Heat exchanger operation state acoustic monitoring method, device and system

Abstract

This invention provides a method, apparatus, and system for acoustic monitoring of heat exchanger operating status, belonging to the field of acoustic monitoring technology. The method includes: acquiring acoustic signals from the heat exchanger during a certain operating time; determining the RMS dispersion entropy and acoustic signature characteristics of the acoustic signals during that operating time based on the acoustic signals; and determining the operating status of the heat exchanger during that operating time based on the RMS dispersion entropy and the acoustic signature characteristics. This invention can identify sudden operating events of heat exchanger tube bundle leakage from strong background noise, achieving accurate judgment of whether there are abnormalities in the heat exchanger, and thus accurately determining the operating status of the heat exchanger.

Description

Technical Field

[0001] This invention relates to the field of acoustic monitoring technology, specifically to an acoustic monitoring method for heat exchanger operating status, an acoustic monitoring device for heat exchanger operating status, an acoustic monitoring system for heat exchanger operating status, an electronic device, and a readable storage medium. Background Technology

[0002] Heat exchange equipment is a critical component in petrochemical production, and its structural integrity directly impacts safety and the company's operational efficiency. Tube bundles in heat exchange equipment can rupture and leak due to corrosion, wear, and other factors, leading to unplanned shutdowns. Current technologies typically involve tube bundle inspection during shutdowns for maintenance, using methods such as rotating ultrasonics and eddy current testing, which cannot achieve real-time monitoring or timely detection of potential safety hazards. While methods that detect tube bundle leaks during operation, such as analyzing the composition of the tube-side medium within the shell-side liquid medium, are costly, time-consuming, and cannot provide rapid and accurate assessments, this approach is also problematic. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, and system for acoustic monitoring of heat exchanger operating status, so as to at least solve the problems mentioned above, such as the inability to achieve real-time monitoring and timely detection of safety hazards during shutdown and maintenance, and the high cost, long cycle, and inability to quickly and accurately determine the composition of the tube-side medium in the shell-side liquid medium.

[0004] To achieve the above objectives, a first aspect of the present invention provides an acoustic monitoring method for the operating status of a heat exchanger, the method comprising:

[0005] Acquire acoustic signals from the heat exchanger over a period of time.

[0006] Based on the acoustic signal, determine the RMS scattering entropy and acoustic signature characteristics of the acoustic signal during the operating time period.

[0007] Based on the RMS dispersion entropy and the acoustic signature, the operating status of the heat exchanger during this operating period is determined.

[0008] Optionally, based on the acoustic signal, the RMS spread entropy of the acoustic signal during the specified operating time is determined, including:

[0009] The acoustic signal is processed by scaling to obtain multiple multi-scale signals;

[0010] Determine the RMS value of each multi-scale signal and form a data sequence;

[0011] The RMS scattering entropy value of the data sequence is calculated and used as the RMS scattering entropy of the acoustic signal.

[0012] Optionally, the RMS scatter entropy value of the data sequence can be calculated using the following formula:

[0013]

[0014] Where RMS is the RMS scattering entropy value; x(t) is the data sequence; and τ is the scaling factor.

[0015] Optionally, based on the acoustic signal, the acoustic signature characteristics of the acoustic signal during the specified operating time are determined, including:

[0016] The acoustic signal is subjected to time-domain transformation, Fourier transform, filtering, and differentiation to obtain the voiceprint features.

[0017] Optionally, the acoustic signal is subjected to time-domain transformation, Fourier transform, filtering, and differentiation to obtain voiceprint features, including:

[0018] Convert the acoustic emission signal into a time-domain signal;

[0019] Based on the time-domain signal, a linear spectrum is obtained using Fourier transform;

[0020] The linear spectrum is converted into a Mel spectrum using a Mel frequency filter bank.

[0021] The logarithmic energy and logarithmic spectrum of the Mel spectrum are taken, and the first derivative is performed to obtain the Mel frequency cepstral coefficients.

[0022] Based on the Mel frequency cepstral coefficients, the voiceprint features are obtained.

[0023] Optionally, based on the RMS dispersion entropy and the acoustic signature characteristics, the operating status of the heat exchanger during this operating period is determined, including:

[0024] The RMS dispersion entropy and the voiceprint features are used as inputs to the leakage identification model to obtain the leakage identification result;

[0025] Based on the leak identification results, the operating status of the heat exchanger during this operating period is determined.

[0026] Optionally, the method further includes:

[0027] Obtain a training dataset, which includes the corresponding RMS scattering entropy and voiceprint features obtained based on historical acoustic signals;

[0028] Based on the training dataset and convolutional neural network, a leak detection model is trained.

[0029] A second aspect of the present invention provides an acoustic monitoring device for the operating status of a heat exchanger, the device comprising:

[0030] The data acquisition module is used to acquire acoustic signals from the heat exchanger over a certain operating period.

