Method and equipment for determining component crack abnormity, device and program product

Abstract

The embodiment of the invention provides a method and equipment for determining component crack abnormity, a device and a program product. The method comprises the following steps: acquiring a first signal and a second signal generated by a target component in a target time period; signal conversion operation is executed on the first signal and the second signal to obtain a target signal, and the signal conversion operation comprises filtering processing and waveform conversion processing executed on the first signal and the second signal; and under the condition that the crack signal characteristics of the target assembly are extracted from the target signal, determining crack abnormity of the target assembly in the target time period based on the crack signal characteristics. According to the invention, the problems of low assembly crack detection efficiency and low accuracy in the prior art are solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of compression equipment, and more specifically, to a method and equipment, a device, and a program product for determining component crack anomalies. Background Art

[0002] Compression equipment is a device that compresses gas to a certain pressure. During the operation of compression equipment, the gas compression components will be subjected to greater stress, which will lead to abnormal cracks in the gas compression components.

[0003] In the related art, manual visual inspection is usually used to determine the crack anomaly of the gas compression component. However, the manual visual inspection method has the problems of low efficiency and low accuracy. Summary of the invention

[0004] The embodiments of the present application provide a method and equipment, a device, and a program product for determining component crack abnormalities, so as to at least solve the problems of low efficiency and low accuracy in detecting component cracks in related technologies.

[0005] According to one embodiment of the present application, a method for determining a crack abnormality of a component is provided, comprising: obtaining a first signal and a second signal generated by a target component within a target time period, wherein the target component is a gas compression component in a target compression device, the first signal is used to represent an acoustic emission signal generated when the target component is operating in the target compression device, and the second signal is used to track the position of the target component when it is operating in the target compression device; performing a signal conversion operation on both the first signal and the second signal to obtain a target signal, wherein the signal conversion operation includes filtering processing and waveform transformation processing performed on the first signal and the second signal; and determining the crack abnormality of the target component within the target time period based on the crack signal characteristics when the crack signal characteristics of the target component are extracted from the target signal.

[0006] In an exemplary embodiment, obtaining a first signal and a second signal generated by a target component within a target time period includes: when the target component is operating in the target compression device, detecting the acoustic emission signal generated by the target component during the force application process by a first sensing device to obtain the first signal; when the first signal is generated, measuring a time-to-digital conversion signal generated by the target component during operation by a second sensing device to obtain the second signal; wherein both the first sensing device and the second sensing device are deployed in the target compression device.

[0007] In an exemplary embodiment, a signal conversion operation is performed on both the first signal and the second signal to obtain a target signal, including: mapping the first signal and the second signal to a signal coordinate space, and fusing the first signal and the second signal in the signal coordinate space to obtain a fused signal, wherein the signal coordinate space includes a first coordinate axis for representing the first signal and a second coordinate axis for representing the second signal; performing signal enhancement on the fused signal by a signal enhancement device in the signal coordinate space to obtain an enhanced signal; performing the filtering process on the enhanced signal by a signal filter in the signal coordinate space to remove the noise signal in the enhanced signal to obtain a filtered signal; performing the waveform transformation process on the filtered signal to obtain the target signal.

[0008] In an exemplary embodiment, the waveform transformation processing is performed on the filtered signal to obtain the target signal, including: performing N-level decomposition on the filtered signal to determine the high-frequency signal and the low-frequency signal of the filtered signal in each level of decomposition, wherein N is a natural number greater than or equal to 1; performing a convolution operation on the high-frequency signal of the filtered signal in each level of decomposition to obtain N convolved high-frequency signals, wherein the length of the convolved high-frequency signal is greater than the length of the high-frequency signal; and reconstructing the N convolved high-frequency signals and the low-frequency signal in the filtered signal to obtain the target signal.

[0009] In an exemplary embodiment, extracting the crack signal characteristics of the target component from the target signal includes: performing multi-level signal decomposition on the target signal to obtain approximate coefficients and detail coefficients of each level of decomposition; and determining the crack signal characteristics using the approximate coefficients and detail coefficients of each level of decomposition.

[0010] In an exemplary embodiment, the crack signal characteristics are determined using the approximate coefficients and detail coefficients of each level of decomposition, including: calculating the energy of the approximate coefficients and detail coefficients of each level of decomposition to obtain an energy characteristic, wherein the energy characteristic is used to represent the local energy distribution of the target signal; calculating the variance of the approximate coefficients and detail coefficients of each level of decomposition to obtain a variance characteristic, wherein the variance characteristic is used to represent the local variation degree of the target signal; calculating the entropy value of the approximate coefficients and detail coefficients of each level of decomposition to obtain an entropy characteristic, wherein the entropy characteristic is used to represent the complexity and uncertainty of the target signal; performing Fourier transform operation on the approximate coefficients and detail coefficients of each level of decomposition to calculate the power spectrum of the target signal, wherein the power spectrum is used to represent the frequency distribution of the target signal; determining at least one of the following as the crack signal characteristic: the energy characteristic, the variance characteristic, the entropy characteristic, and the power spectrum.

[0011] In an exemplary embodiment, when the crack signal characteristics of the target component are extracted from the target signal, the crack anomaly of the target component within the target time period is determined based on the crack signal characteristics, including: comparing the crack signal characteristics with a priori graph, wherein the priori graph includes known damage; determining the damage degree and damage category of the target component based on the comparison result; and determining the crack anomaly of the target component within the target time period according to the damage degree and the damage category.

[0012] In an exemplary embodiment, after extracting the crack signal characteristics of the target component from the target signal and determining the crack abnormality of the target component within the target time period based on the crack signal characteristics, the method further includes: triggering abnormal prompt information when the abnormality level of the crack abnormality of the target component within the target time period is greater than a preset level, wherein the abnormal prompt information is used to prompt the start of an operation to stop the operation of the target component; and generating repair information when it is determined that the target component stops running, wherein the repair information includes information for repairing the crack abnormality.

