calculated in the Crossland model, the corresponding parameters in the Crossland model are directly calculated by using the experimental data without a pretreatment method, and the final model result is calculated. Comparing the two results, if the difference between the two results is larger, redefining the R value (or increasing a regulation coefficient), and performing pretreatment again; if the difference between the two is not large, the loop program is exited, the existing R value is determined to be suitable for the calculation of the mechanical model, and the preprocessing of the data of the model can refer to the R value (under the condition that other important parameters are similar).
On the basis of this embodiment, as shown in fig. 3, the specific steps of step E are:
e1, judging whether the current group of data points are the kinking and inverting points of the filtering path; if yes, reserving the group of data, storing the group of data in the Mth point of the new experiment data file, recalculating the filtering direction N, and performing the step C, wherein M represents the sequence of the new experiment data, and the M is gradually increased along with the storage of the new experiment data, wherein M is less than or equal to N; if not, the group of data is not reserved, the M value of the new experimental data file is not changed, and the next step is carried out simultaneously;
e2, determining whether the current data point N ═ N' is near the approximate inflection point obtained in step B, specifically, N is a positive integer and N >2 among the first N points of the approximate inflection point, which is given by the user; if yes, optimizing the filtering direction without using Chebyshev first-order linear approximation; if not, carrying out the next step;
e3, selecting N groups of data, namely N ', N ' +1, N ' +2, and N ' + N, as (Xi, Yi), i ═ 0,1,2, and N, as the interleaving point group needed by fitting the straight line by using chebyshev's best linear approximation;
e4, setting a fitting straight line as Y-b 1-X + c1, selecting residual values e-Yi- (b-Xi + c) |, selecting (X0, Y0), (Xn, Yn), (Xm, Ym) as an initial fitting interleaving point set by using a Remidz algorithm of an interleaving point set, substituting the initial fitting interleaving point set into a residual value equation to obtain an algebraic equation set, and determining initial undetermined coefficients b1, c1 and e1, wherein m is a positive integer, and m is (n +1)/2 or n/2;
e5, calibrating the data points (Xi, Yi), if (Xk, Yk) exists in the data points (Xi, Yi), so that e ═ Yk- (b1 × Xk + c1) | ═ max | Yi- (k1 × Xi + c1) | > e1, replacing any one of (X0, Y0), (Xn, Yn), (Xm, Ym) with (Xk, Yk), and enabling new data point groups to be distributed on two sides of a straight line in a staggered mode in sequence according to the initial data sequence, and then carrying out the next step; otherwise, skipping the next step e6, and performing step e 7;
e6, substituting the new data point group obtained in e5 for the old point group, substituting the new data point group into a residual value equation in e4, and calculating new values of b1, c1 and e 1. if the obtained value of e1 is within the allowable error range and is approximately equal to the value of e1 obtained last time, stopping searching, otherwise, repeating the step of e 5;
e7, obtaining a best fit straight line expression Y-b 1X + c1, and determining a next data filtering direction n 1;
e8, the center of the filtering circle moves along the direction n 1.
On the basis of this embodiment, the experimental data obtaining request in step a1 further includes an experimental data set, data exchange can be performed at one time, the obtained experimental data is data in a user-defined data file, and the obtained experimental data is a certain type of data set in a plurality of user-defined data files.
