A method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment

Abstract

The present invention relates to the technical field of electronic digital data processing, and more specifically to a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment. The method comprises the following steps: determining an IMF segmented signal of an original acoustic wave signal emitted by the pressure-bearing special equipment; determining the signal authenticity and distortion of the IMF segmented signal; determining a reference frequency curve and an abnormal frequency curve using the distortion degree and a spectrum curve; determining the significance of equipment defects of the pressure-bearing special equipment using the reference frequency curve, the abnormal frequency curve, and the frequency peak; determining the importance of reconstructing the IMF component signals using the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defects; and determining, using the reconstruction importance, which IMF component signals need to be removed or retained to reconstruct the original acoustic wave signal. The present invention more accurately removes noise signals and retains defect signals of the pressure-bearing special equipment, thereby improving the accuracy of noise reduction and reconstruction of acoustic emission signals.

Description

Technical Field

[0001] The present invention relates to the technical field of electric digital data processing, and in particular to a method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment. Background Art

[0002] Currently, online inspection and monitoring technologies for pressure-bearing special equipment are extremely scarce. Common inspection methods, such as X-ray, ultrasound, and infrared imaging, are costly, require offline testing, and are susceptible to significant environmental impact. These methods are unable to monitor damage to pressure-bearing special equipment during operation. Therefore, timely monitoring of the safety status of pressure-bearing special equipment is crucial. Acoustic emission testing technology can monitor overall structural defects in pressure-bearing special equipment and has been successfully used for regular offline inspections of pressure-bearing special equipment to assess defect status.

[0003] However, due to the complexity of on-site monitoring noise, the collected signal components are complex, with equipment defect signals and environmental noise signals intermingled within the original acoustic signal. This makes it difficult to effectively extract and identify the defect signal, resulting in low system reliability for monitoring and early warning. Currently, most inspection methods for pressure-bearing special equipment rely on offline testing, which is inefficient and costly. Furthermore, the signal processing methods employed are limited to traditional methods such as wavelet denoising and Fourier transforms, which are suboptimal for signal noise reduction and fail to accurately reflect the equipment defect signal. Although variational mode decomposition (VMD) can achieve a certain degree of noise reduction, the complexity of the noise means that the reconstructed acoustic signal still cannot accurately reflect the actual structural defects of pressure-bearing special equipment. Both the noise signal and the equipment defect signal appear as anomalies in the original acoustic signal, but the noise signal is considered interference and needs to be excluded during reconstruction, while the equipment defect signal needs to be retained. Existing noise reduction methods struggle to distinguish between the noise signal and the equipment defect signal, resulting in low accuracy in defect detection of pressure-bearing special equipment using the reconstructed acoustic signal. Summary of the Invention

[0004] In order to solve the technical problem that it is difficult to distinguish between noise signals and equipment defect signals in acoustic wave signals, resulting in low accuracy of defect detection for pressure-bearing special equipment in reconstructed acoustic wave signals, the purpose of the present invention is to provide a method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment. The technical solutions adopted are as follows:

[0005] The present invention provides a method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment, the method comprising:

[0006] Determine the IMF component signal of the original sound wave signal emitted by the pressure-bearing special equipment and the IMF segmented signal obtained by dividing it;

[0007] Determine the signal authenticity of the IMF segmented signal, and use the signal authenticity to determine the distortion degree of the IMF segmented signal;

[0008] Using the distortion degree and spectrum curve of the IMF segmented signal, the corresponding reference frequency curve and abnormal frequency curve are determined;

[0009] Determine the significance of equipment defects of pressure-bearing special equipment using the reference frequency curve, abnormal frequency curve and their corresponding frequency peaks;

[0010] The importance of reconstructing the IMF component signals is determined by using the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defects.

[0011] The reconstruction importance is used to determine the IMF component signals that need to be removed or retained to reconstruct the original sound wave signal.

[0012] Furthermore, determining the signal authenticity of the IMF segmented signal includes:

[0013] Get the RMS and average signal levels of the IMF segmented signal;

[0014] The signal authenticity is calculated using the RMS and average signal levels.

[0015] Furthermore, the method of determining the distortion degree of the IMF segmented signal by using the signal authenticity degree includes:

[0016] Determine the difference in signal authenticity between each IMF segment signal;

[0017] The distortion degree of IMF segmented signal is determined by using the difference in the true degree of the signal.

[0018] Furthermore, the method of determining the corresponding reference frequency curve and abnormal frequency curve by using the distortion degree and spectrum curve of the IMF segmented signal includes:

[0019] Determine an IMF segment normal signal and an IMF segment abnormal signal corresponding to the IMF segment signal;

[0020] The reference frequency curve of the IMF component signal is calculated using the distortion degree and spectrum curve of the IMF segmented normal signal.

[0021] The abnormal frequency curve of the IMF component signal is calculated using the distortion degree and spectrum curve of the IMF segmented abnormal signal.

