CN110162853A - A kind of experiment of machanics data pre-processing method based on mechanical model - Google Patents
A kind of experiment of machanics data pre-processing method based on mechanical model Download PDFInfo
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Abstract
A, the experiment of machanics data pre-processing method based on mechanical model that the invention discloses a kind of receives 1-N group experimental data and stores；B, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials, identifies the substantially inflection point in data filtering path and stores；C, when N=1, taking this group of experimental data point is initial point and inceptive filtering circle centre point；D, next group of experimental data point is chosen, nontrivial influence parameter R is determined and carries out data filtering；E, next step filtering direction is determined by optimization that kink, the identification of rollback point and Chebyshev's first-order linear approach；F, it is filtered along data path, repeats step C and step D, obtain filtered data and store.The present invention has the advantage that effectively enhancing improves analysis of experimental data speed for the Data Processing in Experiment and analysis method of mechanical model, solve the problems, such as that current bulk redundancy experimental data influences the computational efficiency of important parameter in mechanical model.
Description
Technical field
The invention belongs to experiment of machanics data pre-processing fields, and in particular to a kind of experiment of machanics number based on mechanical model
According to pre-treating method.
Background technique
In the experiment of machanics test for material property, need to implement different test sides to each feature of material
Method, and obtain relevant experimental data, when related data amount is larger be need to carry out pre-treatment to data, thus obtain it is relatively simple but
Accurate experimental data, so that the later period carries out reasonable material property analysis.
But influence of the existing experimental data in collection process there are bulk redundancy experimental data to data-handling efficiency,
Such as the small size Value Data such as acquisition noise, while the material experiment data obtained by experiment are because of loading environment different data amount
It is very big, and experiment of machanics data volume crosses conference and requires computing resource high, big data processing capacity is low.Through retrieving, the patent No.
A kind of processing method of material fatigue life test data of CN102798568A, a kind of metal structure of patent No. CN107967400A
Fatigue data processing and analysis method for reliability, a kind of track data mistake in setting filtering of patent No. 109001779A section
Filtering method and its device, unresolved drawbacks described above.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to now provide a kind of experiment of machanics number based on mechanical model
According to pre-treating method, reached by the pre-treatment to experimental data in the case where guaranteeing test data precision, effectively enhancing needle
To the Data Processing in Experiment and analysis method of mechanical model, analysis of experimental data speed is improved, solves current bulk redundancy experiment
The computational efficiency problem of important parameter in data influence mechanical model.
In order to solve the above technical problems, the technical solution adopted by the present invention are as follows: a kind of experiment of machanics based on mechanical model
Data pre-processing method, includes the following steps,
A, it receives 1-N group experimental data and stores；
B, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials, is identified in data filtering path substantially
Inflection point simultaneously stores；
C, when N=1, taking this group of experimental data point is initial point and inceptive filtering circle centre point；
D, next group of experimental data point is chosen, nontrivial influence parameter R is determined and carries out data filtering；
E, filtering side in next step is determined by optimization that kink, the identification of rollback point and Chebyshev's first-order linear approach
To；
F, it is filtered along data path, repeats step C and step D, obtain filtered data and store.
Further, the specific steps of step A are as follows:
A1, the 1-N group experimental data for receiving client transmission are requested, and the experimental data obtains request and includes
The tension-torsion fatigue experiment data of Crossland model solve corresponding axial direction and torsional load when special parameter in mechanical model
The load that is acquired in data and experimentation, displacement, and find out direct stress and shear stress；
A2, for specific mechanical model, model parameter solution procedure is determined according to model concrete form, judgement was for should
Experimental data selected by mechanical model should according to include in request after server receives the request of data of client sending
Mechanical model determines the solution procedure of the experimental data；
A3, selection N group experimental data, this data are the axial force and shearing force that the measurement of tension-torsion fatigue experiment obtains, if
Setting N=1-N indicates Data Processing in Experiment sequence, and N is stepped up with the reading of experimental data.
