An ultrasonic guided wave probabilistic imaging algorithm and system based on instantaneous energy features

Abstract

The application discloses an ultrasonic guided wave probability imaging algorithm and system based on instantaneous energy characteristics, and comprises the following steps: acquiring a plurality of ultrasonic guided wave signals of a region to be detected; acquiring damage indexes of the signals according to the ultrasonic guided wave signals; dividing detection paths of the ultrasonic guided wave signals according to the damage indexes; obtaining weight values of the ultrasonic guided wave signals directly passing through damage through an elliptical probability distribution function; obtaining weight values of the ultrasonic guided wave signals not passing through damage through a Gaussian distribution function based on energy; and obtaining a damage image of the region to be detected according to the weight values and the damage indexes of the signals. The application realizes accurate detection of damage.

Description

Technical Field

[0001] This invention relates to the field of damage detection technology, and in particular to an ultrasonic guided wave probabilistic imaging algorithm and system based on instantaneous energy characteristics. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Carbon fiber composites, due to their excellent material properties such as light weight, high specific strength, high temperature resistance, corrosion resistance, and strong design capabilities, have been widely used in aircraft fuselages, high-speed trains, subway car bodies, bogies, and other fields. The weight ratio of carbon fiber composites used has become one of the important indicators for measuring the advancement of high-end equipment. However, defects may occur in carbon fiber composite structures during the complex manufacturing process. During long-term service, due to the effects of external impacts, fatigue loads, and harsh working environments, the material may delaminate and develop fatigue cracks, resulting in a decline in material performance and functional degradation, seriously affecting the safety and reliability of equipment. Therefore, research on health monitoring and damage detection technologies for composite material structures is of great significance.

[0004] Currently, the inspection of structural components in equipment typically employs a shutdown and maintenance approach, which suffers from poor sensitivity and blind spots. Ultrasonic guided waves, with their advantages of low cost, long propagation distance, and sensitivity to damage, are particularly suitable for online monitoring and have become a hot research area in the field of structural nondestructive testing. The principle of ultrasonic guided wave detection technology based on piezoelectric elements is as follows: utilizing the piezoelectric effect of the element, a modulated electrical signal is converted into element deformation, generating an ultrasonic guided wave excitation signal within the structure. Damage in the structure alters the propagation mode of the ultrasonic guided wave signal, which is reflected in the received response signal. Damage is identified by extracting the damage-induced changes in the response signal, and damage is located using a localization algorithm. Methods for extracting damage features from ultrasonic guided wave response signals mainly include time-domain and frequency-domain methods. Time-domain signal analysis methods extract signal characteristic parameters from the time domain, such as reflected echo, time-of-flight delay, and energy attenuation. This method is computationally simple, fast, and has low requirements for terminal equipment performance, but it ignores some nonlinear and non-stationary damage information, resulting in significant errors. Frequency domain signal analysis methods extract signal characteristic parameters from the frequency domain, such as harmonics, modulation spectrum sidebands, and resonant frequency shifts. However, the Fourier transform, the basis of frequency domain methods, requires that the signal be stationary and periodic.

[0005] The probability-based reconstruction algorithm has advantages such as low requirement for prior structural knowledge, good applicability, low computational cost, fast computation speed, and sensitivity to different types of damage, making it one of the most widely researched and applied ultrasonic guided wave damage localization and identification algorithms.

[0006] However, the probability distribution function of the probability test-based reconstruction algorithm has certain limitations. Its distribution decreases from the sensor's detection path towards both sides according to a pre-defined rule. This rule is based on the assumption that the closer the damage is to the sensor's detection path, the greater the damage index. However, during the propagation of ultrasonic guided waves in carbon fiber composite materials, due to their anisotropic characteristics, the damage characteristics do not completely follow this rule. Therefore, this can introduce significant errors into the localization results, making it not particularly reliable in practical engineering applications. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes an ultrasonic guided wave probabilistic imaging algorithm and system based on instantaneous energy characteristics. The algorithm considers the relationship between the detection path of the ultrasonic guided wave signal and the location of the damage, and calculates the weight of each signal according to different relationships. Based on the weight of each signal, the damage image of the area to be detected is accurately obtained, thereby improving the damage localization accuracy.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] Firstly, an ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics is proposed, including:

[0010] Acquire multiple ultrasonic guided wave signals from the area to be detected;

[0011] Based on each ultrasonic guided wave signal, the damage index of each signal is obtained;

[0012] The detection paths for each ultrasonic guided wave signal are divided according to the damage index;

[0013] The weights of each ultrasonic guided wave signal whose path directly passes through the damage are obtained by using the elliptic probability distribution function.