[0013] According to another embodiment of the present application, a target compression device is provided, comprising: a target component, a first sensing device, a second sensing device and a processor, wherein the first sensing device is used to detect an acoustic emission signal generated by the target component during a force-bearing process when the target component is operating in the target compression device to obtain a first signal; the second sensing device is used to measure a time-to-digital conversion signal generated by the target component during operation when the first signal is generated to obtain a second signal; and the processor is used to implement the steps of the method when executed.

[0014] According to another embodiment of the present application, a device for determining a crack abnormality of a component is provided, comprising: a first acquisition module, used to acquire a first signal and a second signal generated by a target component within a target time period, wherein the target component is a gas compression component in a target compression device, and the first signal is used to represent an acoustic emission signal generated by the target component when it is operating in the target compression device, and the second signal is used to track the position of the target component when it is operating in the target compression device; a first conversion module, used to perform a signal conversion operation on both the first signal and the second signal to obtain a target signal, wherein the signal conversion operation includes filtering processing and waveform transformation processing performed on the first signal and the second signal; a first determination module, used to determine the crack abnormality of the target component within the target time period based on the crack signal characteristics when the crack signal characteristics of the target component are extracted from the target signal.

[0015] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0016] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0017] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0018] Through the present application, by acquiring the first signal and the second signal generated by the target component within the target time period, wherein the target component is a gas compression component in the target compression device, the first signal is used to represent the acoustic emission signal generated when the target component is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device; and performing signal conversion operations on both the first signal and the second signal to obtain the target signal; in the case where the crack signal characteristics of the target component are extracted from the target signal, the crack anomaly of the target component within the target time period is determined based on the crack signal characteristics. Not only the acoustic emission signal is monitored, but also the position signal is combined, which helps to more accurately locate the source of the crack anomaly and improve the accuracy of fault diagnosis. Thereby solving the problem of low efficiency and low accuracy in determining the crack anomaly of the gas compression component in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a hardware structure block diagram of a server device of a method for determining component crack abnormality in an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for determining component crack anomaly according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of an acoustic emission sensor according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a waveform of a signal acquired by an acoustic emission sensor according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of signal fusion according to an embodiment of the present application;

[0024] Figure 6 is a schematic diagram of signal filtering according to an embodiment of the present application;

[0025] Figure 7 is a schematic diagram of calculating a spectrum at each position according to an embodiment of the present application;

[0026] Figure 8 is a schematic diagram of waveform comparison according to an embodiment of the present application;

[0027] Fig. 9 is a schematic diagram of a time-domain acoustic emission signal and a time-frequency spectrum according to an embodiment of the present application;

[0028] Fig.10 is a schematic diagram of sub-band AE signals at different stages according to an embodiment of the present application;

[0029] Fig.11 is a structural diagram of a target compression device according to an embodiment of the present application;

[0030] Fig.12 It is a structural block diagram of a device for determining component crack abnormality according to an embodiment of the present application.

[0031] Description of reference numerals:

[0032] Target component 1101; first sensor device 1102; second sensor device 1103; processor 1104. DETAILED DESCRIPTION

[0033] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0035] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 1 is a hardware structure block diagram of a server device for a method of determining component crack abnormality in an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the server device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0036] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining component crack anomalies in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0037] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] In this embodiment, a method for determining component crack anomaly is provided. Figure 2 is a flow chart of a method for determining component crack abnormality according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0039] Step S202, obtaining a first signal and a second signal generated by a target component within a target time period, wherein the target component is a gas compression component in a target compression device, the first signal is used to represent an acoustic emission signal generated by the target component when it is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device;

[0040] Optionally, the target compression device may be a compressor, a compression pump, etc., for example, a reciprocating compressor. The target component may be a gas compression component in a compressor, or a gas compression component in a compression pump, wherein the gas compression component may be a pump body, a piston rod, a piston, a cylinder, a gas valve, etc., for example, a piston rod in a reciprocating compressor.

[0041] Optionally, the target time period may be any time period during which the target compression device is running. For example, the target time period may be 1 day, 1 week, 1 month, 1 year, etc. during which the target compression device is running. Alternatively, the target time period may be any time during which the target compression device is running.

[0042] Optionally, the acoustic emission signal generated when the target component is running in the target compression device may be a stress wave signal generated when the target component is running in the target compression device, or may be an acoustic emission signal generated when the target component is running in the target compression device. The position signal of the target component when it is running in the target compression device may be the position information of the target component when it is running in the target compression device, or may be the position coordinates of the target component when it is running in the target compression device. For example, the first signal includes but is not limited to an acoustic emission signal (Acoustic Emission, referred to as AE), and the second signal includes but is not limited to a time-to-digital converter signal (Time-to-Digital Converter, referred to as TDC) signal. Among them, the AE signal refers to the elastic wave generated by the material when it is subjected to external stress or internal defect changes due to the inhomogeneity of the microstructure, such as cracks, deformation or fracture. The TDC signal is an electronic device that measures time intervals and converts time intervals into digital signals.

[0043] Step S204, performing a signal conversion operation on both the first signal and the second signal to obtain a target signal, wherein the signal conversion operation includes performing a filtering process and a waveform transformation process on the first signal and the second signal;

[0044] Optionally, the signal conversion operation may be a signal conversion operation performed by a signal converter, and the signal converter may be a signal conversion device, a signal conversion module, etc.

[0045] Optionally, the signal conversion operation may be a filtering process and a waveform transformation process performed on the first signal and the second signal. The waveform transformation process may be a Fourier transform process, a wavelet transform process, a Walsh transform process, a Laplace transform process, a Hilbert transform process, etc. performed on the first signal and the second signal. Among them, Fourier transform is to convert the signal from the time domain to the frequency domain, and is used to analyze the frequency components of the signal. Wavelet transform is to decompose the signal into wavelet coefficients of different scales and positions, and is used for non-stationary signal analysis. The filtering process may be a filtering process performed on the first signal and the second signal, and the filtering process may be a filtering process performed by a signal converter, and the filtering process may be a low-pass filtering process, a high-pass filtering process, a band-pass filtering process, a band-stop filtering process, etc. performed on the first signal and the second signal. Among them, low-pass filtering allows low-frequency signals to pass through and blocks high-frequency signals. It is used to remove noise or unnecessary high-frequency components. High-pass filtering allows high-frequency signals to pass through and blocks low-frequency signals. It is used to extract the high-frequency part of the signal. Band-pass filtering only allows signals within a specific frequency range to pass through, and is used to selectively pass signals of a specific frequency band. Band-stop filtering blocks signals within a specific frequency range and allows other frequencies to pass, and is used to suppress interference in a specific frequency band.

[0046] Step S206, when the crack signal characteristics of the target component are extracted from the target signal, the crack abnormality of the target component within the target time period is determined based on the crack signal characteristics.

[0047] Optionally, the target signal may be a signal obtained by converting the first signal and the second signal, or may be a fusion signal of signals obtained by converting the first signal and the second signal.

[0048] Optionally, the crack signal feature may be a crack signal feature of a target component, or a characteristic value of the crack signal feature of the target component, wherein the characteristic value of the crack signal feature may be an energy feature, a variance feature, an entropy feature, a power spectrum, etc.

[0049] In this embodiment, the execution subject of the above steps can be a terminal, a server, a specific processor set in the terminal or server, or a processor or processing device set relatively independently from the terminal or server, but is not limited thereto. For example, a processor set in the target compression component.

[0050] Through the above steps, by acquiring the first signal and the second signal generated by the target component within the target time period, wherein the target component is a gas compression component in the target compression device, the first signal is used to represent the acoustic emission signal generated when the target component is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device; and performing signal conversion operations on both the first signal and the second signal to obtain the target signal; in the case where the crack signal characteristics of the target component are extracted from the target signal, the crack anomaly of the target component within the target time period is determined based on the crack signal characteristics. Not only the acoustic emission signal is monitored, but also the position signal is combined, which helps to more accurately locate the source of the crack anomaly and improve the accuracy of fault diagnosis. Thereby solving the problem of low efficiency and low accuracy in determining the crack anomaly of the gas compression component in the prior art.

[0051] In an exemplary embodiment, obtaining a first signal and a second signal generated by a target component within a target time period includes: when the target component is operating in the target compression device, detecting the acoustic emission signal generated by the target component during the force application process by a first sensing device to obtain the first signal; when the first signal is generated, measuring a time-to-digital conversion signal generated by the target component during operation by a second sensing device to obtain the second signal; wherein both the first sensing device and the second sensing device are deployed in the target compression device.

[0052] Optionally, the first sensing device includes but is not limited to an acoustic emission sensor, which can detect the acoustic emission signals generated by the target component during the stress process. These signals can be used to analyze and evaluate the stress-strain behavior of the material, crack propagation, and structural integrity. For example, Figure 3 As shown in FIG. 1 , it is a schematic diagram of the acoustic emission sensor in this embodiment. Figure 4 The figure below is a waveform diagram of the signal obtained by the acoustic emission sensor. The acoustic emission sensor is a piezoelectric acoustic emission sensor with an integrated preamplifier. The integrated preamplifier has a gain of 34 dB and supports pulse transmission for automatic sensor testing. Its response characteristics show two peaks at 190kHz and 350kHz, accompanied by anti-resonance at 200kHz and 400kHz, and has high sensitivity over a wide frequency range.

[0053] Optionally, the second sensing device is used to measure and analyze signals, including time-to-digital conversion signals, and convert time intervals into digital values. During operation, the target component may generate time-to-digital conversion signals, which need to be measured and recorded by the second sensing device.

[0054] For example, the first sensing device is an acoustic wave sensor, and the second sensing device is a position sensor, both of which are mounted at the end of the piston cylinder. The number of each is one. The installation can be mounted by magnetic attraction. It can also be fastened by screws to the outer housing of the piston cylinder.

[0055] In this embodiment, the first sensor device detects the acoustic emission signal generated by the target component during the force process, and the second sensor device measures the time-to-digital conversion signal generated by the target component during operation. Therefore, the internal state of the target component when it is under force can be timely understood by combining the acoustic emission signal and the time-to-digital conversion signal, and anomalies can be detected before a fault occurs.

[0056] In an exemplary embodiment, a signal conversion operation is performed on both the first signal and the second signal to obtain a target signal, including: mapping the first signal and the second signal to a signal coordinate space, and fusing the first signal and the second signal in the signal coordinate space to obtain a fused signal, wherein the signal coordinate space includes a first coordinate axis for representing the first signal and a second coordinate axis for representing the second signal; performing signal enhancement on the fused signal by a signal enhancement device in the signal coordinate space to obtain an enhanced signal; performing the filtering process on the enhanced signal by a signal filter in the signal coordinate space to remove the noise signal in the enhanced signal to obtain a filtered signal; performing the waveform transformation process on the filtered signal to obtain the target signal.

[0057] Optionally, this embodiment requires converting the signal into a numerical representation, for example, converting the acoustic emission signal into a spectrum diagram. Figure 5 As shown, the sound signal AE and the position angle signal TDC enter the inter-process communication (IPC) at the same time, and the signals correspond to ensure the position corresponding to the crack, where the horizontal axis is the numerical representation of the TDC signal conversion, and the vertical axis is the numerical representation of the AE signal conversion. There are many fusion methods, including weighted averaging, principal component analysis (PCA), Kalman filtering, etc. According to the fusion algorithm, a new fusion signal is generated. This signal should integrate the information of the two original signals and may be superior to the individual signals in some aspects.

[0058] Optionally, in the above signal coordinate space, the above fused signal is enhanced by a signal enhancement device to obtain an enhanced signal; and then the enhanced signal is filtered by a signal filter to remove the noise signal in the enhanced signal to obtain a filtered signal. For example, the original signal can be processed by adaptive filtering, wavelet transform, spectrum enhancement, etc. to enhance its features, for example, Figure 6 As shown, by amplifying certain frequency components or removing noise. After signal enhancement, there may still be noise in the signal, or the enhancement process may introduce new noise. The role of the signal filter is to remove these unwanted signal components and retain the useful signal. The filter can be low-pass, high-pass, band-pass or band-stop, etc. The appropriate filter type is selected according to the characteristics and requirements of the signal. Through filter processing, the noise in the signal can be reduced and the clarity and accuracy of the signal can be improved.

[0059] It should be noted that in actual operation, signal enhancement and filtering may need to be adjusted according to the characteristics of the signal and the application scenario. For example, if the signal contains multiple frequency components, a bandpass filter may be needed to remove the unnecessary frequency range. At the same time, the enhancement algorithm also needs to be selected according to the characteristics of the signal to ensure that the noise component is not over-enhanced.

[0060] In this embodiment, by fusing, enhancing and filtering the first signal and the second signal in the signal coordinate space, the obtained target signal can more accurately reflect the cracks of the target component.

[0061] In an exemplary embodiment, the waveform transformation processing is performed on the filtered signal to obtain the target signal, including: performing N-level decomposition on the filtered signal to determine the high-frequency signal and the low-frequency signal of the filtered signal in each level of decomposition, wherein N is a natural number greater than or equal to 1; performing a convolution operation on the high-frequency signal of the filtered signal in each level of decomposition to obtain N convolved high-frequency signals, wherein the length of the convolved high-frequency signal is greater than the length of the high-frequency signal; and reconstructing the N convolved high-frequency signals and the low-frequency signal in the filtered signal to obtain the target signal.

[0062] Optionally, in this embodiment, the waveform transformation processing of the filtered signal can be performed by wavelet transformation to obtain the target signal. In the wavelet transformation of this embodiment, a fully scalable modulation window can be used to solve the problem of signal cutting. Figure 7As shown, the window moves along the signal and the spectrum is calculated at each position. This process is then repeated multiple times, using a slightly shorter (or longer) window for each new period. The end result will be multiple time-frequency representations of the signal, all of which have different resolutions. Because of the collection of these representations, it can be called multi-resolution analysis. Wavelets are generated from a single basic wavelet (\psi(t)), the so-called mother wavelet, by scaling and translating.

[0063] The detailed steps of the wavelet transform process include:

[0064] S1, multi-level decomposition: Select a wavelet basis function (for example, Daubechies, Haar, etc.). Perform N-level wavelet decomposition on the filtered signal. At each level of decomposition, the signal is divided into two components: high frequency (details) and low frequency (approximation). The high frequency component contains the detailed information of the filtered signal, such as edges, textures, etc. The low frequency component contains the general information of the filtered signal. Multi-level decomposition of wavelets is the process of decomposing a signal into different frequency components. Through multi-scale analysis, the signal can be decomposed into a series of approximate components and detail components. At each level of decomposition, the signal (x[n]) is filtered by a low-pass filter (h[n]) and a high-pass filter (g[n]), and then downsampled (i.e., taking a value every other sample). The decomposition process is as follows: [a_{j+1}[n]=\sum_{k}h[k]a_j[2n-k]][d_{j+1}[n]=\sum_{k}g[k]a_j[2n-k]], where (a_j[n]) is the approximate component of the (j)th level, (d_j[n]) is the detail component of the (j)th level, (h[k]) is the low-pass filter coefficient, and (g[k]) is the high-pass filter coefficient.

[0065] S2, convolution operation: convolution operation is performed on the high-frequency signal of each level. Convolution is a mathematical operation used in signal processing, which can be convolved with a specific kernel to enhance or smooth the signal. The result of convolution is usually an increase in signal length, because the width of the convolution kernel affects the length of the output signal.

[0066] S3, Reconstruct the signal: reconstruct the high-frequency signal after convolution with the low-frequency signal of the original signal. The reconstruction process is to recombine the decomposed signal to recover an approximate or improved version of the original signal. Reconstruction usually involves the use of an inverse wavelet transform, which can be a synthesis or inverse filtering process. In addition, signal reconstruction is the inverse process of decomposition, by upsampling the approximate component and the detail component (that is, inserting a zero between each sample) and filtering, and then adding the results to restore the original signal. The reconstruction process is as follows:

[0067] [a_j[n]=\sum_{k}h'[k]a_{j+1}[2n-k]+\sum_{k}g'[k]d_{j+1}[2n-k]], where (h'[k]) is the reconstructed low-pass filter coefficient and (g'[k]) is the reconstructed high-pass filter coefficient.

[0068] The wavelet analysis in this embodiment can provide both time and frequency information of the signal. Wavelet analysis can analyze the signal at different scales, thereby capturing the details of different frequency components. It can adapt to the analysis of non-stationary signals. Through wavelet transform, the signal can be decomposed into different frequency components in this embodiment, and the signal can be purified by removing high-frequency noise components, thereby providing more sophisticated analysis and processing capabilities.

[0069] In an exemplary embodiment, extracting the crack signal characteristics of the target component from the target signal includes: performing multi-level signal decomposition on the target signal to obtain approximate coefficients and detail coefficients of each level of decomposition; and determining the crack signal characteristics using the approximate coefficients and detail coefficients of each level of decomposition.

[0070] Optionally, in this embodiment, the target signal may be decomposed into multiple levels through the wavelet transform described above. The specific process is similar to the above process and will not be described in detail here.

[0071] Optionally, the crack signal characteristics are determined using the approximate coefficients and detail coefficients of each level of decomposition, including: calculating the energy of the approximate coefficients and detail coefficients of each level of decomposition to obtain an energy characteristic, wherein the energy characteristic is used to represent the local energy distribution of the target signal; calculating the variance of the approximate coefficients and detail coefficients of each level of decomposition to obtain a variance characteristic, wherein the variance characteristic is used to represent the local variation degree of the target signal; calculating the entropy value of the approximate coefficients and detail coefficients of each level of decomposition to obtain an entropy characteristic, wherein the entropy characteristic is used to represent the complexity and uncertainty of the target signal; performing Fourier transform operation on the approximate coefficients and detail coefficients of each level of decomposition to calculate the power spectrum of the target signal, wherein the power spectrum is used to represent the frequency distribution of the target signal; determining at least one of the following as the crack signal characteristic: the energy characteristic, the variance characteristic, the entropy characteristic, and the power spectrum.

[0072] Optionally, this embodiment performs multi-level decomposition on the target signal through wavelet transform, wherein the signal is decomposed into approximate coefficients and detail coefficients of different scales.

[0073] The energy signature is obtained by calculating the sum of the squares of the approximation coefficient and detail coefficient at each level of decomposition. The energy signature reflects the local energy distribution of the signal and can be used to identify areas of concentrated energy in the signal, which may be associated with defects such as cracks.

[0074] The variance feature is obtained by calculating the variance of the approximate coefficients and detail coefficients of each level of decomposition. The variance reflects the degree of local variation of the signal at different scales and can be used to identify inhomogeneous areas in the signal.

[0075] The entropy feature is obtained by calculating the entropy of the approximate coefficient and the detail coefficient of each level of decomposition. Entropy is a concept in information theory that is used to measure the complexity and uncertainty of a signal. A high entropy value usually means that the signal is more complex and uncertain.

[0076] By performing Fourier transform on the approximate coefficients and detail coefficients of each level of decomposition, the power spectrum of the signal can be obtained. The power spectrum describes the energy distribution of the signal at different frequencies and can be used to identify the frequency components in the signal.

[0077] Based on the features calculated above, the features of the crack signal can be determined. These features may include energy features, variance features, entropy features, and power spectrum. By analyzing these features, defects such as cracks in the signal can be identified. Figure 8 As shown, compared with the actual waveform characteristics of the piston rod, it can meet the actual characteristic comparison of the wave.

[0078] In an exemplary embodiment, when the crack signal characteristics of the target component are extracted from the target signal, the crack anomaly of the target component within the target time period is determined based on the crack signal characteristics, including: comparing the crack signal characteristics with a priori graph, wherein the priori graph includes known damage; determining the damage degree and damage category of the target component based on the comparison result; and determining the crack anomaly of the target component within the target time period according to the damage degree and the damage category.

[0079] Optionally, the collected crack signal characteristics are compared with known damage patterns. These known damage patterns may come from historical data, experimental results, or theoretical studies, and they represent characteristic signals under different damage states. By comparing the results, the damage level and category of the target component can be evaluated. The damage level may refer to the severity of the damage, while the damage category may refer to the type of damage (e.g., crack, fracture, fatigue, etc.). Based on the damage level and category, the abnormal behavior of the crack within a specific time period can be predicted. This may involve the use of statistical models, machine learning algorithms, or expert systems to predict the growth rate of the crack, the possible fracture time, etc.

[0080] For example, Fig. 9 The figure shows a schematic diagram of the time-domain acoustic emission signal and the time-frequency spectrum in this embodiment, where (a) is the time-domain acoustic emission signal in the stage without crack extension; (b) is the time-frequency spectrum of the acoustic emission signal in the stage without crack extension; (c) is the time-domain acoustic emission signal in the stage of rapid crack extension; (d) is the time-frequency spectrum of the signal in the stage of rapid crack extension. Fig. 9 By comparing the signals in the four areas, it is possible to determine whether there is damage and the type of damage.

[0081] For example, Fig.10 Figure 2 is a schematic diagram of sub-band AE signals (70-150kHz) at different stages. (a) is the area without crack extension, (b) is the early stage of crack extension, and (c) is the stage close to fracture.

[0082] This embodiment compares the crack signal characteristics with the prior graph, determines the damage degree and damage category of the target component based on the comparison result, and determines the crack anomaly of the target component within the target time period according to the damage degree and damage category. This improves the accuracy of detection, reduces the workload of manual detection, increases the detection speed, and can timely discover potential safety hazards. It can effectively prevent and reduce structural failures caused by crack expansion, ensuring the safety and reliability of the structure.

[0083] In an exemplary embodiment, after extracting the crack signal characteristics of the target component from the target signal and determining the crack abnormality of the target component within the target time period based on the crack signal characteristics, the method further includes: triggering abnormal prompt information when the abnormality level of the crack abnormality of the target component within the target time period is greater than a preset level, wherein the abnormal prompt information is used to prompt the start of an operation to stop the operation of the target component; and generating repair information when it is determined that the target component stops running, wherein the repair information includes information for repairing the crack abnormality.

[0084] Optionally, during the crack detection and analysis process, if the target component is found to have a crack anomaly level exceeding a preset level within a target time period, the system should immediately trigger an abnormal prompt message. Such prompt messages are critical for operators because they can serve as a direct instruction to start and stop the operation of the target component. Once it is confirmed that the operation of the target component needs to be stopped, the next step is to generate repair information. These repair messages should contain detailed instructions on how to repair the detected crack anomalies. This may include the location, size, type of the crack, and the recommended repair method. For example, the repair information may recommend the use of specific welding techniques, the use of specific filler materials, or other non-destructive repair measures. In some cases, it may also be necessary to provide guidance on how to safely remove and replace damaged components. In addition, in order to improve the efficiency and accuracy of repair work, machine learning techniques can be used to analyze the characteristics of cracks and predict the best repair strategy. These technologies are able to learn crack patterns from large amounts of data and provide more accurate repair suggestions.

[0085] This embodiment can effectively manage and repair structural cracks through timely abnormal prompts and detailed repair information, thereby ensuring the safety and reliability of the system.

[0086] In this embodiment, a target compression device is also provided, such as Fig.11 As shown, the device includes: a target component 1101, a first sensor device 1102, a second sensor device 1103 and a processor 1104, wherein:

[0087] The first sensing device 1102 is used to detect the acoustic emission signal generated by the target component 1101 during the force-bearing process when the target component 1101 is running in the target compression device, so as to obtain a first signal;

[0088] The second sensing device 1103 is used to measure the time-to-digital conversion signal generated by the target component 1101 during operation when the first signal is generated, to obtain a second signal;

[0089] The processor 1104 is configured to implement the steps of the method described above during execution.

[0090] Optionally, the target compression device may be a compressor, a compression pump, etc., for example, a reciprocating compressor. The target component may be a gas compression component in a compressor, or a gas compression component in a compression pump, wherein the gas compression component may be a pump body, a piston rod, a piston, a cylinder, a gas valve, etc., for example, a piston rod in a reciprocating compressor.

[0091] Optionally, the target time period may be any time period during which the target compression device is running. For example, the target time period may be 1 day, 1 week, 1 month, 1 year, etc. during which the target compression device is running. Alternatively, the target time period may be any time during which the target compression device is running.

[0092] Optionally, the acoustic emission signal generated when the target component is running in the target compression device may be a stress wave signal generated when the target component is running in the target compression device, or may be an acoustic emission signal generated when the target component is running in the target compression device. The position signal of the target component when it is running in the target compression device may be the position information of the target component when it is running in the target compression device, or may be the position coordinates of the target component when it is running in the target compression device. For example, the first signal includes but is not limited to an acoustic emission signal (Acoustic Emission, referred to as AE), and the second signal includes but is not limited to a time-to-digital converter signal (Time-to-Digital Converter, referred to as TDC) signal. Among them, the AE signal refers to the elastic wave generated by the material when it is subjected to external stress or internal defect changes due to the inhomogeneity of the microstructure, such as cracks, deformation or fracture. The TDC signal is an electronic device that measures time intervals and converts time intervals into digital signals.

[0093] Optionally, the signal conversion operation may be a signal conversion operation performed by a signal converter, and the signal converter may be a signal conversion device, a signal conversion module, etc.

[0094] Optionally, the signal conversion operation may be a filtering process and a waveform transformation process performed on the first signal and the second signal. The waveform transformation process may be a Fourier transform process, a wavelet transform process, a Walsh transform process, a Laplace transform process, a Hilbert transform process, etc. performed on the first signal and the second signal. Among them, Fourier transform is to convert the signal from the time domain to the frequency domain, and is used to analyze the frequency components of the signal. Wavelet transform is to decompose the signal into wavelet coefficients of different scales and positions, and is used for non-stationary signal analysis. The filtering process may be a filtering process performed on the first signal and the second signal, and the filtering process may be a filtering process performed by a signal converter, and the filtering process may be a low-pass filtering process, a high-pass filtering process, a band-pass filtering process, a band-stop filtering process, etc. performed on the first signal and the second signal. Among them, low-pass filtering allows low-frequency signals to pass through and blocks high-frequency signals. It is used to remove noise or unnecessary high-frequency components. High-pass filtering allows high-frequency signals to pass through and blocks low-frequency signals. It is used to extract the high-frequency part of the signal. Band-pass filtering only allows signals within a specific frequency range to pass through, and is used to selectively pass signals of a specific frequency band. Band-stop filtering blocks signals within a specific frequency range and allows other frequencies to pass, and is used to suppress interference in a specific frequency band.

[0095] Optionally, the target signal may be a signal obtained by converting the first signal and the second signal, or may be a fusion signal of signals obtained by converting the first signal and the second signal.

[0096] Optionally, the crack signal feature may be a crack signal feature of a target component, or a characteristic value of the crack signal feature of the target component, wherein the characteristic value of the crack signal feature may be an energy feature, a variance feature, an entropy feature, a power spectrum, etc.

[0097] Through the above-mentioned device, the processor obtains the first signal and the second signal generated by the target component within the target time period, wherein the target component is a gas compression component in the target compression device, the first signal is used to represent the acoustic emission signal generated when the target component is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device; and performs signal conversion operations on both the first signal and the second signal to obtain the target signal; in the case of extracting the crack signal characteristics of the target component from the target signal, the crack anomaly of the target component within the target time period is determined based on the crack signal characteristics. Not only the acoustic emission signal is monitored, but also the position signal is combined, which helps to more accurately locate the source of the crack anomaly and improve the accuracy of fault diagnosis. Thereby solving the problem of low efficiency and low accuracy in determining the crack anomaly of the gas compression component in the prior art.

[0098] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0099] In this embodiment, a device for determining component crack anomalies is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0100] Fig.12 is a structural block diagram of a device for determining component crack abnormality according to an embodiment of the present application, such as Fig.12 As shown, the device comprises:

[0101] A first acquisition module 1202 is used to acquire a first signal and a second signal generated by a target component within a target time period, wherein the target component is a gas compression component in a target compression device, the first signal is used to represent an acoustic emission signal generated by the target component when it is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device;

[0102] A first conversion module 1204 is used to perform a signal conversion operation on the first signal and the second signal to obtain a target signal, wherein the signal conversion operation includes filtering and waveform transformation performed on the first signal and the second signal;

[0103] The first determination module 1206 is used to determine the crack abnormality of the target component within the target time period based on the crack signal characteristics after extracting the crack signal characteristics of the target component from the target signal.

[0104] Through the above device, by acquiring the first signal and the second signal generated by the target component within the target time period, wherein the target component is a gas compression component in the target compression device, the first signal is used to represent the acoustic emission signal generated when the target component is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device; and performing signal conversion operations on both the first signal and the second signal to obtain the target signal; in the case of extracting the crack signal characteristics of the target component from the target signal, the crack anomaly of the target component within the target time period is determined based on the crack signal characteristics. Not only the acoustic emission signal is monitored, but also the position signal is combined, which helps to more accurately locate the source of the crack anomaly and improve the accuracy of fault diagnosis. Thereby solving the problem of low efficiency and low accuracy in determining the crack anomaly of the gas compression component in the prior art.

[0105] In an exemplary embodiment, the first acquisition module 1202 includes: a first detection unit, which is used to detect the acoustic emission signal generated by the target component during the force process through a first sensor device when the target component is running in the target compression device, so as to obtain the first signal; a first measurement unit, which is used to measure the time-to-digital conversion signal generated by the target component during the operation process through a second sensor device when the first signal is generated, so as to obtain the second signal; wherein the first sensor device and the second sensor device are both deployed in the target compression device.

[0106] In an exemplary embodiment, the first conversion module 1204 includes: a first mapping unit, used to map the first signal and the second signal into a signal coordinate space, and fuse the first signal and the second signal in the signal coordinate space to obtain a fused signal, wherein the signal coordinate space includes a first coordinate axis for representing the first signal and a second coordinate axis for representing the second signal; a first fusion unit, used to perform signal enhancement on the fused signal through a signal enhancement device in the signal coordinate space to obtain an enhanced signal; a first filtering unit, used to perform the filtering processing on the enhanced signal through a signal filter in the signal coordinate space to remove the noise signal in the enhanced signal to obtain a filtered signal; a first transformation unit, used to perform the waveform transformation processing on the filtered signal to obtain the target signal.

[0107] In an exemplary embodiment, the first transformation unit includes: a first decomposition subunit, used to perform N-level decomposition on the filtered signal to determine the high-frequency signal and the low-frequency signal of the filtered signal in each level of decomposition, wherein N is a natural number greater than or equal to 1; a first calculation subunit, used to perform a convolution operation on the high-frequency signal of the filtered signal in each level of decomposition to obtain N convolved high-frequency signals, wherein the length of the convolved high-frequency signal is greater than the length of the high-frequency signal; a first reconstruction subunit, used to reconstruct the N convolved high-frequency signals and the low-frequency signal in the filtered signal to obtain the target signal.

[0108] In an exemplary embodiment, the first determination module 1206 includes: a first decomposition unit, used to perform multi-level signal decomposition on the target signal to obtain approximate coefficients and detail coefficients of each level of decomposition; a first determination unit, used to determine the crack signal characteristics using the approximate coefficients and detail coefficients of each level of decomposition.

[0109] In an exemplary embodiment, the first determination unit includes: a second calculation subunit, used to calculate the energy of the approximate coefficients and the detail coefficients of each level of decomposition to obtain an energy feature, wherein the energy feature is used to represent the local energy distribution of the target signal; a third calculation subunit, used to calculate the variance of the approximate coefficients and the detail coefficients of each level of decomposition to obtain a variance feature, wherein the variance feature is used to represent the local variation degree of the target signal; a fourth calculation subunit, used to calculate the entropy value of the approximate coefficients and the detail coefficients of each level of decomposition to obtain an entropy feature, wherein the entropy feature is used to represent the complexity and uncertainty of the target signal; a first transformation subunit, used to perform Fourier transform operation on the approximate coefficients and the detail coefficients of each level of decomposition to calculate the power spectrum of the target signal, wherein the power spectrum is used to represent the frequency distribution of the target signal; a first determination subunit, used to determine at least one of the following as the crack signal feature: the energy feature, the variance feature, the entropy feature, the power spectrum.

[0110] In an exemplary embodiment, the first determination module 1206 includes: a first comparison module, used to compare the crack signal characteristics with a priori graphics, wherein the prior graphics include known damage; a second determination module, used to determine the damage degree and damage category of the target component based on the comparison result; and a third determination module, used to determine the crack anomaly of the target component within the target time period according to the damage degree and the damage category.

[0111] In an exemplary embodiment, the above-mentioned device also includes: a first trigger module, which is used to extract the crack signal characteristics of the above-mentioned target component from the above-mentioned target signal, determine the crack abnormality of the above-mentioned target component within the above-mentioned target time period based on the above-mentioned crack signal characteristics, and trigger abnormal prompt information when the abnormal level of the crack abnormality of the above-mentioned target component within the above-mentioned target time period is greater than a preset level, wherein the above-mentioned abnormal prompt information is used to prompt the operation of starting to stop the operation of the above-mentioned target component; a first generation module, which is used to generate repair information when it is determined that the above-mentioned target component stops running, wherein the above-mentioned repair information includes information for repairing the above-mentioned crack abnormality.

[0112] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0113] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0114] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0115] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0116] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0117] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0118] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0119] The embodiments of the present application also provide a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of any one of the above method embodiments.