Further, the specific steps of step B are as follows:
B1, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials；
B2, it keeps nontrivial influence parameter R constant, is then always R0, carry out step C, step D, step E, step F；
B3, previous step obtain filtered data, as substantially inflection point, and are stored.
Further, the specific steps of step D are as follows:
D1, N group experimental data is chosen；
D2, n-th nontrivial influence parameter area RN is determined according to corresponding N group mechanical model parameter, described first
Nontrivial influence parameter area R1 is determined according to corresponding mechanical model by material properties；
D3, selection N group experimental data, compare first nontrivial influence parameter area R1 described in step d2, and judgement should
Whether data retain, judgement specifically:
The following formula of Crossland model
Wherein α c, β c are material constants, and σ max is hydrostatic pressure maximum value, and △ τ mises is Von Mises shear stress width,
Setting △ τ mises/2, RN value for nontrivial influence parameter area RN according to Crossland mechanical model can be according to corresponding mechanics
Model is determined by material properties, compares N group experimental data and the relationship of RN, if N group experimental data coverage is right
It answers outside the n-th nontrivial influence parameter area RN of mechanical model, then carries out step E, if N group experimental data coverage exists
In the n-th nontrivial influence parameter area RN of corresponding mechanical model, does not then retain the data, filter the round heart and do not move, again
Define R value, recirculation step d1 to d3.
Further, the specific steps of step E are as follows:
E1, the current group data point of judgement whether be filtration path kink and rollback point；If it is, retaining this group of number
According to and being stored in the M point of new experimental data files, while recalculating filtering direction n, and carry out step C, wherein M table
Show new experimental data sequence, M is stepped up with the preservation of new experimental data, wherein M≤N；If it is not, then not retaining this
Group data, the M value of new experimental data files is constant, while carrying out next step；
E2, judge near substantially inflection point that whether current data point N=N ' obtains in stepb, specially substantially inflection point
Preceding n point among, wherein n is positive integer, and n > 2 are given by user；If so, being forced without using Chebyshev's first-order linear
Nearly optimization filtering direction；If it is not, then carrying out next step；
E3, selection N=N ', '+2 N=N '+1, N=N, the total n group data of N=N '+n are denoted as (Xi, Yi) respectively,
I=0,1,2, n, as intercrossing point group needed for utilizing Chebyshev's Best linear approximation fitting a straight line；
E4, fitting a straight line are set as Y=b1*X+c1, residual value e=| Yi- (b*Xi+c) |, with the Li meter Zi of intercrossing point group
Algorithm selects (X0, Y0), (Xn, Yn), and (Xm, Ym) is substituted into residual equation as the intercrossing point group being initially fitted and obtained algebra
Equation group determines initial undetermined coefficient b1, and c1, e1 value, wherein m is positive integer, m=(n+1)/2 or n/2；
E5, calibration data point (Xi, Yi), if wherein there is (Xk, Yk) makes e=| Yk- (b1*Xk+c1) |=max | Yi-
(k1*Xi+c1) | > e1 then replaces (X0, Y0) with (Xk, Yk), (Xn, Yn), any one in (Xm, Ym) makes new data
Point group is successively interspersed according to primary data sequence in the two sides of straight line, then carries out next step；Otherwise it skips in next step
Rapid e6 carries out step e7；
E6, new data point group obtained in e5 is replaced into old group, substitutes into the residual equation in e4, calculates new b1, c1,
If the e1 value that e1 value is acquired is to the extent permitted by the error, and approximately equal with e1 value that last time is acquired, then stop search, it is no
Then repeat e5 step；
E7, best-fitting straight line expression formula Y=b1*X+c1 is obtained, so that it is determined that next step data filtering direction n1；
E8, the round heart of filtering are moved along direction n1.
Further, it further includes experimental data set that experimental data, which obtains request, in step a1, can disposably carry out data
Exchange.
Further, the experimental data for obtaining request is user automatically with the data in data file.
Further, the experimental data of request is obtained as some categorical data collection in multiple user's self-defining data files
It closes.