[0022] Furthermore, the determining of the IMF segment normal signal and the IMF segment abnormal signal corresponding to the IMF segment signal includes:

[0023] Input the IMF segmentation signal into the box plot to obtain the corresponding IMF segmentation normal signal and IMF segmentation abnormal signal;

[0024] The IMF segmented normal signal and the IMF segmented abnormal signal are respectively subjected to short-time Fourier transform to obtain a spectrum curve of the IMF segmented normal signal and a spectrum curve of the IMF segmented abnormal signal.

[0025] Furthermore, the use of the reference frequency curve and the abnormal frequency curve and their corresponding frequency peaks to determine the significance of equipment defects of the pressure-bearing special equipment includes:

[0026] Determining the degree of deformation between the reference frequency curve and the abnormal frequency curve with respect to the frequency distribution;

[0027] The significance of equipment defects of pressure-bearing special equipment is calculated using the frequency peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

[0028] Furthermore, determining the degree of deformation of the frequency distribution between the reference frequency curve and the abnormal frequency curve includes:

[0029] Determining a reference frequency bandwidth of a reference frequency curve and frequency segment intervals into which the reference frequency bandwidth is divided;

[0030] Determine the difference in cumulative amount between the reference frequency curve and the abnormal frequency curve in each identical frequency segment interval;

[0031] The difference between each cumulative amount is used to determine the degree of deformation of the reference frequency curve and the abnormal frequency curve in the frequency distribution.

[0032] Furthermore, the calculation of the significance of equipment defects of pressure-bearing special equipment using the frequency peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve includes:

[0033] Obtaining first global maximum values corresponding to the reference frequency curve and the abnormal frequency curve;

[0034] Determine the fitting curve corresponding to the first global maximum;

[0035] Determine the frequency peak corresponding to the second global maximum in the fitting curve;

[0036] The significance of equipment defects of pressure-bearing special equipment is calculated using the frequency peaks, number of peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

[0037] Furthermore, the determining the importance of reconstructing the IMF component signal by utilizing the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defect includes:

[0038] The reconstruction importance of the IMF component signal is calculated using the Pearson correlation coefficient between the original acoustic wave signal and the IMF segmented signal at the same segment position and the significance of the equipment defects.

[0039] Furthermore, the use of the reconstruction importance to determine the IMF component signal that needs to be removed or retained to reconstruct the original sound wave signal includes:

[0040] The IMF component signals whose reconstruction importance is less than a preset importance threshold are removed, and the IMF component signals whose reconstruction importance is greater than or equal to the preset importance threshold are retained to reconstruct the original sound wave signal.

[0041] The present invention has the following beneficial effects:

[0042] The present invention obtains IMF segmented signals by segmenting each IMF component signal after decomposing the original acoustic wave signal of pressure-bearing special equipment, analyzing the acoustic emission data characteristics of each segment, comparing the stability of the characteristic changes in the time series, and obtaining the signal distortion. Combining the different performances of equipment defects and noise in frequency, the performance of each IMF segmented signal for equipment defects and noise is obtained, and the significance of equipment defects is obtained. This is used as a weight to adjust the correlation threshold judgment between the IMF component signal and the original acoustic wave signal, completing the screening of the IMF components, more accurately achieving the removal of noise signals and the retention of pressure-bearing special equipment defect signals, and significantly improving the accuracy of acoustic emission signal noise reduction and reconstruction. At the same time, based on the embodiments of the present invention, online real-time detection of pressure-bearing special equipment can be achieved, improving detection efficiency and reducing detection costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 A flowchart of a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment provided by one embodiment of the present invention;

[0045] Figure 2 A detailed flow chart of step S2 in a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment provided by one embodiment of the present invention;

[0046] Figure 3 A detailed flow chart of step S3 in a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment provided by one embodiment of the present invention;

[0047] Figure 4 A detailed flow chart of step S4 in a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment provided by one embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of the hardware operating environment of the acoustic emission monitoring data acquisition and processing equipment for pressure-bearing special equipment involved in the embodiment of the present invention;

[0049] Figure 6 This is a schematic diagram of the framework structure of the acoustic emission monitoring data acquisition and processing system for pressure-bearing special equipment involved in the embodiment of the present invention;

[0050] Figure 7 Schematic diagram of a reference frequency curve and an abnormal frequency curve involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] The following describes in detail a method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment provided by the present invention in conjunction with the accompanying drawings.

[0054] Example 1:

[0055] For the data collection and processing method for acoustic emission monitoring of pressure-bearing special equipment provided by the present invention, please refer to Figure 1 , which shows a flowchart of the steps of a method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment provided by an embodiment of the present invention.

[0056] The method comprises:

[0057] Step S1, determining an IMF component signal of an original sound wave signal emitted by a pressure-bearing special equipment and an IMF segmented signal obtained by dividing the original sound wave signal;

[0058] In this embodiment, the original sound wave signal emitted by the pressure-bearing special equipment as an acoustic emission source can be obtained through the acoustic emission sensor, and the corresponding sampling frequency can be 50 Hz.