[0031] The data conversion module is used to determine the RMS spread entropy and acoustic signature characteristics of the acoustic signal of the heat exchanger during the operating time period based on the acoustic signal.

[0032] The operating status determination module is used to determine the operating status of the heat exchanger during the specified operating time period based on the RMS dispersion entropy and the acoustic signature characteristics.

[0033] Optionally, the running status determination module is specifically used for:

[0034] The RMS dispersion entropy and the voiceprint features are used as inputs to the leakage identification model to obtain the leakage identification result;

[0035] Based on the leak identification results, the operating status of the heat exchanger during this operating period is determined.

[0036] A third aspect of the present invention provides an acoustic monitoring system for the operating status of a heat exchanger, the system comprising:

[0037] Multiple acoustic sensors are mounted on the wall of the heat exchanger, and all the acoustic sensors are located at the ends of the tube bundle of the heat exchanger.

[0038] The aforementioned acoustic monitoring device for heat exchanger operation status is electrically connected to the acoustic sensor.

[0039] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described acoustic monitoring method for heat exchanger operating status.

[0040] On the other hand, the present invention provides a readable storage medium storing instructions for causing a machine to perform the above-described acoustic monitoring method for heat exchanger operating status.

[0041] This technical solution acquires the acoustic signal of the heat exchanger during a certain operating period to obtain the corresponding RMS scattering entropy and acoustic signature features. Based on the RMS scattering entropy and acoustic signature features, the operating status of the heat exchanger during that operating period can be determined. This allows for the identification of sudden operating events such as heat exchanger tube bundle leakage from strong background noise, enabling accurate judgment of whether there are any abnormalities in the heat exchanger and thus accurately determining the operating status of the heat exchanger.

[0042] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0043] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0044] Figure 1 This is a flowchart of the acoustic monitoring method for heat exchanger operating status provided by the present invention;

[0045] Figure 2 This is a flowchart of determining RMS spread entropy based on acoustic signals provided by the present invention;

[0046] Figure 3 This is a flowchart of the process for determining voiceprint features based on acoustic signals provided by the present invention;

[0047] Figure 4 This is a schematic diagram of the acoustic monitoring device for heat exchanger operating status provided by the present invention;

[0048] Figure 5 This is a schematic diagram of the acoustic monitoring system for heat exchanger operation status provided by the present invention.

[0049] Explanation of reference numerals in the attached figures

[0050] 10 - Data acquisition module; 20 - Data conversion module;

[0051] 30 - Operating status determination module; 41 - Heat exchanger;

[0052] 42-Acoustic sensor; 43-Acoustic monitoring device for heat exchanger operating status. Detailed Implementation

[0053] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0054] In the embodiments of the present invention, unless otherwise stated, directional terms such as "up," "down," "left," and "right" generally refer to the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use.

[0055] The terms “first”, “second”, etc., are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0056] The terms "parallel" and "perpendicular" do not mean that the components must be absolutely parallel or perpendicular, but rather that they can be slightly tilted. For example, "parallel" simply means that its direction is more parallel than "perpendicular," not that the structure must be completely parallel, but that it can be slightly tilted.

[0057] The terms "horizontal," "vertical," and "sag" do not imply that a component must be absolutely horizontal, vertical, or sagging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," not that the structure must be completely horizontal, but can be slightly tilted.

[0058] Furthermore, terms like "roughly" and "basically" are used to indicate that the content does not require absolute precision, but rather allows for a certain degree of deviation. For example, "roughly equal" does not simply mean absolute equality; in actual production and operation, achieving absolute "equality" is difficult, and a certain degree of deviation is generally present. Therefore, besides absolute equality, "roughly equal to" also includes the aforementioned situation where a certain degree of deviation exists. Using this as an example, in other cases, unless otherwise specified, terms like "roughly" and "basically" have similar meanings.

[0059] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0060] Figure 1 This is a flowchart of the acoustic monitoring method for heat exchanger operating status provided by the present invention; Figure 2 This is a flowchart of determining RMS spread entropy based on acoustic signals provided by the present invention; Figure 3 This is a flowchart of the process for determining voiceprint features based on acoustic signals provided by the present invention; Figure 4 This is a schematic diagram of the acoustic monitoring device for heat exchanger operating status provided by the present invention; Figure 5 This is a schematic diagram of the acoustic monitoring system for heat exchanger operation status provided by the present invention.