[0014] The weights of each ultrasonic guided wave signal without damage are obtained by using an energy-based Gaussian distribution function.

[0015] Based on the weights of each signal and the damage index, a damage image of the area to be detected is obtained.

[0016] Secondly, an ultrasonic guided wave probabilistic imaging system based on instantaneous energy characteristics is proposed, including:

[0017] The ultrasonic guided wave signal acquisition module is used to acquire multiple ultrasonic guided wave signals from the area to be detected.

[0018] The damage index acquisition module is used to acquire the damage index of each ultrasonic guided wave signal based on each signal.

[0019] The signal path division module is used to divide the detection path of each ultrasonic guided wave signal according to the damage index;

[0020] The signal weight acquisition module is used to obtain the weights of each ultrasonic guided wave signal whose path directly passes through the damage through an elliptic probability distribution function; and to obtain the weights of each ultrasonic guided wave signal whose path does not pass through the damage through an energy-based Gaussian distribution function.

[0021] The damage image acquisition module is used to obtain the damage image of the area to be detected based on the weights of each signal and the damage index.

[0022] Thirdly, an electronic device is proposed, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, complete the steps described in the ultrasonic guided wave probabilistic imaging method based on instantaneous energy characteristics.

[0023] Fourthly, a computer-readable storage medium is proposed for storing computer instructions, which, when executed by a processor, complete the steps described in the ultrasonic guided wave probabilistic imaging method based on instantaneous energy characteristics.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] 1. This invention considers the relationship between the ultrasonic guided wave signal detection path and the location of the damage, and proposes a Gaussian distribution function based on instantaneous energy. This optimizes the problem that the Gaussian distribution function in probabilistic imaging algorithms requires flight time and wave velocity information, making it unusable in carbon fiber composites. By employing multi-probability distribution function fusion imaging, this invention improves upon traditional probabilistic imaging methods, overcoming the shortcomings caused by the mismatch between the probability distribution law and the damage index. This enhances its feasibility and reliability in practical applications of structural damage detection, significantly reducing operational risks and maintenance costs, and possesses broad application prospects and engineering value.

[0026] 2. This invention utilizes the characteristic of Hilbert-Huang transform to adaptively extract nonlinear and nonstationary damage information, and proposes a damage index based on instantaneous energy, which makes the extracted damage information more comprehensive and the damage characteristics more accurate.

[0027] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0029] Figure 1 Here is a flowchart of the detection process of the method disclosed in Example 1;

[0030] Figure 2 This refers to the ultrasonic guided wave detection system disclosed in Example 1;

[0031] Figure 3 This is a schematic diagram illustrating the principle of the method disclosed in Example 1;

[0032] Figure 4 The contour plot of the elliptic probability distribution function EW weights obtained in Example 1;

[0033] Figure 5 The contour plot of the Gaussian distribution function GW weights obtained in Example 1;

[0034] Figure 6 This is a diagram showing the sensor layout and simulated damage location in Example 1;

[0035] Figure 7 The damage index based on instantaneous energy obtained in Example 1;

[0036] Figure 8 The damage imaging results obtained in Example 1 are shown. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0039] Example 1

[0040] To achieve accurate detection and localization of damage in the area to be detected, this embodiment discloses an ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics, such as... Figure 1 , Figure 3 As shown, it includes:

[0041] S1: Acquire multiple ultrasonic guided wave signals from the area to be detected.

[0042] Multiple ultrasonic guided wave signals from the area to be tested are acquired by an ultrasonic guided wave detection system mounted on the area to be tested.

