Acoustic emission signal frequency domain migration-based conveyor belt hidden damage identification method

Abstract

The invention relates to the technical field of conveyer belt nondestructive testing, and discloses a conveyer belt hidden damage identification method based on acoustic emission signal frequency domain migration, which comprises the following steps: collecting acoustic emission signals in the operation of a conveyer belt in real time, carrying out time-frequency analysis to construct a time-frequency energy matrix, and migrating a frequency band division rule of a historical working condition to a current signal through a domain adaptation algorithm in transfer learning to form a self-adaptive feature sensitive frequency band, comparing a noise spectrum fingerprint database to remove frequency band components overlapped with noise, and inputting residual frequency bands into the lightweight convolutional network to output a recognition result. As the acoustic emission signal frequency band of the hidden damage of the conveyor belt dynamically shifts along with damage evolution and working condition change, the method can sense signal energy distribution in real time and dynamically adjust and focus on the frequency band which can best represent damage characteristics, so that the problems of incomplete characteristic extraction and misjudgment caused by frequency band solidification are avoided.

Description

Technical Field

[0001] The present invention relates to a method for identifying hidden damage of a conveyor belt based on frequency-domain migration of acoustic emission signals, belonging to the technical field of non-destructive testing of conveyor belts. Background Art

[0002] As a widely used material conveying equipment in modern industrial production, the safe and stable operation of the conveyor belt is crucial for ensuring production efficiency; however, due to the influence of long-term operation, material impact and environmental factors, hidden damages such as micro-cracks and fiber fractures often occur inside the conveyor belt. These early damages are difficult to be directly observed by the naked eye. If not discovered and processed in time, they are very likely to develop into serious faults, resulting in production interruption and safety accidents. Therefore, early and accurate identification of the hidden damage of the conveyor belt has important practical significance.

[0003] At present, using acoustic emission technology to detect the damage of the conveyor belt has become an important means. Acoustic emission refers to the elastic waves released when materials deform or fracture under stress. By analyzing the characteristics of acoustic emission signals, the damage state of materials can be judged; in the prior art, the acoustic emission signal identification method for hidden damage of the conveyor belt usually focuses on frequency-domain analysis. Its basic principle is to perform time-frequency transformation on the collected acoustic emission signals, such as using methods like wavelet packet decomposition, to extract the energy or amplitude characteristics within a specific frequency band, and judge whether there is damage based on these characteristics. However, the problems existing in these methods in practical applications are as follows: 1. Most of the existing acoustic emission damage identification methods rely on pre-set fixed frequency bands for analysis. However, the materials of the conveyor belt are not completely uniform, and the operating environment is complex and changeable. The effective frequency band of the acoustic emission signal generated by hidden damage will dynamically shift with the evolution of the damage and the interference of environmental noise. This leads to the situation that if only the fixed frequency band is analyzed, the characteristic information of the damage may not be comprehensively captured, and even misjudgment may occur. 2. The operating environment of the conveyor belt is usually relatively harsh, with various noise interferences such as mechanical vibrations and material impacts. The frequency bands of these noise signals often highly overlap with the frequency band of the hidden damage signal, making it difficult to effectively separate the two using traditional filtering methods; the common practice in the industry is to set thresholds or rely on manual experience to screen signals, but this method has obvious subjectivity and is prone to missing weak early damage signals, resulting in a relatively high missed detection rate. 3. The existing damage identification models are usually trained based on single working conditions or laboratory data. However, during the actual operation of the conveyor belt, the working conditions such as load, speed, and environmental temperature will change significantly; these changes will affect the characteristics of the acoustic emission signals, resulting in the model trained based on single working conditions being difficult to adapt to the changing working conditions on site, with insufficient migration ability of frequency-domain characteristics and poor generalization performance of the model.

[0004] Therefore, how to overcome the limitations of the fixed frequency domain features in the prior art, effectively separate the noise and damage signals, and improve the adaptability of the model under different working conditions has become the technical problem to be solved by the present invention. Summary of the Invention

[0005] The present invention provides a method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals, and its main purpose is to solve the problems of incomplete extraction of frequency domain features, difficult effective separation of noise interference, and poor cross-working condition adaptability.

