Equipment flaw detection system, method and equipment based on acoustic soliton waves and medium

Abstract

The invention discloses an equipment flaw detection system, method and equipment based on acoustic soliton waves and a medium, mainly relates to the technical field of equipment flaw detection, and is used for solving the problems of signal attenuation, waveform expansion, low flaw detection precision and the like of an existing scheme in a complex structure, deep detection and high-frequency noise environment. In addition, according to the scheme, the problem that the defect position is mainly displayed, and the defect cannot be further analyzed is mainly solved. Comprising a nonlinear piezoelectric exciter used for determining the frequency, the amplitude and the excitation time of acoustic solitons; the acoustic soliton wave transmitting module is used for exciting an acoustic soliton wave; the acoustic soliton wave receiving module is used for converting the acoustic soliton waves into electric signals; the signal processing module is connected with the acoustic soliton wave receiving module and is used for extracting time domain features and frequency domain features of the acoustic soliton waves; and determining whether the detected object has a potential defect and determining the degree and position of the potential defect and the representation form of the defect.

Description

Technical Field

[0001] The present application relates to the technical field of equipment flaw detection, and in particular to an equipment flaw detection system, method, equipment and medium based on acoustic soliton waves. Background Art

[0002] The ultrasonic flaw detection system is a system that uses ultrasonic technology to detect, locate and evaluate internal defects in materials.

[0003] Existing ultrasonic flaw detection systems mainly use traditional elastic wave methods for defect detection. These traditional methods mainly process the echo signals received from the object being detected in at least three signal channels, wherein the echo signals are scaled to different degrees along each channel, in such a way that the dynamic range of the associated A / D converter system is increased and extended, which eliminates the need to use a large number of analog high-pass and low-pass filters and variable gain amplifiers. This reduces complexity and avoids performance limitations. The digital-to-analog converter samples input signals of different scales, and the selection circuit selects the output of the digital output obtained by the analog-to-digital converter with the highest gain but no overflow. The digital outputs are seamlessly merged to produce an output that can be displayed as a scanning display, which shows the location of the defect.

[0004] However, the above solutions often face problems such as signal attenuation, waveform expansion and low flaw detection accuracy when facing complex structures, deep detection and high-frequency noise environments. In addition, the above solutions mainly display the location of defects and cannot conduct further analysis of the defects. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present application provides a device flaw detection system, method, device and medium based on acoustic soliton waves to solve the problems of signal attenuation, waveform expansion and low flaw detection accuracy often faced by the existing solutions in the face of complex structures, deep detection and high-frequency noise environments. In addition, the above-mentioned solutions mainly display the location of defects and cannot further analyze the defects.

[0006] In a first aspect, the present application provides a device flaw detection system based on acoustic soliton waves, the system comprising: A nonlinear piezoelectric actuator is used to obtain the material properties and detection requirements of the object under test, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave; a plurality of acoustic soliton wave transmitting modules placed within a preset range of the object under test are connected to the nonlinear piezoelectric actuator to convert the electrical signal of the acoustic soliton wave into mechanical vibration to excite the acoustic soliton wave; an acoustic soliton wave receiving module is used to convert the acoustic soliton wave into an electrical signal after capturing the acoustic soliton wave emitted by the acoustic soliton wave transmitting module; a signal processing module is connected to the acoustic soliton wave receiving module to perform time domain and frequency domain analysis on the electrical signal to extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; through the time domain characteristics and frequency domain characteristics, it is determined whether the object under test has potential defects and the degree, location and manifestation of the potential defects.

[0007] The novel flaw detection system provided by the embodiment of the present application, the nonlinear piezoelectric exciter in the system can flexibly adjust the frequency, amplitude and excitation time of the acoustic soliton wave according to the material properties and detection requirements of the object to be tested, so that the flaw detection system can adapt to the detection requirements of different materials and structures, and improve the flexibility and adaptability of the system. By adopting the nonlinear wave form of acoustic soliton wave, and utilizing its stable waveform and strong penetration, the problems of signal attenuation, waveform expansion and low flaw detection accuracy faced by traditional ultrasonic flaw detection in complex structures and deep detection are effectively solved. Acoustic soliton waves can penetrate materials more deeply while maintaining the stability of the waveform, thereby improving the detection capability and accuracy of deep defects. In addition to locating defects, the system of the present application can also extract the characteristic information of acoustic soliton waves through time domain and frequency domain analysis, and then determine whether the object to be tested has potential defects and the degree, location and manifestation of the defects, which helps users to more accurately evaluate the state and performance of the object to be tested.