[0043] Ultrasonic guided wave testing systems, such as Figure 2As shown, it includes: a piezoelectric ceramic sensor, a signal generator, a power amplifier, and a data acquisition card. The waveform emitted by the signal generator is amplified by the power amplifier. The piezoelectric ceramic sensor is placed at the area to be detected to receive the waveform signal amplified by the power amplifier and generate a corresponding response signal, which is sent to the data acquisition card. The signal in the data acquisition card is the ultrasonic guided wave signal to be acquired in this embodiment.

[0044] In practice, multiple piezoelectric ceramic sensors are symmetrically attached to the edge of the area to be detected using epoxy resin adhesive to form a sensor network. Each piezoelectric ceramic sensor can emit ultrasonic guided wave signals.

[0045] The waveform signal emitted by the signal generator is a Hanning window modulated sine wave. Due to the low-frequency accumulation excitation mode, the ultrasonic guided wave in the area to be tested has the fewest guided wave modes, which facilitates ultrasonic guided wave signal analysis. In order to make the waveform signal emitted by the signal generator more sensitive to damage, before the waveform signal is emitted by the signal generator for damage detection, the ultrasonic guided wave signal is excited and collected in the area to be tested by traversal to find the signal modulation parameter with obvious wave packet separation, large signal amplitude and sensitivity to damage. The waveform signal emitted by the signal generator using the signal modulation parameter is more sensitive to damage detection in the area to be tested.

[0046] S2: Obtain the damage index of each ultrasonic guided wave signal.

[0047] The process of obtaining the damage index is as follows:

[0048] S21: Perform Hilbert-Huang transform on each ultrasonic guided wave signal to obtain the Hilbert spectrum of each signal.

[0049] The Hilbert-Huang transform consists of two parts: empirical mode decomposition and the Hilbert transform. The process of obtaining the Hilbert spectrum of a signal is as follows:

[0050] (1) Empirical mode decomposition (EMD) is performed on the ultrasonic guided wave signal to obtain multiple intrinsic mode function (EMF) components. For an ultrasonic guided wave signal f(t) acquired from a sensor, it can be decomposed into k EMF components and a residual signal. The expression for EMF decomposition is:

[0051]

[0052] In the formula, c i r(t) is the i-th intrinsic mode function component, r(t) is the residual signal, and t is the time series of the ultrasonic guided wave signal.

[0053] (2) Perform a Hilbert transform on each eigenmode function component to obtain the Hilbert spectrum of each component, where each eigenmode function component corresponds to a unique Hilbert spectrum H.i (t,f i ).

[0054] (3) The Hilbert spectrum of the signal is formed by the Hilbert spectrum of all components of the obtained ultrasonic guided wave signal. It is a three-dimensional graph consisting of time, instantaneous frequency and instantaneous amplitude.

[0055] S22: Integrate the Hilbert spectrum of each signal to obtain the instantaneous energy curve of each signal.

[0056] The expression for the instantaneous energy curve is:

[0057]

[0058] In the formula, IE(t) is the instantaneous energy curve, and f is the frequency.

[0059] The damage index of each signal is obtained based on the instantaneous energy curve of each signal.

[0060] S23: Obtain the damage index of each signal based on the instantaneous energy curve of each signal.

[0061] The damage index represents the magnitude of the damage quantification. The sign of the damage index represents the increase or decrease of instantaneous energy after the structure is damaged. A negative sign indicates an increase in energy, and a positive sign indicates an decrease in energy.

[0062] The expression for the Damage Index ERDI is:

[0063]

[0064] In the formula, IE x (t) represents the instantaneous energy curve of the reference signal, IE y (t) is the instantaneous energy curve of the damage signal, that is, the instantaneous energy curve of the ultrasonic guided wave signal obtained by S1.

[0065] The reference signal is the ultrasonic guided wave signal acquired before the damage occurred.

[0066] The instantaneous energy curve of the reference signal is obtained by performing S21 and S22 calculations on the reference signal.

[0067] S3: Divide the detection paths of each ultrasonic guided wave signal according to the damage index.