[0006] To achieve the above object, a method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals provided by the present invention includes the following steps: Step 1, collect in real time the acoustic emission signals generated during the operation of the conveyor belt, and perform continuous wavelet time-frequency analysis to construct a time-frequency energy matrix reflecting the distribution of signal energy in the time domain and frequency domain; Step 2, by analyzing the energy density gradient in the time-frequency energy matrix, dynamically identify the frequency band regions where the signal energy changes significantly, and use these frequency band regions as candidate regions for potential hidden damage feature frequency bands to achieve a preliminary tracking of the frequency domain energy flow of the acoustic emission signals; Step 3, use the frequency band energy entropy and the energy correlation between adjacent frequency bands to perform self-organizing migration on the candidate regions for potential hidden damage feature frequency bands, specifically including dynamically merging adjacent frequency bands with similar energy entropy values, and performing secondary splitting according to the energy correlation between frequency bands to form sensitive frequency bands that can adaptively represent the hidden damage features under the current working conditions, so as to achieve dynamic focusing of frequency domain features; Step 4, construct and dynamically maintain a noise spectrum fingerprint library, which extracts the spectrum features of background noise signals collected under different working conditions and updates them using a dynamic self-evolution mechanism. The dynamic self-evolution mechanism includes that during real-time operation, when it is detected that the similarity between the current environmental noise spectrum and all records in the fingerprint library is lower than a preset threshold, an incremental acquisition of the noise spectrum is automatically triggered, stable noise feature segments are selected through verification of the stability of the frequency band energy distribution, and spatio-temporal correlation constraints are combined with the operating state parameters of the conveyor belt to eliminate transient interference noise irrelevant to the current working condition, and finally a lightweight fingerprint fusion strategy is used for updating; Step 5, compare the similarity between the adaptive feature sensitive frequency band and the noise spectrum in the noise spectrum fingerprint library to decouple the noise signal and the damage signal. Through spectrum similarity comparison, the frequency domain components highly overlapping with the noise are removed from the dynamically divided frequency bands to obtain the remaining frequency bands containing only potential damage information; Step 6, perform normalization processing on the energy of the remaining frequency bands to eliminate the influence of signal amplitude differences under different working conditions; Step 7: Input the remaining frequency bands after energy normalization into a pre-trained lightweight convolutional neural network. The lightweight convolutional neural network adapts the frequency domain features learned under historical working conditions to the current working condition through the domain adaptation algorithm in transfer learning, and outputs the recognition result of whether there is a hidden damage on the conveyor belt.

[0007] Preferably, in the self-organizing migration step, the calculation of the frequency band energy entropy is used to evaluate the concentration degree of the energy distribution within the frequency band. Frequency bands with similar energy entropy values have similar damage characteristics in terms of physical meaning.

[0008] Preferably, the spatio-temporal correlation constraint includes performing correlation analysis on the timestamps of the newly added noise segments and the historical noise data, as well as the operating state parameters of the conveyor belt, to distinguish between persistent environmental noise and occasional interference.

[0009] Preferably, the training data of the lightweight convolutional neural network includes artificially simulated hidden damage signals under multiple working conditions, and realizes cross-environment generalization through transfer learning. The domain adaptation algorithm is used to align the feature distribution of the source domain (historical working condition) to the target domain (current working condition).

[0010] Preferably, the initial construction of the noise spectral fingerprint library adopts a spatio-temporal homology verification mechanism, including synchronously recording the operating state of the conveyor belt, environmental temperature and humidity, and equipment position coordinates, constructing a spatio-temporal correlation matrix, then dividing the collected noise spectra into several subsets according to the spatio-temporal correlation matrix, and only retaining the segments with the standard deviation of the spectral energy distribution within each subset less than a preset threshold, and through cross-modal cross-verification, using the low-frequency features of the vibration sensor to eliminate abnormal noise patterns.

[0011] Preferably, the energy density gradient detection is realized by calculating the difference between the energy values of adjacent frequency bands and judging whether the difference exceeds a preset gradient threshold to identify the energy mutation frequency bands.

[0012] Preferably, the frequency band energy entropy The calculation formula is: , where represents the proportion of the energy density of the th frequency point within the frequency band to the total energy density of the frequency band.

[0013] Preferably, the operating state parameters of the conveyor belt include but are not limited to the load size and running speed of the conveyor belt.