Beneficial effects of the present invention are as follows:
1, experiment of machanics data pre-processing method of the invention helps mechanical model in conjunction with mechanical model and theoretical method
Exploitation and application, including analysis initial experiment data on mechanical model influence；Given continuity experimental data determines and subsequent sentences
The quasi- changing rule with experimental data of calibration；According to the parameter regulation data processing criterion in specific mechanical model；Quantization
Superiority-inferiority of the mechanical model parameter to Data Processing in Experiment；Optimize experimental data pre-treatment side according to experimental data and mechanical model
Method.The present invention is reached by the pre-treatment to experimental data in the case where guaranteeing test data precision, and effectively enhancing is directed to power
The Data Processing in Experiment and analysis method of model are learned, analysis of experimental data speed is improved, solves current bulk redundancy experimental data
The computational efficiency problem of important parameter in mechanical model is influenced, the present invention improves experimental data in the case where limited resources and is directed to
The big data processing capacity of specific mechanical model improves user experience.
2, the present invention chooses suitable nontrivial influence parameter area R value, guarantees to mention under the premise of theory of mechanics calculated result
Height greatly improves the operational capability that mechanical model is directed to real data for the processing capacity of experiment of machanics data.
3, step E redefines a filtering direction in dynamic filtration, i.e. fitting straight line, so that each point
Maximum distance to this straight line is minimum, that is, carries out Chebyshev's first-order linear to it and approach, to obtain more accurately
Filtration path result.
Detailed description of the invention:
The following examples can make professional and technical personnel that the present invention be more fully understood, but therefore not send out this
It is bright to be limited among the embodiment described range.
Fig. 1 is a kind of system flow chart of the experiment of machanics data pre-processing method based on mechanical model of the present invention.
Fig. 2 is a kind of experiment of machanics data pre-processing method based on mechanical model of the present invention for a certain mechanics mould
The system flow schematic diagram of type.
Fig. 3 is a kind of Chebyshev's optimum linear of the experiment of machanics data pre-processing method based on mechanical model of the present invention
The flow diagram of optimization filtering direction.
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book is understood other advantages and efficacy of the present invention easily.
A kind of experiment of machanics data pre-processing method based on mechanical model is shown as shown in Figure 1, Figure 2, is included the following steps,
A, it receives 1-N group experimental data and stores；
B, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials, is identified in data filtering path substantially
Inflection point simultaneously stores；
C, when N=1, taking this group of experimental data point is initial point and inceptive filtering circle centre point；
D, next group of experimental data point is chosen, nontrivial influence parameter R is determined and carries out data filtering；
E, filtering side in next step is determined by optimization that kink, the identification of rollback point and Chebyshev's first-order linear approach
To；
F, it is filtered along data path, repeats step C and step D, obtain filtered data and store.
On the basis of the present embodiment, the specific steps of step A are as follows:
A1, the 1-N group experimental data for receiving client transmission are requested, and the experimental data obtains request and includes
The tension-torsion fatigue experiment data of Crossland model solve corresponding axial direction and torsional load when special parameter in mechanical model
The load that is acquired in data and experimentation, displacement, and find out direct stress and shear stress；
A2, for specific mechanical model, model parameter solution procedure is determined according to model concrete form, judgement was for should
Experimental data selected by mechanical model should according to include in request after server receives the request of data of client sending
Mechanical model determines the solution procedure of the experimental data；
A3, selection N group experimental data, this data are the axial force and shearing force that the measurement of tension-torsion fatigue experiment obtains, if
Setting N=1-N indicates Data Processing in Experiment sequence, and N is stepped up with the reading of experimental data.
On the basis of the present embodiment, the specific steps of step B are as follows:
B1, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials；
B2, it keeps nontrivial influence parameter R constant, is then always R0, carry out step C, step D, step E, step F；
B3, previous step obtain filtered data, as substantially inflection point, and are stored.
