CN110470742B - Accurate detection method for defects of pipeline elbow - Google Patents
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Abstract
The invention discloses an accurate detection method of a pipeline elbow defect, which comprises the steps of sequentially applying Chirp signals to a single excitation sensor to obtain Chirp excitation response signals; extracting narrow-band pulse signals of guided waves under different central frequencies from a Chirp excitation response signal by using a narrow-band pulse extraction method, carrying out mode identification on wave packets in the extracted narrow-band pulse signals, and then confirming the central frequency of narrow-band extraction with the aim of obtaining a pure S0 mode; establishing a pipeline elbow tomography forward model, and calculating the guided wave flight time by using a second-order difference forward algorithm; then, combining a steepest descent inversion method, analyzing and processing the narrow-band pulse extraction signal based on the flight time characteristic of the signal, and then identifying and extracting defect characteristic information of the analysis and processing result; and finally, displaying the depth and surface appearance characteristics of the defects to finish the detection of the defects of the elbow of the pipeline. The invention can realize accurate, effective, comprehensive and rapid detection of the defects of the pipe elbows.
Description
Technical Field
The invention belongs to the field of transportation pipeline defect detection, and relates to an accurate detection method for a pipeline elbow defect.
Background
As the fifth transportation industry after railways, roads, water transportation and aviation, pipeline transportation has extremely important roles in the industries such as petrifaction, power generation and the like and daily life due to the unique advantages of pipeline transportation. However, such pipelines usually work in severe environments such as high temperature, high pressure or electric corrosion, and the like, and the aging of the equipment structure inevitably causes damage of different degrees, further causes major accidents, brings great economic loss, and causes casualties. The pipeline elbow is used as an important component of a pipeline network, the defects of the pipeline elbow are effectively detected, and the method has important significance for perfecting the detection of the transportation pipeline.
The ultrasonic guided wave has long propagation distance, high detection speed, small attenuation and high efficiency, has the advantages of detecting the whole wall thickness, and is greatly applied and researched in the field of nondestructive detection of pipelines. Nowadays, more and more domestic and foreign scholars perform simulation or experimental research on the propagation characteristics of ultrasonic guided waves in pipe elbows.
The A.DEMMA of the British empire theory university researches the influence of the pipeline elbow on the transmission and reflection coefficients of the guided waves through finite element analysis, proves the reflection and transmission performance of the ultrasonic guided waves in the pipeline elbow and provides a research idea for the application of the ultrasonic guided waves in the pipeline.
The existing ultrasonic guided wave signal excitation mode is that sensors are uniformly arranged at two ends of a pipeline elbow in the circumferential direction respectively, one end of each ultrasonic guided wave signal excitation mode is an excitation sensor arranged in an annular array, and the other end of each ultrasonic guided wave signal excitation mode is a receiving sensor arranged in an annular array; on the other hand, research shows that the ultrasonic guided wave excited by the existing excitation means cannot detect the inner side region of the pipe elbow no matter the mode is the S0 mode or the T mode, so new and effective guided wave excitation technology and signal processing method need to be researched to realize accurate, effective, comprehensive and rapid detection of the defects of the pipe elbow.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for accurately detecting the defects of the pipeline elbow, which can realize accurate, effective, comprehensive and rapid detection of the defects in the pipeline elbow.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a method for accurately detecting defects of a pipeline elbow comprises the following steps:
s1, building an ultrasonic guided wave detection system for the pipe elbow; the method comprises the following steps that a built pipeline elbow ultrasonic guided wave detection system is utilized, Chirp signals are sequentially applied to a single excitation sensor in the pipeline elbow ultrasonic guided wave detection system, and a receiving sensor in the pipeline elbow ultrasonic guided wave detection system acquires Chirp excitation response signals; extracting narrow-band pulse signals of guided waves under different central frequencies from a Chirp excitation response signal by using a narrow-band pulse extraction method, carrying out mode identification on wave packets in the extracted narrow-band pulse signals, confirming the central frequency of narrow-band extraction with the aim of obtaining a pure S0 mode, and obtaining narrow-band pulse extraction signals according to the central frequency;
s2, establishing a pipeline elbow tomography forward model, and calculating the guided wave flight time by using a second-order difference forward algorithm; then, analyzing and processing the narrow-band pulse extraction signal obtained in the step S1 based on the flight time characteristic of the signal by combining a steepest descent inversion method, and then identifying and extracting defect characteristic information of the analysis and processing result; and finally, displaying the depth and surface appearance characteristics of the defects to finish the detection of the defects of the elbow of the pipeline.