[0068] In carbon fiber composites, the instantaneous energy of ultrasonic guided waves exhibits the following characteristics: When the sensor path, i.e., the detection path of the ultrasonic guided wave signal, directly passes through the damage, instantaneous energy attenuation occurs due to propagation obstruction; when the sensor path is near the damage, i.e., the detection path of the ultrasonic guided wave signal does not pass through the damage, the ultrasonic guided wave signal that does not pass through the damage is not directly affected by the damage, and a portion of the scattered wave generated by the damage is superimposed on the ultrasonic signal that directly passes through the damage, causing an increase in instantaneous energy. Based on this characteristic, the detection paths of ultrasonic guided wave signals are divided into two categories according to the sign of the damage index, specifically:

[0069] When the damage coefficient is positive, it is determined that the detection path of the ultrasonic guided wave signal directly passes through the damage.

[0070] When the damage coefficient is negative, it is determined that the detection path of the ultrasonic guided wave signal does not pass through the damage.

[0071] S4: Obtain the weights of each ultrasonic guided wave signal whose path directly passes through the damage using the elliptic probability distribution function EW.

[0072] The expression for the elliptic probability distribution function EW is:

[0073]

[0074]

[0075] In the formula, EW k [R k [x,y)] represents the weights of the elliptic probability distribution function obtained by the k-th ultrasonic guided wave signal at point (x,y), where (x,y) are the coordinates of the pixel in the currently calculated detection region. ak ,y ak ) and (x sk ,y sk R represents the coordinates of the excitation sensor and the receiving sensor that generate the k-th ultrasonic guided wave signal, respectively. k (x,y) represents the distance coefficients from the pixel to the excitation and reception sensors, D ak (x,y) and D sk (x, y) represent the distances from point (x, y) to the excitation sensor and the receiving sensor, respectively. k β is the distance between the excitation sensor and the receiving sensor, and β is the elliptic shape factor.

[0076] The weights of each ultrasonic guided wave signal without path damage are obtained by using an energy-based Gaussian distribution function.

[0077] The expression for the energy-based Gaussian distribution function GW is:

[0078]

[0079] z k (x,y)=R k (x,y)-|ERDI k |,

[0080] In the formula, GW k [z k [x,y)] represents the Gaussian probability distribution function weights of the k-th ultrasonic guided wave signal at point (x,y), where (x,y) are the coordinates of the pixel in the currently calculated detection region, and z... k (x,y) represents the distance-damage coefficient from the pixel to the excitation and reception sensors, σ is the standard deviation, and ERDI is the distance-damage coefficient. k Let be the damage index of the k-th sensor path.

[0081] S5: Obtain the damage image of the area to be detected based on the weights of each signal and the damage index.

[0082] Specifically, based on the weights of each signal, the damage index, and the fusion probability imaging model, fusion probability imaging is performed, and the resulting image is plotted to obtain the damage image of the area to be detected.

[0083] The fusion probabilistic imaging model is as follows:

[0084]

[0085] In the formula, P(x,y) is the probability that pixel (x,y) has damage, p k (x,y) represents the probability of damage at pixel (x,y) calculated from the k-th ultrasonic guided wave signal, N represents the total number of ultrasonic guided wave signals, I represents the number of ultrasonic guided wave signals whose path passes through the damage, and J represents the number of ultrasonic guided wave signals whose path does not pass through the damage.

[0086] The horizontal and vertical coordinates of the damage image represent the row and column numbers of the probability matrix, respectively. The value of each pixel corresponds to the value of an element in the probability matrix. The location of the maximum value is defined as the detected damage location coordinates. In other words, the coordinates of the largest pixel in the damage image are the damage location coordinates.

[0087] The accuracy of the method disclosed in this embodiment was verified by using a test bench.

[0088] First, with dimensions of 450mm*450mm*2.4mm and a layup pattern of [0 / 90], 3s A circular detection area with a diameter of 200 mm is set on a carbon fiber composite plate. This circular detection area serves as the detection area. Twelve piezoelectric ceramic sensors are symmetrically attached to the edge of the area using epoxy resin adhesive, forming a sensor network. Figure 6As shown, there are a total of 66 sensor paths. In the experiment, a method of attaching mass blocks was used to change the local rigidity of the tested structure, simulating structural damage.