On the basis of the present embodiment, the specific steps of step D are as follows:
D1, N group experimental data is chosen；
D2, n-th nontrivial influence parameter area RN is determined according to corresponding N group mechanical model parameter, described first
Nontrivial influence parameter area R1 is determined according to corresponding mechanical model by material properties；
D3, selection N group experimental data, compare first nontrivial influence parameter area R1 described in step d2, and judgement should
Whether data retain, judgement specifically:
The following formula of Crossland model:
Wherein α c, β c are material constants, and σ max is hydrostatic pressure maximum value, and △ τ mises is Von Mises shear stress width,
Setting △ τ mises/2, RN value for nontrivial influence parameter area RN according to Crossland mechanical model can be according to corresponding mechanics
Model is determined by material properties, compares N group experimental data and the relationship of RN, if N group experimental data coverage is right
It answers outside the n-th nontrivial influence parameter area RN of mechanical model, then carries out step E, if N group experimental data coverage exists
In the n-th nontrivial influence parameter area RN of corresponding mechanical model, does not then retain the data, filter the round heart and do not move.
In this step, as R value reduces, remaining experimental data amount is larger, and Crossland Model Parameter is by experiment number
According to being affected, calculated result is closer to practical notional result, but data amount of analysis is larger, especially when experimental data amount is larger
When seriously affect operation efficiency；As R value increases, remaining experimental data amount is less, and Crossland Model Parameter is by experiment number
Smaller according to influencing, calculated result is further off in practical notional result, but data amount of analysis is smaller, especially when experimental data amount is larger
When greatly improve operation efficiency, calculating of the updated experimental data in Crossland model is not straight by pre-treating method
It connects to calculate in Crossland model using experimental data and corresponds to parameter, and calculate final mask result.Two results are compared
Compared with such as the two difference is larger, then needs to redefine R value (can also increase regulation coefficient), re-start pre-treatment operation；If
The two is not much different, then exits cyclic program, determines that existing R value is suitble to the calculating for this mechanical model, for this model
Data pre-processing can be referring to this R value size (in the case where other important parameters are similar).
On the basis of the present embodiment, as shown in figure 3, the specific steps of step E are as follows:
E1, the current group data point of judgement whether be filtration path kink and rollback point；If it is, retaining this group of number
According to and being stored in the M point of new experimental data files, while recalculating filtering direction n, and carry out step C, wherein M table
Show new experimental data sequence, M is stepped up with the preservation of new experimental data, wherein M≤N；If it is not, then not retaining this
Group data, the M value of new experimental data files is constant, while carrying out next step；
E2, judge near substantially inflection point that whether current data point N=N ' obtains in stepb, specially substantially inflection point
Preceding n point among, wherein n is positive integer, and n > 2 are given by user；If so, being forced without using Chebyshev's first-order linear
Nearly optimization filtering direction；If it is not, then carrying out next step；
E3, selection N=N ', '+2 N=N '+1, N=N, the total n group data of N=N '+n are denoted as (Xi, Yi) respectively,
I=0,1,2, n, as intercrossing point group needed for utilizing Chebyshev's Best linear approximation fitting a straight line；
E4, fitting a straight line are set as Y=b1*X+c1, residual value e=| Yi- (b*Xi+c) |, with the Li meter Zi of intercrossing point group
Algorithm selects (X0, Y0), (Xn, Yn), and (Xm, Ym) is substituted into residual equation as the intercrossing point group being initially fitted and obtained algebra
Equation group determines initial undetermined coefficient b1, and c1, e1 value, wherein m is positive integer, m=(n+1)/2 or n/2；
E5, calibration data point (Xi, Yi), if wherein there is (Xk, Yk) makes e=| Yk- (b1*Xk+c1) |=max | Yi-
(k1*Xi+c1) | > e1 then replaces (X0, Y0) with (Xk, Yk), (Xn, Yn), any one in (Xm, Ym) makes new data
Point group is successively interspersed according to primary data sequence in the two sides of straight line, then carries out next step；Otherwise it skips in next step
Rapid e6 carries out step e7；
E6, new data point group obtained in e5 is replaced into old group, substitutes into the residual equation in e4, calculates new b1, c1,
If the e1 value that e1 value is acquired is to the extent permitted by the error, and approximately equal with e1 value that last time is acquired, then stop search, it is no
Then repeat e5 step；
E7, best-fitting straight line expression formula Y=b1*X+c1 is obtained, so that it is determined that next step data filtering direction n1；
E8, the round heart of filtering are moved along direction n1.
