Acoustic emission signal extraction method and device, terminal equipment and storage equipment

Abstract

The invention relates to the technical field of signal processing, discloses an acoustic emission signal extraction method, and particularly relates to an acoustic emission signal extraction method and device, terminal equipment and storage equipment. Through combination of a particle swarm optimization algorithm and a simulated annealing algorithm, harmonic wavelet parameters can be adaptively optimized, and arc acoustic emission signals can be accurately extracted. According to the method, the signal extraction precision can be remarkably improved, noise interference is reduced, and therefore the welding quality is represented more accurately.

Description

Technical Field

[0001] The present application relates to the technical field of signal processing, and is a method for extracting an acoustic emission signal, and specifically is a method, an apparatus, a terminal device and a storage device for extracting an acoustic emission signal. Background Art

[0002] The traditional welding process information contained in the welding arc voltage and current signal is too little to fully characterize the complex dynamic physical process of welding. The acoustic emission signal is used with a higher sampling frequency and contains more information. The acoustic emission signal generated by the welding process is a signal emitted from the inside of the welding process, which can characterize many factors of the welding quality. As one of the by-products of the welding process, the welding arc acoustic emission signal carries a lot of information about the welding process, so it is a valuable and potential source signal for online monitoring of welding quality.

[0003] Currently, it is difficult to effectively extract arc acoustic emission signals that reflect welding quality because there are many acoustic emission signal sources and complex background noise in the welding process. Summary of the invention

[0004] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide an acoustic emission signal extraction method, apparatus, terminal device and storage device, which can effectively extract the acoustic emission signal of the aluminum alloy double-pulse MIG arc.

[0005] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, a method for extracting an acoustic emission signal is provided, which is applied to an aluminum alloy double-pulse arc generating device, the method comprising: acquiring an arc current signal, and performing harmonic wavelet packet extraction on the arc current signal based on multiple groups of optimal harmonic wavelet parameters to obtain multiple groups of reference signals, and demodulating the extracted reference signals; updating the multiple groups of optimal harmonic wavelet parameters based on a particle swarm optimization algorithm and a simulated degradation algorithm to obtain sample entropy values ​​for the extracted multiple groups of reference signals, respectively, and traversing matching combinations of multiple groups of optimal harmonic wavelet parameters based on a greedy algorithm; selecting the harmonic wavelet parameter with the smallest entropy value from the multiple groups of parameters as the target harmonic wavelet parameter, and using the target harmonic wavelet parameter and the corresponding arc acoustic emission signal as the target arc acoustic emission signal.

[0007] Furthermore, based on the particle swarm optimization algorithm and the simulated degradation algorithm, the optimal multiple groups of harmonic wavelet parameters are updated, including: initializing the parameter sample set, the key parameters of the particle swarm algorithm and the key parameters of the simulated annealing; performing particle swarm optimization and simulated annealing optimization based on the initialized parameters; and cyclically iterating the particle swarm optimization and simulated annealing optimization process until the objective function converges and returns the final optimal parameter combination.

[0008] Furthermore, the key parameters of the particle swarm algorithm include particle swarm position, particle swarm velocity, particle swarm global optimal position, and particle swarm historical optimal position.

[0009] Furthermore, the key parameters of simulated annealing include an initial temperature of simulated annealing.

[0010] Furthermore, the particle swarm optimization based on the initialized parameters includes: based on the following formula:

[0011] v i (t+1)=ωv i (t)+c1r1(p i -x i (t))+c2r2(gx i (t)),

[0012] x i (t+1)=x i (t)+v i (t+1), update the speed and position of each particle in the particle swarm, calculate the objective function value corresponding to the new position of each particle, and judge the relationship between the objective function value and the historical best position and the global best position; when the objective function value is better than the historical best position, update the historical best position; when the objective function value is better than the current global best position, update the global best position; where v i (t) is the velocity of particle i at time t; x i (t) is the position of particle i at time t; ω is the inertia weight, which controls the global and local exploration capabilities of the particle search space; c1 and c2 are learning factors, which represent the degree of dependence of the particle on its own experience and the group experience respectively; r1 and r2 are random numbers between [0,1]; p i is the historical best position of particle i; g is the global best position.

[0013] Furthermore, the simulated annealing optimization based on the initialized parameters includes: selecting a particle from a particle population; generating a neighborhood solution for the particle, and according to the following formula: Calculate the acceptance probability, and decide whether to accept the domain solution as a new solution based on the acceptance probability; if accepted, update the position of the particle and the historical best position, and reduce the temperature according to the annealing strategy; where x is the current solution of the particle selected in the particle swarm, and x′ is the domain solution of the particle.