[0008] In one implementation of the present application, the nonlinear piezoelectric actuator includes an evaluation module: Used to take the material properties and detection requirements of the object to be tested as the input of the trained preset neural network algorithm to obtain the frequency, amplitude and excitation time of the output acoustic soliton wave; Among them, the sample data for training the preset neural network algorithm are material properties, detection requirements, frequency, amplitude and excitation time of acoustic soliton waves.

[0009] In one implementation of the present application, the signal processing module includes a first defect determination unit, It is used to determine whether there are potential defects by identifying whether the received acoustic soliton wave is distorted.

[0010] In one implementation of the present application, the signal processing module includes a distance determination unit, a first degree determination unit, a second degree determination unit, and a performance determination unit: A distance determination unit for obtaining the maximum correlation value between the transmitted acoustic soliton wave and the received acoustic soliton wave ; , calculate the delay ;in, = - , represents the time of the emitted acoustic soliton wave, express The acoustic soliton wave signal emitted at any moment, represents the time of the received acoustic soliton wave, express Acoustic soliton wave signals received at all times; Determine the distance between the defect position and the acoustic soliton wave transmitting module that transmits the acoustic soliton wave through a preset corresponding relationship between the time delay and the distance between the defect position; The first degree determination unit is used to obtain the receiving signal corresponding to the acoustic soliton wave receiving module When the maximum amplitude is greater than a preset first amplitude threshold, it is determined that the defect is at a preset severity level, or the defect is located at a depth lower than a preset first depth; when the maximum amplitude is less than a preset second amplitude threshold, it is determined that the defect is at a preset light level, or the defect is located at a depth higher than a preset second depth; The second degree determination unit is used by the formula: , calculate the standard deviation corresponding to the received acoustic soliton wave; in, represents the acoustic soliton wave signal received at time t, Indicates the center position of the acoustic soliton wave signal; Determine the degree of defect based on the proportional relationship between standard deviation and degree of defect; Or, the degree of the defect can be determined by the inverse relationship between the center frequency of the frequency domain feature and the degree of the defect; The performance determination unit is used to obtain the waveform width in the time domain characteristics. When the waveform width is less than a preset first width threshold, it is determined that the performance of the defect corresponds to a point or elongated crack defect; when the waveform width is greater than or equal to a preset second width threshold, it is determined that the performance of the defect corresponds to a smooth defect interface.

[0011] In a second aspect, the present application provides a device flaw detection method based on acoustic soliton waves, which is applied to a device flaw detection system based on acoustic soliton waves, and the method comprises: The material properties and detection requirements of the object under test are obtained, and then the frequency, amplitude and excitation time of the acoustic soliton wave are determined; the electrical signal of the acoustic soliton wave is converted into mechanical vibration by placing a number of acoustic soliton wave transmitting modules within a preset range of the object under test to excite the acoustic soliton wave; the acoustic soliton wave is captured and transmitted by the acoustic soliton wave receiving module, and the captured acoustic soliton wave is converted into an electrical signal; the electrical signal is analyzed in the time domain and frequency domain to extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; through the time domain characteristics and frequency domain characteristics, it is determined whether the object under test has potential defects and the degree, location and manifestation of the potential defects.

[0012] In one implementation of the present application, the material properties and detection requirements of the object to be tested are obtained, and then the frequency, amplitude and excitation time of the acoustic soliton wave are determined, which specifically includes: The material properties and detection requirements of the object to be tested are used as inputs of the trained preset neural network algorithm to obtain the frequency, amplitude and excitation time of the output acoustic soliton wave; Among them, the sample data for training the preset neural network algorithm are material properties, detection requirements, frequency, amplitude and excitation time of acoustic soliton waves.

[0013] In one implementation of the present application, the time domain characteristics and frequency domain characteristics are used to determine whether the object under test has potential defects and the degree, location and manifestation of the potential defects, including: By identifying whether the received acoustic soliton wave is distorted, it is determined whether there is a potential defect.

[0014] In one implementation of the present application, whether the object under test has potential defects and the degree, location and manifestation of the potential defects are determined through time domain features and frequency domain features, which specifically also includes: Get the maximum correlation value between the transmitted acoustic soliton wave and the received acoustic soliton wave ; , calculate the delay ;in, = - , represents the time of the emitted acoustic soliton wave, express The acoustic soliton wave signal emitted at any moment, represents the time of the received acoustic soliton wave, express Acoustic soliton wave signals received at all times; Determine the distance between the defect position and the acoustic soliton wave transmitting module that transmits the acoustic soliton wave through a preset corresponding relationship between the time delay and the defect position distance; Get the received signal corresponding to the acoustic soliton wave receiving module When the maximum amplitude is greater than a preset first amplitude threshold, it is determined that the defect is at a preset severity level, or the defect is located at a depth lower than a preset first depth; when the maximum amplitude is less than a preset second amplitude threshold, it is determined that the defect is at a preset light level, or the defect is located at a depth higher than a preset second depth; By formula: , calculate the standard deviation corresponding to the received acoustic soliton wave; in, represents the acoustic soliton wave signal received at time t, Indicates the center position of the acoustic soliton wave signal; Determine the degree of defect based on the proportional relationship between standard deviation and degree of defect; Or, the degree of the defect can be determined by the inverse relationship between the center frequency of the frequency domain feature and the degree of the defect; The waveform width in the time domain feature is obtained. When the waveform width is less than a preset first width threshold, it is determined that the defect manifestation corresponds to a point-shaped or elongated crack defect; when the waveform width is greater than or equal to a preset second width threshold, it is determined that the defect manifestation corresponds to a smooth defect interface.

[0015] In a third aspect, the present application provides a device flaw detection device based on acoustic soliton waves, the device comprising: A processor; and a memory having executable codes stored thereon, which, when executed, causes the processor to execute any of the above-mentioned methods for detecting equipment flaws based on acoustic soliton waves.

[0016] In a fourth aspect, the present application provides a non-volatile computer storage medium having computer instructions stored thereon, which when executed implement a device flaw detection method based on acoustic soliton waves as described above.

[0017] Those skilled in the art can understand that the present application has at least the following beneficial effects: This application proposes a device flaw detection system, method, device and medium based on acoustic soliton waves. The nonlinear piezoelectric actuator in the application can flexibly adjust the frequency, amplitude and excitation time of the acoustic soliton waves according to the material properties and detection requirements of the object to be tested, so that the flaw detection system can adapt to the detection requirements of different materials and structures, and improve adaptability. By adopting the nonlinear wave form of acoustic soliton waves and utilizing its stable waveform and strong penetration, the problems of signal attenuation, waveform expansion and low flaw detection accuracy faced by traditional ultrasonic flaw detection in complex structures and deep detection are effectively solved. Acoustic soliton waves can penetrate materials more deeply while maintaining the stability of the waveform, thereby improving the detection capability and accuracy of deep defects. In addition to locating defects, this application can also extract the characteristic information of acoustic soliton waves through time domain and frequency domain analysis, and then determine whether there are potential defects in the object to be tested and the degree, location and manifestation of the defects, which helps users to more accurately evaluate the state and performance of the object to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which: Figure 1 It is a schematic diagram of the internal structure of an equipment flaw detection system based on acoustic soliton waves provided in an embodiment of the present application.

[0019] Figure 2 It is a flow chart of a device flaw detection method based on acoustic soliton waves provided in an embodiment of the present application.

[0020] Figure 3 It is a schematic diagram of the internal structure of a device flaw detection device based on acoustic soliton waves provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can only be implemented through the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present disclosure.

[0022] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] This application Figure 1 The present invention provides an equipment flaw detection system based on acoustic soliton waves. Figure 1 As shown, the system provided in the embodiment of the present application mainly includes: The nonlinear piezoelectric actuator 110 is used to obtain the material properties of the object under test and the detection requirements, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave.

[0025] It should be noted that the nonlinear piezoelectric actuator 110 is an existing device or apparatus that can output the frequency, amplitude and excitation time of the acoustic soliton wave according to the material properties of the object to be tested and the detection requirements. For example, a preset neural network algorithm can be used to take the material properties of the object to be tested and the detection requirements as input to output the required acoustic soliton wave parameters.

[0026] More specifically, the evaluation module in the nonlinear piezoelectric actuator 110 can use the material properties and detection requirements of the object under test as inputs to a trained preset neural network algorithm to obtain the frequency, amplitude and excitation time of the output acoustic soliton wave; wherein the sample data for training the preset neural network algorithm are material properties, detection requirements, frequency, amplitude and excitation time of the acoustic soliton wave.

[0027] It can be understood by those skilled in the art that the nonlinear piezoelectric actuator 110 can flexibly adjust the frequency, amplitude and excitation time of the acoustic soliton wave according to the material properties of the object to be tested and the detection requirements, so that the flaw detection system can adapt to the detection requirements of different materials and structures, thereby improving adaptability.

[0028] The soliton wave transmitting module is connected to the nonlinear piezoelectric actuator 110 and is placed within a preset range of the object to be measured. The preset range can be placed on the surface of the object to be measured. It is an existing device or apparatus that can convert the electrical signal of the acoustic soliton wave into mechanical vibration to excite the acoustic soliton wave.

[0029] The acoustic soliton wave receiving module 130 captures the acoustic soliton wave emitted by the acoustic soliton wave transmitting module 120 and then converts the acoustic soliton wave into an electrical signal.

[0030] It should be additionally explained that the acoustic soliton wave receiving module 130 may be an existing device or apparatus capable of receiving external acoustic soliton waves.

[0031] The signal processing module 140 is connected to the acoustic soliton wave receiving module 130, and is used to perform time domain and frequency domain analysis on the electrical signal, and extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; through the time domain characteristics and frequency domain characteristics, it is determined whether the object under test has potential defects and the degree, location and manifestation of the potential defects.

[0032] As an example, a solution for determining whether there is a potential defect may be: The first defect determination unit in the signal processing module 140 is used to determine whether there is a potential defect by identifying whether the received acoustic soliton wave is distorted.

[0033] Specifically, the presence of distortion determines the presence of a potential defect.

[0034] As a second example, the signal processing module 140 includes a distance determination unit, a first degree determination unit, a second degree determination unit, and a performance determination unit.

[0035] The distance determination unit can obtain the maximum correlation value between the transmitted acoustic soliton wave and the received acoustic soliton wave. .

[0036] By formula: , calculate the delay .in, = - , represents the time of the emitted acoustic soliton wave, express The acoustic soliton wave signal emitted at any moment, represents the time of the received acoustic soliton wave, express The acoustic soliton wave signal received at each moment.

[0037] The distance between the defect position and the acoustic soliton wave transmitting module 120 that transmits the acoustic soliton wave is determined through the preset corresponding relationship between the time delay and the distance of the defect position.

[0038] The first degree determination unit can obtain the received signal corresponding to the acoustic soliton wave receiving module 130 when the maximum amplitude is greater than a preset first amplitude threshold, it is determined that the defect is at a preset severity level, or the defect is located at a depth lower than a preset first depth; when the maximum amplitude is less than a preset second amplitude threshold, it is determined that the defect is at a preset light level, or the defect is located at a depth higher than a preset second depth.

[0039] It should be supplemented that the specific values ​​of the preset first amplitude threshold, the preset first depth, the preset second amplitude threshold, and the preset second depth can be obtained by those skilled in the art through multiple experiments.

[0040] The second degree determination unit can be determined by the formula: , calculate the standard deviation of the received acoustic soliton wave.

[0041] in, represents the acoustic soliton wave signal received at time t, Indicates the center position of the acoustic soliton wave signal; The degree of defect is determined based on the proportional relationship between the standard deviation and the degree of defect; or, the degree of defect is determined through the inverse relationship between the center frequency of the frequency domain feature and the degree of defect.

[0042] Among them, the performance determination unit can obtain the waveform width in the time domain characteristics. When the waveform width is less than the preset first width threshold, it is determined that the performance form of the defect corresponds to a point or elongated crack defect; when the waveform width is greater than or equal to the preset second width threshold, it is determined that the performance form of the defect corresponds to a smooth defect interface.

[0043] It should be noted that the specific values ​​of the preset first width threshold and the preset second width threshold can be obtained by those skilled in the art through multiple experiments.

[0044] In addition, the embodiment provides a device flaw detection method based on acoustic soliton waves, such as Figure 2 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 110: Obtain material properties and detection requirements of the object to be tested, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave.

[0045] This step can be specifically as follows: The material properties and detection requirements of the object to be tested are used as inputs of a trained preset neural network algorithm to obtain the frequency, amplitude and excitation time of the output acoustic soliton wave; wherein the sample data for training the preset neural network algorithm are the material properties, detection requirements, frequency, amplitude and excitation time of the acoustic soliton wave.

[0046] Step 120 , by placing a plurality of acoustic soliton wave transmitting modules within a preset range of the object to be measured, the electrical signal of the acoustic soliton wave is converted into mechanical vibration to excite the acoustic soliton wave.

[0047] Step 130: Capture the transmitted acoustic soliton wave through the acoustic soliton wave receiving module, and convert the captured acoustic soliton wave into an electrical signal.

[0048] Step 140: Perform time domain and frequency domain analysis on the electrical signal to extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; determine whether the object under test has potential defects and determine the degree, location and manifestation of the potential defects through the time domain characteristics and frequency domain characteristics.

[0049] Among them, through the time domain characteristics and frequency domain characteristics, it is determined whether the object under test has potential defects and the degree, location and manifestation of the potential defects. Specifically, it can be: By identifying whether the received acoustic soliton wave is distorted, it is determined whether there is a potential defect.

[0050] Among them, through the time domain characteristics and frequency domain characteristics, it is determined whether the object under test has potential defects and the degree, location and manifestation of the potential defects. Specifically, it can also be: Get the maximum correlation value between the transmitted acoustic soliton wave and the received acoustic soliton wave ; By formula: , calculate the delay ;in, = - , represents the time of the emitted acoustic soliton wave, express The acoustic soliton wave signal emitted at any moment, represents the time of the received acoustic soliton wave, express The acoustic soliton wave signal received at each moment.

[0051] The distance between the defect position and the acoustic soliton wave transmitting module that transmits the acoustic soliton wave is determined through a preset corresponding relationship between the time delay and the distance of the defect position.

[0052] Get the received signal corresponding to the acoustic soliton wave receiving module when the maximum amplitude is greater than a preset first amplitude threshold, it is determined that the defect is at a preset severity level, or the defect is located at a depth lower than a preset first depth; when the maximum amplitude is less than a preset second amplitude threshold, it is determined that the defect is at a preset light level, or the defect is located at a depth higher than a preset second depth.

[0053] By formula: , calculate the standard deviation of the received acoustic soliton wave; where, represents the acoustic soliton wave signal received at time t, Indicates the center position of the acoustic soliton wave signal.

[0054] The degree of defect is determined based on the proportional relationship between the standard deviation and the degree of defect; or, the degree of defect is determined through the inverse relationship between the center frequency of the frequency domain feature and the degree of defect.

[0055] The waveform width in the time domain feature is obtained. When the waveform width is less than a preset first width threshold, it is determined that the defect manifestation corresponds to a point-shaped or elongated crack defect; when the waveform width is greater than or equal to a preset second width threshold, it is determined that the defect manifestation corresponds to a smooth defect interface.

[0056] The above is a method embodiment of the present application. Based on the same inventive concept, the present application embodiment also provides a device flaw detection device based on acoustic soliton waves. Figure 3 As shown, the device includes: a processor; and a memory, on which executable codes are stored. When the executable codes are executed, the processor executes a device flaw detection method based on acoustic soliton waves as in the above-mentioned embodiment.