It further includes experimental data set that experimental data, which obtains request, on the basis of the present embodiment, in step a1, can be disposable
Data exchange is carried out, the experimental data for obtaining request is user automatically with the data in data file, obtains the experiment number of request
According to for some categorical data set in multiple user's self-defining data files.
Experiment of machanics data pre-processing method of the invention helps mechanical model in conjunction with mechanical model and theoretical method
Exploitation and application, including analysis initial experiment data influence mechanical model；Given continuity experimental data, determines subsequent judgement
Standard with experimental data changing rule；According to the parameter regulation data processing criterion in specific mechanical model；Quantify power
Model parameter is learned to the superiority-inferiority of Data Processing in Experiment；Optimize experimental data pre-treatment side according to experimental data and mechanical model
Method.The present invention is reached by the pre-treatment to experimental data in the case where guaranteeing test data precision, and effectively enhancing is directed to power
The Data Processing in Experiment and analysis method of model are learned, analysis of experimental data speed is improved, solves current bulk redundancy experimental data
The computational efficiency problem of important parameter in mechanical model is influenced, the present invention improves experimental data in the case where limited resources and is directed to
The big data processing capacity of specific mechanical model improves user experience.The present invention chooses suitable nontrivial influence parameter area R
Value guarantees to improve the processing capacity for experiment of machanics data under the premise of theory of mechanics calculates knot, greatly improves mechanical model needle
To the operational capability of real data.Step E redefines a filtering direction in dynamic filtration, that is, is fitted one directly
Line so that the maximum distance of each point to this straight line is minimum, that is, carries out Chebyshev's first-order linear to it and approaches, thus
Obtain more accurate filtration path result.
What the present invention was not disclosed in detail partly belongs to techniques known.
Above-described embodiment is presently preferred embodiments of the present invention, is not a limitation on the technical scheme of the present invention, as long as
Without the technical solution that creative work can be realized on the basis of the above embodiments, it is regarded as falling into the invention patent
Rights protection scope in.
Claims (8)
1. a kind of experiment of machanics data pre-processing method based on mechanical model, which is characterized in that include the following steps,
A, it receives 1-N group experimental data and stores；
B, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials, identifies the substantially inflection point in data filtering path
And it stores；
C, when N=1, taking this group of experimental data point is initial point and inceptive filtering circle centre point；
D, next group of experimental data point is chosen, nontrivial influence parameter R is determined and carries out data filtering；
E, next step filtering direction is determined by optimization that kink, the identification of rollback point and Chebyshev's first-order linear approach；
F, it is filtered along data path, repeats step C and step D, obtain filtered data and store.
2. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 1, which is characterized in that institute
State the specific steps of step A are as follows:
A1, the 1-N group experimental data for receiving client transmission are requested, and the experimental data obtains request and includes
The tension-torsion fatigue experiment data of Crossland model solve corresponding axial direction and torsional load when special parameter in mechanical model
The load that is acquired in data and experimentation, displacement, and find out direct stress and shear stress；
A2, for specific mechanical model, model parameter solution procedure is determined according to model concrete form, judgement is directed to the mechanics
Experimental data selected by model, after server receives the request of data of client sending, according to the mechanics for including in request
Model determines the solution procedure of the experimental data；
A3, selection N group experimental data, this data are the axial force and shearing force that the measurement of tension-torsion fatigue experiment obtains, and N=is arranged
1-N indicates Data Processing in Experiment sequence, and N is stepped up with the reading of experimental data.
3. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 1, which is characterized in that institute
State the specific steps of step B are as follows:
B1, the previously given nontrivial influence parameter R0 based on the fatigue limit of materials；
B2, it keeps nontrivial influence parameter R constant, is then always R0, carry out step C, step D, step E, step F；
B3, previous step obtain filtered data, as substantially inflection point, and are stored.
4. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 1, which is characterized in that institute
State the specific steps of step D are as follows:
D1, N group experimental data is chosen；
D2, n-th nontrivial influence parameter area RN is determined according to corresponding N group mechanical model parameter, described first invalid
Affecting parameters range R1 is determined according to corresponding mechanical model by material properties；
D3, selection N group experimental data, compare first nontrivial influence parameter area R1 described in step d2, judge the data
Whether retain, judge specifically:
The following formula of Crossland model
Wherein α c, β c are material constants, and σ max is hydrostatic pressure maximum value, and △ τ mises is Von Mises shear stress width, according to
Nontrivial influence parameter area RN is set △ τ mises/2, RN value by Crossland mechanical model can be according to corresponding mechanical model
It is determined by material properties, compares N group experimental data and the relationship of RN, if N group experimental data coverage is to stress
Outside the n-th nontrivial influence parameter area RN for learning model, then step E is carried out, if N group experimental data coverage is in correspondence
In the n-th nontrivial influence parameter area RN of mechanical model, does not then retain the data, filter the round heart and do not move, redefine R
Value, recirculation step d1 to d3.
5. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 1, which is characterized in that institute
State the specific steps of step E are as follows:
E1, the current group data point of judgement whether be filtration path kink and rollback point；If it is, retain this group of data, and
It is stored in the M point of new experimental data files, while recalculating filtering direction n, and carries out step C, wherein M indicates new real
Data sequence is tested, M is stepped up with the preservation of new experimental data, wherein M≤N；If it is not, then do not retain this group of data,
The M value of new experimental data files is constant, while carrying out next step；
E2, judge near substantially inflection point that whether current data point N=N ' obtains in stepb, the specially substantially preceding n of inflection point
Among a point, wherein n is positive integer, and n > 2 are given by user；If so, not using Chebyshev's first-order linear optimization
Filtering direction；If it is not, then carrying out next step；
E3, selection N=N ', '+2 N=N '+1, N=N, the total n group data of N=N '+n are denoted as (Xi, Yi), i=respectively
0,1,2, n, as intercrossing point group needed for utilizing Chebyshev's Best linear approximation fitting a straight line；
E4, fitting a straight line are set as Y=b1*X+c1, residual value e=| Yi- (b*Xi+c) |, calculated with the Li meter Zi of intercrossing point group
Method selects (X0, Y0), (Xn, Yn), and (Xm, Ym) is substituted into residual equation as the intercrossing point group being initially fitted and obtained algebra side
Journey group determines initial undetermined coefficient b1, and c1, e1 value, wherein m is positive integer, m=(n+1)/2 or n/2；
E5, calibration data point (Xi, Yi), if wherein there is (Xk, Yk) makes e=| Yk- (b1*Xk+c1) |=max | Yi- (k1*
Xi+c1) | > e1 then replaces (X0, Y0) with (Xk, Yk), (Xn, Yn), any one in (Xm, Ym) makes new group of data points
It is successively interspersed according to primary data sequence in the two sides of straight line, then carries out next step；Otherwise next step e6 is skipped,
Carry out step e7；
E6, new data point group obtained in e5 is replaced into old group, substitutes into the residual equation in e4, calculates new b1, c1, e1
Value then stops search, otherwise if the e1 value acquired is to the extent permitted by the error, and approximately equal with e1 value that last time is acquired
Repeat e5 step；
E7, best-fitting straight line expression formula Y=b1*X+c1 is obtained, so that it is determined that next step data filtering direction n1；
E8, the round heart of filtering are moved along direction n1.
6. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 2, it is characterised in that: institute
Stating experimental data in step a1 and obtaining request further includes experimental data set, can disposably carry out data exchange.
7. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 6, it is characterised in that: institute
Stating and obtaining the experimental data of request is user automatically with the data in data file.
8. a kind of experiment of machanics data pre-processing method based on mechanical model according to claim 6, it is characterised in that: institute
Stating and obtaining the experimental data of request is some categorical data set in multiple user's self-defining data files.
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