Method for optimizing recurrence plot threshold and extracting characteristic parameters based on particle swarm optimization

Abstract

The invention discloses a recurrence plot threshold value optimization and characteristic parameter extraction method based on a particle swarm algorithm, and belongs to the field of nondestructive testing. According to the method, an ultrasonic back scattering signal is collected through a set of ultrasonic signal collection system, phase space reconstruction is carried out on the ultrasonic back scattering signal, and the Euclidean distance between any two time vectors in the phase space is calculated. And setting particle swarm parameters and convergence conditions, and optimizing the recurrence plot threshold by adopting a particle swarm optimization algorithm. And performing recurrence plot drawing on the ultrasonic back scattering signal by using the optimized threshold value. And performing quantitative calculation on the recursion plot by using a recursion quantitative analysis method, extracting characteristic parameters, and finally constructing a mapping relation between the characteristic parameters and the material degradation damage level, thereby realizing evaluation of different material degradation levels. The defect that the recursion threshold value is set only manually is overcome, the ultrasonic back scattering signals can be accurately and comprehensively analyzed, accurate extraction of recursion characteristic parameters is achieved, and the method has great significance in developing an acoustic evaluation method for material deterioration damage.

Description

Technical Field

[0001] The present invention is a method for optimizing the recursive graph threshold and feature parameter extraction based on particle swarm algorithm, which can optimize the recursive graph threshold selection and belongs to the field of non-destructive evaluation. Therefore, the extracted optimized recursive feature parameters can be used to accurately characterize the material degradation damage level. Background Art

[0002] High-temperature, pressure-bearing, hot-walled pipelines are prone to material degradation damage during service. Common metal materials include carbon steel and low-alloy steel. Material degradation will cause changes in the microstructure of metal materials, including material texture, grain size, and grain shape. Ultrasonic backscatter signals are sensitive to changes in the microstructure of materials. By extracting the characteristic parameters of ultrasonic backscatter signals, the material degradation level can be effectively evaluated. Since ultrasonic backscatter signals are nonlinear and non-stationary signals, it is difficult to extract characteristic parameters in the time domain and frequency domain. Recursion graphs can effectively identify the characteristics of ultrasonic backscatter signals and evaluate the material degradation level of metal materials to ensure the safe operation of equipment. Therefore, the ultrasonic backscatter signals of different material degradation levels are plotted through recursion graphs, and the recursive characteristic parameters are extracted using recursive analysis methods. This is of great significance for studying the extraction of characteristic parameters of recursion graphs and developing acoustic evaluation methods for material degradation levels.

[0003] At present, the recursive graph of ultrasonic backscatter signals is mostly drawn by manually setting the threshold and extracting the characteristic parameters of the recursive graph using the recursive analysis method. However, due to the inaccurate human judgment in the actual threshold setting and the lack of effective setting of the ultrasonic backscatter signal threshold, the recursive characteristic parameters cannot be effectively extracted during calculation.

[0004] The present invention proposes a method for optimizing the recursive graph threshold and feature parameter extraction based on a particle swarm algorithm. Backscattered signals are collected by ultrasonic detection equipment, and the embedding dimension and delay time are determined by the false neighbor method and the average mutual information method, and the distance between any two ultrasonic backscattered signal vectors in the phase space is calculated. The particle swarm parameters and convergence conditions are set, and the recursive graph threshold is optimized by the particle swarm optimization algorithm. The recursive graph of the ultrasonic backscattered signal is drawn using the optimized threshold. The recursive quantitative analysis method is used to quantitatively calculate the recursive graph, extract the feature parameters, and finally construct a mapping relationship between the feature parameters and the material degradation damage level. On the basis of the traditional recursive graph, the present invention introduces a particle swarm optimization algorithm to optimize the recursive graph threshold, which is of great significance in studying the selection of recursive graph thresholds, extracting recursive feature parameters, and developing acoustic evaluation methods for material degradation levels. Summary of the invention

