Mechanical seal operation state monitoring data transmission method and device

Abstract

The application discloses a mechanical seal operation state monitoring data transmission method and device, and relates to the field of signal processing.The application performs sparse representation on collected acoustic emission data signals by using a sparse matrix, and then performs compressed sampling on the AE data signals after sparse representation by using an observation matrix to achieve the purpose of dimension reduction.Based on the characteristics of background noise, the acoustic emission data signals are preprocessed by adopting a suitable filtering mode before sparse representation, so that the accuracy of subsequent state judgment is improved, data redundancy is effectively reduced, and the data transmission efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of signal processing, and in particular to a method and apparatus for data transmission for monitoring the operating status of mechanical seals. Background Technology

[0002] Mechanical seals are rotary shaft sealing devices commonly used in compressors, reactors, and other similar equipment. Their working principle involves the contact, rotation, and compensation of the moving and stationary rings on the sealing end faces to achieve leakage prevention. Therefore, the contact state of the sealing end faces determines the performance of the mechanical seal. During operation, the mechanical seal generates sound at a specific frequency due to end-face friction. The frequency of this sound varies depending on the friction pair materials, operating speed, and end-face conditions. Therefore, the operating state of the friction pair can be determined by analyzing the acoustic emission signal data, thereby monitoring for mechanical seal failure. Based on the acoustic emission (AE) signal generated by the high-speed rotation and friction of the moving and stationary rings of the mechanical seal, the operating state of the seal can be determined.

[0003] However, acoustic emission testing is a non-destructive testing method. Its signal is a high-frequency, rapidly changing signal, which requires a high data sampling frequency. Generally, the amount of acoustic emission data collected is huge and there is data redundancy, which is very unfavorable for data transmission.

[0004] Therefore, how to provide a more efficient method for transmitting acoustic emission signals is an urgent problem to be solved. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a method and apparatus for transmitting data to monitor the operating status of mechanical seals.

[0006] In a first aspect, this application provides a method for data transmission for monitoring the operating status of a mechanical seal, the method comprising: Acquire the raw acoustic emission signal of the target device; The original acoustic emission signal is filtered based on the operating status of the target device to obtain the filtered acoustic emission signal. The filtered acoustic emission signal is sparsely represented to obtain sparse signal data. Compressed signal data is obtained by compressing and sampling the sparse data using the observation matrix; Transmit compressed signal data to the target terminal; The compressed signal data is reconstructed to obtain the reconstructed signal data.

[0007] Optionally, the step of filtering the original acoustic emission signal based on the device operating status of the target device to obtain a filtered acoustic emission signal specifically includes: Obtain device information and operating status information of the target device; Based on the device information and operating status information, extract the corresponding background noise features from the background noise database; Based on the device information, the acoustic emission signal characteristics of different types of friction pair materials available for the target device are extracted from the acoustic emission database. Based on the extracted background noise features and acoustic emission signal features, determine the filtering range to match the current target device; The original acoustic emission signal is filtered based on the filtering range to obtain the filtered acoustic emission signal.

[0008] Optionally, both the background noise features and the acoustic emission signal features include energy features and frequency features. The step of determining the filtering range matching the current target device based on the extracted background noise features and acoustic emission signal features specifically includes: The steps involve determining whether there are overlapping intervals between the energy and frequency characteristics of the acoustic emission signal and the energy and frequency characteristics of the background noise. If there is no overlap, the energy threshold range and frequency threshold range covered by the background noise features are used as the filtering range; If there is overlap, the non-overlapping portion of the energy threshold range and frequency threshold range covered by the background noise features is used as the filtering range.

[0009] Optionally, the method for constructing the background noise database is as follows: For different types of target equipment without mechanical seals, audio signals during operation are collected under different working conditions; Extracting signal features from audio signals; The extracted signal features are bound to the device information and operating status information of the target device at the time of extraction, and saved as background noise feature data in the background noise database.

[0010] Optionally, the method for constructing the acoustic emission database is as follows: Obtain the types of friction pair materials available for different types of target equipment; For each target device, theoretical acoustic emission signals generated during operation using different types of available friction pair materials and under different working conditions were acquired. Extracting signal characteristics from theoretical acoustic emission signals; The extracted signal features are bound to the target device's equipment information and the type of friction pair material, and saved as acoustic emission signal features in the acoustic emission database.