In S1, the process of extracting the narrowband pulse signals of the guided waves at different center frequencies from the Chirp excitation response signal is as follows:
determining an ideal pulse excitation signal as sd(t), ideal pulse excitation signal sd(t) response signal Rd(ω) is:
wherein R isc(omega) is a frequency domain expression form of a Chirp excitation response signal, Sd(ω) is the Fourier transform, S, of an ideal pulsed excitation signalc(ω) is a Fourier transform form of the Chirp signal, and G (ω) is in the frequency domainA band-pass filter;
then to the ideal pulse excitation signal sd(t) response signal Rd(omega) carrying out inverse Fourier transform to obtain an ideal pulse excitation signal sd(t) narrow-band pulse signal Rd(t)。
In S1, the process of performing mode identification on the wave packet in the extracted narrowband pulse signal is as follows:
performing time-frequency transformation on the extracted narrow-band pulse signal to obtain the arrival time of each frequency point and the flight time t of a wave packet in the narrow-band pulse signalfAnd the arrival time satisfies the following equation:
tf＝ta-te
wherein, teFor the emission time, t, of each frequency point of the ideal pulse excitation signalaIs the arrival time of the wave packet;
then according to the time of flight t of the wave packet in the narrow-band pulse signalfCalculating the propagation velocity v of the wave packet:
wherein larcIs the arc length of the pipe elbow;
and then carrying out mode identification on the wave packet in the extracted narrow-band pulse signal according to the propagation velocity v of the wave packet and the ultrasonic guided wave dispersion curve.
In S1, the procedure for identifying the center frequency of the narrowband extraction to obtain the pure S0 mode is as follows:
selecting an S0 mode as a mode of ultrasonic detection of the pipe elbow, wherein the purity ξ of the S0 mode is as follows:
wherein A is1Amplitude of wave packet of S0 mode, A2A wave packet amplitude of the a0 mode;
the narrowband extracted center frequency is then determined based on the relationship between the purity level ξ and the narrowband extracted center frequency.
The Chirp signal is a signal with continuous and linear variation of instantaneous frequency along with time, and the mathematical expression of the Chirp signal is as follows:
in the formula: w (t) is a rectangular window function; f. of0Is the starting frequency; b is the signal frequency domain width; t is the signal duration;
the expression form of the Chirp excitation response signal in the frequency domain is as follows:
Rc(ω)＝H(ω)Sc(ω)
wherein R isc(omega) is a frequency domain expression form of a Chirp excitation response signal; scAnd (omega) is a Fourier transform form of the Chirp signal.
In S2, the process of establishing the pipe elbow tomography forward model is as follows:
unfolding along the outer ridge line of the pipeline elbow, establishing a pipeline elbow tomography forward modeling model, and obtaining a mapping relation between a three-dimensional coordinate system { O, x, y, z } and a two-dimensional coordinate system { O, x ', y' } of the pipeline elbow tomography forward modeling model, wherein the mapping relation is as follows:
wherein R is the bending radius of the pipeline elbow, and R is the inner diameter of the pipeline elbow.