[0014] Furthermore, the decision of whether to accept the domain solution as the new solution based on the reception probability includes: calculating the current solution objective function value and the domain solution objective function value corresponding to the current solution and the domain solution according to the current solution and the domain solution; if the domain solution objective function value is better than the current solution objective function value, then taking the domain solution objective function value as the new solution; if the domain solution objective function value is not better than the current solution objective function value, then taking the domain solution objective function value as the new solution with probability Accept the domain solution as the new solution; where f(x) is the objective function value of the current solution and f(x′) is the objective function value of the domain solution.

[0015] In a second aspect, an acoustic emission signal extraction device is provided, which is applied to an aluminum alloy double-pulse arc generating device, and the device includes: a reference signal acquisition module, which is used to perform harmonic wavelet packet extraction on the acquired arc current signal based on multiple groups of optimal harmonic wavelet parameters to obtain multiple groups of reference signals, and demodulate the extracted reference signals; a screening module, which is used to calculate sample entropy values ​​for the extracted multiple groups of reference signals respectively, and traverse the matching combinations of multiple groups of optimal harmonic wavelet parameters based on a greedy algorithm; a target signal determination module, which is used to screen out the harmonic wavelet parameters with the smallest entropy value from the multiple groups of parameters, and based on and corresponding arc acoustic emission signal as the target arc acoustic emission signal.

[0016] According to a third aspect, a terminal device is provided, comprising: a memory and a processor coupled to the memory, wherein the processor is configured to execute any one of the above-mentioned acoustic emission signal extraction methods based on instructions stored in the memory.

[0017] In a fourth aspect, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method for extracting acoustic emission signals described in any one of the above items is implemented.

[0018] In the technical solution provided in the embodiment of the present application, by combining the particle swarm optimization algorithm and the simulated annealing algorithm, the harmonic wavelet parameters can be adaptively optimized to accurately extract the arc acoustic emission signal. The present invention can significantly improve the accuracy of signal extraction, reduce noise interference, and thus more accurately characterize the welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] The methods, systems and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numbers represent similar mechanisms in the various views of the accompanying drawings.

[0021] Figure 1 It is a flow chart of the acoustic emission signal extraction method provided in an embodiment of the present application.

[0022] Figure 2 It is the original acoustic emission signal image of the welding process.

[0023] Figure 3 It is the processed acoustic emission signal image.

[0024] Figure 4 It is a schematic diagram of the structure of the acoustic emission signal extraction device provided in an embodiment of the present application.

[0025] Figure 5 It is a schematic diagram of the terminal device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0027] In the following detailed description, numerous specific details are set forth by way of example in order to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that the present application may be practiced without these details. In other cases, well-known methods, procedures, systems, compositions and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

[0028] Flowcharts are used in the present application to illustrate the execution process performed by the system according to the embodiment of the present application. It should be clearly understood that the execution process of the flowchart may not be performed in order. On the contrary, these execution processes may be performed in reverse order or simultaneously. In addition, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0029] Before further describing the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are described. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0030] (1) In response, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0031] (2) Based on is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or have a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0032] Double pulse MIG (double pulse molten inert gas) welding is an important processing technology for aluminum alloy structure welding. In the process of aluminum alloy DP-MIG welding, the arc stability determines the arc force, heat amplitude and distribution during the welding process, which directly affects the weld formation and the quality of the welded joint. Therefore, when welding, the arc acoustic emission signal needs to be accurately and quickly extracted to meet the welding needs.

[0033] With regard to this technical background, an embodiment of the present application provides an adaptive arc sound emission signal extraction method, which can improve the signal extraction accuracy and enhance the real-time performance and efficiency of the arc sound emission signal.

[0034] See also Figure 1 , this method includes the following steps:

[0035] Step S110: Acquire an arc current signal, perform harmonic wavelet packet extraction on the arc current signal based on multiple groups of optimal harmonic wavelet parameters, obtain multiple groups of reference signals, and demodulate the extracted reference signals.

[0036] In the embodiment of the present application, multiple groups of optimal harmonic wavelet parameters are updated based on a particle swarm optimization algorithm and a simulated degradation algorithm.

[0037] Among them, particle swarm optimization is an optimization algorithm based on swarm intelligence. It finds the optimal solution by simulating the behavior of bird flocks. Each particle represents a possible solution, and the optimal parameter combination is found by continuously updating its speed and position. Simulated annealing algorithm is an optimization algorithm based on the material annealing process. By allowing suboptimal solutions to be accepted with a certain probability, it is possible to jump out of the local optimal solution and gradually approach the global optimal solution.