[0005] The purpose of the present invention is to propose a method for optimizing the recursive graph threshold and characteristic parameter extraction based on a particle swarm algorithm. For different material degradation levels, this method combines the particle swarm optimization algorithm to select the recursive graph threshold, and realizes the accurate extraction of recursive characteristic parameters under different material degradation levels. It overcomes the shortcomings of only manually setting the recursive threshold, can accurately and comprehensively analyze the ultrasonic backscattered signal, and realize the accurate extraction of recursive characteristic parameters, which is of great significance to the development of acoustic evaluation methods for material degradation damage.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A particle swarm algorithm-based method for optimizing a recursive graph threshold and extracting characteristic parameters comprises the following steps: an ultrasonic signal acquisition system composed of a set of ultrasonic detection equipment, an oscilloscope and a digital signal acquisition card is used to acquire ultrasonic backscatter signals, which are time series signals; a phase space reconstruction is performed on the time series signals, and the Euclidean distance between any two ultrasonic backscatter signal vectors in the phase space is calculated; the Euclidean distance is subtracted from the recursive threshold, and the difference is converted into a two-dimensional matrix, i.e., a recursive graph matrix, by using a Heavside function; particle swarm parameters and convergence conditions are set, and the recursive graph threshold is optimized by using a particle swarm optimization algorithm; a recursive graph of the ultrasonic backscatter signal is drawn by using the optimized recursive graph threshold; a recursive quantitative analysis method is used to quantitatively calculate the recursive graph, and characteristic parameters are extracted, and finally a correlation between the characteristic parameters and the material degradation damage level is constructed to achieve the evaluation of the degradation levels of different materials.

[0008] The specific steps are as follows:

[0009] Step 1: Use an ultrasonic signal acquisition system consisting of ultrasonic testing equipment, an oscilloscope and a digital signal acquisition card to collect ultrasonic backscatter signals of high-temperature pressure hot-wall pipes with different material degradation. The ultrasonic backscatter signals are used as the time series for subsequent research.

[0010] Step 2: Calculate the stable point and the position corresponding to the first local minimum by using the false neighbor method and the average mutual information method, and use this position as the final value of the embedding dimension m and the delay time τ;

[0011] The false neighbor method is to reconstruct a time series to obtain an m-dimensional phase space, and record the phase point X n The nearest neighbor point is X η(n) , the distance between the phase point and the nearest neighbor point is expressed as:

[0012] X η(n) -X n (m) (1)

[0013] After the phase space becomes m+l dimensions, the distance between phase points is expressed as:

[0014]

[0015] Calculate the ratio B of the distance between the phase point in the m+1-dimensional phase space and the phase point in the m-dimensional phase space, define the threshold as Br, and when B>Br, the point is a false neighbor. m is the optimal embedding dimension selected.

[0016] The average mutual information method selects the τ corresponding to the local minimum of the mutual information function that is reached for the first time. The mutual information function expression is:

[0017]

[0018] Using the embedding dimension m and delay time τ calculated above, the ultrasonic backscattered signal is reconstructed according to the phase space reconstruction technology of Takens embedding theorem, as shown in formula (4):

[0019]

[0020] Where, X i is the i-th phase point, m is the embedding dimension of the ultrasonic backscatter signal, and τ is the delay time of the ultrasonic backscatter signal. The number of reconstructed phase space vectors M = n-(m-1), n ​​is the sampling point of the ultrasonic backscatter signal, the rows in the matrix are the vectors reconstructed by adding the embedding dimension m, and the columns in the matrix are the vectors reconstructed by adding the delay time τ.

[0021] The ultrasonic backscattered signal vector X obtained by phase space reconstruction i , each vector represents a point in the phase space. The distance between any two ultrasonic backscatter signal vectors in the phase space is calculated, and the recursive matrix is ​​obtained as shown in the following equations (5) and (6):

[0022] R i,j (ε) = Θ(ε-||x i -x j ||),i,j=1,...,N (5)

[0023]

[0024] Where ε is the recursive graph threshold, Θ(x) is the Heaviside function, and x i , x j is the reconstructed ultrasonic backscatter signal vector.