In S2, the process of calculating the guided wave flight time using the second order difference correction algorithm includes the steps of:
s2.1, according to the pipeline elbow tomography forward model, obtaining an anisotropic sound velocity field model c' (theta) as follows:
wherein c is the propagation velocity of the guided wave,
s2.2, obtaining an equation of an engineering function according to the mapping relation and the anisotropic sound velocity field model as follows:
s2.3, solving the equation of the equation by using a second order difference algorithm to obtain a flight time matrix of the whole area, wherein:
the algorithm for solving the equation of the function uses the entropy to meet the ascending region, and the equation of the function is expressed as follows:
wherein
Representing the backward difference operator in the x-direction,
representing a forward difference operator in the x-direction,
representing a forward difference operator in the y-direction,
representing a backward difference operator in the y-direction;
the basic form of the second order difference operator is:
in S2, analyzing and processing the narrow-band pulse extraction signal obtained in S1 based on the flight time characteristic of the signal by combining a steepest descent inversion method, and then identifying and extracting defect characteristic information of the analysis and processing result; finally, the process of displaying the depth and surface topography of the defect comprises the following steps:
s2.4, obtaining a travel time matrix T of the receiving sensor according to the flight time matrix;
s2.5, determining an inversion problem as follows:
d＝A(T)
wherein A is a nonlinear operator, and d is a wall thickness matrix of the whole area of the pipeline elbow;
s2.6, setting a travel time error functional as follows:
φ(d)＝||A(d)-T||
s2.7, setting the gradient delta of the travel time error functionalnDetermining the optimal direction of the steepest descent inversion method, and iterating a wall thickness matrix d of the whole area of the pipeline elbow, wherein the iteration format is as follows:
dn+1＝dn-ηnδn
wherein, ηnFor search step size, ηnCalculated by the following formula:
ηn＝arg{min[φ(dn+ηδn)]}
wherein the content of the first and second substances,
when the error reaches a given level, the iterative process is terminated, the calculation result is mapped into the three-dimensional model, and the depth and the surface topography of the defect are displayed.
Compared with the prior art, the invention has the following beneficial effects:
in the method for accurately detecting the defects of the pipeline elbow, the chirp signal is used, so that the signal-to-noise ratio of the detection signal is improved, the detection efficiency can be improved, and the chirp signal is sequentially applied to a single excitation sensor in an ultrasonic guided wave detection system of the pipeline elbowThat is, the received guided wave signal expands n compared with the traditional array simultaneous excitation method2And (n) the number of the sensors arranged in the circumferential direction, more information containing the shape and the feature of the elbow is obtained. In the aspect of signal processing, the characteristic of guided wave S0 modal flight time is extracted, the proposed second-order difference algorithm utilizes the flight time information of grid nodes, the appearance information of the elbow can be inverted more accurately, and the defect detection result is more visual. It should be noted that the invention adopts the ultrasonic guided wave as the detection means, and can complete accurate, effective, comprehensive and rapid detection of the defects of the pipe elbow based on the characteristics of full wave field characteristic and rapid propagation speed of the ultrasonic guided wave.
Drawings
FIG. 1 is a schematic structural diagram of an ultrasonic guided wave detection system for a pipe elbow built in the invention;
fig. 2 is a time domain diagram of a chirp signal in the present invention;
fig. 3 is a frequency domain diagram of a chirp signal in the present invention;
fig. 4 is a time-frequency domain diagram of a chirp signal in the present invention;
FIG. 5 is a mapping of a three-dimensional model to a two-dimensional model of a pipe elbow in the present invention;
fig. 6 is a diagram illustrating a specific process of detecting a pipe bend according to the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples to verify the effectiveness of the invention in its application, but the examples are not intended to limit the invention.
Referring to fig. 6, the high-precision detection method for the defects of the pipe elbow based on the ultrasonic guided waves comprises the following steps:
Applying Chirp signals shown in fig. 2 to a single excitation sensor in sequence, and acquiring Chirp excitation response signals by a receiving sensor, wherein the Chirp signals are broadband excitation signals, and a time domain diagram, a frequency domain diagram and a time-frequency domain diagram of the Chirp signals are respectively shown in fig. 2, fig. 3 and fig. 4; and then, extracting narrow-band pulse signals of the guided waves under different central frequencies from the Chirp excitation response signals with high efficiency by using a narrow-band pulse extraction method, carrying out mode identification on wave packets in the extracted narrow-band pulse signals, confirming the central frequency of narrow-band extraction with the aim of obtaining a pure S0 mode, and obtaining narrow-band pulse extraction signals according to the central frequency.