[0057] Specifically, a nonlinear piezoelectric exciter on the server side is used to obtain the material properties and detection requirements of the object under test, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave; a plurality of acoustic soliton wave transmitting modules placed within a preset range of the object under test are connected to the nonlinear piezoelectric exciter to convert the electrical signal of the acoustic soliton wave into mechanical vibration to excite the acoustic soliton wave; an acoustic soliton wave receiving module is used to convert the acoustic soliton wave into an electrical signal after capturing the acoustic soliton wave emitted by the acoustic soliton wave transmitting module; a signal processing module is connected to the acoustic soliton wave receiving module to perform time domain and frequency domain analysis on the electrical signal to extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; through the time domain characteristics and frequency domain characteristics, it is determined whether there are potential defects in the object under test and the degree, location and manifestation of the potential defects.

[0058] In addition, an embodiment of the present application further provides a non-volatile computer storage medium on which executable instructions are stored. When the executable instructions are executed, the above-mentioned device flaw detection method based on acoustic soliton waves is implemented.

[0059] So far, the technical solutions of the present disclosure have been described in combination with the above multiple embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present disclosure is not limited to these specific embodiments. Without departing from the technical principles of the present disclosure, those skilled in the art can split and combine the technical solutions in the above-mentioned various embodiments, and can also make equivalent changes or replacements to the relevant technical features. Any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principle of the present disclosure will fall within the protection scope of the present disclosure.

Claims

1. An equipment flaw detection system based on acoustic soliton waves, characterized in that: The system comprises: Nonlinear piezoelectric actuators are used to obtain the material properties of the object being tested and the detection requirements, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave; A plurality of acoustic soliton wave transmitting modules are placed within a preset range of the object to be measured and connected to a nonlinear piezoelectric actuator to convert the electrical signal of the acoustic soliton wave into mechanical vibration to excite the acoustic soliton wave; The acoustic soliton wave receiving module is used to convert the acoustic soliton wave into an electrical signal after capturing the acoustic soliton wave emitted by the acoustic soliton wave transmitting module; The signal processing module is connected to the acoustic soliton wave receiving module and is used to perform time domain and frequency domain analysis on the electrical signal and extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; through the time domain characteristics and frequency domain characteristics, it is determined whether there are potential defects in the object under test and the degree, location and manifestation of the potential defects.

2. The equipment flaw detection system based on acoustic soliton waves according to claim 1 is characterized in that: The nonlinear piezoelectric actuator includes an evaluation module: Used to take the material properties and detection requirements of the object to be tested as the input of the trained preset neural network algorithm to obtain the frequency, amplitude and excitation time of the output acoustic soliton wave; Among them, the sample data for training the preset neural network algorithm are material properties, detection requirements, frequency, amplitude and excitation time of acoustic soliton waves.

3. The equipment flaw detection system based on acoustic soliton waves according to claim 1 is characterized in that: The signal processing module includes a first defect determination unit, It is used to determine whether there are potential defects by identifying whether the received acoustic soliton wave is distorted.

4. The equipment flaw detection system based on acoustic soliton waves according to claim 1 is characterized in that: The signal processing module includes a distance determination unit, a first degree determination unit, a second degree determination unit, and a performance determination unit: A distance determination unit for obtaining the maximum correlation value between the transmitted acoustic soliton wave and the received acoustic soliton wave ; , calculate the delay ;in, = - , represents the time of the emitted acoustic soliton wave, express The acoustic soliton wave signal emitted at any moment, represents the time of the received acoustic soliton wave, express Acoustic soliton wave signals received at all times; Determine the distance between the defect position and the acoustic soliton wave transmitting module that transmits the acoustic soliton wave through a preset corresponding relationship between the time delay and the distance between the defect position; The first degree determination unit is used to obtain the receiving signal corresponding to the acoustic soliton wave receiving module When the maximum amplitude is greater than a preset first amplitude threshold, it is determined that the defect is at a preset severity level, or the defect is located at a depth lower than a preset first depth; when the maximum amplitude is less than a preset second amplitude threshold, it is determined that the defect is at a preset light level, or the defect is located at a depth higher than a preset second depth; The second degree determination unit is used by the formula: , calculate the standard deviation corresponding to the received acoustic soliton wave; in, represents the acoustic soliton wave signal received at time t, Indicates the center position of the acoustic soliton wave signal; Determine the degree of defect based on the proportional relationship between standard deviation and degree of defect; Or, the degree of the defect can be determined by the inverse relationship between the center frequency of the frequency domain feature and the degree of the defect; The performance determination unit is used to obtain the waveform width in the time domain characteristics. When the waveform width is less than a preset first width threshold, it is determined that the performance of the defect corresponds to a point or elongated crack defect; when the waveform width is greater than or equal to a preset second width threshold, it is determined that the performance of the defect corresponds to a smooth defect interface.

