CN103335814A - Inclination angle measurement error data correction system and method of experimental model in wind tunnel - Google Patents
Inclination angle measurement error data correction system and method of experimental model in wind tunnel Download PDFInfo
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Abstract
The invention discloses an inclination angle measurement error data correction system and method of an experimental model in a wind tunnel when the experimental model is placed in the wind tunnel to be subjected to a blowing experiment. The inclination angle measurement data correction system comprises a data preprocessing module, a model training module and an error data correction module, and the correction method comprises three steps: data preprocessing, model training and error data correcting. The invention provides a multi-stage and multi-model fusion error correction method based on a neural network model as a basic model, the model fitting degree is higher, the calculation result is more accurate, the nonlinear error, caused by vibration of the model under the wind action, in data measured actually by an inclination angle sensor can be corrected well, and the correction error of the correction system can be controlled within 0.004-0.01 degree near the 0 degree.
Description
Technical field
The present invention relates to empirical model in a kind of wind-tunnel, empirical model measurement of dip angle error information update the system and modification method in the wind-tunnel in the blowing experimentation.
Background technology
At present, the measuring technique of supersonic speed and transonic wind tunnel model is applied in the production and test of civilian goods more and more, and is also more and more higher to requirement and the precision of measuring technology.Because the wind tunnel test process is the aerodynamic experiment of a complexity, the test process link is many, tested person instrument and test environment and tester's technology grasp levels etc. are factor affecting in many ways, and test result, measuring accuracy and accuracy are all brought influence and error.How to obtain test data accurately, reject the precision influence that environment causes, this measuring technique to supersonic speed and transonic wind tunnel itself is most important.
Being fixed in the wind tunnel model on the support has individual angle α with surface level, is referred to as the inclination angle, and the inner gravity sensor that can use a model is measured.Obliquity sensor is used for the actual angle of attack of measurement model after loading, is positioned at detection system foremost.Angle of attack measured value is one of basic data of other numerical operation of detection system, and its accuracy directly has influence on the accuracy of detection system test data.And in the wind tunnel test of reality, model can be subjected to from external force influence all around, and the moment loading that the external force influence produces can make the gravity sensor at model inclination angle measure the generation error on model.Just under the influence of various external force factors, there is error in the measured data of obliquity sensor.By analysis, this error mainly is because model has been subjected to the oscillatory torque on the different directions, to such an extent as to the working environment of obliquity sensor is no longer stable, so have error by the resulting digital signal of this sensor, can not reflect the angle of attack value of model really.Therefore, the raw digital signal that obtains by obliquity sensor can not be directly used in other numerical evaluation, need carry out correction-compensation to this signal, to guarantee that test data can truly reflect tested situation.
In order to reduce the margin of error, when the bucking-out system modeling, usually adopt nonlinear mathematical model, and generally do not adopt linear model.In the nonlinear model system, data error compensation method commonly used has successive approximation method, least square polynomial curve fitting method, adaptive filter method, error of fitting compensation formula method, cubic spline interpolation and with neural network method etc.
Because the model wind-engaging produces complicacy and the certainty of the vibration moment that produces, the obliquity sensor data always exist because model vibrates the error that produces, and this error normally complicated function or the model of a plurality of variablees.If remove to try to achieve this function with conventional mathematics approximating method, the key issue that will solve is except carrying out rational mathematical description to possible variable so, more to excavate as much as possible the variable that exists in the environment, also need precise math model to describe relation between these variablees in addition, it is very difficult for explication that vibration error model of fit in the wind-tunnel is carried out the people, and the extended capability of model also can not get guaranteeing.In the recent period, data error correction technique field proposes the new technology that the employing nerual network technique carries out the match of error compensation value.Because neural network does not need precise math model, non-linear mapping capability is good, is good at and learns useful knowledge from inputoutput data, discloses data characteristics, handles enchancement factor.Utilize neural network method how to solve the relation between the data of description environment and data error, by neural network is trained fully, utilize the neural network self-organization to optimize ability of modeling, constantly carry out self-organization optimization with training data, thereby form error compensation model optimum and that adaptive faculty is strong, promote Accuracy Error data correction ability.