The mechanical experiment data preprocessing method is combined with a mechanical model and a theoretical method, and helps development and application of the mechanical model, including analysis of influence of initial experiment data on the mechanical model; giving continuous experimental data, and determining the change rule of the subsequent judgment standard along with the experimental data; adjusting data processing judgment standards according to parameters in the specific mechanical model; quantifying the superiority and inferiority of the parameters of the mechanical model to the experimental data processing; and optimizing the experimental data preprocessing method according to the experimental data and the mechanical model. According to the invention, through the pretreatment of the experimental data, under the condition of guaranteeing the accuracy of the experimental data, the experimental data processing and analyzing method aiming at the mechanical model is effectively enhanced, the analysis speed of the experimental data is improved, the problem that the calculation efficiency of important parameters in the mechanical model is influenced by a large amount of redundant experimental data at present is solved, the big data processing capacity of the experimental data aiming at the specific mechanical model is improved under the condition of limited resources, and the user experience is improved. The method selects a proper invalid influence parameter range R value, improves the processing capacity of the mechanical experiment data on the premise of ensuring the calculation of a mechanical theory, and greatly improves the calculation capacity of a mechanical model for actual data. Step E, redefining a filtering direction under the condition of dynamic filtering, namely fitting a straight line to enable the maximum distance from each point to the straight line to be minimum, namely performing Chebyshev first-order linear approximation on the straight line, and accordingly obtaining a more accurate filtering path result.
Portions of the invention not disclosed in detail are well within the skill of the art.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the technical solutions of the present invention, so long as the technical solutions can be realized on the basis of the above embodiments without creative efforts, which should be considered to fall within the protection scope of the patent of the present invention.
Claims (4)
1. A mechanics experimental data preprocessing method based on mechanics model is characterized by comprising the following steps,
A. receiving and storing 1-N groups of experimental data; the method comprises the following specific steps:
a1, receiving 1-N groups of experimental data acquisition requests sent by a client, wherein the experimental data acquisition requests comprise tension-torsion fatigue experimental data of a Crossland model, axial and torsion load data corresponding to specific parameters in a mechanical model and load and displacement acquired in the experimental process, and solving positive stress and shear stress;
a2, determining a model parameter solving process according to a model concrete form for a concrete mechanical model, judging experimental data selected for the mechanical model, and determining the solving process of the experimental data according to the mechanical model contained in a request after a server receives a data request sent by a client;
a3, selecting the Nth group of experimental data, wherein the data are axial force and shearing force obtained by tension-torsion fatigue experiment measurement, N is set to be 1-N to represent the processing sequence of the experimental data, and N is gradually increased along with the reading of the experimental data;
B. presetting an invalid influence parameter R0 based on the material fatigue limit, and identifying and storing approximate inflection points in the data filtering path; the method comprises the following specific steps:
b1, presetting an ineffective influence parameter R0 based on the fatigue limit of the material;
b2, keeping the invalid influence parameter R unchanged, and then always keeping the invalid influence parameter R to be R0, and performing the steps C, D, E and F;
b3, obtaining the filtered data in the previous step, namely the data is the approximate inflection point, and storing the data;
C. when N is 1, taking the group of experimental data points as an initial point and an initial filtering circle center point;
D. selecting a next group of experimental data points, determining an invalid influence parameter R and filtering data; the method comprises the following specific steps:
d1, selecting the Nth group of experimental data;
d2, determining an Nth invalid influence parameter range RN according to the corresponding Nth set of mechanical model parameters, wherein the first invalid influence parameter range R1 is determined by material properties according to the corresponding mechanical model;
d3, selecting the Nth group of experimental data, and comparing the first invalid influence parameter range R1 in the step d2 to judge whether the data is reserved, wherein the judgment specifically comprises the following steps:
the Crossland model is formulated as follows.