[0038] The embodiment of the present application combines the above two algorithms, obtains multiple parameter combinations through a particle swarm algorithm, and then uses a simulated degradation algorithm to iteratively update the obtained multiple parameter combinations to obtain the final target parameters.

[0039] The target parameters are key parameters for wavelet signal extraction, specifically m, n and k. That is, in this embodiment, the optimal harmonic wavelet parameters are m, n and k, and step S110 is how to obtain the final optimal harmonic wavelet parameters m, n and k.

[0040] Specifically, the following steps are included for updating the optimal multiple groups of harmonic wavelet parameters through the particle swarm optimization algorithm and the simulated degradation algorithm:

[0041] Step S111: Initialize the parameter sample set, the key parameters of the particle swarm algorithm and the key parameters of the simulated annealing.

[0042] Step S112: performing particle swarm optimization and simulated annealing optimization based on the initialized parameters.

[0043] Step S113. The particle swarm optimization and simulated annealing optimization processes are iterated repeatedly until the objective function converges, and the final optimal parameter combination is returned.

[0044] Among them, in this embodiment, the parameter sample set is {m}, {n} and {k}; the key parameters for the particle swarm algorithm include the particle group position, the particle group speed, the particle group global optimal position, and the particle group historical optimal position; the key parameters for simulated annealing include the initial temperature of simulated annealing.

[0045] The particle swarm optimization process of step S112 is based on the following formula for each particle:

[0046] v i (t+1)=ωv i (t)+c1r1(p i -x i (t))+c2r2(gx i (t)),

[0047] x i (t+1)=x i (t)+v i (t+1), update the speed and position of each particle in the particle swarm, calculate the objective function value corresponding to the new position of each particle, and judge the relationship between the objective function value and the historical best position and the global best position; when the objective function value is better than the historical best position, update the historical best position; when the objective function value is better than the current global best position, update the global best position; where v i (t) is the velocity of particle i at time t; x i(t) is the position of particle i at time t; ω is the inertia weight, which controls the global and local exploration capabilities of the particle search space; c1 and c2 are learning factors, which represent the degree of dependence of the particle on its own experience and the group experience respectively; r1 and r2 are random numbers between [0,1]; p i is the historical best position of particle i; g is the global best position.

[0048] The steps for simulated annealing optimization are to select a particle from the particle population, generate a neighborhood solution for the particle, and calculate the solution according to the following formula: Calculate the acceptance probability, and decide whether to accept the domain solution as a new solution based on the acceptance probability; if accepted, update the position of the particle and the historical best position, and reduce the temperature according to the annealing strategy; where x is the current solution of the particle selected in the particle swarm, and x′ is the domain solution of the particle.

[0049] The process of step S113 is to repeat the two optimization processes in step S112 until the maximum number of iterations is reached or the objective function converges, and return the final global optimal position g, that is, the optimal parameter combination m, n and k.

[0050] The optimal parameter combinations m, n and k obtained through steps S111 to S113 are used to extract harmonic wavelet packets from the signal according to different parameter matching, and the extracted arc acoustic emission signal is demodulated.

[0051] Step S120: Calculate sample entropy values ​​for the extracted multiple groups of reference signals respectively, and traverse matching combinations of multiple groups of optimal harmonic wavelet parameters based on a greedy algorithm.

[0052] Step S130. Filter out the harmonic wavelet parameter with the smallest entropy value from the multiple groups of parameters as the target harmonic wavelet parameter, and use the target harmonic wavelet parameter and the corresponding arc acoustic emission signal as the target arc acoustic emission signal.

[0053] The sample entropy value of the extracted signal obtained in step S110 is calculated, with an embedding dimension of m=2 and a tolerance of r=0.2*std(signal). The greedy algorithm is combined to traverse all parameter matching combinations to screen out the signal data with the smallest entropy value, i.e., the arc acoustic emission signal.

[0054] See also Figure 2 and Figure 3 , which are respectively the original acoustic emission signal image of the welding process before processing and the acoustic emission signal image after processing through steps S110 to S130.

[0055] From the above two images, it can be seen that the processed acoustic emission signal image reduces the interference of noise on the signal in signal expression, making the signal expression clearer and more accurate.

[0056] The acoustic emission signal extraction method provided in this embodiment can adaptively optimize the harmonic wavelet parameters and accurately extract the arc acoustic emission signal by combining the particle swarm optimization algorithm and the simulated annealing algorithm. The present invention can significantly improve the accuracy of signal extraction and reduce noise interference, thereby more accurately characterizing the welding quality. The embodiment of the present invention makes the extraction process of arc acoustic emission signals more efficient. Compared with traditional methods, the present invention has higher efficiency and response speed in real-time monitoring of welding quality.