And 2, establishing a pipeline elbow tomography forward model, calculating the guided wave flight time by using a second-order difference forward algorithm, analyzing and processing the narrowband pulse extraction signal obtained in the step 1 based on the flight time characteristic of the signal by combining a steepest descent inversion method, identifying and extracting defect characteristic information of the analyzed and processed result, and finally displaying the depth and surface topography characteristics of the defect to finish the detection of the pipeline elbow defect.
The specific process of the step 1 of the invention is as follows:
the adopted Chirp signal refers to a signal with continuous and linear variation of instantaneous frequency along with time, and the mathematical expression of the Chirp signal is as follows:
in the formula: w (t) -a rectangular window function; f. of0-a starting frequency; b-signal frequency domain width; T-Signal duration.
The expression form of the Chirp excitation response signal in the frequency domain is as follows:
Rc(ω)＝H(ω)Sc(ω)
wherein R isc(omega) is a frequency domain expression form of a Chirp excitation response signal; scAnd (omega) is a Fourier transform form of the Chirp signal.
Let sd(t) is an ideal pulse excitation signal, and for the same guided wave detection system, an ideal pulse mechanism signal sd(t) response signal Rd(ω) is:
Rd(ω)＝H(ω)Sd(ω)
wherein the response signal Rd(ω) in the form of a frequency domain representation; sd(omega) is an ideal pulse excitation signal sd(t) Fourier transform.
Ideal pulse excitation signal sd(t) known response signal RdThe (ω) frequency domain expression is:
wherein G (ω) is considered as a band pass filter in the frequency domain;
next, the result of the evaluation is responded to the signal R, with all parameters being knownd(omega) carrying out inverse Fourier transform to obtain an ideal pulse excitation signal sd(t) response signal Rd(t) the response signal RdAnd (t) is the narrow-band pulse signal.
Setting the response of 5 periodic sinusoidal signals modulated by a Hanning window as an extraction object, an ideal pulse excitation signal sdThe expression of (t) is:
wherein f iscIs the extracted center frequency.
Performing time-frequency transformation on the extracted narrow-band pulse signal to obtain the arrival time of each frequency point, namely the instantaneous frequency of the signal and the arrival time of each frequency point respectively correspond to the projections of the ridge line on the frequency and the time axis;
the time of flight and the time of arrival of a wave packet in the narrowband pulsed signal satisfy the following equation:
tf＝ta-te
wherein, tfTime of flight of a wave packet, taIs the arrival time of a wave packet, teThe emitting time of each frequency point of the ideal pulse excitation signal is obtained.
Then according to the time of flight t of the wave packet in the narrow-band pulse signalfCalculating the propagation speed of the wave packet:
where v is the wave packet propagation velocity,/arcIs the arc length of the pipe elbow;
secondly, performing mode identification on the wave packet in the extracted narrowband pulse signal according to the propagation velocity v of the wave packet and the ultrasonic guided wave dispersion curve;
then, an S0 mode is selected as a mode of ultrasonic detection of the pipe elbow, a parameter ξ represents the purity degree of the S0 mode, and the expression of the purity degree ξ is as follows:
wherein A is1Amplitude of wave packet of S0 mode, A2A wave packet amplitude of the a0 mode;
the narrowband extracted center frequency is then determined based on the relationship between the purity level ξ and the narrowband extracted center frequency.
