CN111766305A - Ultrasonic signal similarity analysis method for interface contact state evaluation - Google Patents
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Abstract
The invention discloses an ultrasonic signal similarity analysis method for interface contact state evaluation, belongs to the field of ultrasonic nondestructive testing, and represents the change degree of an interface contact state through a similarity function. The detection method comprises the steps that a broadband signal is utilized to excite an ultrasonic transducer, and an interface reflection signal is received through the ultrasonic transducer; carrying out average storage operation on the detection signal by an oscilloscope; and the computer is responsible for controlling the pressure tester to change the external loading pressure of the interface. Processing the detection signals by using a similarity analysis method, obtaining the results of the similarity functions under different external load pressures after determining the analysis range and the reference signals, normalizing the results, sequencing according to the sensitivity degrees of the similarity functions, selecting the similarity function with the best sensitivity degree as a characterization parameter, and finally realizing the quantitative nondestructive detection of the contact state of the interface of the multilayer structure.
Description
Technical Field
The invention belongs to the field of ultrasonic nondestructive detection, and particularly relates to an ultrasonic signal similarity analysis method which is suitable for quantitative evaluation of an interface contact state.
Background
Due to the wide application of engineering and the extreme importance to the structure safety, the evaluation of the interface characteristics of the multilayer pressure-bearing structure is always a hotspot and difficult problem in the field of nondestructive testing. Due to the strong penetrating power of ultrasonic waves and the high sensitivity to structural discontinuity, the ultrasonic technology becomes a feasible means for detecting the interface characteristics of the multilayer structure. Based on the reflection and transmission characteristics of ultrasonic waves at a contact interface, a spring model uses rigidity to characterize the contact state of a solid interface, and is a basic model which can be used for interface contact state description. Scholars at home and abroad have carried out detection experiment research on various interface contact problems based on a spring model [1-4 ]. For example, Her [1] and others have applied the reflectance method to the analysis processing of interface signals to evaluate the strength of the adhesive layer of the multilayer structure. Ishii [2,3] develops theoretical analysis and detection experiments on the ultrasonic detection of the rigidity of the interface of the multi-layer plate structure, and researches the change relationship between the reflection coefficient and the transmission coefficient along with the rigidity of the epoxy interface layer. Research results show that the interface contact state can be detected by using the reflection coefficient/transmission coefficient, but the detection sensitivity is low, and the quantitative evaluation of the interface contact state is difficult to realize [4 ].
As a fuzzy comprehensive evaluation method, similarity analysis is widely applied in the fields of image processing, signal analysis and the like. The similarity analysis fully utilizes the global statistical information of the signals in the time domain/frequency domain, the extracted information is more comprehensive, and the characteristics of the signals can be more fully reflected. In recent years, scholars at home and abroad try to apply a similarity analysis method to the field of ultrasonic detection. Cepel R5, 6 and the like apply similarity analysis to (Pearson correlation) C-scan data processing, and defect imaging in steel/aluminum plates under extremely low signal-to-noise ratio is realized. Aiming at the problem of porosity detection in the composite material, the change rule of the similarity function (Euclidean distance) of the ultrasonic signal along with the porosity is researched in morning [7], and the extraction and characterization of the weak signal under high noise are realized. Son J G8 and the like propose to classify ultrasonic images of different tissues by taking Euclidean distance of a logarithmic power spectrum as a characteristic parameter. Xiao B9, etc. proposes one spectrum distribution similarity analysis method, which is applied in detecting coarse crystal material crack. Chenzhenhua [10] et al applied similarity function (spearman correlation) analysis to nonlinear ultrasonic signal analysis and studied the relationship between defect perimeter and nonlinear coefficient.
Aiming at the problem of detecting the interface characteristics of a multilayer pressure-bearing structure, the invention provides an ultrasonic signal similarity analysis method for evaluating the interface contact state under broadband ultrasonic excitation.
Disclosure of Invention
The invention provides an ultrasonic signal similarity analysis method for interface contact state evaluation, which aims to solve the problems of small reflection coefficient and low detection sensitivity of ultrasonic waves at a multilayer structure interface. And exciting ultrasonic waves by using the broadband signals, analyzing interface reflection signals, establishing a corresponding relation of a similarity function along with the change of the interface contact state, and finally realizing effective evaluation of the interface contact state.
The basic principle of the broadband ultrasonic similarity detection method provided by the invention is as follows:
changes in the interface state of the intermediate layers of the multilayer structure can result in micro-changes in the interface stiffness and inter-layer thickness, thereby causing changes in the amplitude of the ultrasonic time/frequency domain signal. When the broadband ultrasonic excitation is used, interface information contained in the interface reflection signals is rich, the similarity difference of the ultrasonic signals in different interface states is obvious, and the effective evaluation of the interface states can be realized by analyzing the similarity degree of the two signals.