[0061] Example 1

[0062] This invention provides an acoustic monitoring method for the operating status of a heat exchanger, such as... Figure 1 As shown, the method includes:

[0063] Step 1: Acquire acoustic signals from the heat exchanger during a certain operating period;

[0064] Specifically, in this embodiment, the acoustic signal is acquired by an acoustic sensor. During normal operation of the heat exchanger, the acoustic signal monitored is a stable fluid flow acoustic signal A. When a tube bundle leak occurs, the internal and external pressure difference causes the medium to flow through the leak hole and friction leak hole, which superimposes with the flow of the heat exchange fluid to form an acoustic signal B. For the identification of a leak event, it is necessary to accurately identify this subtle change.

[0065] Step 2: Based on the acoustic signal, determine the RMS scattering entropy and acoustic signature characteristics of the acoustic signal during the specified operating time.

[0066] Specifically, in this embodiment, such as Figure 2 As shown, the RMS scattering entropy of the acoustic signal is obtained in the following manner, specifically including:

[0067] Step 211: Perform scale-scaling on the acoustic signal to obtain multiple multi-scale signals;

[0068] In the process of scaling, the scale factor is usually set to 20 or 30.

[0069] Step 212: Determine the RMS value of each multi-scale signal and form a data sequence;

[0070] Specifically, the root mean square (RMS) of a multi-scale signal x(t) that varies continuously with time over the time interval (0, t) can be expressed as:

[0071]

[0072] Where rms is the root mean square RMS; x(t) is the acoustic signal; and τ is the scaling factor.

[0073] Meanwhile, the following formula data sequence is used:

[0074]

[0075] Step 213: Calculate the RMS scattering entropy value of the data sequence as the RMS scattering entropy of the acoustic signal.

[0076] In step 213, the RMS scatter entropy value of the data sequence is calculated using the following formula:

[0077]

[0078] Where RMS is the RMS scatter entropy value; x(t) is the data sequence; τ is the scale factor, which takes a positive integer value. If τ = 1, it is the original time-domain signal. When τ > 1, the original time-domain signal is divided into τ scaled sequences of length N / τ. By coarsening the original signal, τ multi-scale signals are obtained. However, in the coarsening process, when τ is too small, it is difficult to completely extract the state feature information from the picked-up switch machine vibration signal. As the scale factor τ increases, if τ is too large, the number of discrete points in the resulting new sequence will be large. The entropy value is greatly reduced, which may lead to the loss of the feature information represented by the obtained entropy value. Therefore, a coarse-grained method of continuous shifting and averaging can be adopted to improve the accuracy and computational stability of the entropy value. m, c, and d are constants. When extracting multi-scale RMS scatter entropy, four parameters need to be selected: number of categories c, embedding dimension m, time delay d, and scale factor τ. Usually, the embedding dimension m and the number of categories c should not be too small or too large. m is usually 2 or 3, c is an integer between 4 and 8, and the time delay d is generally 1. The length of the data to be processed should be greater than 2000.

[0079] In this embodiment, to address the complex changes in acoustic signals after leakage, RMS scatter entropy is calculated quickly, is less affected by abrupt signal changes, and considers the magnitude relationship between amplitudes. To improve the recognizability of leakage signals, RMS scatter entropy information of acoustic detection signals at different scales is extracted. Combined with the low-frequency continuity and other characteristics of leakage acoustic signals, the signal data is coarsely processed, and a multi-scale RMS scatter entropy feature extraction method based on RMS is established, which can ensure the accuracy of the data.

[0080] Specifically, in this embodiment, the acoustic signature features of the acoustic signal are obtained in the following manner, including:

[0081] The acoustic signal is subjected to time-domain transformation, Fourier transform, filtering, and differentiation to obtain the voiceprint features.

[0082] More specifically, such as Figure 3 As shown, it includes:

[0083] Step 221: Convert the acoustic emission signal into a time-domain signal;

[0084] Specifically, time-domain signals can describe the relationship between mathematical functions or physical signals and time. For example, the time-domain waveform of an acoustic signal can express how the signal changes over time.

[0085] Step 222: Based on the time-domain signal, obtain the linear spectrum using Fourier transform;

[0086] The Fourier transform can be performed using either the Discrete Fourier Transform or the Fast Fourier Transform. Alternatively, the Laplace transform or the Z-transform can be used.

[0087] Step 223: Convert the linear spectrum into a Mel spectrum using a Mel frequency filter bank;

[0088] Specifically, because frequency domain signals have a lot of redundancy, filter banks can simplify the amplitude in the frequency domain, using a single value to represent each frequency band. This is achieved by multiplying the amplitude spectrum obtained from the FFT with the frequency of each filter and accumulating the results; the resulting value represents the energy of the frame data in the corresponding frequency band of that filter.