Wherein α c and β c are material constants, σ max is a maximum hydrostatic pressure value, Δ τ Mises is a shear stress amplitude of Von Mises, an invalid influence parameter range RN is set to Δ τ Mises/2 according to a crossbar mechanical model, an RN value can be determined by material properties according to a corresponding mechanical model, the relation between the Nth group of experimental data and RN is compared, if the influence range of the Nth group of experimental data is outside the Nth invalid influence parameter range RN of the corresponding mechanical model, the step E is carried out, if the influence range of the Nth group of experimental data is within the Nth invalid influence parameter range RN of the corresponding mechanical model, the data is not retained, the center of a filtering circle does not move, the R value is redefined, and the steps d1 to d3 are recycled;
E. determining the next filtering direction through the kink, the identification of the reversal point and the optimization of Chebyshev first-order linear approximation; the method comprises the following specific steps:
e1, judging whether the current group of data points are the kinking and inverting points of the filtering path; if yes, reserving the group of data, storing the group of data in the Mth point of the new experiment data file, recalculating the filtering direction N, and performing the step C, wherein M represents the sequence of the new experiment data, and the M is gradually increased along with the storage of the new experiment data, wherein M is less than or equal to N; if not, the group of data is not reserved, the M value of the new experimental data file is not changed, and the next step is carried out simultaneously;
e2, determining whether the current data point N ═ N' is near the approximate inflection point obtained in step B, specifically, N is a positive integer and N >2 among the first N points of the approximate inflection point, which is given by the user; if yes, optimizing the filtering direction without using Chebyshev first-order linear approximation; if not, carrying out the next step;
e3, selecting N groups of data, namely N ', N ' +1, N ' +2, and N ' + N, as (Xi, Yi), i ═ 0,1,2, and N, as the interleaving point group needed by fitting the straight line by using chebyshev's best linear approximation;
e4, setting a fitting straight line as Y-b 1-X + c1, selecting residual values e-Yi- (b-Xi + c) |, selecting (X0, Y0), (Xn, Yn), (Xm, Ym) as an initial fitting interleaving point set by using a Remidz algorithm of an interleaving point set, substituting the initial fitting interleaving point set into a residual value equation to obtain an algebraic equation set, and determining initial undetermined coefficients b1, c1 and e1, wherein m is a positive integer, and m is (n +1)/2 or n/2;
e5, calibrating the data points (Xi, Yi), if (Xk, Yk) exists in the data points (Xi, Yi), so that e ═ Yk- (b1 × Xk + c1) | ═ max | Yi- (k1 × Xi + c1) | > e1, replacing any one of (X0, Y0), (Xn, Yn), (Xm, Ym) with (Xk, Yk), and enabling new data point groups to be distributed on two sides of a straight line in a staggered mode in sequence according to the initial data sequence, and then carrying out the next step; otherwise, skipping the next step e6, and performing step e 7;
e6, substituting the new data point group obtained in e5 for the old point group, substituting the new data point group into a residual value equation in e4, calculating new values of b1, c1 and e1, if the obtained value of e1 is within the allowable error range and is approximately equal to the value of e1 obtained last time, stopping searching, otherwise, repeating the step of e 5;
e7, obtaining a best fit straight line expression Y-b 1X + c1, and determining a next data filtering direction n 1;
e8, moving the center of the filtering circle along the direction n 1;
F. and D, filtering along the data path, and repeating the step C and the step D to obtain filtered data and storing the filtered data.
2. The mechanical model-based mechanical experiment data preprocessing method as claimed in claim 1, wherein: the experimental data obtaining request in the step a1 further includes an experimental data set, and data exchange can be performed at one time.
3. The mechanical model-based mechanical experiment data preprocessing method as claimed in claim 1, wherein: and the acquired requested experimental data is data in a user-defined data file.