[0120] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0121] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0122] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a component crack anomaly, characterized in that: include: Acquire a first signal and a second signal generated by a target component within a target time period, wherein the target component is a gas compression component in a target compression device, the first signal is used to represent an acoustic emission signal generated by the target component when it is running in the target compression device, and the second signal is used to track the position of the target component when it is running in the target compression device; Performing a signal conversion operation on both the first signal and the second signal to obtain a target signal, wherein the signal conversion operation includes filtering processing and waveform transformation processing performed on the first signal and the second signal; In a case where the crack signal feature of the target component is extracted from the target signal, the crack abnormality of the target component within the target time period is determined based on the crack signal feature.

2. The method according to claim 1, characterized in that Acquiring a first signal and a second signal generated by a target component within a target time period, including: When the target component is running in the target compression device, the acoustic emission signal generated by the target component during the force application process is detected by a first sensing device to obtain the first signal; When the first signal is generated, a time-to-digital conversion signal generated by the target component during operation is measured by a second sensing device to obtain the second signal; Wherein, the first sensing device and the second sensing device are both deployed in the target compression device.

3. The method according to claim 1, characterized in that Performing a signal conversion operation on both the first signal and the second signal to obtain a target signal includes: Mapping the first signal and the second signal into a signal coordinate space, and fusing the first signal and the second signal in the signal coordinate space to obtain a fused signal, wherein the signal coordinate space includes a first coordinate axis for representing the first signal and a second coordinate axis for representing the second signal; Performing signal enhancement on the fused signal by a signal enhancement device in the signal coordinate space to obtain an enhanced signal; Performing the filtering process on the enhanced signal by using a signal filter in the signal coordinate space to remove the noise signal in the enhanced signal to obtain a filtered signal; The waveform transformation process is performed on the filtered signal to obtain the target signal.

4. The method according to claim 3, characterized in that Performing the waveform transformation process on the filtered signal to obtain the target signal includes: Performing N-level decomposition on the filtered signal to determine a high-frequency signal and a low-frequency signal in each level of decomposition of the filtered signal, wherein N is a natural number greater than or equal to 1; Performing a convolution operation on the high-frequency signal of the filtered signal in each level of decomposition to obtain N convolved high-frequency signals, wherein the length of the convolved high-frequency signal is greater than the length of the high-frequency signal; Reconstruct the N convolved high-frequency signals and the low-frequency signal in the filtered signal to obtain the target signal.

5. The method according to claim 1, characterized in that Extracting crack signal features of the target component from the target signal includes: Performing multi-level signal decomposition on the target signal to obtain approximate coefficients and detail coefficients of each level of decomposition; The crack signal characteristics are determined using the approximate coefficients and detail coefficients of each level of decomposition.

6. The method according to claim 5, characterized in that Determining the crack signal characteristics using the approximate coefficients and detail coefficients of each level of decomposition includes: Calculating the energy of the approximate coefficients of each level of decomposition and the detail coefficients to obtain energy features, wherein the energy features are used to represent the local energy distribution of the target signal; Calculating the variance of the approximate coefficients and the detail coefficients of each level of decomposition to obtain a variance feature, wherein the variance feature is used to represent the local variation degree of the target signal; Calculating the entropy values ​​of the approximate coefficients of each level of decomposition and the detail coefficients to obtain entropy features, wherein the entropy features are used to represent the complexity and uncertainty of the target signal; Performing Fourier transform operation on the approximate coefficients of each level of decomposition and the detail coefficients to calculate the power spectrum of the target signal, wherein the power spectrum is used to represent the frequency distribution of the target signal; At least one of the following is determined as the crack signal feature: the energy feature, the variance feature, the entropy feature, and the power spectrum.

7. The method according to claim 1, characterized in that In the case where the crack signal feature of the target component is extracted from the target signal, determining the crack abnormality of the target component within the target time period based on the crack signal feature includes: Comparing the crack signal feature with a priori graph, wherein the priori graph includes known damage; Determining the damage degree and damage category of the target component based on the comparison result; The crack anomaly of the target component within the target time period is determined according to the damage degree and the damage category.

8. The method according to claim 1, characterized in that In the case where the crack signal feature of the target component is extracted from the target signal, after determining the crack abnormality of the target component within the target time period based on the crack signal feature, the method further includes: When the abnormal level of the crack abnormality of the target component within the target time period is greater than a preset level, triggering abnormal prompt information, wherein the abnormal prompt information is used to prompt the operation of starting and stopping the operation of the target component; In the case where it is determined that the target component stops operating, repair information is generated, wherein the repair information includes information for repairing the crack anomaly.

9. A target compression device, characterized in that: include: A target component, a first sensor device, a second sensor device, and a processor, wherein: The first sensing device is used to detect an acoustic emission signal generated by the target component during a force-bearing process when the target component is running in the target compression device, so as to obtain a first signal; The second sensing device is used to measure the time-to-digital conversion signal generated by the target component during operation when the first signal is generated, so as to obtain a second signal; The processor is used to implement the steps of the method described in any one of claims 1 to 8 when executed.

10. A device for determining component crack anomalies, characterized in that: include: A first acquisition module, configured to acquire a first signal and a second signal generated by a target component within a target time period, wherein the target component is a gas compression component in a target compression device, the first signal is used to represent an acoustic emission signal generated by the target component when it is running in the target compression device, and the second signal is used to track a position of the target component when it is running in the target compression device; A first conversion module, configured to perform a signal conversion operation on both the first signal and the second signal to obtain a target signal, wherein the signal conversion operation includes filtering processing and waveform transformation processing performed on the first signal and the second signal; The first determination module is used to determine the crack abnormality of the target component within the target time period based on the crack signal feature after extracting the crack signal feature of the target component from the target signal.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 8 when executed by a processor.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 8 are implemented.

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