[0089] First, obtain the ultrasonic guided wave signal of the circular detection area without attaching the mass block, which serves as the reference signal.

[0090] Next, a mass block is attached to the circular detection area to simulate structural damage, and then the ultrasonic guided wave signal of the circular detection area is obtained as the damage signal.

[0091] The instantaneous energy curves of the signals are obtained by calculating the reference signal and the damage signal according to S21 and S22 respectively, and the damage index obtained during the process is as follows: Figure 7 As shown. Then, based on the acquired instantaneous energy curve, the damage index of the damage signal is calculated, and the detection path category of the damage signal is determined according to the damage index;

[0092] The weights of each damage signal along the path directly passing through the mass block are obtained using the elliptic probability distribution function EW. The contour plot of these weights is shown below. Figure 4 As shown.

[0093] The weights of each damage signal along the path that does not pass through the mass block are obtained using an energy-based Gaussian distribution function. The contour plot of these weights is shown below. Figure 5 As shown.

[0094] The damage image of the circular detection area is obtained according to step S5, such as... Figure 8 As shown, the identified mass block position is the same as the actual position of the mass block pasted in the circular area, verifying the accuracy of the method in this embodiment for damage detection.

[0095] This embodiment discloses a method that considers the relationship between the ultrasonic guided wave signal path and the location of the damage. It proposes a Gaussian distribution function based on instantaneous energy, optimizing the problem that the Gaussian distribution function in probabilistic imaging algorithms requires flight time and wave velocity information, making it unusable in carbon fiber composites. By employing multi-probability distribution function fusion imaging, the traditional probabilistic imaging method is improved, overcoming its shortcomings caused by the mismatch between the probability distribution law and the damage index. This enhances its feasibility and reliability in practical applications of structural damage detection, significantly reducing operational risks and maintenance costs, and possesses broad application prospects and engineering value.

[0096] The method disclosed in this embodiment also utilizes the characteristic of Hilbert-Huang transform to adaptively extract nonlinear and nonstationary damage information, and proposes a damage index based on instantaneous energy, so that the extracted damage information is more comprehensive and the damage characteristics are more accurate, thereby improving the accuracy of damage identification.

[0097] Example 2

[0098] In this embodiment, an ultrasonic guided wave probabilistic imaging system based on instantaneous energy characteristics is disclosed, comprising:

[0099] The ultrasonic guided wave signal acquisition module is used to acquire multiple ultrasonic guided wave signals from the area to be detected.

[0100] The damage index acquisition module is used to acquire the damage index of each ultrasonic guided wave signal based on each signal.

[0101] The signal path division module is used to divide the detection path of each ultrasonic guided wave signal according to the damage index;

[0102] The signal weight acquisition module is used to obtain the weights of each ultrasonic guided wave signal whose path directly passes through the damage through an elliptic probability distribution function; and to obtain the weights of each ultrasonic guided wave signal whose path does not pass through the damage through an energy-based Gaussian distribution function.

[0103] The damage image acquisition module is used to obtain the damage image of the area to be detected based on the weights of each signal and the damage index.

[0104] Example 3

[0105] In this embodiment, an electronic device is disclosed, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the processor executes the computer instructions, it completes the steps of the ultrasonic guided wave probabilistic imaging method based on instantaneous energy characteristics disclosed in Embodiment 1.