The specific process of the step 2 of the invention is as follows:
as shown in fig. 5, unfolding along the outer ridge of the pipe elbow, establishing a pipe elbow tomography forward model, and obtaining a mapping relation between a three-dimensional coordinate system { O, x, y, z } and a two-dimensional coordinate system { O, x ', y' } of the pipe elbow tomography forward model, wherein:
wherein R and R are respectively the bending radius of the elbow and the inner diameter of the pipeline;
according to the tomography forward model of the pipe elbow, based on the differential geometry theory, an anisotropic acoustic velocity field model c' (theta) is obtained as follows:
wherein c is the propagation velocity of the guided wave,
obtaining an equation of an engineering function according to the mapping relation and the anisotropic sound velocity field model as follows:
solving the equation of the equation by using a second-order difference algorithm to obtain a flight time matrix of the whole area, wherein:
the solution of the equation of the function uses an algorithm whose entropy satisfies the ascending region, and the equation of the function is expressed as:
wherein the content of the first and second substances,
representing the backward difference operator in the x-direction,
representing a forward difference operator in the x-direction,
representing a forward difference operator in the y-direction,
representing a backward difference operator in the y-direction;
the basic form of the second order difference operator is:
obtaining a travel time matrix T of the receiving sensor according to the flight time matrix;
the inversion problem was determined to be:
d＝A(T)
wherein A is a nonlinear operator, and d is a wall thickness matrix of the whole area of the pipeline elbow;
setting a travel time error functional as follows:
φ(d)＝||A(d)-T||
setting the gradient delta of the travel time error functionalnDetermining the optimal direction of the steepest descent inversion method, and iterating a wall thickness matrix d of the whole area of the pipeline elbow, wherein the iteration format is as follows:
dn+1＝dn-ηnδn
η thereinnFor search step size, ηnCalculated by the following formula:
ηn＝arg{min[φ(dn+ηδn)]}
wherein the content of the first and second substances,
when the error reaches a set level, the iterative process terminates, i.e.: phi (d) is less than or equal to epsilon0When the iteration process is terminated, epsilon0Is at a set level;
and finally, mapping the calculation result to a three-dimensional model, and visually displaying the appearance information of the pipeline display elbow.
Claims (7)
1. The method for accurately detecting the defects of the pipeline elbow is characterized by comprising the following steps of:
s1, building an ultrasonic guided wave detection system for the pipe elbow; the method comprises the following steps that a built pipeline elbow ultrasonic guided wave detection system is utilized, Chirp signals are sequentially applied to a single excitation sensor in the pipeline elbow ultrasonic guided wave detection system, and a receiving sensor in the pipeline elbow ultrasonic guided wave detection system acquires Chirp excitation response signals; extracting narrow-band pulse signals of guided waves under different central frequencies from a Chirp excitation response signal by using a narrow-band pulse extraction method, carrying out mode identification on wave packets in the extracted narrow-band pulse signals, confirming the central frequency of narrow-band extraction with the aim of obtaining a pure S0 mode, and obtaining narrow-band pulse extraction signals according to the central frequency;
s2, establishing a pipeline elbow tomography forward model, and calculating the guided wave flight time by using a second-order difference forward algorithm; then, analyzing and processing the narrow-band pulse extraction signal obtained in the step S1 based on the flight time characteristic of the signal by combining a steepest descent inversion method, and then identifying and extracting defect characteristic information of the analysis and processing result; finally, displaying the depth and surface appearance characteristics of the defects to finish the detection of the defects of the elbow of the pipeline;
in S2, the process of establishing the pipe elbow tomography forward model is as follows:
unfolding along the outer ridge line of the pipeline elbow, establishing a pipeline elbow tomography forward modeling model, and obtaining a mapping relation between a three-dimensional coordinate system { O, x, y, z } and a two-dimensional coordinate system { O, x ', y' } of the pipeline elbow tomography forward modeling model, wherein the mapping relation is as follows:
wherein R is the bending radius of the pipeline elbow, and R is the inner diameter of the pipeline elbow.
2. The method of claim 1, wherein in step S1, the process of extracting the narrowband pulse signals of the guided waves at different center frequencies from the Chirp excitation response signal is as follows:
determining an ideal pulse excitation signal as sd(t), ideal pulse excitation signal sd(t) response signal Rd(ω) is:
wherein R isc(omega) is a frequency domain expression form of a Chirp excitation response signal, Sd(ω) is the Fourier transform, S, of an ideal pulsed excitation signalc(omega) is a Fourier transform form of a Chirp signal, and G (omega) is a band-pass filter in a frequency domain;
then to the ideal pulse excitation signal sd(t) response signal Rd(omega) carrying out inverse Fourier transform to obtain an ideal pulse excitation signal sd(t) narrow-band pulse signal Rd(t)。
3. The method of claim 1, wherein in step S1, the mode identification of the wave packet in the extracted narrow-band pulse signal is performed as follows:
performing time-frequency transformation on the extracted narrow-band pulse signal to obtain the arrival time of each frequency point and the flight time t of a wave packet in the narrow-band pulse signalfAnd the arrival time satisfies the following equation:
tf＝ta-te
wherein, teFor the emission time, t, of each frequency point of the ideal pulse excitation signalaIs a wave packet toReaching the time;
then according to the time of flight t of the wave packet in the narrow-band pulse signalfCalculating the propagation velocity v of the wave packet:
wherein larcIs the arc length of the pipe elbow;
and then carrying out mode identification on the wave packet in the extracted narrow-band pulse signal according to the propagation velocity v of the wave packet and the ultrasonic guided wave dispersion curve.