A broadband excitation (such as a Chirp signal) is applied to the multilayer component, and reflected signals under different interface states are picked up. And determining a reference signal, and performing similarity analysis on the detection signal and the reference signal in different interface states to obtain a similarity coefficient in a corresponding interface state. The method selects five similarity functions of Pearson correlation, Euclidean distance, cosine similarity, Kendel correlation and Spireman correlation for comparative analysis. In the following analysis, xiAnd yiRespectively, an interface reflection reference signal and a detection signal.
1) Pearson correlation
Pearson correlation is a statistic that reflects the degree of linear correlation between two variables, expressed as follows.
Wherein n is the number of signal points, sx，syIs the standard deviation of the two signals.
2) Euclidean distance
The euclidean distance is expressed as a spatial linear distance of two points, and is expressed as follows.
3) Cosine similarity
The cosine similarity evaluates the similarity by calculating the cosine value of an included angle of two vectors, the larger the value is, the larger the included angle is, the smaller the similarity is, and the expression is shown in the specification.
4) Kendel correlation
Kendel correlation is a statistic for describing the correlation level of two signals, mainly focuses on the trend of two adjacent data points of the signals, and ignores the influence brought by the amplitude of the signals. When two points satisfy xi>xj,yi>yjOr xi<xj,yi<yjOtherwise, the two points are considered to be inconsistent. The expression is
Where C represents the number of points where the two signals coincide and D represents the number of points where the two signals do not coincide.
5) Spearman correlation
The spearman similarity is derived from the Euclidean distance between two signal points, when the number of the signal points is more, the accuracy of spearman correlation is higher, and the expression is
After the similarity coefficient is obtained, a change rule curve of each similarity function along with the interface state is established, sensitive characteristic parameters are preferably selected, and the characteristic parameters are used for quantitative characterization of the contact state of the multilayer structure interface.
The technical scheme of the invention is as follows:
the device adopted by the invention is shown in figure 1 and comprises an arbitrary function generator 1, a voltage amplifier 2, a duplexer 3, an ultrasonic transducer 4, an oscilloscope 6, a computer 7 and a pressure tester 8. Firstly, connecting a function generator 1 with a voltage amplifier 2 for output amplification of an excitation signal; the output end of the voltage amplifier 2 is connected with the input end of the duplexer 3; the output end of the duplexer 3 is connected with the ultrasonic transducer 4 and used for exciting and collecting detection signals; the receiving end of the duplexer 3 is connected with the oscilloscope 6 and used for collecting interface reflection signals; the data obtained by the oscilloscope 6 is transmitted to the computer 7, and the computer analyzes and processes the data; the computer 7 is also connected with a pressure tester 8 to control the change of the external load pressure range.
The broadband ultrasonic similarity detection method provided by the invention is realized by the following steps, and the flow chart is shown in figure 2:
1) the structure to be tested is a multi-layer structural member formed by two materials, and an organic silicon film is used as an intermediate layer. The external load pressure is applied to the multilayer structural part by a pressure tester, the change rule of the interface contact state along with the pressure is analyzed, and the loading range is controlled by a computer.
2) The Chirp signal is applied to an ultrasonic transducer through a voltage amplifier and a duplexer to excite a broadband ultrasonic signal, and a coupling agent is coated between the transducer and the multilayer structure.
3) And after receiving the interface reflection signal, the ultrasonic transducer transmits the interface reflection signal to an oscilloscope through a duplexer, and stores the detection signal after average processing.
4) Changing the external load pressure, repeating the steps 2) to 3) to obtain detection signals under each pressure, and selecting the detection signal under the maximum pressure as a reference signal.
5) And carrying out similarity analysis on the detection signals by the computer, and determining the detection signal range and the frequency domain bandwidth of the similarity analysis. And calculating similarity function values corresponding to interface contact states under different external load pressures according to the formulas (1) to (5), and further obtaining a change curve of each similarity function along with the external load pressure.
6) And after the variation curves are normalized, sorting according to the sensitivity degree of each similarity curve, and selecting a similarity function with the best sensitivity as an interface contact state characterization parameter.
Drawings
FIG. 1 is a system diagram of a detection device.
In the figure, 1, an arbitrary function generator, 2, a voltage amplifier, 3, a duplexer, 4, an ultrasonic transducer, 5, a multilayer structural part, 6, an oscilloscope, 7, a computer, 8 and a compression testing machine are shown.