[0106] Example 4

[0107] In this embodiment, a computer-readable storage medium is disclosed for storing computer instructions, which, when executed by a processor, complete the steps described in the ultrasonic guided wave probabilistic imaging method based on instantaneous energy characteristics disclosed in Embodiment 1.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics, characterized in that, include: Acquire multiple ultrasonic guided wave signals from the area to be detected; Based on each ultrasonic guided wave signal, the damage index of each signal is obtained; The detection paths for each ultrasonic guided wave signal are divided according to the damage index; The weights of each ultrasonic guided wave signal whose path directly passes through the damage are obtained by using the elliptic probability distribution function. The weights of each ultrasonic guided wave signal without passing through damage are obtained by using an energy-based Gaussian distribution function. The expression for the energy-based Gaussian distribution function is: ， ， In the formula, For the first k An ultrasonic guided wave signal in The weights of the Gaussian probability distribution function obtained at the points. These are the coordinates of the pixels in the currently calculated detection region. To calculate the distance-damage coefficient from a pixel to the excitation and reception sensors, Standard deviation For the first k Damage index of each sensor path; Based on the weights of each signal, the damage index, and the fusion probability imaging model, the damage image of the region to be detected is obtained. The fusion probability imaging model is as follows: , In the formula, For pixels The probability of damage. For the first k The ultrasonic guided wave signal was calculated at pixel point. The probability of damage. N This represents the total amount of ultrasonic guided wave signal. I This represents the number of ultrasonic guided wave signals whose path directly passes through the damage. J This represents the number of ultrasonic guided wave signals that do not pass through damage.

2. The ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics as described in claim 1, characterized in that, The process of obtaining the damage index is as follows: Hilbert-Huang transform was performed on each ultrasonic guided wave signal to obtain the Hilbert spectrum of each signal; Integrating the Hilbert spectrum of each signal yields the instantaneous energy curve of each signal; The damage index of each signal is obtained based on the instantaneous energy curve of each signal.

3. The ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics as described in claim 2, characterized in that, The process of obtaining the Hilbert spectrum of the signal is as follows: Empirical mode decomposition is performed on the ultrasonic guided wave signal to obtain multiple intrinsic mode function components; Perform a Hilbert transform on each intrinsic mode function component to obtain the Hilbert spectrum of each component; The Hilbert spectrum of all components constitutes the Hilbert spectrum of the ultrasonic guided wave signal.

4. The ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics as described in claim 1, characterized in that, When the damage coefficient is positive, it is determined that the detection path of the ultrasonic guided wave signal directly passes through the damage. When the damage coefficient is negative, it is determined that the detection path of the ultrasonic guided wave signal does not pass through the damage.

5. The ultrasonic guided wave probabilistic imaging algorithm based on instantaneous energy characteristics as described in claim 1, characterized in that, The coordinates of the largest pixel in the damaged image are the location coordinates of the damage.

6. An ultrasonic guided wave probabilistic imaging system based on instantaneous energy characteristics, characterized in that, include: The ultrasonic guided wave signal acquisition module is used to acquire multiple ultrasonic guided wave signals from the area to be detected. The damage index acquisition module is used to acquire the damage index of each ultrasonic guided wave signal based on each signal. The signal path division module is used to divide the detection path of each ultrasonic guided wave signal according to the damage index; The signal weight acquisition module is used to obtain the weights of each ultrasonic guided wave signal whose path directly passes through the damage using an elliptic probability distribution function; and to obtain the weights of each ultrasonic guided wave signal whose path does not pass through the damage using an energy-based Gaussian distribution function. The expression for the energy-based Gaussian distribution function is as follows: ， ， In the formula, For the first k An ultrasonic guided wave signal in The weights of the Gaussian probability distribution function obtained at the points. These are the coordinates of the pixels in the currently calculated detection region. To calculate the distance-damage coefficient from a pixel to the excitation and reception sensors, Standard deviation For the first k Damage index of each sensor path; The damage image acquisition module is used to obtain the damage image of the region to be detected based on the weights of each signal, the damage index, and the fusion probability imaging model. The fusion probability imaging model is as follows: , In the formula, For pixels The probability of damage. For the first k The ultrasonic guided wave signal was calculated at pixel point. The probability of damage. N This represents the total amount of ultrasonic guided wave signal. I This represents the number of ultrasonic guided wave signals whose path directly passes through the damage. J This represents the number of ultrasonic guided wave signals that do not pass through damage.

7. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, complete the steps of the ultrasonic guided wave probabilistic imaging method based on instantaneous energy characteristics as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the steps of the ultrasonic guided wave probabilistic imaging method based on instantaneous energy characteristics as described in any one of claims 1-5.

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