[0059] The existing particle swarm optimization algorithm can be used to obtain the number of modes and penalty factors of the variational mode decomposition (VMD) algorithm. The original acoustic wave signal is decomposed using the VMD parameters obtained above to obtain multiple IMF (Intrinsic Mode Function) component signals of the original acoustic wave signal. The center frequency of each IMF component signal can also be obtained simultaneously. The IMF component signals are sorted from small to large (low frequency to high frequency) according to their center frequency and recorded as IMF1, IMF2, etc. Next, the IMF component signals are segmented using an autocorrelation algorithm to obtain IMF segmented signals.

[0060] Step S2, determining the signal authenticity of the IMF segmented signal, and determining the distortion degree of the IMF segmented signal using the signal authenticity;

[0061] For details, please refer to Figure 2 , the step S2 comprises:

[0062] Step S21, obtaining the root mean square and average signal levels of the IMF segmented signal;

[0063] Step S22: Calculate the signal authenticity using the root mean square and average signal level.

[0064] If pressure-bearing special equipment is free of defects and operates normally and stably, the corresponding signal amplitude fluctuations will be small. For example, within the normal pressure range of a pressure vessel, the acoustic emission signal amplitude generated by lattice slip and dislocation within the material is relatively constant. Therefore, the amplitude changes of the IMF components after VMD decomposition should also be stable, and the amplitude of the components should remain stable over time. However, the presence of environmental noise can cause significant, irregular fluctuations in the monitored signal, randomly increasing or decreasing the amplitude and masking the true signal changes.

[0065] Based on the aforementioned laws of noise's impact on acoustic signals, the amplitude corresponding to the IMF component signal data at each acquisition moment is obtained. For the segmented IMF component signal, the higher the RMS (Root Mean Square) of the signal amplitude in that segment, the lower the ASL (Average Signal Level) of the corresponding segment. This more closely reflects the true, normal signal of the pressure-bearing special equipment corresponding to the IMF component, and the greater the signal authenticity. The authenticity of the IMF segmented signal can be specifically reflected in the following ways:

[0066]

[0067] Indicates the authenticity of the jth segment signal of the i-th IMF component; represents the jth segment under the i-th IMF component; Indicates the RMS value under this segment; Represents the ASL value under this segment; it should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations in the embodiment of the present invention, when encountering a situation where the denominator is 0, it is necessary to add a parameter adjustment factor greater than 0 to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0068] Different IMF components correspond to normal operating signals with different frequency characteristics. An IMF component with a larger amplitude may correspond to energy release at a specific frequency during normal equipment operation, while a smaller ASL indicates that this energy release is within a normal, short-term range, without sustained high-intensity signal interference, which is consistent with the characteristics of normal equipment structure operation. The smaller the ASL and the larger the RMS amplitude, the more likely the IMF is a normal acoustic emission signal for pressure-bearing special equipment; otherwise, it is an abnormal signal. In other words, the true degree of the signal can be reflected by the true degree of normality of the signal. The larger the value, the more true and normal the signal, and vice versa.

[0069] In addition, the method of determining the distortion degree of the IMF segmented signal by using the signal authenticity degree includes:

[0070] Determine the difference in signal authenticity between each IMF segment signal;

[0071] The distortion degree of IMF segmented signal is determined by using the difference in the true degree of the signal.

[0072] During normal operation, the physical processes within pressure-bearing special equipment, such as the flow of the medium and normal friction between components, generate relatively stable acoustic emission signals (acoustic wave signals). These signals have specific frequency and energy characteristics. The same IMF component, obtained through VMD decomposition, represents a signal component with similar frequency characteristics. Due to the stability of the normal operation process, the signal energy and amplitude changes corresponding to this IMF component are also relatively stable, thereby maintaining relative temporal stability in the ASL and RMS values. Therefore, under normal circumstances, the signal authenticity under the same IMF should remain stable. However, environmental noise is typically generated randomly, with its frequency and amplitude varying irregularly over time. These noise signals are superimposed on the acoustic emission signals generated by normal equipment operation, disrupting the signal's regularity. When a mixed signal containing noise is decomposed through VMD, the noise affects the signal characteristics of the same IMF component. The degree of signal distortion in each IMF segment can be further evaluated using the following methods:

[0073]

[0074] The signal authenticity of the jth segment is compared with the signal authenticity of the other segments under the same IMF component. If there is a significant difference between the authenticity of the same frequency band and the authenticity of the other frequency bands, the two different segments themselves have a higher signal authenticity. Therefore, the IMF segment signal reflects the local anomaly of the IMF component of the VMD decomposition of the pressure-bearing special equipment in the time series.

[0075] In the above formula, Indicates the distortion degree of the jth segment signal of the i-th IMF component; represents the number of segments of the i-th IMF component; represents the true degree of the jth segment signal representing the i-th IMF component, represents the first IMF component The authenticity of the segmented signal.

[0076] By comparing the authenticity of the same IMF segment signal with that of the other segments, if there is significant environmental noise in the signal segment, the IMF signal of the segment will be obviously distorted. It reflects the local distortion of the signal in this segment. The larger the value, the more obvious the RMS and ASL differences between the signals of the jth segment and the rest of the segments under the same IMF segmentation. As a result, the waveform of the signal in this segment is significantly different from that of the rest of the segments, indicating significant signal distortion. On the contrary, the smaller the value, the stronger the consistency of the signals in each time period under this IMF component, which means that the possibility of abnormality is smaller, indicating that the IMF signal in this segment has no significant distortion and is a normal VMD decomposition signal in the acoustic emission detection process.