Fig. 2 is a flowchart of a method for detecting similarity of broadband ultrasound.
Fig. 3 shows the time domain similarity detection result.
Fig. 4 shows the result of the frequency domain similarity detection.
FIG. 5 is a schematic diagram of experimental system setup realized by the method.
Detailed Description
The invention is further illustrated below with reference to specific experiments:
the experiment implementation process comprises the following steps:
1. establishing an experiment system: an experimental system is built according to a system diagram of the detection device shown in fig. 1, and the system comprises an arbitrary function generator 1, a voltage amplifier 2, an ultrasonic transducer 3, a duplexer 4, an oscilloscope 6, a computer 7 and a pressure tester 8. Firstly, connecting a function generator 1 with a voltage amplifier 2 for output amplification of an excitation signal; the output end of the voltage amplifier 2 is connected with the input end of the duplexer 3; the output end of the duplexer 3 is connected with the ultrasonic transducer 4 and used for exciting and collecting detection signals; the receiving end of the duplexer 3 is connected with the oscilloscope 6 and used for collecting interface reflection signals; the data obtained by the oscilloscope 6 is transmitted to the computer 7 for analysis and processing of the data; the computer 7 is also connected with a pressure tester 8 to control the change of the external load pressure range.
2. Selecting a detection test piece: the tested multilayer structure consists of an aluminum block and a graphite block, wherein the thickness of the aluminum block is 50 mm; the thickness of the graphite block is 30mm, the thickness of the organic silicon film is 100 mu m, and the cross section dimensions are all 145mm multiplied by 145 mm.
3. Setting detection parameters: the ultrasonic transducer is adhered to a test piece through a coupling agent, the excitation signal is a Chirp broadband signal, the frequency range of the signal is 0-10 MHz, and the excitation time is 10 microseconds. The excitation signal is amplified by a voltage amplifier to have an amplitude of 50 Vpp. The received detection signals are stored by an oscilloscope, and the average frequency is 128 times.
4. Multilayer structure interface characteristic detection experiment: and starting the function generator, the voltage amplifier and the oscilloscope, realizing effective isolation of the excitation signal and the received signal by the duplexer, and storing the acquired detection signal by the oscilloscope. Similarly, the computer is used to control the variation of the external load pressure, the variation range is 0.25-2.5MPa, the above detection steps are repeated and the signals are stored.
5. And (3) similarity analysis: firstly, determining the range of signal similarity analysis, wherein the range of a detection signal is 15-25 mu s, and the bandwidth of a frequency domain is 0-10 MHz. And (3) calculating similarity function values corresponding to interface contact states under different external load pressures according to formulas (1) to (5) to obtain time domain and frequency domain similarity along with pressure change curves.
6. And (3) analyzing an experimental result: the normalized similarity detection results are shown in fig. 3 and 4, and the euclidean distance in the time domain similarity is most sensitive to pressure changes. The feasibility of the broadband ultrasonic similarity detection method for quantitative detection of the multilayer structure interface state is demonstrated.
The above is a typical application of the present invention, and the application of the present invention is not limited thereto.
Reference to the literature
[1]Her S C,Lin Y C.Assessment of Adhesive Bond Strength Using theUltrasonic Technique[J]. The Journal of Adhesion,2014,90(5-6):545-554
[2]Ishii Y,Biwa S.Ultrasonic evaluation of interlayer interfacialstiffness of multilayered structures[J].Journal of Applied Physics,2012,111(8):89
[3]Ishii Y,Biwa S,Biwa.Evaluation of interlayer interfacial stiffnessand layer wave velocity of multilayered structures by ultrasonic spectroscopy[J].The Journal of the Acoustical Society of America,2014,136(1):183-91
[4]Holmes C,Drinkwater B W.The use of ultrasound to measure contactstiffness and pressure in large contacting interfaces[C]//AipConference.American Institute of Physics,2003, 657:1072-1077.
[5]Cepel R,Ho K,Rinker B,et al.Ultrasonic Detection Using CorrelationImages[J]. 2006.26(894):657-664.
[6]Cepel R,Ho K,Rinker B,et al.Spatial Correlation Coefficient Imagesfor Ultrasonic Detection[J].IEEE Transactions on Ultrasonics,Ferroelectricsand Frequency Control,2007, 54(9):1841-1850.
[7] In the morning, Kinsjie, Linli. ultrasonic backscattering signal recursive quantitative analysis nondestructive characterization CFRP pore distribution simulation [ J ] composite material academic newspaper, 2018,35(10): 159-.
[8]Son J G,Kim N C.Organ recognition in ultrasound images using logpower spectrum[J]. Proceedings of Spie the International Society for OpticalEngineering,2003,4687:387-394.