[0014] Preferably, the preset threshold is an empirical value range determined through the analysis of the historical operation data of different types of conveyor belts and the characteristics of hidden damage acoustic emission signals.

[0015] Preferably, the recognition result includes the probability value of whether there is a hidden damage on the conveyor belt. When the probability value is greater than or equal to the set damage probability threshold, it is determined that there is a hidden damage on the conveyor belt, and an alarm signal can be output.

[0016] Compared with the problems described in the background art, the beneficial effects of the present invention are as follows: 1. Introduce the dynamic tracking and self-organizing migration mechanism of the frequency-domain energy flow to overcome the limitations of relying on fixed frequency band analysis in the prior art; since the frequency band of the acoustic emission signal of the hidden damage of the conveyor belt will dynamically shift with the damage evolution and working conditions changes, this method can real-time sense the signal energy distribution, and dynamically adjust and focus on the frequency band that can best characterize the damage characteristics, thus avoiding the problems of incomplete feature extraction and misjudgment caused by fixed frequency bands, and significantly improving the accuracy and integrity of the recognition of early hidden damage of the conveyor belt.

[0017] 2. Aiming at the problem of serious noise interference and high overlap between the frequency band and the damage signal in the operating environment of the conveyor belt, the method of the present invention constructs a noise spectrum fingerprint library based on dynamic self-evolution and spatio-temporal correlation constraints; this mechanism can effectively decouple the noise components from the dynamically divided feature-sensitive frequency bands, avoiding the loss of useful signals that may be caused by traditional filtering methods, thus significantly enhancing the signal-to-noise ratio of the damage features in a complex industrial background, reducing the subjectivity and missed detection rate of manual experience screening, and realizing a more objective and reliable extraction of damage signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the method for identifying hidden damage of the conveyor belt based on the frequency-domain migration of acoustic emission signals of the present invention; Figure 2 It is a schematic diagram of the dynamic migration mechanism of the method for identifying hidden damage of the conveyor belt based on the frequency-domain migration of acoustic emission signals of the present invention; Figure 3 It is a flowchart of self-organizing migration to form a sensitive frequency band of the present invention.

[0019] The realization of the object, functional characteristics and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The embodiment of the present application provides a method for identifying hidden damage of the conveyor belt based on the frequency-domain migration of acoustic emission signals, including the following steps: Step 1, continuously collect the acoustic emission signals generated during the operation of the conveyor belt in real time, and perform continuous wavelet time-frequency analysis to construct a time-frequency energy matrix reflecting the distribution of signal energy in the time domain and the frequency domain; Step 2, by analyzing the energy density gradient in the time-frequency energy matrix, dynamically identify the frequency band regions where the signal energy changes significantly, and use these frequency band regions as candidate regions for potential hidden damage feature frequency bands, to achieve a preliminary tracking of the frequency-domain energy flow of the acoustic emission signal; Step 3, use the frequency band energy entropy and the energy correlation between adjacent frequency bands to perform self-organizing migration on the candidate regions for potential hidden damage feature frequency bands, specifically including dynamically merging adjacent frequency bands with similar energy entropy values, and performing secondary splitting according to the energy correlation between frequency bands, to form sensitive frequency bands that can adaptively characterize the hidden damage features under the current working conditions, and achieve dynamic focusing of frequency-domain features; Step 4, construct and dynamically maintain a noise spectrum fingerprint library, which extracts the spectrum features of background noise signals collected under different working conditions and updates them using a dynamic self-evolution mechanism. The dynamic self-evolution mechanism includes, during real-time operation, when it is detected that the similarity between the current environmental noise spectrum and all records in the fingerprint library is lower than a preset threshold, automatically trigger incremental acquisition of the noise spectrum, screen out stable noise feature segments through verification of the stability of the frequency band energy distribution, and perform spatio-temporal correlation constraints in combination with the conveyor belt operating state parameters to eliminate transient interference noise irrelevant to the current working conditions, and finally update using a lightweight fingerprint fusion strategy; Step 5, compare the similarity between the adaptive feature sensitive frequency band and the noise spectrum in the noise spectrum fingerprint library to decouple the noise signal and the damage signal. Through spectrum similarity comparison, eliminate the frequency domain components that highly overlap with the noise from the dynamically divided frequency bands, and obtain the remaining frequency bands that only contain potential damage information; Step 6, perform normalization processing on the energy of the remaining frequency bands to eliminate the influence of signal amplitude differences under different working conditions; Step 7, input the remaining frequency bands after energy normalization into a pre-trained lightweight convolutional neural network. The lightweight convolutional neural network adapts the frequency-domain features learned under historical working conditions to the current working conditions through the domain adaptation algorithm in transfer learning, and outputs the recognition result of whether there is hidden damage on the conveyor belt.