[0077] Step S3, using the distortion degree and spectrum curve of the IMF segmented signal to determine the corresponding reference frequency curve and abnormal frequency curve;

[0078] For details, please refer to Figure 3 , the step S3 comprises:

[0079] Step S31, determining an IMF segment normal signal and an IMF segment abnormal signal corresponding to the IMF segment signal;

[0080] More specifically, step S31 includes:

[0081] Input the IMF segmentation signal into the box plot to obtain the corresponding IMF segmentation normal signal and IMF segmentation abnormal signal;

[0082] The IMF segmented normal signal and the IMF segmented abnormal signal are respectively subjected to short-time Fourier transform to obtain a spectrum curve of the IMF segmented normal signal and a spectrum curve of the IMF segmented abnormal signal.

[0083] If pressure-bearing special equipment has defects, such as crack propagation or corrosion, it will generate periodic acoustic emission signals. As the defects develop, energy release gradually increases, leading to a higher ASL. For example, during crack propagation in pressure-bearing special equipment, new fracture surfaces are constantly generated, continuously releasing energy, causing the acoustic emission signal to maintain high intensity for a longer period of time, thereby increasing the signal's ASL value. However, environmental noise during acoustic emission testing also causes the signal's ASL value to be excessively high. Since the importance of the signal cannot be determined, the IMF components used for signal reconstruction are inaccurately selected, interfering with the acoustic emission test signal's ability to detect defects in pressure-bearing special equipment.

[0084] To address this issue, in this embodiment, one of the IMF component signals is selected and a boxplot anomaly detection algorithm is employed. The segmented signal and distortion level of each segment of this component signal are used as input for the boxplot, and the abnormal segments within this IMF component signal are output. This results in a time series of normal and abnormal IMF segmented signals. These abnormal IMF segmented signals represent signal segments with noise interference or signal segments indicating defects in pressure-bearing special equipment.

[0085] The IMF segmented normal signal and the IMF segmented abnormal signal are transformed by STFT (Short-Time Fourier Transform) respectively to obtain the spectral characteristics and spectral curves corresponding to the IMF segmented normal signal and the IMF segmented abnormal signal respectively.

[0086] Step S32, using the distortion degree and spectrum curve of the IMF segmented normal signal, calculate and obtain a reference frequency curve of the IMF component signal;

[0087] Step S33 : using the distortion degree and the spectrum curve of the IMF segmented abnormal signal, calculate and obtain the abnormal frequency curve of the IMF component signal.

[0088] Because different segments of the same IMF component signal reside within the same frequency bandwidth, if the equipment is operating normally, it will vibrate at its natural frequency or a specific frequency associated with its operating state, thereby generating an acoustic emission signal. Therefore, the frequencies of the acoustic emission signals generated over different time periods are consistent. For example, when a pressure vessel is under stable pressure, the vibration frequency of its shell is relatively fixed. These natural vibration frequencies give the acoustic emission signal a frequency concentration, and after VMD decomposition, the frequencies of the IMF components also exhibit this concentration.

[0089] In the above situation, if there is environmental noise, it will be superimposed on the acoustic emission signal of normal equipment operation. The IMF component originally concentrated near a specific frequency will be interfered with by the noise, making the frequency distribution wider and the energy distribution divergent. If it manifests as a crack defect in pressure-bearing special equipment, the equipment crack defect will generate acoustic emission signals of specific frequencies during the expansion process. These signals are related to factors such as the size of the crack, the expansion rate, and the material properties. In the spectrum of the IMF component, obvious peaks will appear at specific frequencies related to the crack. For example, when the crack expands rapidly, significant peaks may appear in the frequency band of 800kHz-1MHz. These peaks reflect the rapid release of energy during the crack expansion process.

[0090] In this regard, taking the reference frequency curve as an example, the following methods can be used to obtain the reference frequency curve:

[0091]

[0092] F represents the reference frequency curve of the IMF component signal; The spectrum curve of the jth segment signal (here refers to any IMF segment normal signal) of the IMF component signal; Indicates the reciprocal of the distortion degree of the j-th segment signal. The smaller the distortion degree, the greater the reference value of the spectrum curve, reflecting the corresponding spectrum curve under normal conditions of the device. is the weight of the jth signal segment; the frequency spectrum curves of all IMF segmented normal signals of the IMF component are summed to obtain the reference frequency curve F of the IMF component, which reflects the frequency characteristics of the IMF component of the pressure-bearing special equipment in the normal state during the acoustic emission detection process. A curve can be represented by a sequence of data points.

[0093] Similarly, the abnormal frequency curve of the IMF component signal can also be obtained , I will not go into details here.

[0094] Step S4, using the reference frequency curve and the abnormal frequency curve and their corresponding frequency peaks, determining the significance of equipment defects of the pressure-bearing special equipment;

[0095] For details, please refer to Figure 4 , the step S4 comprises:

[0096] Step S41, determining the degree of deformation of the frequency distribution between the reference frequency curve and the abnormal frequency curve;

[0097] More specifically, the step S41 includes:

[0098] Determining a reference frequency bandwidth of a reference frequency curve and frequency segment intervals into which the reference frequency bandwidth is divided;

[0099] Determine the difference in cumulative amount between the reference frequency curve and the abnormal frequency curve in each identical frequency segment interval;

[0100] The difference between each cumulative amount is used to determine the degree of deformation of the reference frequency curve and the abnormal frequency curve in the frequency distribution.

[0101] Please refer to Figure 7 , obtain the reference frequency bandwidth of the reference frequency curve. The reference frequency bandwidth is the minimum value of the frequency in the frequency curve and maximum value The interval formed , the reference frequency bandwidth is evenly divided to obtain each frequency segment interval.

[0102] Select any frequency segmentation interval to obtain the degree of deformation of the reference frequency distribution under this frequency segmentation interval and the abnormal frequency distribution under this segmentation interval:

[0103]

[0104] Indicates the degree of deformation, reflecting the difference between the IMF segmented abnormal signal and the IMF segmented normal signal in the frequency segmentation interval, reflecting the degree of deformation of the frequency distribution, and also reflecting the interference of noise on the IMF component signal; F represents the reference frequency curve of the IMF component signal, Indicates abnormal frequency curve; represents the integral over frequency, ∈ ,and In the range Any equally divided frequency segment interval.

[0105] If all deformation degrees are similar within the reference frequency bandwidth of the IMF component signal, the possibility of environmental noise is greater; on the contrary, if there are significant differences between the deformation degrees, it means that there is a significant change in the frequency distribution, and thus the possibility of a real defect in the pressure-bearing special equipment is greater.

[0106] The standard deviation of the deformation can also be obtained:

[0107]

[0108] Indicates the standard deviation of the deformation degree of different frequency segments within the reference frequency bandwidth.

[0109] In step S42, the significance of equipment defects of the pressure-bearing special equipment is calculated using the frequency peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

[0110] More specifically, step S42 includes:

[0111] Obtaining first global maximum values corresponding to the reference frequency curve and the abnormal frequency curve;

[0112] Determine the fitting curve corresponding to the first global maximum;

[0113] Determine the frequency peak corresponding to the second global maximum in the fitting curve;

[0114] The significance of equipment defects of pressure-bearing special equipment is calculated using the frequency peaks, number of peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

[0115] Taking the reference frequency curve as an example, obtain the overall maximum value in the reference frequency curve F (for the sake of distinction, here called the first overall maximum value), apply polynomial fitting to the first overall maximum value, obtain a fitting curve, obtain the second overall maximum value of the fitting curve, and determine the frequency corresponding to the second maximum value. The fitting curve reflects the distribution of frequency, and the obtained second maximum value reflects the peak value of the frequency, avoiding the interference of local peaks. Peak value is obtained by It is a two-dimensional coordinate. It should be noted that there may be multiple peaks, and the number of peaks can be expressed as .

[0116] Similarly, the frequency peak corresponding to the abnormal frequency curve is obtained and peak number .

[0117] Compare the consistency of the reference frequency curve and the abnormal frequency curve. If the two frequency curves are similar in different frequency segment intervals, the frequency distribution of the corresponding IMF components remains basically consistent, and there is no obvious difference in the shape of the frequency distribution, then it means that the frequency segment interval is more likely to be a device defect; otherwise, it means that the segment is more likely to be affected by noise.

[0118] Specifically, the significance of equipment defects of pressure-bearing special equipment can be determined by the following methods:

[0119]

[0120] Indicates the initial significance of equipment defects and reflects the possibility of equipment defects; Represents the i-th frequency peak of the reference frequency curve; represents the jth frequency peak of the abnormal frequency curve, The norm of the difference between the two; Indicates taking the minimum norm; Indicates the standard deviation of the deformation degree of different frequency segment intervals under the reference frequency bandwidth; and Respectively represent the number of peaks corresponding to the reference frequency curve and the abnormal frequency curve; is the positive correlation normalization function.

[0121] If there is an obvious difference in peak distribution between the abnormal frequency curve and the reference frequency curve in a certain frequency segment, and the number of peaks in its frequency distribution increases significantly, then the abnormal frequency curve reflects the real defect signal of the pressure-bearing special equipment in this frequency segment.

[0122] In order to more comprehensively and accurately measure the significance of equipment defects reflected by the overall abnormal frequency curve, the following methods can be used:

[0123]

[0124] The significance of the final equipment defects of pressure-bearing special equipment; Indicates the initial device defect significance; It represents the mean of all deformation degrees and reflects the interference of environmental noise on the abnormal frequency curve. The greater the interference, the lower the accuracy of the defect signal judgment of pressure-bearing equipment.

[0125] Set the equipment defect significance under normal operation of the equipment Correspondingly, a value greater than 1 can be judged as an equipment defect anomaly, and a value less than 1 can be judged as a noise anomaly.

[0126] Step S5, determining the importance of reconstructing the IMF component signal by using the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defect;

[0127] Specifically, step S5 includes:

[0128] The reconstruction importance of the IMF component signal is calculated using the Pearson correlation coefficient between the original acoustic wave signal and the IMF segmented signal at the same segment position and the significance of the equipment defects.

[0129] Based on the above process, the characteristic performance of IMF segmented abnormal signal for equipment defects is determined. The similarity between IMF segmented abnormal signal and original acoustic wave signal at the same segment position is compared. If there is a strong noise performance in the same segment position ( <1), then the environmental noise signal needs to be removed. The noise signal obtained in the IMF reconstruction process should exclude the interference of the corresponding IMF component signal and reduce the similarity with the original sound wave signal. If there is a strong equipment defect in the same segment position ( >1), although the similarity between the above signals is weak, it still shows the real structural defects of the pressure-bearing equipment. Such signals should be retained, so the similarity between the corresponding IMF component signal and the original signal should be amplified;

[0130] Specifically, the following methods can be used to determine which IMF component signals need to be retained and which need to be removed:

[0131]

[0132] Indicates the reconstruction importance of the i-th IMF component signal; represents the Pearson correlation coefficient between the j-th segment of the original sound wave signal and the j-th segment of the i-th IMF component signal, where the segment division of the original sound wave signal and the signal division of the i-th IMF component are at the same corresponding position; Significance of equipment defects for pressure-bearing special equipment.

[0133] Step S6: using the reconstruction importance, determining the IMF component signal that needs to be removed or retained to reconstruct the original sound wave signal.

[0134] Specifically, step S6 includes:

[0135] The IMF component signals whose reconstruction importance is less than a preset importance threshold are removed, and the IMF component signals whose reconstruction importance is greater than or equal to the preset importance threshold are retained to reconstruct the original sound wave signal.

[0136] The reconstruction importance of each IMF component signal is obtained. A threshold of 0.8 (adjustable) can be set. IMF component signals with an importance greater than or equal to 0.8 participate in the reconstruction of the acoustic emission detection data (original acoustic wave signal), while IMF component signals with an importance less than 0.8 do not participate in the reconstruction of the acoustic emission detection data, thus completing the selection of the intrinsic mode function. Finally, the retained IMF component signals are reconstructed to obtain the denoised acoustic wave reconstructed signal.

[0137] In addition, supplementarily, the steps for detecting specific equipment defects reflected by the acoustic wave signals of pressure-bearing special equipment can be as follows:

[0138] 1. Feature extraction

[0139] Time domain features: Calculate the time domain statistical features of the reconstructed signal, such as mean, variance, root mean square, peak value, kurtosis, etc. These features can reflect the strength and fluctuation of the signal.

[0140] Frequency domain features: Perform frequency domain analysis such as Fourier transform on the reconstructed signal to extract frequency domain features such as center frequency, frequency bandwidth, and spectrum peak to understand the frequency distribution characteristics of the signal.

[0141] Time-frequency domain features: Time-frequency analysis methods such as wavelet transform and short-time Fourier transform are used to obtain time-frequency domain features, such as time-frequency energy distribution and time-frequency peaks, to more comprehensively describe the changes of signals at different times and frequencies.

[0142] 2. Construct feature vector

[0143] The extracted time domain, frequency domain, and time-frequency domain features are combined to form a multi-dimensional feature vector to comprehensively characterize the characteristics of the reconstructed signal.

[0144] 3. Defect identification and classification

[0145] Establish a fault model or database: Collect acoustic emission signal data of pressure-bearing special equipment with known defect types and degrees, process and extract features according to the above steps, and establish a corresponding defect feature model or database, which includes feature vectors and corresponding defect information under different defect states.

[0146] Select a classification algorithm: Use an appropriate classification algorithm, such as support vector machine (SVM), artificial neural network (ANN), decision tree, etc., to compare and analyze the extracted feature vector of the signal to be detected with the fault model or database.

[0147] Model training and validation: Use known defect data to train and validate the classification algorithm, and adjust the model parameters so that it can accurately identify different defect types and degrees.

[0148] Defect detection and diagnosis: The reconstructed acoustic emission signal of actual pressure-bearing special equipment that has been processed and feature extracted is input into the trained classification model. The model outputs the corresponding diagnostic results such as defect type, location, severity, etc.

[0149] Result Evaluation and Verification: Defect detection results are evaluated and verified through actual testing, dissection verification, and comparison with other nondestructive testing methods to ensure their accuracy and reliability. If the results are inaccurate or unreliable, the signal processing process, feature extraction methods, classification models, etc. need to be reviewed and optimized and improved.

[0150] The present invention obtains IMF segmented signals by segmenting each IMF component signal after decomposing the original acoustic wave signal of pressure-bearing special equipment, analyzing the acoustic emission data characteristics of each segment, comparing the stability of the characteristic changes in the time series, and obtaining the signal distortion. Combining the different performances of equipment defects and noise in frequency, the performance of each IMF segmented signal for equipment defects and noise is obtained, and the significance of equipment defects is obtained. This is used as a weight to adjust the correlation threshold judgment between the IMF component signal and the original acoustic wave signal, completing the screening of the IMF components, more accurately achieving the removal of noise signals and the retention of pressure-bearing special equipment defect signals, and significantly improving the accuracy of acoustic emission signal noise reduction and reconstruction. At the same time, based on the embodiments of the present invention, online real-time detection of pressure-bearing special equipment can be achieved, improving detection efficiency and reducing detection costs.

[0151] Example 2:

[0152] The embodiment of the present invention also provides a data acquisition and processing device for acoustic emission monitoring of pressure-bearing special equipment. The device can be a data computing and processing device such as a computer, a server, a programmable logic controller, or a combination of multiple devices.

[0153] like Figure 5 As shown, Figure 5 It is a structural diagram of the hardware operating environment of the acoustic emission monitoring data acquisition and processing equipment for pressure-bearing special equipment involved in the embodiment of the present invention.

[0154] like Figure 5As shown, the pressure-bearing special equipment acoustic emission monitoring data acquisition and processing device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a control panel. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as a disk drive. The memory 1005 may also be a storage device independent of the processor 1001. The memory 1005, which serves as a computer storage medium, may include a program for collecting and processing the pressure-bearing special equipment acoustic emission monitoring data.

[0155] Those skilled in the art will understand that Figure 5 The hardware structure shown in the figure does not constitute a limitation of the device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0156] Continue to refer to Figure 5 , Figure 5 The memory 1005 as a computer-readable storage medium may include an operating system, a user interface module, a network communication module, and a pressure-bearing special equipment acoustic emission monitoring data acquisition and processing program.

[0157] exist Figure 5 In the embodiment, the network communication module is mainly used to connect to the server and can communicate data with the server; and the processor 1001 can call the pressure-bearing special equipment acoustic emission monitoring data acquisition and processing program stored in the memory 1005, and execute the steps in the above embodiments.

[0158] The hardware structure of the above-mentioned pressure-bearing special equipment acoustic emission monitoring data acquisition and processing equipment is used to implement various embodiments of the pressure-bearing special equipment acoustic emission monitoring data acquisition and processing method of the present invention.

[0159] In addition, the present invention also provides a pressure-bearing special equipment acoustic emission monitoring data acquisition and processing system, please refer to Figure 6 The pressure-bearing special equipment acoustic emission monitoring data acquisition and processing system includes:

[0160] The signal decomposition module A10 is used to determine the IMF component signal of the original sound wave signal emitted by the pressure-bearing special equipment and the IMF segmented signal obtained by dividing the original sound wave signal;

[0161] Signal processing module A20 is used to determine the signal authenticity of the IMF segmented signal and determine the degree of distortion of the IMF segmented signal using the signal authenticity; determine the corresponding reference frequency curve and abnormal frequency curve using the distortion degree and spectrum curve of the IMF segmented signal; determine the significance of equipment defects of pressure-bearing special equipment using the reference frequency curve and abnormal frequency curve and their corresponding frequency peaks; and determine the importance of reconstruction of the IMF component signal using the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defect;

[0162] The sound wave reconstruction module A30 is used to determine the IMF component signal that needs to be removed or retained to reconstruct the original sound wave signal by using the reconstruction importance.

[0163] Furthermore, the signal processing module A20 is further configured to:

[0164] Get the RMS and average signal levels of the IMF segmented signal;

[0165] The signal authenticity is calculated using the RMS and average signal levels.

[0166] Furthermore, the signal processing module A20 is further configured to:

[0167] Determine the difference in signal authenticity between each IMF segment signal;

[0168] The distortion degree of IMF segmented signal is determined by using the difference in the true degree of the signal.

[0169] Furthermore, the signal processing module A20 is further configured to:

[0170] Determine an IMF segment normal signal and an IMF segment abnormal signal corresponding to the IMF segment signal;

[0171] The reference frequency curve of the IMF component signal is calculated using the distortion degree and spectrum curve of the IMF segmented normal signal.

[0172] The abnormal frequency curve of the IMF component signal is calculated using the distortion degree and spectrum curve of the IMF segmented abnormal signal.

[0173] Furthermore, the signal processing module A20 is further configured to:

[0174] Input the IMF segmentation signal into the box plot to obtain the corresponding IMF segmentation normal signal and IMF segmentation abnormal signal;

[0175] The IMF segmented normal signal and the IMF segmented abnormal signal are respectively subjected to short-time Fourier transform to obtain a spectrum curve of the IMF segmented normal signal and a spectrum curve of the IMF segmented abnormal signal.

[0176] Furthermore, the signal processing module A20 is further configured to:

[0177] Determining the degree of deformation between the reference frequency curve and the abnormal frequency curve with respect to the frequency distribution;

[0178] The significance of equipment defects of pressure-bearing special equipment is calculated using the frequency peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

[0179] Furthermore, the signal processing module A20 is further configured to:

[0180] Determining a reference frequency bandwidth of a reference frequency curve and frequency segment intervals into which the reference frequency bandwidth is divided;

[0181] Determine the difference in cumulative amount between the reference frequency curve and the abnormal frequency curve in each identical frequency segment interval;

[0182] The difference between each cumulative amount is used to determine the degree of deformation of the reference frequency curve and the abnormal frequency curve in the frequency distribution.

[0183] Furthermore, the signal processing module A20 is further configured to:

[0184] Obtaining first global maximum values corresponding to the reference frequency curve and the abnormal frequency curve;

[0185] Determine the fitting curve corresponding to the first global maximum;

[0186] Determine the frequency peak corresponding to the second global maximum in the fitting curve;

[0187] The significance of equipment defects of pressure-bearing special equipment is calculated using the frequency peaks, number of peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

[0188] Furthermore, the signal processing module A20 is further configured to:

[0189] The reconstruction importance of the IMF component signal is calculated using the Pearson correlation coefficient between the original acoustic wave signal and the IMF segmented signal at the same segment position and the significance of the equipment defects.

[0190] Furthermore, the acoustic wave reconstruction module A30 is further configured to:

[0191] The IMF component signals whose reconstruction importance is less than a preset importance threshold are removed, and the IMF component signals whose reconstruction importance is greater than or equal to the preset importance threshold are retained to reconstruct the original sound wave signal.

[0192] The specific implementation of the pressure-bearing special equipment acoustic emission monitoring data collection and processing system of the present invention is basically the same as the various embodiments of the pressure-bearing special equipment acoustic emission monitoring data collection and processing method described above, and will not be repeated here.

[0193] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a program for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment. When executed by a processor, the program implements the steps of the aforementioned method for collecting and processing acoustic emission monitoring data for pressure-bearing special equipment.

[0194] Among them, the method implemented when the pressure-bearing special equipment acoustic emission monitoring data collection and processing program is executed can refer to the various embodiments of the pressure-bearing special equipment acoustic emission monitoring data collection and processing method of the present invention, and will not be repeated here.

[0195] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0196] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0197] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0198] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the scope of protection of the present invention.

Claims

1. A method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment, characterized in that: The method comprises: Determine the IMF component signal of the original sound wave signal emitted by the pressure-bearing special equipment and the IMF segmented signal obtained by dividing it; Obtaining the root mean square (RMS) and average signal levels of the IMF segmented signals, and using the RMS and average signal levels to calculate the signal authenticity; determining the signal authenticity differences between the various IMF segmented signals, and using the signal authenticity differences to determine the distortion degree of the IMF segmented signals; Determine the IMF segmented normal signal and the IMF segmented abnormal signal corresponding to the IMF segmented signal, calculate the reference frequency curve of the IMF component signal using the distortion degree and spectrum curve of the IMF segmented normal signal, and calculate the abnormal frequency curve of the IMF component signal using the distortion degree and spectrum curve of the IMF segmented abnormal signal; Determine the reference frequency bandwidth of the reference frequency curve and the frequency segment intervals into which the reference frequency bandwidth is divided, determine the difference in cumulative quantities between the reference frequency curve and the abnormal frequency curve in each of the same frequency segment intervals; use the differences in each cumulative quantity to determine the degree of deformation of the reference frequency curve and the abnormal frequency curve in the frequency distribution; use the frequency peak values and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve respectively to calculate the significance of equipment defects of the pressure-bearing special equipment; The importance of reconstructing the IMF component signals is determined by using the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defects. The reconstruction importance is used to determine the IMF component signals that need to be removed or retained to reconstruct the original sound wave signal.

2. The method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment according to claim 1 is characterized in that: The determining of the IMF segment normal signal and the IMF segment abnormal signal corresponding to the IMF segment signal includes: Input the IMF segmentation signal into the box plot to obtain the corresponding IMF segmentation normal signal and IMF segmentation abnormal signal; The IMF segmented normal signal and the IMF segmented abnormal signal are respectively subjected to short-time Fourier transform to obtain a spectrum curve of the IMF segmented normal signal and a spectrum curve of the IMF segmented abnormal signal.

3. The method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment according to claim 1 is characterized in that: The calculation of the significance of equipment defects of pressure-bearing special equipment using the frequency peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve includes: Obtaining first global maximum values corresponding to the reference frequency curve and the abnormal frequency curve; Determine the fitting curve corresponding to the first global maximum; Determine the frequency peak corresponding to the second global maximum in the fitting curve; The significance of equipment defects of pressure-bearing special equipment is calculated using the frequency peaks, number of peaks and deformation degrees corresponding to the reference frequency curve and the abnormal frequency curve.

4. The method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment according to claim 1 is characterized in that: The method of determining the importance of reconstructing the IMF component signal by utilizing the correlation between the original acoustic wave signal and the IMF segmented signal and the significance of the equipment defect comprises: The reconstruction importance of the IMF component signal is calculated using the Pearson correlation coefficient between the original acoustic wave signal and the IMF segmented signal at the same segment position and the significance of the equipment defects.

5. The method for collecting and processing acoustic emission monitoring data of pressure-bearing special equipment according to claim 1 is characterized in that: The method of utilizing the reconstruction importance to determine the IMF component signal that needs to be removed or retained to reconstruct the original sound wave signal includes: The IMF component signals whose reconstruction importance is less than a preset importance threshold are removed, and the IMF component signals whose reconstruction importance is greater than or equal to the preset importance threshold are retained to reconstruct the original sound wave signal.

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