Summary of the invention
The objective of the invention is to improve the performance of existing Error Compensation Technology, provide a kind of and can effectively revise in the obliquity sensor measured data nonlinearity erron that causes owing to vibration, and inclination angle value round-off error controlled near 0 angle empirical model measurement of dip angle error information update the system and modification method in the wind-tunnel within 0.004～0.01 precision.
The objective of the invention is to be achieved through the following technical solutions: empirical model measurement of dip angle error information update the system in the wind-tunnel, it comprises with lower module:
Data preprocessing module: the input data are carried out pre-service, extract vibrations error character vector, systematic error correction model training process and data correction process all comprise data preprocessing module, but pretreatment module is finished different operating in VEC training process and data correction process;
The model training module: setting up with angle, frequency and amplitude according to the training data that collects in advance and target data is the input variable parameter, error correction values is the joint error correction model of output, and this module comprises that aspect of model vector extracts submodule and model training submodule;
Data correction module: import data according to each section that data preprocessing module in the error correction process obtains, extract the proper vector of each section input data, and proper vector imported the joint error correction model that precondition obtains, obtain error correction values, modified value is added to original input, obtains revised data.
The modification method of empirical model measurement of dip angle error information in the wind-tunnel, it comprises data pre-service, model training and three steps of data correction, model training and data correction all comprise data preprocessing module, but pretreatment module is finished different operating in model training and data correction;
The data pre-service comprises following substep in the described model training step:
S101: loaded targets data and training data be to update the system, and target data curve and training data curve are carried out down-sampling;
S102: the curve to target data curve and training data carries out wavelet transformation and smoothing processing respectively, obtains their high and low frequency data;
S103: the low-and high-frequency data to the target data curve ask poor with the low-and high-frequency of all training data curves respectively, and input (high frequency poor) and (low frequency poor) output data set of obtaining for training pattern close;
The data pre-service comprises following substep in the described data correction step:
S201: loaded targets data and each wait to revise data to system, and to aim curve and treat that fair curve carries out down-sampling;
S202: to aim curve and treat that fair curve carries out wavelet transformation and smoothing processing, obtain their high and low frequency data respectively;
S203: the low-and high-frequency data to aim curve ask poor with the low-and high-frequency for the treatment of fair curve respectively, obtain input (high frequency poor) data and the reference curve of VEC;
S204: the result according to end-point detection carries out segmentation to the input data that obtain;
Described model training step comprises following substep:
S301: according to the input data that pre-service in the model training step obtains, extract and revise feature;
S302: the output data that obtain with pretreatment module in the model training process are output valve, the correction that obtains among the S301 is characterized as input value, the composing training data acquisition, system ties up as an element variable with each of correction proper vector value, be that the model training of this element variable is gathered with the training data subset division that characterizes each element variable data variation in the training data set, training with each element variable respectively is the input variable parameter, the output data are N (N=element variable number) unit variation model of output valve, and are stored as intermediate result;
S303: point centered by each eigenwert in each unit variable training data subclass that the S302 division obtains, within the specific limits to its interpolation, the new characteristic value collection of each element variable after obtaining respectively to insert;
S304: with the new characteristic value collection of each element variable that obtains among the S303, the unit variation model of bringing the S302 acquisition respectively into obtains the output valve of each model, and with each new element variable characteristic value collection and each model output valve, together constitute one dimension and change the training data set, and storage is used for follow-up model training;
S305: utilize the one dimension that S304 obtains to change the training data set, based on the Krging Algorithm for Surface Fitting, the match M(M=that is three-dimensional coordinate with any two element variable values and corresponding model output valve respectively
) individual space curved surface, the vector of each point for being constituted by 2 dimension element variable values and corresponding model output valve in the curved surface;
S306: K the point of respectively sampling on each curved surface in each space curved surface of the M that in S305, generates, constitute the error information training set of KxM element, each point is a four-dimensional vector in the set, with this error information training set training error correction model Q, and the system that is stored in is used for the error information makeover process;
Described data correction comprises following substep:
S401: extract the input data that the data pre-service obtains in the data correction step, and to each input segmentation, organize data into the long certain matrix of row;
S402: adopt same procedure to extract the eigenwert of every segment data with S301, and bring the eigenwert of each segment data into error correction values that the VEC that trains obtains each segment data;
S403: the error correction values of each segment data is connected, form complete error correction values, and carry out up-sampling and form final calibration corrections;
S404: the data addition of final calibration corrections and grandfather tape correction obtains revised obliquity sensor data.