[0011] Optionally, the method further includes: The reconstructed signal data is compared with the original acoustic emission signal to determine the similarity of the effective signals, thus obtaining an evaluation result of the data transmission effect.

[0012] Optionally, the method further includes: Based on the evaluation results, the filtering range is adjusted.

[0013] Secondly, this application provides a data transmission device for monitoring the operating status of a mechanical seal, the device comprising: The signal acquisition unit is used to acquire the raw acoustic emission signal of the target device. The signal filtering unit is used to filter the original acoustic emission signal based on the operating status of the target device to obtain a filtered acoustic emission signal. The sparse representation unit is used to perform sparse representation on the filtered acoustic emission signal to obtain sparse signal data. The signal compression unit is used to compress and sample the sparse data using the observation matrix to obtain compressed signal data. A signal transmission unit is used to transmit compressed signal data to the target terminal; The signal reconstruction unit is used to reconstruct compressed signal data to obtain reconstructed signal data.

[0014] Thirdly, this application provides an electronic device, comprising: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the method as described in the first aspect.

[0015] Fourthly, this application provides a computer-readable storage medium storing program code that can be invoked by a processor to execute the method described in the first aspect.

[0016] In summary, this application includes the following beneficial technical effects: To improve the data transmission efficiency of acoustic emission (AE) signals, a compressed sensing-based acoustic emission data processing method is proposed. This method utilizes a sparse matrix to sparsely represent the acquired AE data signals, and then uses an observation matrix to compress and sample the sparsely represented AE data signals to achieve dimensionality reduction. Based on the characteristics of background noise, an appropriate filtering method is adopted to preprocess the AE data signals. This improves the accuracy of subsequent state judgment and effectively reduces data redundancy, thereby increasing data transmission efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for transmitting data to monitor the operating status of a mechanical seal, as provided in an embodiment of this application.

[0018] Figure 2 This is a flowchart of a method for filtering raw acoustic emission signals provided in an embodiment of this application.

[0019] Figure 3 This is a functional block diagram of the mechanical seal operation status monitoring data transmission device provided in the embodiments of this application.

[0020] Figure 4 This is a structural block diagram of an electronic device for implementing the mechanical seal operation status monitoring data transmission method according to an embodiment of the present invention.

[0021] Figure 5 This is a structural block diagram of a computer-readable storage medium for storing or carrying program code implementing the mechanical seal operation status monitoring data transmission method according to an embodiment of the present application.

[0022] Figure label: Signal acquisition unit 110; signal filtering unit 120; sparse representation unit 130; signal compression unit 140; signal transmission unit 150; signal reconstruction unit 160; electronic device 300; processor 310; memory 320; computer-readable storage medium 400; program code 410. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] like Figure 1 As shown, a method for monitoring the operating status of a mechanical seal according to an embodiment of the present invention includes: Step S101: Acquire the raw acoustic emission signal of the target device.

[0027] The raw acoustic emission signal is acquired by a sensor installed on the target device, preferably positioned as close as possible to the dynamic and static rings of the target device. For example, the acoustic emission probe (sensor) can be installed at the opening of the outer pressure cover of the mechanical seal.

[0028] Step S102: Filter the original acoustic emission signal based on the device operating status of the target device to obtain a filtered acoustic emission signal.

[0029] Filtering can effectively remove noise components from the acquired raw acoustic emission signal, thereby reducing the amount of data that needs to be transmitted and improving the accuracy of subsequent sealing status judgment based on the acquired information.

[0030] Based on this, choosing a more suitable filtering method can further improve the effect.

[0031] like Figure 2 As shown, in a preferred embodiment of the present invention, step S102 specifically includes: Step S201: Obtain the device information and operating status information of the target device; When a mechanical seal is in operation, the friction on its end face generates sound signals within a certain frequency band. Furthermore, the frequency of this sound varies depending on the type of target equipment, the materials used in the friction pair, the operating speed, and the condition of the end face. On the other hand, other components of the target equipment also generate noise during operation. This noise primarily originates from bearing friction, high-pressure fluid scouring, motor operation noise, and other mechanical vibrations. The noise generated will also differ depending on the type of target equipment and its operating conditions. Therefore, it is necessary to first assess the actual situation on-site before selecting a suitable filtering method.