[0057] Because the acoustic emission signals generated during the welding process contain a large amount of information, through the method of the embodiment of the present application, these signals can be effectively extracted and demodulated, so that they are associated with the arc current signal in the time domain, thereby providing richer data support for the online monitoring of welding quality. In addition, the present invention is not only applicable to the double-pulse MIG welding process of aluminum alloys, but can also be extended to other types of welding process quality detection. Its adaptive parameter optimization and signal extraction methods have good applicability and robustness in different welding environments. And the present invention can associate the extracted arc acoustic emission signals with the arc current signals in the time domain through the resonance demodulation technology, thereby verifying the accuracy and reliability of the arc acoustic emission signals. This provides strong technical support for the online monitoring of welding quality.

[0058] See also Figure 4 Based on step S110-step S130, a virtual device for execution is also provided, which is an acoustic emission signal extraction device 400. This device can be configured in any software product to execute the process of step S110-step S130. This device can be a separate plug-in module or a complete computer product. This device includes:

[0059] A reference signal acquisition module 410 is used to extract harmonic wavelet packets from the acquired arc current signal based on multiple groups of optimal harmonic wavelet parameters to obtain multiple groups of reference signals, and demodulate the extracted reference signals;

[0060] A screening module 420 is used to calculate sample entropy values ​​for the extracted multiple groups of reference signals respectively, and traverse matching combinations of multiple groups of optimal harmonic wavelet parameters based on a greedy algorithm;

[0061] The target signal determination module 430 is used to select the harmonic wavelet parameter with the smallest entropy value from the multiple groups of parameters, and use the corresponding arc acoustic emission signal as the target arc acoustic emission signal.

[0062] A method for processing EEG signals provided in an embodiment of the present application collects EEG signals.

[0063] See also Figure 5, the above method can also be integrated into the provided terminal device 500, and the device may have relatively large differences due to different configurations or performances, and may include one or more processors 501 and memory 502, and the memory 502 may store one or more storage applications or data. Among them, the memory 502 can be a temporary storage or a permanent storage. The application stored in the memory 502 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the terminal device. Furthermore, the processor 501 can be configured to communicate with the memory 502, and the terminal device executes a series of computer executable instructions in the memory 502. The terminal device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input or output interfaces 505, one or more keyboards 506, etc.

[0064] In a specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer executable instructions in the terminal device, and the one or more programs are configured to be executed by one or more processors, including computer executable instructions for performing the following:

[0065] Acquire an arc current signal, perform harmonic wavelet packet extraction on the arc current signal based on multiple groups of optimal harmonic wavelet parameters, obtain multiple groups of reference signals, and demodulate the extracted reference signals;

[0066] The sample entropy values ​​of the extracted multiple groups of reference signals are calculated respectively, and the matching combinations of multiple groups of optimal harmonic wavelet parameters are traversed based on the greedy algorithm;

[0067] A harmonic wavelet parameter with the smallest entropy value is screened out from multiple groups of parameters as a target harmonic wavelet parameter, and an arc acoustic emission signal based on the target harmonic wavelet parameter and the corresponding arc acoustic emission signal is a target arc acoustic emission signal.

[0068] The following is a detailed introduction to the various components of the processor:

[0069] In this embodiment, the processor is an application specific integrated circuit (ASIC), or is configured to implement one or more integrated circuits of the embodiments of the present application, such as: one or more microprocessors (digital signal processors, DSP), or, one or more field programmable gate arrays (field programmable gate array, FPGA).

[0070] Optionally, the processor may execute various functions by running or executing a software program stored in the memory and calling data stored in the memory, such as executing the above-mentioned Figure 1 The method shown.

[0071] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.

[0072] Among them, the memory is used to store the software program that executes the solution of the present application, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0073] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor, or may exist independently and be coupled to the processing unit through the interface circuit of the processor, and the embodiments of the present application do not specifically limit this.

[0074] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device, and the actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0075] In addition, the technical effects of the processor can refer to the technical effects of the method described in the above method embodiment, which will not be repeated here.

[0076] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0077] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0078] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0079] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0080] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0081] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0086] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks.