4. The method of claim 1, wherein the identification of the center frequency of the narrow-band extraction in S1 for the purpose of obtaining a pure S0 mode is performed as follows:
selecting an S0 mode as a mode of ultrasonic detection of the pipe elbow, wherein the purity ξ of the S0 mode is as follows:
wherein A is1Amplitude of wave packet of S0 mode, A2A wave packet amplitude of the a0 mode;
the narrowband extracted center frequency is then determined based on the relationship between the purity level ξ and the narrowband extracted center frequency.
5. The method for accurately detecting the defects of the pipe elbows of claim 1, wherein the Chirp signal is a signal with the instantaneous frequency continuously and linearly changing along with the time, and the mathematical expression of the Chirp signal is as follows:
in the formula: w (t) is a rectangular window function; f. of0Is the starting frequency; b is the signal frequency domain width; t is the signal duration;
the expression form of the Chirp excitation response signal in the frequency domain is as follows:
Rc(ω)＝H(ω)Sc(ω)
wherein R isc(omega) is a frequency domain expression form of a Chirp excitation response signal; scAnd (omega) is a Fourier transform form of the Chirp signal.
6. The method of claim 1, wherein the step of calculating the guided wave flight time using the second order difference correction algorithm in step S2 comprises the steps of:
s2.1, according to the pipeline elbow tomography forward model, obtaining an anisotropic sound velocity field model c' (theta) as follows:
wherein c is the propagation velocity of the guided wave,
s2.2, obtaining an equation of an engineering function according to the mapping relation and the anisotropic sound velocity field model as follows:
s2.3, solving the equation of the equation by using a second order difference algorithm to obtain a flight time matrix of the whole area, wherein:
the algorithm for solving the equation of the function uses the entropy to meet the ascending region, and the equation of the function is expressed as follows:
wherein
Representing the backward difference operator in the x-direction,
representing a forward difference operator in the x-direction,
representing a forward difference operator in the y-direction,
representing a backward difference operator in the y-direction;
the basic form of the second order difference operator is:
7. the method of claim 6, wherein in step S2, in combination with the steepest descent inversion method, the narrow-band pulse extracted signal obtained in step S1 is analyzed based on the time-of-flight characteristics of the signal, and then the result of the analysis is identified and extracted as the defect characteristic information; finally, the process of displaying the depth and surface topography of the defect comprises the following steps:
s2.4, obtaining a travel time matrix T of the receiving sensor according to the flight time matrix;
s2.5, determining an inversion problem as follows:
d＝A(T)
wherein A is a nonlinear operator, and d is a wall thickness matrix of the whole area of the pipeline elbow;
s2.6, setting a travel time error functional as follows:
φ(d)＝||A(d)-T||
s2.7, setting the gradient delta of the travel time error functionalnDetermining the optimal direction of the steepest descent inversion method, and iterating a wall thickness matrix d of the whole area of the pipeline elbow, wherein the iteration format is as follows:
dn+1＝dn-ηnδn
wherein, ηnFor search step size, ηnCalculated by the following formula:
ηn＝arg{min[φ(dn+ηδn)]}
wherein the content of the first and second substances,
when the error reaches a given level, the iterative process is terminated, the calculation result is mapped into the three-dimensional model, and the depth and the surface topography of the defect are displayed.
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