[0022] Preferably, in the self-organizing migration step, the calculation of the frequency band energy entropy is used to evaluate the concentration degree of the energy distribution within the frequency band, and frequency bands with similar energy entropy values have similar damage features in terms of physical meaning.

[0023] Preferably, the spatio-temporal correlation constraint includes performing correlation analysis on the time stamps of the new noise segments and the historical noise data and the conveyor belt operating state parameters to distinguish persistent environmental noise and occasional interference.

[0024] Preferably, the training data of the lightweight convolutional neural network includes artificially simulated hidden damage signals under multiple working conditions, and cross-environment generalization is achieved through transfer learning. A domain adaptation algorithm is used to align the feature distribution of the source domain (historical working conditions) to the target domain (current working conditions).

[0025] Preferably, the initial construction of the noise spectrum fingerprint library adopts a spatio-temporal homology verification mechanism, including synchronously recording the operating state of the conveyor belt, environmental temperature and humidity, and equipment position coordinates, constructing a spatio-temporal correlation matrix, then dividing the collected noise spectra into several subsets according to the spatio-temporal correlation matrix, and only retaining the segments with the standard deviation of the spectral energy distribution within each subset less than a preset threshold. Through cross-modal cross-verification, abnormal noise patterns are eliminated using the low-frequency characteristics of vibration sensors.

[0026] Preferably, the energy density gradient detection is achieved by calculating the difference between the energy values of adjacent frequency bands and determining whether the difference exceeds a preset gradient threshold to identify the frequency bands with sudden energy changes.

[0027] Preferably, the energy entropy of the frequency band is calculated by the formula: , where represents the proportion of the energy density of the th frequency point in the frequency band to the total energy density of the frequency band.

[0028] Preferably, the operating state parameters of the conveyor belt include, but are not limited to, the load size and running speed of the conveyor belt.

[0029] Preferably, the preset threshold is an empirical value range determined by analyzing the historical operation data of different types of conveyor belts and the characteristics of hidden damage acoustic emission signals.

[0030] Preferably, the recognition result includes the probability value of whether there is hidden damage on the conveyor belt. When the probability value is greater than or equal to the set damage probability threshold, it is determined that there is hidden damage on the conveyor belt, and an alarm signal can be output.

[0031] Example 1: In step 2, the time-frequency energy matrix constructed in step 1 is analyzed to dynamically identify the frequency band regions where the signal energy changes; for each moment in the time-frequency energy matrix, calculate the energy difference between adjacent frequency bands, that is, the energy density gradient. Set a gradient threshold , when the energy difference between adjacent frequency bands is greater than this threshold, it is considered that the energy in this frequency band region has changed significantly, and these frequency band regions with significant energy changes are marked as candidate regions for potential hidden damage feature frequency bands, and the gradient threshold The setting can be based on the statistical analysis of historical lossless and lossy conveyor belt acoustic emission signals, and select the empirical value range that can effectively distinguish the energy mutation; for example, the distribution of the energy density gradient under normal working conditions and typical damage conditions can be statistically analyzed, and the gradient value range with the highest discrimination degree is set as .