[0025] Step 3: Set the initial parameters of the particle swarm optimization of the recursive graph threshold for high-temperature pressurized hot-wall pipelines, including the number of particles in the particle swarm, the particle swarm calculation speed, the number of iterations of the recursive graph threshold, and the recursive graph threshold range.

[0026] Based on the above parameter settings, the global optimal value and local optimal value in the population are calculated according to the fitness function, and the velocity and position of the particle are calculated. The formulas are shown in (7) and (8):

[0027] v id (t+1)=w·v id (t)+c1·rand()·(pbest(t)-x id (t))+c2·rand()·(gbest-x id (t)) (7)

[0028] x id (t+1)=v id (t+1)+x id (t) (8)

[0029] Calculate the fitness function values ​​of all particles in the new position, compare and update the global optimal value. Select the local optimal value in the particle swarm based on the comparison between the current fitness function value and the previous fitness function value.

[0030] Repeat the above steps until the objective function reaches the minimum value when the threshold number of iterations is met, and the optimization selection of the recursive graph threshold is completed.

[0031] Step 4: Use the optimized recursion graph threshold to draw a recursion graph of the ultrasonic backscatter signal.

[0032] Step 5: Use the recursive quantitative analysis method to quantitatively calculate the recursive graph, extract the characteristic parameters, and finally construct the mapping relationship between the characteristic parameters and the material degradation damage level. The mathematical model is: where x i is the material degradation level, y i is the recursive feature parameter.

[0033] The present invention can achieve the following beneficial effects:

[0034] Based on the traditional recursive graph, the particle swarm optimization algorithm is introduced to optimize the threshold of the recursive graph, realizing the accurate selection of the threshold in the recursive graph. The optimized threshold is reset to extract the recursive feature parameters, and a comprehensive mapping model between the recursive feature parameters and different levels of material degradation is constructed to realize the acoustic evaluation of the material degradation level. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention provides a flow chart.

[0036] Figure 2 The present invention provides an ultrasonic backscatter signal.

[0037] Figure 3 The present invention provides an algorithm iteration curve graph.

[0038] Figure 4 The present invention provides an optimized threshold recursive graph.

[0039] Figure 5 The present invention provides a recursive characteristic parameter of degradation levels of different materials. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the embodiments of the present invention. The following embodiments are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited by them.

[0041] Figure 1 This is a flow chart of a method for optimizing the threshold value of a recursive graph and extracting characteristic parameters based on a particle swarm algorithm of the present invention. The specific implementation method is as follows:

[0042] (1) Using an ultrasonic signal acquisition system consisting of ultrasonic testing equipment, an oscilloscope, and a digital signal acquisition card, ultrasonic backscatter signals of degraded samples of different materials were collected, and the backscatter signals between 21 μs and 32 μs were intercepted. The backscatter signals were used as the time series for subsequent research. Figure 2 shown.

[0043] (2) The point where the network tends to be stable and the position where the network first reaches the local minimum are calculated by the false neighbor method and the average mutual information method. This position is used as the final value of the embedding dimension and delay time.

[0044] (3) Set the initial parameters of the particle swarm, including the number of particles in the particle swarm is 30, the position of the particles is randomly set, the number of threshold iterations is 50, and the threshold range is 0 to 10*σ (σ is the standard deviation of the signal). After iterative calculation of the optimal threshold, the fitness value reaches the minimum value. The iteration curve of the particle swarm optimization algorithm is as follows: Figure 3 shown.

[0045] (4) The optimized threshold is used to draw a recursive graph of the ultrasonic backscattering signals of samples with different material degradation levels. The results of the recursive graph of the material degradation of the high-temperature pressure-bearing hot wall pipeline level 1 are shown in Figure 2. Figure 4 As shown, the horizontal and vertical axes are the number of sampling points, and the image is symmetrically distributed about the main diagonal.

[0046] (5) The optimized threshold recursion graph is quantitatively calculated using the recursive quantitative analysis method to extract characteristic parameters, namely, recursion rate, determination rate, recursion entropy and layered rate.

[0047] (6) Repeat steps (1) to (5) to draw recursive graphs for samples of four different grades of high-temperature pressure-bearing hot-wall pipeline material degradation, and complete the recursive parameter extraction. The normalized results of the extracted recursive characteristic parameters are as follows: Figure 5 As shown in the figure, as the material degradation level increases, the recursive feature parameter gradually increases and has a good linear relationship. It can be seen that the optimized recursive threshold optimization method and material degradation evaluation method proposed in the present invention are feasible.

[0048] The above description is only a preferred embodiment of the present invention. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing recursive graph threshold and feature parameter extraction based on particle swarm algorithm, characterized in that: The steps include: Step 1: using an ultrasonic signal acquisition system to collect ultrasonic backscatter signals, the ultrasonic backscatter signals are used as a time series of subsequent research signals; Step 2: Determine the embedding dimension and delay time by using the false neighbor method and the average mutual information method, reconstruct the time series in step 1 by using the phase space reconstruction technology, and calculate the distance between any two vectors in the phase space; Step 3: Set the particle swarm parameters and convergence conditions, and use the particle swarm optimization algorithm to optimize the recursive graph threshold; Step 4: Use the optimized threshold to recursively draw a graph of the ultrasonic backscatter signal; Step 5: Use the recursive quantitative analysis method to quantitatively calculate the recursive graph, extract the characteristic parameters, and finally construct a mapping relationship between the characteristic parameters and the material degradation damage level.

2. According to claim 1, a method for optimizing recursive graph threshold and feature parameter extraction based on particle swarm algorithm is characterized in that: Ultrasonic testing equipment is used to collect ultrasonic backscatter signals of samples with different levels of material degradation, and the signals are used as time series for subsequent recursive analysis.

3. The method for optimizing recursive graph threshold and feature parameter extraction based on particle swarm algorithm according to claim 1, characterized in that: The specific steps for determining the embedding dimension and delay time are as follows: a. Use the false neighbor method to calculate the embedding dimension of the above time series. When the changes of different phase points tend to be stable, select this value as the optimal embedding dimension; b. Calculate the delay time of the above time series using the mutual information method, and select the position when the mutual information function first reaches the local minimum from the calculation results; c. Using the embedding dimension and delay time calculated above, the time series is reconstructed in phase space according to the embedding theorem, as shown in the following formula (1): Where, X i is the i-th phase point, m is the embedding dimension of the ultrasonic backscatter signal, τ is the delay time of the ultrasonic backscatter signal; the number of reconstructed phase space vectors M = n-(m-1), n ​​is the sampling point of the ultrasonic backscatter signal, the rows in the matrix are the vectors reconstructed by adding the embedding dimension m, and the columns in the matrix are the vectors reconstructed by adding the delay time τ; d. Calculate the distance between any two points in the phase space and obtain the recursive matrix R ij , as shown in the following formula (2): R i,j (e)=Θ(e-||x i -x j ||),i,j=1,...,N (2) ε is the recursive graph threshold, Θ(x) is the Heaviside function, x i 、x j is the reconstructed ultrasonic backscatter signal vector.

4. The method for optimizing recursive graph threshold and feature parameter extraction based on particle swarm algorithm according to claim 1, characterized in that: Initialize various parameters in the particle swarm algorithm, define the convergence conditions, and update the moving speed and position of each particle to make the convergence conditions reach the minimum value.

5. The method for optimizing recursive graph threshold and feature parameter extraction based on particle swarm algorithm according to claim 1, characterized in that: The optimized threshold is used to draw the recursive map of ultrasonic backscattered signals.

6. The method for optimizing recursive graph threshold and feature parameter extraction based on particle swarm algorithm according to claim 1, characterized in that: The recursive quantitative analysis method is used to extract characteristic parameters of the recursive graph, and a comprehensive mapping model between the recursive characteristic parameters and the material degradation damage level is constructed.

Images