[0032] By obtaining the equipment information and operating status information of the target device, a preliminary analysis of the above situation can be performed.

[0033] Step S202: Extract the corresponding background noise features from the background noise database based on the device information and operating status information; By acquiring the device information and operational status information, we can determine the model of the target device being monitored and its current operational status. Using these two dimensions as labels, we can retrieve the characteristics of the background noise in that scenario from a pre-established background noise database. This database contains pre-saved background noise data for different types of target devices under different operational states. This data is stored in the database with device information and operational status information as labels, and therefore can be retrieved using the acquired device information and operational status information.

[0034] Specifically, the method for constructing the background noise database is as follows: For different types of target equipment without mechanical seals, audio signals during operation are collected under different working conditions; Extracting signal features from audio signals; The extracted signal features are bound to the device information and operating status information of the target device at the time of extraction, and saved as background noise feature data in the background noise database.

[0035] Step S203: Based on the device information, extract the acoustic emission signal characteristics of different types of friction pair materials available for the target device from the acoustic emission database; For different types of target equipment, their equipment parameters, available friction pair materials, and various executable operating states are all known. Based on this information, the theoretical acoustic emission signals generated by each target equipment operating under different working conditions and using different types of available friction pair materials can be obtained. Based on these theoretical acoustic emission signals, their signal characteristics can be obtained. The calculation of the theoretical acoustic emission signals can be performed using relevant simulation software with the known initial parameters, or it can be obtained through theoretical calculations after collecting a large amount of real data. The theoretical acoustic emission signals are also used when determining the operating state of mechanical seals. The acoustic emission database pre-stores the acoustic emission signal characteristics for different types of target equipment. This data is identified by the target equipment information and friction pair material type, and can be retrieved using the acquired equipment information and friction pair material type. Specifically, the method for constructing the acoustic emission database is as follows: Obtain the types of friction pair materials available for different types of target equipment; For each target device, theoretical acoustic emission signals generated during operation using different types of available friction pair materials and under different working conditions were acquired. Extracting signal characteristics from theoretical acoustic emission signals; The extracted signal features are bound to the target device's equipment information and the type of friction pair material, and saved as acoustic emission signal features in the acoustic emission database.

[0036] Step S204: Determine the filtering range to match the current target device based on the extracted background noise features and acoustic emission signal features; By comparing the extracted background noise features with the acoustic emission signal features, a higher filtering range suitable for the current target device can be selected, thereby filtering the original acoustic emission signal.

[0037] As a preferred embodiment of the present invention, step S204 specifically includes: The steps involve determining whether there are overlapping intervals between the energy and frequency characteristics of the acoustic emission signal and the energy and frequency characteristics of the background noise. If there is no overlap, the energy threshold range and frequency threshold range covered by the background noise features are used as the filtering range; If there is overlap, the non-overlapping portion of the energy threshold range and frequency threshold range covered by the background noise features is used as the filtering range.

[0038] In practical implementation scenarios, we found that the original acoustic emission signal used for subsequent judgment of the mechanical seal's operating status, i.e., the useful signal portion, is mainly a pulse-type signal, while other background noise is a continuous signal. Therefore, the two signals have certain differences in energy and frequency characteristics. By setting an appropriate filtering range, the invalid components of the original acoustic emission signal can be effectively and significantly filtered out.

[0039] The obtained background noise features and acoustic emission signal features are compared along two dimensions: energy and frequency, to determine if there are overlapping intervals. Generally, pulses are usually composed of high-energy instantaneous signals, so energy-based filtering can better locate effective information. On the other hand, by setting an appropriate energy threshold and using an energy monitoring algorithm to find portions exceeding that threshold, these portions are likely to be pulse signals. Regarding frequency, based on statistical analysis of actual collected data, effective signals typically have higher frequencies than noise, which can be filtered by setting an appropriate bandwidth. Combining filtering along these two dimensions yields better results.

[0040] However, due to differences in equipment information and operating status, the background noise characteristics generated during operation vary among different target devices. Therefore, feature data from background noise databases and acoustic emission databases are needed to assist in the judgment.