[0032] In step 3, self-organizing migration is performed on the candidate area of the potential hidden damage feature frequency band identified in step 2 to form a sensitive frequency band that can adaptively characterize the hidden damage features under the current working conditions; first, calculate the energy entropy of each frequency band , and its calculation formula is as follows: , where represents the proportion of the energy density of the th frequency point in the frequency band to the total energy density of the frequency band; the energy entropy is used to evaluate the concentration degree of the energy distribution within the frequency band. Adjacent frequency bands with similar energy entropy values have similar damage characteristics in physical meaning. Therefore, adjacent frequency bands with the difference in energy entropy values less than the preset entropy value threshold can be dynamically merged; the setting of the entropy value threshold can be determined according to the analysis of the energy distribution characteristics of the acoustic emission signal frequency bands in different damage states. Secondly, in order to more finely focus on the damage characteristics, the energy correlation between adjacent frequency bands also needs to be considered, and calculate the energy correlation coefficient between adjacent frequency bands; if the energy correlation coefficient between adjacent frequency bands is less than the preset correlation threshold , it indicates that the energy change trends of these two adjacent frequency bands are quite different and may contain different damage feature information, and secondary splitting is required. The setting of the correlation threshold can be determined based on the analysis of the correlation between the acoustic emission signal frequency bands of different damage types; by dynamically merging adjacent frequency bands with similar energy entropy values and performing secondary splitting according to the energy correlation between frequency bands, a sensitive frequency band that can adaptively characterize the hidden damage features under the current working conditions is finally formed.

[0033] In step 4, a noise spectrum fingerprint library is constructed and dynamically maintained. This fingerprint library collects the background noise signals under different working conditions, extracts their spectrum characteristics, and is updated using a dynamic self-evolution mechanism. During real-time operation, when it is detected that the similarity between the current environmental noise spectrum and all the records in the fingerprint library is lower than the preset similarity threshold , the incremental acquisition of the noise spectrum is automatically triggered, and the similarity threshold The settings can be determined based on the analysis of the spectral characteristics of different environmental noises; the newly collected noise segments are screened to obtain stable noise feature segments through the verification of the stability of the frequency band energy distribution, and the spatio-temporal correlation constraints are combined with the operating state parameters of the conveyor belt to eliminate transient interference noises irrelevant to the current working conditions. Finally, a lightweight fingerprint fusion strategy (for example, weighted averaging of similar noise spectra) is used to update the fingerprint database.

[0034] In step 5, the adaptive feature-sensitive frequency band formed in step 3 is compared with the noise spectra in the noise spectrum fingerprint database, and the similarity between the spectrum of the adaptive feature-sensitive frequency band and each noise spectrum in the fingerprint database can be calculated (for example, by calculating the cosine similarity); for the frequency domain components with a similarity higher than the preset noise similarity threshold they are considered to belong to the noise signal and are removed from the adaptive feature-sensitive frequency band, so as to obtain the remaining frequency band containing only potential damage information. The setting of the noise similarity threshold can be determined based on the analysis of the discrimination between the spectral characteristics of the damage signal and the noise signal.

[0035] Embodiment 2: This embodiment combines Figures 1 to 3 to illustrate the method for identifying hidden damage of the conveyor belt based on the frequency domain migration of acoustic emission signals.

[0036] As Figure 1 shown, first, the acoustic emission signals generated during the operation of the conveyor belt are collected in real time by the sensor. The collected acoustic emission signals are then sent to the time-frequency analysis module for processing to generate a time-frequency energy matrix. Thereafter, the migration engine receives this time-frequency energy matrix and sends a signal requesting noise features to the noise library; the noise library responds to this request and returns fingerprint data to the migration engine. The migration engine extracts pure frequency band features using these data and transmits these features to the recognition network; the recognition network analyzes the received pure frequency band features and outputs a damage probability, which is used by the migration engine to generate a warning instruction. Finally, the warning instruction is transmitted to the time-frequency analysis module to form a closed-loop monitoring and feedback mechanism.

[0037] As Figure 2 shown, the sensor first sends acoustic emission signals to the time-frequency analysis module, which generates a time-frequency energy matrix and transmits it to the migration engine. The migration engine then requests noise features from the noise library, and the noise library returns fingerprint data. Based on these data, the migration engine extracts pure frequency band features and sends them to the recognition network. The recognition network analyzes and outputs a damage probability, and the recognition network transmits the damage probability to the alarm, which triggers a warning and finally sends the warning information to the monitoring center to achieve a more comprehensive monitoring and alarm function.

[0038] As Figure 3 shown,Figure 3 The process of self-organizing migration to form sensitive frequency bands is elaborated in detail. The whole process starts from two parallel steps: adjacent frequency band merging and candidate frequency band input. After adjacent frequency band merging, correlation analysis is carried out, and a judgment is made according to the analysis results: if the correlation is low, the frequency band is split again; otherwise, the merging is maintained. At the same time, after the candidate frequency band is input, energy entropy calculation is carried out, and it is judged whether the entropy values are close: if the entropy values are close, the process returns to the adjacent frequency band merging step; otherwise, it remains independent. The final link of the process is the output of sensitive frequency bands, which can adaptively characterize the hidden damage characteristics under the current working conditions.