4. The mechanical model-based mechanical experiment data preprocessing method as claimed in claim 1, wherein: the experimental data of the request is a certain type of data set in a plurality of user-defined data files.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910383017.2A CN110162853B (en) | 2019-05-08 | 2019-05-08 | Mechanical experiment data preprocessing method based on mechanical model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910383017.2A CN110162853B (en) | 2019-05-08 | 2019-05-08 | Mechanical experiment data preprocessing method based on mechanical model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110162853A CN110162853A (en) | 2019-08-23 |
| CN110162853B true CN110162853B (en) | 2021-02-09 |
Family
ID=67633782
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910383017.2A Active CN110162853B (en) | 2019-05-08 | 2019-05-08 | Mechanical experiment data preprocessing method based on mechanical model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110162853B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112485028B (en) * | 2019-09-12 | 2023-06-02 | 上海三菱电梯有限公司 | Feature spectrum extraction method of vibration signal and mechanical fault diagnosis analysis method |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102798568B (en) * | 2012-07-27 | 2014-12-24 | 中国航空工业集团公司北京航空材料研究院 | Method for processing material fatigue life test data |
| US10409930B2 (en) * | 2015-09-24 | 2019-09-10 | Harman International Industries, Incorporated | Techniques for improving swept sine analyses |
| CN106295071A (en) * | 2016-08-29 | 2017-01-04 | 同济大学 | A kind of Experiments of Machanics data pre-processing method based on mechanical model |
| CN107967400B (en) * | 2017-12-21 | 2021-04-09 | 中航沈飞民用飞机有限责任公司 | Metal structure fatigue test data processing and reliability analysis method |
| CN109001779A (en) * | 2018-06-15 | 2018-12-14 | 佛山市竣智文化传播股份有限公司 | The track data filter method and its device in a kind of setting filtering section |
-
2019
- 2019-05-08 CN CN201910383017.2A patent/CN110162853B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110162853A (en) | 2019-08-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109740661B (en) | Oil well moving liquid level self-adaptive prediction method based on fuzzy evaluation | |
| CN112365029B (en) | Missing value processing method for air conditioner load prediction and air conditioner load prediction system | |
| CN110889085A (en) | Intelligent wastewater monitoring method and system based on complex network multiple online regression | |
| CN108074015B (en) | Ultra-short-term prediction method and system for wind power | |
| CN108804334B (en) | Discrete software reliability increase testing and evaluating method based on self-adaptive sampling | |
| CN110162853B (en) | Mechanical experiment data preprocessing method based on mechanical model | |
| CN112051506B (en) | Similar product transferable sample screening method, system and application | |
| CN111790762B (en) | Hot rolled strip steel rolling force setting method based on random forest | |
| CN115271186B (en) | Reservoir water level prediction and early warning method based on delay factor and PSO RNN Attention model | |
| CN115796002A (en) | Strip steel coil defect prediction and diagnosis method based on deep learning | |
| CN112926893A (en) | Horizontal well profile control effect evaluation method based on fuzzy comprehensive evaluation and hierarchical analysis | |
| CN110260914B (en) | Engineering safety monitoring system region division method based on measuring point space-time characteristics | |
| CN113408696A (en) | Fixed point quantization method and device of deep learning model | |
| CN112328971A (en) | Double-peak three-parameter Weibull distribution fitting method based on random number | |
| CN112329272A (en) | Residual life fitting prediction method integrating photovoltaic module degradation and life data | |
| CN116298984A (en) | Lithium ion battery capacity jump point and battery attenuation degree identification method | |
| CN112069045A (en) | Cloud platform software performance prediction method based on hidden Markov | |
| CN115101136A (en) | Large-scale aluminum electrolysis cell global anode effect prediction method | |
| CN115935212A (en) | Adjustable load clustering method and system based on longitudinal trend prediction | |
| CN111368931B (en) | Method for determining learning rate of image classification model | |
| CN110889190B (en) | Performance degradation modeling data volume optimization method facing prediction precision requirement | |
| CN116757337B (en) | House construction progress prediction system based on artificial intelligence | |
| CN116743764B (en) | Industrial Internet identification analysis management system | |
| CN117743965B (en) | Data center energy efficiency optimization method and system based on machine learning | |
| CN116933331B (en) | Multithreaded file hash method based on wasm and web worker |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 226200 No.1, Landao Road, shipbuilding and Marine Industrial Park, Qidong City, Nantong City, Jiangsu Province Applicant after: Nantong Taisheng blue island ocean engineering Co.,Ltd. Address before: 226200 No.1, Landao Road, Yinyang Town, Qidong City, Nantong City, Jiangsu Province Applicant before: NANTONG BLUE ISLAND OFFSHORE Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |