CN111351862A - Ultrasonic measurement calibration method and thickness measurement method - Google Patents

Ultrasonic measurement calibration method and thickness measurement method Download PDF

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CN111351862A
CN111351862A CN202010234363.7A CN202010234363A CN111351862A CN 111351862 A CN111351862 A CN 111351862A CN 202010234363 A CN202010234363 A CN 202010234363A CN 111351862 A CN111351862 A CN 111351862A
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sound velocity
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刘书杰
李长俊
何玉发
贾文龙
许亮斌
刘清友
盛磊祥
段杰浩
郝希宁
吴霞
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China National Offshore Oil Corp CNOOC
CNOOC Research Institute Co Ltd
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CNOOC Research Institute Co Ltd
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    • G01—MEASURING; TESTING
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Abstract

The invention relates to an ultrasonic measurement calibration method and a thickness measurement method. The ultrasonic measurement calibration method comprises the following steps: A. collecting a sample of the corresponding relation between the sound velocity and the temperature of the measured ultrasonic wave; B. establishing an artificial neural network model framework of the sound velocity and the temperature; C. performing parameter adjustment of the model framework: correcting the weight and the bias of the artificial neural network according to a neuron gradient descent method; correcting the sound velocity mean square error of the model frame by weight and bias according to a neuron gradient descent method; D. and C, performing regression processing on the model frame of the sound velocity and the temperature according to the correction parameters obtained in the step C to obtain a regression model of the sound velocity and the temperature after calibration. The invention fits an algorithm model for predicting the sound velocity through the temperature, and further can be used for reducing the measurement error of the thickness of an object caused by the change of the temperature, so that the measured thickness can be more accurate.

Description

Ultrasonic measurement calibration method and thickness measurement method
Technical Field
The invention relates to the technical field of petroleum pipe detection, in particular to an ultrasonic measurement calibration method and a thickness measurement method.
Background
The Non Destructive Testing (NTD) is a method for inspecting and Testing the structure, properties, state, and types, properties, quantity, shape, position, size, distribution, and changes of defects inside and on the surface of a test piece by using special techniques and equipment without damaging or affecting the usability of the test piece and without damaging the inside of the test piece.
For the current ultrasonic nondestructive detection, many problems to be solved exist, which can be roughly divided into ①, most of the conditions are that a detection instrument is manually held to detect when the detection is carried out, time and labor are wasted, ② is difficult to reproduce the detection result aiming at a specific position, ③ is that a test tool needs to be calibrated in advance when each test is carried out, ④ ultrasonic nondestructive measurement is greatly influenced by temperature and easily causes errors, generally, the former three problems can be solved by designing new wall thickness real-time monitoring equipment, and aiming at the last problem, in the real-time thickness measurement process, the temperature change amplitude under certain environments is large, the influence of the temperature on sound velocity directly influences the thickness measurement precision, once the thickness measurement value is inaccurate, the equipment can be possibly failed, and a series of safety problems are caused.
At present, because the influence factor of temperature on the sound velocity is not easy to remove, the problem of temperature influence is not considered in the existing measurement, however, the error caused by the temperature on the measurement cannot be ignored, and particularly in the field needing accurate measurement, high attention needs to be paid, so that the calibration of the thickness measurement error caused by the temperature is very important.
Disclosure of Invention
In view of this, the present invention provides an ultrasonic measurement calibration method and a thickness measurement method, so as to calibrate ultrasonic measurements at different temperatures and further accurately measure wall thicknesses at different temperatures.
The invention firstly provides an ultrasonic measurement calibration method, which mainly comprises the following steps:
A. collecting a sample of the corresponding relation between the sound velocity and the temperature of the measured ultrasonic wave;
B. establishing an artificial neural network model framework of the sound velocity and the temperature;
C. performing parameter adjustment of the model framework: correcting the weight and the bias of the artificial neural network according to a neuron gradient descent method;
D. and C, performing regression processing on the model frame of the sound velocity and the temperature according to the correction parameters obtained in the step C to obtain a calibrated regression model of the sound velocity and the temperature.
The ultrasonic value obtained in the regression model is the value close to the actual ultrasonic sound velocity after the temperature factor is calibrated.
Further, in the step C,
let the number of sample sets be q, n be the training times, and let the sound velocity of the expected output be tkThe actual output speed of sound is ykThe performance function of the model framework is mean square error E, then
Figure BDA0002428608890000021
Further, in the step C, the modified weight and bias are respectively:
Figure BDA0002428608890000022
Figure BDA0002428608890000023
wherein
Figure BDA0002428608890000024
Represents the weight from the jth node of level l to the ith node of level l +1,
Figure BDA0002428608890000025
indicating the bias of the ith node of the ith layer.
According to one embodiment of the invention, in said step B, the back propagation of the error is used for the parametric determination of the model.
In the step B, the model framework is an artificial neural network. Training is carried out through training data in the sample, so that the purposes of inputting sample temperature data and outputting sound velocity can be achieved. And verifying the calculation accuracy of the artificial neural network through the verification data in the sample, and continuously improving the calculation accuracy of the artificial neural network to enable the output sound velocity data to be close to the sound velocity data (ideal value) in the sample.
According to an embodiment of the present invention, in the step B, the parameters of iteration number, minimum gradient and failure number are also set (i.e. the number of neurons in the input layer, hidden layer and output layer of the artificial neural network is changed).
The invention also provides an ultrasonic thickness measuring method, which comprises the following steps: and carrying out temperature detection on the measured object, applying the detected temperature value to the regression model to obtain the sound velocity of the ultrasonic wave corresponding to the temperature value, and calculating to obtain the thickness of the measured object by utilizing the sound velocity and the time of the ultrasonic wave passing through the measured object.
According to one embodiment of the invention, the ultrasonic thickness measurement is performed by means of pulse reflection.
According to one embodiment of the invention, the pulse reflection method comprises: the ultrasonic probe is contacted with a measured object through a coupling agent, a pulse signal is sent to the measured object through the mainboard, an ultrasonic signal is transmitted into the measured object and reflected when contacting the bottom surface of the measured object, the ultrasonic probe receives the signal, the time of the signal in the object is detected, and the thickness can be calculated.
According to one embodiment of the invention, the thickness of the object to be measured is a wall thickness, the wall thickness being:
Figure BDA0002428608890000026
wherein t is the time of the ultrasonic signal in the measured object, h is the thickness of the measured object, and v is the sound velocity of the ultrasonic obtained in the regression model.
The invention provides a novel nonlinear model of the corresponding relation between the sound velocity and the temperature of ultrasonic waves in a measured object based on artificial neural network modeling. After a model rough frame is set, model correction is carried out on the model rough frame by using a data set, correction of weight and bias between neurons is carried out on the model rough frame, MSE (mean square error) of expected output and actual output continuously advances towards a decreasing direction, an ending condition is regulated by setting limiting conditions such as maximum failure times, iteration times and the like, and finally an algorithm for predicting the sound velocity according to the temperature is obtained. Under the known time, the sound velocity is obtained by using the algorithm, and the wall thickness of the object can be measured by using a measuring method such as a pulse reflection method.
According to the invention, the relation between the temperature and the sound velocity is explained by analyzing and processing the acquired data set, and an algorithm model for predicting the sound velocity through the temperature is fitted, so that the measurement error of the thickness of the object caused by the change of the temperature can be reduced, and the measured thickness can be more accurate.
According to the invention, the regression equation can be obtained by only obtaining array discrete data for operation, the corresponding relation between the required temperature and the sound velocity is obtained, the problem that the data acquisition point taking is discrete and cannot be continuous is solved, the calibration-free measurement can be realized, and the labor burden during measurement is reduced.
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FIG. 1 is a flow chart illustrating sound speed calibration according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating weight bias connections of a model framework according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the objects, features and advantages of the invention can be more clearly understood. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the present invention, but are merely intended to illustrate the spirit of the technical solution of the present invention.
The invention provides a temperature sound velocity calibration method especially for petroleum pipe thickness measurement, aiming at overcoming the defect that temperature factors influence the measurement precision in the prior art. A large number of data sets are analyzed and processed, the relation between the temperature and the sound velocity is explained, and an algorithm model for predicting the sound velocity through the temperature is fitted to reduce the measurement error of the thickness of a measured object such as a petroleum pipe caused by the change of the temperature.
The invention provides a nonlinear model of the corresponding relation between the sound velocity and the temperature of a novel ultrasonic wave in a metal material based on artificial neural network modeling. After a model rough frame is set, model correction is carried out on the model rough frame by using a data set, correction of weight and bias between neurons is carried out on the model rough frame, MSE (mean square error) of expected output and actual output continuously advances towards a decreasing direction, an ending condition is regulated by setting limiting conditions such as maximum failure times, iteration times and the like, and finally an algorithm for predicting the sound velocity according to the temperature is obtained. Under the basis of known time, the sound velocity is obtained by using the algorithm, and then the wall thickness of the object can be measured by using a pulse reflection method.
The practice of the invention generally comprises the steps of:
the first step is as follows: and (4) carrying out data acquisition by using a standard test block to obtain an enough sound velocity temperature relation sample, and storing the sample as a sample set.
The second step is that: and establishing an algorithm artificial neural network model framework.
The model framework is an artificial neural network which is not trained by sample data, can be called as an initial artificial neural network, and comprises an input layer (inputting temperature sample data), a hidden layer and an output layer (outputting sound speed data).
The algorithm used by the invention is an algorithm using signal forward propagation and error backward propagation, the principle is that the backward propagation of the error is used for determining the parameters of the model, and the specific implementation steps are as follows:
the method comprises the steps of setting an algorithm main body frame, selecting a Training Function (Training Function), wherein the algorithm is an algorithm which enables errors to be minimum as far as possible, using a Training lm, setting Hidden layer related parameters such as the number of layers and the number of nodes of a Hidden layer, selecting an Activation Function which is generally called an excitation Function, giving nonlinear elements to neurons, solving the problem of linear inseparability, selecting a performance Function which is a performance Function, and generally selecting an MSE (mean Square error) which is a mean Square error. Finally, various parameters are set, such as iteration times, minimum gradient, failure times and the like. This step builds an untrained artificial neural network.
The third step: and adjusting algorithm parameters.
Namely, the weight and the bias of the artificial neural network are adjusted.
The model framework can be understood as an artificial neural network, the artificial neural network inputs sample temperature data and outputs sound velocity data, the sound velocity data is compared with sample sound velocity data (namely sound velocity data expected to be output), and the mean square error formula E is substituted, wherein the smaller the E is, the closer the sound velocity data output by the artificial neural network is to the sample sound velocity data is, namely, the higher the calculation precision of the artificial neural network is, the closer the output sound velocity value is to the sample value (ideal value). The sample data used in this section is training data.
In order to gradually reduce E, the invention continuously corrects the weight and the bias of the artificial neural network by a neuron gradient descent method. The sample data used in this section is the verification data.
Let the number of sample sets be q, the available error be
Figure BDA0002428608890000041
Let the desired output be tkThe actual output is ykThe performance function selected by the invention is MSE, and the MSE capable of obtaining a single sample is MSE
Figure BDA0002428608890000042
Then
Figure BDA0002428608890000051
The algorithm corrects the weight and the offset according to a gradient descent method, namely
Figure BDA0002428608890000052
Figure BDA0002428608890000053
In formula 2.4
Figure BDA0002428608890000054
Represents the weight from the jth node of level 1 to the ith node of level 1+1, in equation 2.5
Figure BDA0002428608890000055
Indicating the bias of the ith node of level 1.
The core of the algorithm of the invention is to update the algorithm by the two formulas
Figure BDA0002428608890000056
And
Figure BDA0002428608890000057
the algorithm model is close to an ideal value, and the purpose of improving the accuracy of the algorithm is achieved. The weight bias connections of its level 1 and its next level are shown in fig. 2.
The fourth step: and obtaining an algorithm regression model.
The invention uses a sample set, and processes the algorithm according to the formulas 2.4 and 2.5 to obtain the regression thereof.
The regression model is a mature artificial neural network after sample data training and accuracy verification. The artificial neural network at this time has been trained on sample data, and can input arbitrary temperature data (not limited to the temperature data in the sample) to obtain corresponding data conforming to the actual sound velocity.
The obtained regression nonlinearity can be represented by a function and can be represented by the structure and the setting of weight bias (because the artificial neural network is a complex network formed by training, the artificial neural network cannot be represented by the function and can be represented by the structural change (namely the neuron number of the input layer, the hidden layer and the output layer) and the setting of the weight and the bias).
The fifth step: error calibration of thickness measurement.
The thickness measuring method used by the invention is a pulse reflection method, and the principle is as follows: ultrasonic probe passes through the couplant and contacts with the measured object to send pulse signal to the measured object through the mainboard, ultrasonic signal spreads into in the object and takes place the reflection when contacting the object bottom surface, is received the signal by ultrasonic probe, detects out the time that this signal passes through in the object through hardware, marks as t, and gets out the thickness h of measured object, marks v as the sound velocity of ultrasonic wave again, then can obtain formula 2.6:
Figure BDA0002428608890000058
the time t can be detected by hardware, and the unknown quantity is only the ultrasonic sound velocity which can be obtained by the algorithm of the invention.
When the thickness of the pipeline is measured, the measured temperature value is substituted into the regression expression of the algorithm to obtain a sound velocity predicted value, and the thickness value of the object can be calculated according to the expression 2.6.
According to an embodiment of the invention, the data of the tables 1 and 2 can be obtained respectively by performing a detection experiment on the standard test block, and as can be seen from the tables, the method provided by the invention realizes better error compensation at different temperatures, and has the beneficial effects of no calibration, continuous point taking, relatively small measurement error along with temperature change and the like.
TABLE 1
Measurement temperature (. degree. C.) 19.1 20.9 24.9 29 33.1 35
Measuring thickness (mm) 14.9682 14.9884 15.00543 14.99278 15.00796 15.03762
Error magnitude (mm) 0.0318 0.0116 -0.00543 0.00722 -0.00796 -0.03762
TABLE 2
Measurement temperature (. degree. C.) 19.1 20.9 24.9 29 33.1 35
Measuring thickness (mm) 11.94492 11.96104 11.95097 11.97052 11.98264 11.98264
Error magnitude (mm) 0.05508 0.03896 0.04903 0.02948 0.01736 0.01736
According to the invention, the regression equation can be obtained by only obtaining array discrete data for operation, the corresponding relation between the required temperature and the sound velocity is obtained, the problem that the data acquisition point taking is discrete and cannot be continuous is solved, the calibration-free measurement can be realized, and the labor burden during measurement is reduced.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiments are merely illustrative of the present invention, and various components and devices of the embodiments may be changed or eliminated as desired, not all components shown in the drawings are necessarily required, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments described herein, and all equivalent changes and modifications based on the technical solutions of the present invention should not be excluded from the scope of the present invention.

Claims (10)

1. An ultrasonic measurement calibration method, the method comprising:
A. collecting a sample of the corresponding relation between the sound velocity and the temperature of the measured ultrasonic wave;
B. establishing an artificial neural network model framework of the sound velocity and the temperature;
C. performing parameter adjustment of the model framework: correcting the weight and the bias of the artificial neural network according to a neuron gradient descent method;
D. and C, performing regression processing on the model frame of the sound velocity and the temperature according to the correction parameters obtained in the step C to obtain a regression model of the sound velocity and the temperature after calibration.
2. The ultrasonic measurement calibration method according to claim 1, wherein, in the step C,
let the number of sample sets be q, n be the training times, and let the sound velocity of the expected output be taukThe actual output speed of sound is ykThe performance function of the model framework is mean square error E, then
Figure FDA0002428608880000011
3. The ultrasonic measurement calibration method according to claim 1 or 2, wherein in the step C, the modified weight and bias are respectively:
Figure FDA0002428608880000012
Figure FDA0002428608880000013
wherein
Figure FDA0002428608880000014
Represents the weight from the jth node of level l to the ith node of level l +1,
Figure FDA0002428608880000015
indicating the bias of the ith node of the ith layer.
4. The ultrasonic measurement calibration method according to claim 1 or 2, wherein in the step B, the back propagation of the error is utilized for the parameter determination of the model.
5. The ultrasonic measurement calibration method according to claim 4, characterized in that training is performed by training data in the sample to achieve the purpose of inputting sample temperature data and outputting sound speed; and verifying the calculation accuracy of the artificial neural network through the verification data in the sample, and continuously improving the calculation accuracy of the artificial neural network to enable the output sound velocity data to be close to the ideal value of the sound velocity data in the sample.
6. The ultrasonic measurement calibration method according to claim 1, 2 or 4, wherein in the step B, the parameters of iteration number, minimum gradient and failure number are set.
7. An ultrasonic thickness measuring method is characterized in that temperature detection is carried out on a measured object, the detected temperature value is applied to the regression model of any one of claims 1 to 6, the sound velocity of ultrasonic waves corresponding to the temperature value is obtained, and the thickness of the measured object is obtained through calculation by utilizing the sound velocity and the time of the ultrasonic waves passing through the measured object.
8. The method of ultrasonic thickness measurement according to claim 7, wherein the ultrasonic thickness measurement is performed by a pulse reflection method.
9. The method of ultrasonic thickness measurement according to claim 8, wherein the pulse reflection method comprises: the ultrasonic probe is contacted with a measured object through a coupling agent, a pulse signal is sent to the measured object through the mainboard, an ultrasonic signal is transmitted into the measured object and reflected when contacting the bottom surface of the measured object, the ultrasonic probe receives the signal, the time of the signal in the object is detected, and the thickness can be calculated.
10. The method of ultrasonic thickness measurement according to claim 9, wherein the thickness of the object to be measured is a wall thickness, the wall thickness being:
Figure FDA0002428608880000021
wherein t is the time of the ultrasonic signal in the measured object, h is the thickness of the measured object, and v is the sound velocity of the ultrasonic obtained in the regression model.
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