5. A device flaw detection method based on acoustic soliton waves, applied to the device flaw detection system based on acoustic soliton waves according to claim 1, characterized in that: The method comprises: Obtain the material properties and detection requirements of the object to be tested, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave; By placing a number of acoustic soliton wave transmitting modules within a preset range of the object to be measured, the electrical signal of the acoustic soliton wave is converted into mechanical vibration to excite the acoustic soliton wave; The transmitted acoustic soliton wave is captured by the acoustic soliton wave receiving module, and the captured acoustic soliton wave is converted into an electrical signal; Perform time domain and frequency domain analysis on the electrical signal to extract the time domain characteristics and frequency domain characteristics of the acoustic soliton wave; through the time domain characteristics and frequency domain characteristics, determine whether the object under test has potential defects and determine the degree, location and manifestation of the potential defects.

6. The device flaw detection method based on acoustic soliton waves according to claim 5 is characterized in that: Obtain the material properties and detection requirements of the object to be tested, and then determine the frequency, amplitude and excitation time of the acoustic soliton wave, including: The material properties and detection requirements of the object to be tested are used as inputs of the trained preset neural network algorithm to obtain the frequency, amplitude and excitation time of the output acoustic soliton wave; Among them, the sample data for training the preset neural network algorithm are material properties, detection requirements, frequency, amplitude and excitation time of acoustic soliton waves.

7. The device flaw detection method based on acoustic soliton waves according to claim 5 is characterized in that: Through the time domain characteristics and frequency domain characteristics, determine whether the object under test has potential defects and determine the degree, location and manifestation of the potential defects, including: By identifying whether the received acoustic soliton wave is distorted, it is determined whether there is a potential defect.

8. The device flaw detection method based on acoustic soliton waves according to claim 5 is characterized in that: Through the time domain characteristics and frequency domain characteristics, determine whether the object under test has potential defects and determine the degree, location and manifestation of the potential defects, including: Get the maximum correlation value between the transmitted acoustic soliton wave and the received acoustic soliton wave ; , calculate the delay ;in, = - , represents the time of the emitted acoustic soliton wave, express The acoustic soliton wave signal emitted at any moment, represents the time of the received acoustic soliton wave, express Acoustic soliton wave signals received at all times; Determine the distance between the defect position and the acoustic soliton wave transmitting module that transmits the acoustic soliton wave through a preset corresponding relationship between the time delay and the distance between the defect position; Get the received signal corresponding to the acoustic soliton wave receiving module When the maximum amplitude is greater than a preset first amplitude threshold, it is determined that the defect is at a preset severity level, or the defect is located at a depth lower than a preset first depth; when the maximum amplitude is less than a preset second amplitude threshold, it is determined that the defect is at a preset light level, or the defect is located at a depth higher than a preset second depth; By formula: , calculate the standard deviation corresponding to the received acoustic soliton wave; in, represents the acoustic soliton wave signal received at time t, Indicates the center position of the acoustic soliton wave signal; Determine the degree of defect based on the proportional relationship between standard deviation and degree of defect; Or, the degree of the defect can be determined by the inverse relationship between the center frequency of the frequency domain feature and the degree of the defect; The waveform width in the time domain feature is obtained. When the waveform width is less than a preset first width threshold, it is determined that the defect manifestation corresponds to a point-shaped or elongated crack defect; when the waveform width is greater than or equal to a preset second width threshold, it is determined that the defect manifestation corresponds to a smooth defect interface.

9. A device flaw detection device based on acoustic soliton waves, characterized in that: The device comprises: processor; and a memory having executable codes stored thereon, which, when the executable codes are executed, enable the processor to execute the device flaw detection method based on acoustic soliton waves as claimed in any one of claims 5 to 8.

10. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the device flaw detection method based on acoustic soliton waves as described in any one of claims 5 to 8 is implemented.

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