[0039] Example 3: In step 2, analyze the energy density gradient in the time-frequency energy matrix to dynamically identify the frequency band regions where the signal energy changes significantly. For the time-frequency energy matrix obtained by continuous wavelet time-frequency analysis, calculate the energy difference between adjacent frequency bands on each time slice; for example, if at a certain moment , frequency band has an energy of , then its energy density gradient can be obtained by calculating . Preset a gradient threshold , which is an empirical value obtained by statistical analysis of a large number of historical acoustic emission signals of conveyor belts in different health states; when the calculated energy density gradient is greater than , then mark this frequency band as the frequency band region where the energy changes significantly, and include it in the candidate area of potential hidden damage characteristic frequency bands.

[0040] In step 3, perform self-organizing migration on the candidate area of potential hidden damage characteristic frequency bands to form sensitive frequency bands that can adaptively characterize the hidden damage characteristics under the current working conditions. First, calculate the energy entropy of each candidate frequency band , and the calculation formula is: , where represents the proportion of the energy density of the -th frequency point in the frequency band to the total energy density of the frequency band. Set an entropy value difference threshold . For two adjacent candidate frequency bands and , if , then merge these two frequency bands; the setting of the entropy value difference threshold is determined based on the analysis of the energy distribution characteristics of the acoustic emission signal frequency bands in different damage states; then, considering the energy correlation between adjacent frequency bands, calculate the adjacent frequency bands and Energy correlation coefficient in the time domain , if is less than a preset correlation threshold , then the frequency bands and are split into independent sensitive frequency bands; the setting of the correlation threshold is determined based on the analysis of the correlation between frequency bands of acoustic emission signals of different damage types. Through the above dynamic merging and splitting processes, sensitive frequency bands that can adapt to the hidden damage characteristics under the current working conditions are formed.

[0041] In step 4, a noise spectrum fingerprint library is constructed and dynamically maintained. This fingerprint library collects background noise signals during the operation of the conveyor belt under different working conditions and extracts their spectral characteristics; when the similarity between the currently monitored ambient noise spectrum and all existing records in the fingerprint library is lower than a preset similarity threshold , the system will automatically trigger new noise spectrum acquisition. The similarity can be achieved by calculating the cosine similarity of two spectral vectors. The frequency band energy distribution stability of the newly acquired noise spectrum segment is verified, and the frequency bands with stable energy distribution are selected as noise characteristic segments. At the same time, in combination with operating state parameters such as the load size and running speed of the conveyor belt, spatio-temporal correlation constraints are performed to eliminate transient interference noises that do not match the current working conditions. Finally, a weighted average method is used to fuse and update the similar noise spectra in the fingerprint library.

[0042] In step 5, the adaptive feature sensitive frequency band obtained in step 3 is compared with the noise spectra in the noise spectrum fingerprint library. The cosine similarity between the spectrum of the adaptive feature sensitive frequency band and each noise spectrum in the fingerprint library is calculated. For the frequency domain components with a similarity greater than the preset noise similarity threshold , it is considered that this frequency domain component is very likely to originate from noise and is removed from the adaptive feature sensitive frequency band, obtaining the remaining frequency bands that only contain potential damage information.

[0043] In step 7, the remaining frequency bands after energy normalization are input into a pre-trained lightweight convolutional neural network. This network transfers the frequency domain feature knowledge learned under various historical working conditions to the current working condition through the domain adaptation algorithm in transfer learning, so as to accurately identify whether there is hidden damage on the conveyor belt and output the corresponding identification result, such as the probability value of the existence of hidden damage on the conveyor belt.

[0044] Example 4: In this example, frequency domain migration is a process of dynamically adjusting and focusing on the sensitive frequency band that characterizes damage features by using the frequency band division experience under historical working conditions and combining the characteristics of the current signal. This process may include, for example, the following two core elements, such as experience reference: extracting the rules or patterns of frequency band division from historical working condition data as the initial migration basis; dynamic adaptability: on this basis, according to the characteristics of the current signal such as energy distribution, energy entropy, and frequency band correlation, self-organizing migration is carried out, that is, dynamically merging, splitting, and adjusting the frequency bands, and finally forming a sensitive frequency band adapted to the current working condition.