Element variable in the described model training step is for comprising frequency h, amplitude f and angle a.
The invention has the advantages that:
1. make up multistage, multi-model fusion error correcting method based on neural network model, constructed model has quick study and can approach internal model and internal model controller arbitrarily, thereby well the obliquity sensor data are carried out error correction, make model-fitting degree higher, error compensation result is more accurate.
2. by the neural network correction model under the multiple vibration frequency of training and the oscillation intensity, when input obliquity sensor data, based on multi-model and multistage data correction algorithm error obliquity sensor data are revised; Can effectively revise the nonlinearity erron that causes owing to vibration in the obliquity sensor measured data, and the error of tilt modified value is controlled near 0.004～0.01 precision 0 angle.
Description of drawings
Fig. 1 is system framework figure of the present invention;
Data pretreatment process figure in Fig. 2 system model training process;
Data pretreatment process figure in Fig. 3 system data makeover process;
Fig. 4 is model training method process flow diagram of the present invention;
Fig. 5 is the neural network structure synoptic diagram of error information correction model;
Fig. 6 is data correction process flow diagram of the present invention.
Embodiment
Training dataset: in advance by hitting the shake device, under different frequency, amplitude and inclination angle condition, empirical model hit and encourages vibrations, gather obtain under different frequency, various amplitude and different angle
Individual obliquity sensor data.
Target data: the artificial free from error obliquity sensor data of assert.
Further specify technical scheme of the present invention below in conjunction with accompanying drawing, but the content that the present invention protects is not limited to the following stated.
As shown in Figure 1, empirical model measurement of dip angle error information update the system in the wind-tunnel, it comprises with lower module:
Data preprocessing module: the input data are carried out pre-service, extract vibrations error character vector, systematic error correction model training process and data correction process all comprise data preprocessing module, but pretreatment module is finished different operating in VEC training process and data correction process;
The model training module: setting up with angle, frequency and amplitude according to the training data that collects in advance and target data is the input variable parameter, error correction values is the joint error correction model of output, and this module comprises that aspect of model vector extracts submodule and model training submodule;
Data correction module: import data according to each section that data preprocessing module in the error correction process obtains, extract the proper vector of each section input data, and proper vector imported the joint error correction model that precondition obtains, obtain error correction values, modified value is added to original input, obtains revised data.
The modification method of empirical model measurement of dip angle error information in the wind-tunnel, it comprises data pre-service, model training and three steps of data correction, model training and data correction all comprise data preprocessing module, but pretreatment module is finished different operating in model training and data correction;
As shown in Figure 2, the data pre-service comprises following substep in the described model training step:
S101: loaded targets data and training data carry out target data curve and training data curve to update the system
Times down-sampling obtains target data behind the down-sampling and the packed data of each training data;
S102: respectively each packed data is carried out continuous wavelet transform, carry out The disposal of gentle filter then, obtain high and low frequency two parts frequency data of each packed data;
S103: the low-and high-frequency data to the target data curve ask poor with the low-and high-frequency of all training data curves respectively, obtain the input for training pattern
(high frequency poor) and
(low frequency poor) (
) output data set closes;
As shown in Figure 3, the data pre-service comprises following substep in the data correction step:
S201: loaded targets data and each wait to revise data to system, and to aim curve and treat that fair curve carries out
Times down-sampling obtains the target data behind the down-sampling and waits to revise the packed data of data;
S202: to target data and wait that the packed data of revising data carries out continuous wavelet transform, carry out The disposal of gentle filter then respectively, obtain their high and low frequency two parts data;
S203: the low-and high-frequency curve of target data is asked poor with waiting the low-and high-frequency of revising data respectively, obtain the input (high frequency poor) of VEC
Data and reference curve.