[0041] The appropriate filtering range is determined by judging whether there are overlapping intervals between the acoustic emission signal characteristics and the energy and frequency characteristics of the background noise characteristics. Considering the priority preservation of useful signals, if there is overlap, the non-overlapping portion of the energy and frequency threshold ranges covered by the background noise characteristics is used as the filtering range; if there is no overlap, the energy and frequency threshold ranges covered by the background noise characteristics are used as the filtering range.

[0042] Step S205: Filter the original acoustic emission signal based on the filtering range to obtain a filtered acoustic emission signal.

[0043] By filtering the original acoustic emission signal based on the determined filtering range, a filtered acoustic emission signal can be obtained. On this basis, subsequent compressed sensing algorithm processing can be performed.

[0044] Step S103: Perform sparse representation on the filtered acoustic emission signal to obtain sparse signal data.

[0045] The theory of compressed sensing is mainly divided into three parts: sparse representation, signal compression, and signal reconstruction. Among them, sparse representation is the prerequisite for the establishment of compressed sensing theory, signal compression is the key step in the data transmission process, and signal reconstruction is the ultimate goal of compressed sensing.

[0046] Sparse representation of a signal refers to minimizing the number of non-zero elements in the signal after a sparse transformation. If an N*1 signal has no more than k non-zero elements, it can be called a k-sparse signal. Therefore, a signal becomes a discrete signal after a sparse transformation. The choice of sparse matrix or sparse dictionary is crucial in the sparse representation process, and finding a suitable dictionary is the most widely studied topic in compressed sensing theory across various fields. Currently, sparse dictionaries are mainly divided into two categories: fixed dictionaries and learned dictionaries. Typical fixed dictionaries include Fourier dictionaries and discrete cosine dictionaries. The core of the Fast Fourier Transform (FFT) is to convert a time-domain signal into a frequency-domain signal. Therefore, FFT is very effective at reducing the sparsity of signals with limited frequency domain coverage. The Discrete Cosine Transform (DCT) is a special form of the Discrete Fourier Transform (DFT). DCT omits the complex number operations of DFT and implements it by constructing even functions. Compared to DFT, DCT has better frequency domain clustering and can remove unimportant frequency domain signals. K-Singular Value Decomposition (K-SVD) is a mainstream learning algorithm that combines the ideas of k-means and Singular Value Decomposition (SVD). It uses SVD to decompose the error term, and the minimum error term is used as a correction coefficient to update the dictionary elements. The optimal solution is obtained through iteration. In a preferred embodiment of this invention, K-SVD is chosen for sparse representation when the compression ratio is around 0.2-0.4.

[0047] Step S104: Compress and sample the sparse data using the observation matrix to obtain compressed signal data.

[0048] While sparse representation simplifies computation, it doesn't reduce the signal's dimensionality. Introducing an observation matrix unrelated to the sparse matrix, and multiplying it by the sparse signal data, achieves dimensionality reduction and compression. Therefore, finding a suitable observation matrix directly impacts the signal reconstruction result. Common observation matrices are generally classified into two types: deterministic matrices and random matrices. Deterministic matrices include partial Hadamard matrices, while random matrices include Gaussian random matrices and Bernoulli random matrices. In a preferred embodiment of this invention, a Bernoulli random matrix is ​​used as the observation matrix.

[0049] Step S105: Transmit the compressed signal data to the target terminal.

[0050] The target terminal is a device used to monitor the operating status of the mechanical seal, and it can determine the operating status of the mechanical seal based on reconstructed signal data. The target terminal can be set up locally on the target device or as a service terminal set up in the cloud.

[0051] Step S106: Reconstruct the compressed signal data to obtain reconstructed signal data.

[0052] On the target terminal, the compressed signal data is reconstructed.

[0053] Signal reconstruction is essentially the reverse engineering of compressed signal data. Greedy algorithms are a mainstream signal reconstruction method. Their basic idea is to reconstruct the support set of the signal iteratively, obtaining the elements constituting the signal to be estimated through a certain greedy criterion. Greedy algorithms can significantly reduce computational complexity. Orthogonal Matching Pursuit (OMP) is a commonly used greedy algorithm. It mainly consists of three stages: identification, enhancement, and residual update. By repeatedly iterating the signal's support set and continuously updating the residual coefficients, the reconstructed signal is finally obtained.