[0045] The update of the fingerprint database is a complex process involving multiple links, and its core goal is to ensure the accuracy, comprehensiveness, and real-time nature of the fingerprint database. Specifically, the update mechanism includes, for example, the following steps: incremental acquisition trigger: when the similarity between the currently monitored current environmental noise spectrum and all records in the fingerprint database is lower than the preset threshold, trigger the acquisition of a new noise spectrum; stability verification: verify the stability of the frequency band energy distribution of the newly acquired noise segment, and screen out stable noise feature segments to reduce the influence of random noise; spatio-temporal correlation constraint: combine the operating state parameters such as the load size, running speed, environmental temperature and humidity of the conveyor belt to perform spatio-temporal correlation constraint, and eliminate transient interference noise irrelevant to the current working condition; fingerprint fusion: fuse similar noise spectra in the fingerprint database to reduce redundancy and improve the retrieval efficiency. The specific method of fusion can be weighted average, where the weights can be determined according to factors such as the occurrence frequency and duration of the noise spectrum, and all belong to the extended implementation methods known to those of ordinary skill in the art.

[0046] and in the frequency band energy entropy In the calculation formula, energy entropy is used to evaluate the concentration degree of energy distribution within the frequency band. The lower the energy entropy, the more concentrated the energy distribution; the higher the energy entropy, the more dispersed the energy distribution. During the frequency band merging process, since adjacent frequency bands with similar energy entropy values have similar energy distribution characteristics and contain similar damage features, these frequency bands can be merged to simplify subsequent processing and improve the efficiency of feature extraction. The specific merging strategy is as follows: calculate the energy entropy of each candidate frequency band , set an entropy value difference threshold ; for two adjacent candidate frequency bands and , if , then merge these two frequency bands. Here, the selection of the entropy value difference threshold is crucial, which determines the looseness of the merging. If it is smaller, the merging is more strict, and only frequency bands with very close energy entropy will be merged, which can retain more detailed information but may also increase the complexity of subsequent processing. If it is larger, the merging is more relaxed and more frequency bands will be merged, which can simplify subsequent processing but may also result in some loss of detailed information. The specific value of needs to be determined according to the actual application scenario and prior knowledge of damage characteristics, and the optimal value can be selected through experimental analysis and parameter optimization. And the energy density gradient threshold : It is used to identify the frequency band region where the energy changes significantly, and its value can be based on the statistical analysis of historical undamaged and damaged conveyor belt acoustic emission signals. For example, the probability distributions of the energy density gradient under normal working conditions and typical damage conditions can be calculated, and the gradient value range that can most effectively distinguish these two distributions is selected as ; The entropy value threshold is used for frequency band merging, and its value can be based on the analysis of the frequency band energy distribution characteristics of acoustic emission signals in different damage states. For example, the statistical characteristics of the frequency band energy entropy under different damage degrees can be calculated, and the entropy value difference range that can effectively distinguish different damage degrees is selected as ; The correlation threshold : This threshold is used for frequency band splitting, and its value can be based on the analysis of the correlation between frequency bands of acoustic emission signals of different damage types. For example, the statistical characteristics of the energy correlation coefficient between adjacent frequency bands under different damage types can be calculated, and the correlation coefficient range that can effectively distinguish different damage types is selected as ; The noise similarity threshold : It is used for noise separation, and its value can be based on the analysis of the distinguishability of the spectral characteristics of damage signals and noise signals. For example, the similarity distribution of the spectra of damage signals and noise signals can be calculated, and the similarity threshold that can most effectively distinguish these two signals is selected as