[9]Xiao B,Li M,Gongzhang R,et al.Image de-noising via spectraldistribution similarity analysis for ultrasonic non-destructive evaluation[J].2014,1581:1941-1947.
[10] Chenghua, Xiaofeng, Luming, et al, ultrasonic nonlinear area detection technique of micro-lamellar type defects [ J ] application acoustics, 2019,38(02):54-61.
Claims (3)
1. An ultrasonic signal similarity analysis method for interface contact state evaluation is characterized by comprising the following steps: the method comprises the following steps of,
step 1) selecting a multilayer structural member formed by two materials for a structure to be detected, and using an organic silicon film as an intermediate layer; applying external load pressure to the multilayer structural part by a pressure tester, analyzing the change rule of the interface contact state along with the pressure, and controlling the loading range by a computer;
step 2) applying a Chirp signal to an ultrasonic transducer after passing through a voltage amplifier and a duplexer to excite a broadband ultrasonic signal, and coating a coupling agent between the transducer and the multilayer structure;
step 3) after the ultrasonic transducer receives the interface reflection signal, the interface reflection signal is transmitted to an oscilloscope by a duplexer, and after average processing, the detection signal is stored;
step 4) changing external load pressure, repeating the steps 2) to 3) to obtain detection signals under all pressures, and selecting the detection signal under the maximum pressure as a reference signal;
step 5), carrying out similarity analysis on the detection signals by the computer, and determining the detection signal range and the frequency domain bandwidth of the similarity analysis; calculating similarity function values corresponding to interface contact states under different external load pressures according to the similarity function expression to obtain a variation curve of each similarity function along with the external load pressure;
and 6) after the variation curves are normalized, sorting according to the sensitivity of each similarity curve, and selecting a similarity function as an interface contact state characterization parameter.
2. The ultrasonic signal similarity analysis method for interface contact state evaluation according to claim 1, characterized in that: the device for realizing the method comprises an arbitrary function generator, a voltage amplifier, a duplexer, an ultrasonic transducer, an oscilloscope, a computer and a pressure tester; connecting a function generator with a voltage amplifier for output amplification of the excitation signal; the output end of the voltage amplifier is connected with the input end of the duplexer; the output end of the duplexer is connected with the ultrasonic transducer and used for exciting and collecting detection signals; the receiving end of the duplexer is connected with the oscilloscope and used for collecting interface reflection signals; the data obtained by the oscilloscope is transmitted to a computer, and the computer analyzes and processes the data; the computer is connected with the pressure tester to control the change of the external load pressure range.
3. The ultrasonic signal similarity analysis method for interface contact state evaluation according to claim 1, characterized in that: in the step 5), broadband excitation is applied to the multilayer component, and reflected signals in different interface states are picked up; determining a reference signal, and performing similarity analysis on the detection signal and the reference signal in different interface states to obtain a similarity coefficient in a corresponding interface state; selecting Pearson's correlation, Euclidean distanceComparing and analyzing five similarity functions of the distance similarity, the cosine similarity, the Kendel correlation and the Spireman correlation; in the following analysis, xiAnd yiRespectively an interface reflection reference signal and a detection signal;
1) pearson correlation
Pearson's correlation is a statistic reflecting the degree of linear correlation between two variables, expressed as
Wherein n is the number of signal points, sx，syIs the standard deviation of the two signals;
2) euclidean distance
The Euclidean distance is expressed as the space linear distance of two points, and the expression is
3) Cosine similarity
The cosine similarity evaluates the similarity by calculating the cosine value of an included angle between two vectors, the larger the evaluation value is, the larger the included angle is, the smaller the similarity is, and the expression is
4) Kendel correlation
Kendall correlation is statistics for describing correlation levels of two signals, the trend of two adjacent data points of the signals is concerned, and the influence brought by the amplitude of the signals is ignored; when two points satisfy xi>xj,yi>yjOr xi<xj,yi<yjIf not, the two points are not consistent; is expressed as
Wherein C represents the number of points of coincidence of the two signals, and D represents the number of points of non-coincidence of the two signals;
5) spearman correlation
The spearman similarity is derived from the Euclidean distance between two signal points, when the number of the signal points is more, the accuracy of spearman correlation is higher, and the expression is
And after the similarity coefficient is obtained, establishing a change rule curve of each similarity function along with the interface state, selecting sensitive characteristic parameters, and using the sensitive characteristic parameters for quantitative characterization of the interface contact state of the multilayer structure.
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| CN109738512A (en) * | 2019-01-08 | 2019-05-10 | 重庆大学 | Nondestructive detection system and method based on multiple physical field fusion |
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