[0054] As a preferred embodiment of the present invention The reconstructed signal data is compared with the original acoustic emission signal to determine the similarity of the effective signals, thus obtaining an evaluation result of the data transmission effect.

[0055] Specifically, the root mean square error can be used to determine the similarity between the reconstructed signal and the filtered acoustic emission signal, and then the data transmission effect can be comprehensively evaluated based on the signal compression ratio.

[0056] Finally, based on the evaluation results, the filtering range is adjusted, or the algorithm used in the compressed sensing process is adjusted, to obtain the optimal processing method suitable for the mechanical seal acoustic emission signal transmission process of the current target device.

[0057] In summary, the mechanical seal operation status monitoring data transmission method provided in this embodiment utilizes a sparse matrix to sparsely represent the acquired acoustic emission data signal, and then uses an observation matrix to compress and sample the sparsely represented AE data signal to achieve dimensionality reduction. Based on the characteristics of background noise, an appropriate filtering method is adopted to preprocess the acoustic emission data signal, which improves the accuracy of subsequent status judgment and effectively reduces data redundancy, thereby improving data transmission efficiency.

[0058] like Figure 3 As shown, the mechanical seal operation status monitoring data transmission device provided by this invention includes: Signal acquisition unit 110 is used to acquire the original acoustic emission signal of the target device. The signal filtering unit 120 is used to filter the original acoustic emission signal based on the device operating status of the target device to obtain a filtered acoustic emission signal. The sparse representation unit 130 is used to perform sparse representation on the filtered acoustic emission signal to obtain sparse signal data. The signal compression unit 140 is used to compress and sample the sparse data through the observation matrix to obtain compressed signal data. The signal transmission unit 150 is used to transmit compressed signal data to the target terminal; The signal reconstruction unit 160 is used to reconstruct the compressed signal data to obtain reconstructed signal data.

[0059] The mechanical seal operation status monitoring data transmission device provided in this embodiment of the invention is used to implement the above-mentioned mechanical seal operation status monitoring data transmission method. Therefore, the specific implementation method is the same as the above method, and will not be repeated here.

[0060] like Figure 4 The diagram shows a structural block diagram of an electronic device 300 provided in an embodiment of the present invention. This electronic device 300 can be a smartphone, tablet computer, e-reader, or other electronic device capable of running applications. The electronic device 300 in this application may include one or more of the following components: a processor 310, a memory 320, and one or more applications, wherein the one or more applications can be stored in the memory 320 and configured to be executed by one or more processors 310, and the one or more applications are configured to perform the methods described in the foregoing method embodiments.

[0061] Processor 310 may include one or more processing cores. Processor 310 connects to various parts within the electronic device 300 using various interfaces and lines, and performs various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets, or instruction sets stored in memory 320, and by calling data stored in memory 320. Optionally, processor 310 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 310 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 310 and may be implemented separately using a communication chip.

[0062] The memory 320 may include random access memory (RAM) or read-only memory (ROM). The memory 320 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 320 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described below. The data storage area may also store data created by the terminal during use (such as phonebook data, audio and video data, chat log data, etc.).

[0063] like Figure 5 The diagram shows a structural block diagram of a computer-readable storage medium 400 provided in an embodiment of the present invention. The computer-readable medium stores program code 410, which can be called by a processor to execute the methods described in the above method embodiments.

[0064] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 400 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. This program code 410 can be read from or written to one or more computer program products. The program code 410 may be compressed, for example, in a suitable form.

[0065] In summary, the mechanical seal operation status monitoring data transmission method and apparatus provided in this embodiment of the invention utilizes a sparse matrix to sparsely represent the acquired acoustic emission data signal, and then uses an observation matrix to compress and sample the sparsely represented AE data signal to achieve dimensionality reduction. Based on the characteristics of background noise, an appropriate filtering method is adopted to preprocess the acoustic emission data signal, which improves the accuracy of subsequent status judgment on the one hand, and effectively reduces data redundancy on the other hand, thereby improving data transmission efficiency.