[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for extracting acoustic emission signals, characterized in that: Applied to an aluminum alloy double pulse arc generating device, the method comprises: Acquire an arc current signal, and extract harmonic wavelet packets from the arc current signal based on multiple groups of optimal harmonic wavelet parameters to obtain multiple groups of reference signals, and demodulate the extracted reference signals; the multiple groups of optimal harmonic wavelet parameters are updated based on a particle swarm optimization algorithm and a simulated degradation algorithm; The sample entropy values ​​of the extracted multiple groups of reference signals are calculated respectively, and the matching combinations of multiple groups of optimal harmonic wavelet parameters are traversed based on the greedy algorithm; A harmonic wavelet parameter with the smallest entropy value is screened out from multiple groups of parameters as a target harmonic wavelet parameter, and an arc acoustic emission signal based on the target harmonic wavelet parameter and the corresponding arc acoustic emission signal is a target arc acoustic emission signal.

2. The acoustic emission signal extraction method according to claim 1, characterized in that: Based on the particle swarm optimization algorithm and simulated degradation algorithm, the optimal multiple groups of harmonic wavelet parameters are updated, including: Initialize the parameter sample set, the key parameters of the particle swarm algorithm and the key parameters of the simulated annealing; Particle swarm optimization and simulated annealing optimization are performed based on the initialized parameters; The particle swarm optimization and simulated annealing optimization processes are iterated until the objective function converges and the final optimal parameter combination is returned.

3. The acoustic emission signal extraction method according to claim 2, characterized in that: The key parameters of the particle swarm algorithm include particle swarm position, particle swarm velocity, particle swarm global optimal position, and particle swarm historical optimal position.

4. The method for extracting acoustic emission signals according to claim 3, characterized in that: The key parameters of simulated annealing include the initial temperature of simulated annealing.

5. The method for extracting acoustic emission signals according to claim 4, characterized in that: The particle swarm optimization based on the initialized parameters includes: based on the following formula: υ i (t+1)=ωv i (t)+c1r1(p i -x i (t))+c2r2(g-x i (t)), x i (t+1)=x i (t)+v i (t+1), update the speed and position of each particle in the particle swarm, calculate the objective function value corresponding to the new position of each particle, and judge the relationship between the objective function value and the historical best position and the global best position; when the objective function value is better than the historical best position, update the historical best position; when the objective function value is better than the current global best position, update the global best position; where v i (t) is the velocity of particle i at time t; x i (t) is the position of particle i at time t; ω is the inertia weight, which controls the global and local exploration capabilities of the particle search space; c1 and c2 are learning factors, which represent the degree of dependence of the particle on its own experience and the group experience respectively; r1 and r2 are random numbers between [0, 1]; p i is the historical best position of particle i; g is the global best position.

6. The method for extracting acoustic emission signals according to claim 4, characterized in that: The simulated annealing optimization based on the initialized parameters includes: selecting a particle from a particle population; generating a neighborhood solution for the particle, and according to the following formula: Calculate the acceptance probability, and decide whether to accept the domain solution as a new solution based on the acceptance probability; if accepted, update the particle's position and historical best position, and reduce the temperature according to the annealing strategy; where x is the current solution of the particle selected in the particle swarm, and x′ is the domain solution of the particle.

7. The method for extracting acoustic emission signals according to claim 6, characterized in that: The method of determining whether to accept the domain solution as a new solution based on the reception probability includes: calculating the current solution objective function value and the domain solution objective function value corresponding to the current solution and the domain solution according to the current solution and the domain solution, and if the domain solution objective function value is better than the current solution objective function value, then using the domain solution objective function value as the new solution; if the domain solution objective function value is not better than the current solution objective function value, then using the probability Accept the domain solution as the new solution; where f(x) is the objective function value of the current solution and f(x′) is the objective function value of the domain solution.

8. An acoustic emission signal extraction device, characterized in that: Applicable to an aluminum alloy double pulse arc generating device, the device comprising: A reference signal acquisition module is used to extract harmonic wavelet packets from the acquired arc current signal based on multiple groups of optimal harmonic wavelet parameters to obtain multiple groups of reference signals, and demodulate the extracted reference signals; A screening module is used to calculate the sample entropy values ​​of the extracted multiple groups of reference signals respectively, and traverse the matching combinations of multiple groups of optimal harmonic wavelet parameters based on a greedy algorithm; The target signal determination module is used to screen out the harmonic wavelet parameter with the smallest entropy value from multiple groups of parameters, and based on the corresponding arc acoustic emission signal, it is the target arc acoustic emission signal.

9. A terminal device, comprising: A memory and a processor coupled to the memory, wherein the processor is configured to execute the acoustic emission signal extraction method according to any one of claims 1 to 7 based on instructions stored in the memory.

10. A non-volatile computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for extracting acoustic emission signals according to any one of claims 1 to 7 is implemented.

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