S204: the result according to end-point detection will
Be divided into
Section, and every section follow-uply compensated respectively;
As shown in Figure 4, the correction model training step comprises following substep:
S301: the input data that obtain according to pre-service in the model training step obtain
(
), to extract and revise feature, concrete steps are as follows:
S3011: right
Carry out Short Time Fourier Transform, obtain
The frequency data of each point;
S3012: feature extraction: make up P 3 dimensional feature vectors
=
,
,
,
, P=
Count.Wherein
For correspondence
Of frequency data
Point value,
For correspondence
Of amplitude data
Point value,
Be of correspondence
The inclination angle value of individual training data;
S302: obtain with pre-service in the model training step
(
) for exporting data, the correction feature that obtains among the S301
(
) be input value, the composing training data acquisition, system is to revise the proper vector value
Each dimension as an element variable, be that the model training of this element variable is gathered with the training data subset division that characterizes each element variable data variation in the training data set, namely
Variation training data subclass
,
| belong to
Change,
Variation training data subclass
,
| belong to
Change and
Variation training data subclass
,
| belong to
Change }, train respectively with
Be the input variable parameter, the output data are output valve
3 unit variation models
, and be stored as intermediate result;
S303: point centered by each eigenwert in each unit variable training data subclass that the S302 division obtains, use following formula, within the specific limits to its interpolation, the new characteristic value collection of each element variable after obtaining respectively to insert:
=
S304: with the new characteristic value collection of each element variable that obtains among the S303
Bring the unit variation model that S302 obtains respectively into
Obtain the output valve of each model
, and with each new element variable characteristic value collection and each model output valve, together constitute one dimension change the training data set
,
, and storage is used for follow-up model training;
S305: the one dimension that utilizes S304 to obtain changes the training data set, and based on the Krging Algorithm for Surface Fitting, match determines that arbitrarily other two element variable values and corresponding model output valve are three-dimensional coordinate under the one dimension element variable situation respectively, namely (
,
,
), (
,
,
) (
,
,
) 3 space curved surfaces, the vector of each point for being constituted by 2 dimension element variable values and corresponding model output valve in the curved surface;
S306: in 3 space curved surfaces that in S305, generate, each sampling on each curved surface
Individual point (
), constitute
The error information of individual element training set, in the set each point be a four-dimensional vector
,
,
,
, with this error information training set, training as shown in Figure 5
,
,
Be 3 yuan of inputs,
Neural network VEC for monobasic output
, and the system that is stored in is used for the error information makeover process;
As shown in Figure 6, data correction comprises following substep:
S404: the data addition of final calibration corrections and grandfather tape correction obtains revised obliquity sensor data.
S401: each segmentation of the input data that data preprocessing module in the data correction step is obtained carry out regularly for length be
Matrix;
S402: adopt same procedure to extract the eigenwert of each point of every segment data with S301
(
) (
), and with eigenwert
Bring the VEC that trains into
Obtain the error correction values of each point data;
S403: the each point error correction values of each segment data is connected, form complete error correction values, and carry out
Times up-sampling forms final calibration corrections;
S404: the data addition of final calibration corrections and grandfather tape correction obtains revised obliquity sensor data.
Element variable in the described model training step is for comprising frequency h, amplitude f and angle a.
Claims (3)
1. empirical model measurement of dip angle error information update the system in the wind-tunnel, it is characterized in that: it comprises with lower module:
Data preprocessing module: the input data are carried out pre-service, extract vibrations error character vector, systematic error correction model training process and data correction process all comprise data preprocessing module, but pretreatment module is finished different operating in VEC training process and data correction process;
The model training module: setting up with angle, frequency and amplitude according to the training data that collects in advance and target data is the input variable parameter, error correction values is the joint error correction model of output, and this module comprises that aspect of model vector extracts submodule and model training submodule;
Data correction module: import data according to each section that data preprocessing module in the error correction process obtains, extract the proper vector of each section input data, and proper vector imported the joint error correction model that precondition obtains, obtain error correction values, modified value is added to original input, obtains revised data.
2. the modification method of empirical model measurement of dip angle error information in the wind-tunnel, it is characterized in that: it comprises data pre-service, model training and three steps of data correction, model training and data correction all comprise data preprocessing module, but pretreatment module is finished different operating in model training and data correction;
The data pre-service comprises following substep in the described model training step:
S101: loaded targets data and training data be to update the system, and target data curve and training data curve are carried out down-sampling;
S102: the curve to target data curve and training data carries out wavelet transformation and smoothing processing respectively, obtains their high and low frequency data;
S103: the low-and high-frequency data to the target data curve ask poor with the low-and high-frequency of all training data curves respectively, and input (high frequency poor) and (low frequency poor) output data set of obtaining for training pattern close;
The data pre-service comprises following substep in the described data correction step:
S201: loaded targets data and each wait to revise data to system, and to aim curve and treat that fair curve carries out down-sampling;
S202: to aim curve and treat that fair curve carries out wavelet transformation and smoothing processing, obtain their high and low frequency data respectively;
S203: the low-and high-frequency data to aim curve ask poor with the low-and high-frequency for the treatment of fair curve respectively, obtain input (high frequency poor) data and the reference curve of VEC;
S204: the result according to end-point detection carries out segmentation to the input data that obtain;
Described model training step comprises following substep:
S301: according to the input data that pre-service in the model training step obtains, extract and revise feature;
S302: the output data that obtain with pretreatment module in the model training process are output valve, the correction that obtains among the S301 is characterized as input value, the composing training data acquisition, system ties up as an element variable with each of correction proper vector value, be that the model training of this element variable is gathered with the training data subset division that characterizes each element variable data variation in the training data set, training with each element variable respectively is the input variable parameter, the output data are N (N=element variable number) unit variation model of output valve, and are stored as intermediate result;
S303: point centered by each eigenwert in each unit variable training data subclass that the S302 division obtains, within the specific limits to its interpolation, the new characteristic value collection of each element variable after obtaining respectively to insert;
S304: bring the new characteristic value collection of each element variable that obtains among the S303 into output valve that unit variation model that S302 obtains obtains each model respectively, and with each new element variable characteristic value collection and each model output valve, together constitute one dimension and change the training data set, and storage is used for follow-up model training;
S305: utilize the one dimension that S304 obtains to change the training data set, based on the Krging Algorithm for Surface Fitting, the match M(M=that is three-dimensional coordinate with any two element variable values and corresponding model output valve respectively
) individual space curved surface, the vector of each point for being constituted by 2 dimension element variable values and corresponding model output valve in the curved surface;
S306: K the point of respectively sampling on each curved surface in the M that in S305, the generates space curved surface, constitute the error information training set of KxM element, each point is a four-dimensional vector in the set, with this error information training set training error correction model Q, and the system that is stored in is used for the error information makeover process;
Described data correction comprises following substep:
S401: extract the input data that the data pre-service obtains in the data correction step, and to each input segmentation, organize data into the long certain matrix of row;
S402: adopt same procedure to extract the eigenwert of every segment data with S301, and bring the eigenwert of each segment data into error correction values that the VEC that trains obtains each segment data;
S403: the error correction values of each segment data is connected, form complete error correction values, and carry out up-sampling and form final calibration corrections;
S404: the data addition of final calibration corrections and grandfather tape correction obtains revised obliquity sensor data.
3. the modification method of empirical model measurement of dip angle error information in the wind-tunnel according to claim 2 is characterized in that, the element variable in the described model training step is for comprising frequency h, amplitude f and angle a.
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