[0066] In the embodiments disclosed in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0067] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0068] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0070] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for transmitting data to monitor the operating status of a mechanical seal, characterized in that, The method includes: Acquire the raw acoustic emission signal of the target device; The original acoustic emission signal is filtered based on the operating status of the target device to obtain the filtered acoustic emission signal. The filtered acoustic emission signal is sparsely represented to obtain sparse signal data. Compressed signal data is obtained by compressing and sampling the sparse signal data using the observation matrix; Transmit compressed signal data to the target terminal; The compressed signal data is reconstructed to obtain the reconstructed signal data. The step of filtering the original acoustic emission signal based on the device operating status of the target device to obtain the filtered acoustic emission signal specifically includes: Obtain device information and operating status information of the target device; Based on the device information and operating status information, extract the corresponding background noise features from the background noise database; Based on the device information, the acoustic emission signal characteristics of different types of friction pair materials available for the target device are extracted from the acoustic emission database; Based on the extracted background noise features and acoustic emission signal features, determine the filtering range to match the current target device; The original acoustic emission signal is filtered based on the filtering range to obtain the filtered acoustic emission signal; The background noise features and acoustic emission signal features both include energy features and frequency features. The step of determining the filtering range matching the current target device based on the extracted background noise features and acoustic emission signal features specifically includes: Determine whether there are overlapping intervals between the energy and frequency characteristics of the acoustic emission signal and the energy and frequency characteristics of the background noise. If there is no overlap, the energy threshold range and frequency threshold range covered by the background noise features are used as the filtering range; If there is overlap, the non-overlapping portion of the energy threshold range and frequency threshold range covered by the background noise features is used as the filtering range.

2. The method for data transmission for monitoring the operating status of a mechanical seal according to claim 1, characterized in that, The method for constructing the background noise database is as follows: For different types of target equipment without mechanical seals, audio signals during operation are collected under different working conditions; Extracting signal features from audio signals; The extracted signal features are bound to the device information and operating status information of the target device at the time of extraction, and saved as background noise feature data in the background noise database.

3. The method for data transmission for monitoring the operating status of a mechanical seal according to claim 1, characterized in that, The method for constructing the acoustic emission database is as follows: Obtain the types of friction pair materials available for different types of target equipment; For each target device, theoretical acoustic emission signals generated during operation using different types of available friction pair materials and under different working conditions were acquired. Extracting signal characteristics from theoretical acoustic emission signals; The extracted signal features are bound to the target device's equipment information and the friction pair material type, and saved as acoustic emission signal features in the acoustic emission database.

4. The method for data transmission for monitoring the operating status of a mechanical seal according to claim 1, characterized in that, The method further includes: The reconstructed signal data is compared with the original acoustic emission signal to determine the similarity of the effective signals, thus obtaining an evaluation result of the data transmission effect.

5. The method for data transmission for monitoring the operating status of a mechanical seal according to claim 4, characterized in that, The method further includes: Based on the evaluation results, the filtering range is adjusted.

6. A data transmission device for monitoring the operating status of a mechanical seal, characterized in that, The device includes: The signal acquisition unit is used to acquire the raw acoustic emission signal of the target device. A signal filtering unit is used to filter the original acoustic emission signal based on the operating status of the target device to obtain a filtered acoustic emission signal. Specifically, it is used to: acquire the device information and operating status information of the target device; extract corresponding background noise features from a background noise database based on the device information and operating status information; extract acoustic emission signal features of different types of friction pair materials available for the target device from the acoustic emission database based on the device information; determine a filtering range matching the current target device based on the extracted background noise features and acoustic emission signal features; and filter the original acoustic emission signal based on the filtering range to obtain a filtered acoustic emission signal. The background noise features and acoustic emission signal features both include energy features and frequency features. The signal filtering unit is further used to: determine whether the energy features and frequency features of the acoustic emission signal feature overlap with the energy features and frequency features of the background noise feature; if there is no overlap, the energy threshold range and frequency threshold range covered by the background noise feature are used as the filtering range; if there is an overlap, the non-overlapping portion of the energy threshold range and frequency threshold range covered by the background noise feature is used as the filtering range. The sparse representation unit is used to perform sparse representation on the filtered acoustic emission signal to obtain sparse signal data. The signal compression unit is used to compress and sample the sparse signal data through the observation matrix to obtain compressed signal data. A signal transmission unit is used to transmit compressed signal data to the target terminal; The signal reconstruction unit is used to reconstruct compressed signal data to obtain reconstructed signal data.

7. An electronic device, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by a processor to execute the method as described in any one of claims 1-5.

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