[0047] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals, characterized in that: The following steps are involved: Step 1: collect the acoustic emission signal generated by the conveyor belt in real time, perform continuous wavelet time-frequency analysis, and construct a time-frequency energy matrix reflecting the distribution of signal energy in the time domain and frequency domain; Step 2, by analyzing the energy density gradient in the time-frequency energy matrix, dynamically identifying the frequency band regions where the signal energy changes significantly, and taking these frequency band regions as potential candidate frequency bands of hidden damage characteristics, to achieve preliminary tracking of the frequency domain energy flow of the acoustic emission signal; Step 3, using the frequency band energy entropy and the energy correlation between adjacent frequency bands, self-organizing migration is performed on the candidate frequency bands of the potential hidden damage characteristics, specifically including dynamically merging adjacent frequency bands with similar energy entropy values, and performing secondary splitting according to the energy correlation between frequency bands, so as to form a sensitive frequency band that can adaptively characterize the hidden damage characteristics under the current working conditions; Step 4: construct and dynamically maintain a noise spectrum fingerprint library. The noise spectrum fingerprint library collects background noise signals under different working conditions, extracts its spectrum features and updates it using a dynamic self-evolution mechanism. The dynamic self-evolution mechanism includes automatically triggering incremental acquisition of the noise spectrum in real-time operation when it is detected that the similarity between the current environmental noise spectrum and all records in the fingerprint library is lower than a preset threshold. Stable noise feature fragments are screened out through the stability verification of frequency band energy distribution, and the spatiotemporal correlation constraints are combined with the conveyor belt operation state parameters to eliminate transient interference noise irrelevant to the current working condition. Finally, a lightweight fingerprint fusion strategy is used for updating. Step 5, performing a similarity comparison between the adaptive feature sensitive frequency band and the noise spectrum in the noise spectrum fingerprint library to decouple the noise signal and the damage signal, and removing the frequency domain components that highly overlap with the noise from the dynamically divided frequency bands through spectrum similarity comparison to obtain the remaining frequency bands that only contain potential damage information; Step 6, normalizing the energy of the remaining frequency bands to eliminate the influence of signal amplitude differences under different working conditions; Step 7, input the remaining frequency band after energy normalization processing into a pre-trained lightweight convolutional neural network, and the lightweight convolutional neural network adapts the frequency domain features learned under historical working conditions to the current working conditions through the domain adaptation algorithm in transfer learning, and outputs the identification result of whether the conveyor belt has hidden damage.

2. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1 is characterized in that: In the self-organization migration step, the calculation of the frequency band energy entropy is used to evaluate the concentration of energy distribution in the frequency band. Frequency bands with similar energy entropy values ​​have similar damage characteristics in a physical sense.

3. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1 is characterized in that: The spatiotemporal correlation constraints include correlating the newly added noise segments with the timestamps of historical noise data and the conveyor belt operation status parameters to distinguish between continuous environmental noise and occasional interference.

4. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1 is characterized in that: The training data of the lightweight convolutional neural network contains artificially simulated hidden damage signals under multiple working conditions, and cross-environment generalization is achieved through transfer learning. The domain adaptation algorithm is used to align the feature distribution of the source domain to the target domain.

5. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1 is characterized in that: The initial construction of the noise spectrum fingerprint library adopts a spatiotemporal homology verification mechanism, including synchronously recording the operating status of the conveyor belt, ambient temperature and humidity, and equipment location coordinates, constructing a spatiotemporal correlation matrix, and then dividing the collected noise spectrum into several subsets according to the spatiotemporal correlation matrix, and only retaining the fragments in each subset whose standard deviation of the spectrum energy distribution is less than the preset threshold. Through cross-modal cross-validation, the low-frequency characteristics of the vibration sensor are used to eliminate abnormal noise patterns.

6. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1 is characterized in that: Energy density gradient detection recognizes frequency bands with energy mutations by calculating the difference in energy values ​​between adjacent frequency bands and determining whether the difference exceeds a preset gradient threshold.

7. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1 is characterized in that: Frequency band energy entropy The calculation formula is: ， in, Indicates frequency band Neidi The ratio of the energy density of a frequency point to the total energy density of the frequency band.

8. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 3 is characterized in that: The operating state parameters of the conveyor belt include but are not limited to the load size and operating speed of the conveyor belt.

9. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 5 is characterized in that: The preset threshold is an empirical value range determined based on historical operating data of different types of conveyor belts and characteristic analysis of acoustic emission signals of hidden damage.

10. The method for identifying hidden damage of a conveyor belt based on frequency domain migration of acoustic emission signals according to claim 1, characterized in that: The identification result includes a probability value of whether the conveyor belt has hidden damage. When the probability value is greater than or equal to a set damage probability threshold, it is determined that the conveyor belt has hidden damage and an alarm signal can be output.

Images