[0089] Step 224: Take the logarithmic energy and logarithmic spectrum of the Mel spectrum, and perform first-order differentiation to obtain the Mel frequency cepstral coefficients;

[0090] Specifically, in step 224, the cepstrum means: performing a Fourier transform on the time-domain signal, taking the logarithm, and then performing an inverse Fourier transform. It can be divided into complex cepstrum, real cepstrum, and power cepstrum; we use the power cepstrum. Cepstrum analysis can be used to decompose signals, transforming the convolution of two signals into their sum. Specifically, taking the logarithmic energy and logarithmic spectrum of the Mel spectrum and performing first-order differentiation yields the Mel frequency cepstrum coefficients, which can further amplify the difference between the anomalous signal and the generated signal, facilitating the detection of anomalous signals.

[0091] Step 225: Obtain the voiceprint features based on the Mel frequency cepstral coefficients.

[0092] Step 3: Based on the RMS dispersion entropy and the acoustic signature characteristics, determine the operating status of the heat exchanger during this operating period.

[0093] Specifically, in this embodiment, the RMS dispersion entropy and the voiceprint features are used as inputs to the leakage identification model to obtain the leakage identification result;

[0094] Based on the leak identification results, the operating status of the heat exchanger during this operating period is determined.

[0095] More specifically, the input to the leak identification model is the RMS scattering entropy and the acoustic signature features, and the output of the leak identification model is 0 or 1. 0 represents that the signal is not a normal acoustic signal of the heat exchanger tube bundle operation state, i.e., an abnormal signal; 1 represents that the signal is a normal acoustic signal of the heat exchanger tube bundle operation state.

[0096] In this embodiment, the method further includes:

[0097] Obtain a training dataset, which includes the corresponding RMS scattering entropy and voiceprint features obtained based on historical acoustic signals;

[0098] Based on the training dataset and convolutional neural network, a leak detection model is trained.

[0099] Specifically, before real-time monitoring, based on historical acoustic signals collected by acoustic sensors (including acoustic signals from normal operation and abnormal operation of the heat exchanger tube bundles), the corresponding RMS scattering entropy and acoustic signature features are obtained using the method described above. These RMS scattering entropy and acoustic signature features are then used as a training set to train a convolutional neural network, ultimately resulting in a leak detection model. During real-time monitoring, the corresponding RMS scattering entropy and acoustic signature features are first obtained based on the actual collected acoustic signals. These features are then used as input to the leak detection model, with the final output being either 0 or 1. 0 represents that the signal is not a normal acoustic signal indicating normal operation of the heat exchanger tube bundles, i.e., an abnormal signal; 1 represents that the signal is a normal acoustic signal indicating normal operation of the heat exchanger tube bundles.

[0100] More specifically, convolutional neural networks possess translation invariance, enabling them to better capture anomalous signal features. This allows for more accurate and effective identification of abnormal events. A leak detection model is built using a one-dimensional convolutional neural network, consisting of several convolutional layers and fully connected layers, including:

[0101] The received signal is divided according to the window size W, and the windows overlap by Z.

[0102] During the training phase, assuming the input is x, some noise is added to x to generate x~. x~ is then encoded and decoded to reconstruct x′. x and x′ are input to a discriminator, which determines whether the signal belongs to the generated signal or the real signal. Ideally, after training, a generator capable of reconstructing x~ into a distribution similar to x can be obtained.

[0103] During the testing phase, x is input into the generator, and the difference between x and the generator's output x′ is judged. When the difference is greater than a certain value, x is an abnormal signal and the output is 0; when the difference is less than a certain value, x is a normal signal and the output is 1.

[0104] To verify the monitoring effect of the heat exchanger, four sensors were installed on the outer heat exchanger head of Qilu Petrochemical, collecting 800 sets of acoustic signals with different leakage orifice diameters. 200 sets of noise signals were mixed in for identification. The accuracy of conventional models such as the CNN model was 79.50%, the accuracy of the empirical database matching model was 74.60%, and the identification accuracy of the online acoustic monitoring and diagnosis model for heat exchanger tube bundle leakage was 97.3%.

[0105] In another implementation, if the acoustic signal generating component is the same as the tube bundle in working principle, the above-mentioned scheme can also be used for fault analysis, identification and diagnosis, so as to obtain the operating status of the component with the same working principle as the tube bundle.

[0106] Example 2

[0107] This invention provides an acoustic monitoring device for the operating status of a heat exchanger, such as... Figure 4 As shown, the device includes:

[0108] Data acquisition module 10 is used to acquire acoustic signals of the heat exchanger during a certain operating time;

[0109] In this embodiment, the acoustic signal is acquired by an acoustic sensor. During normal operation of the heat exchanger, the acoustic signal monitored is a stable fluid flow acoustic signal A. When a tube bundle leak occurs, the internal and external pressure difference causes the medium to flow through the leak hole and friction leak hole, which superimposes with the flow of the heat exchange fluid to form an acoustic signal B. For the identification of a leak event, it is necessary to accurately identify this subtle change.

[0110] Data conversion module 20 is used to determine the RMS spread entropy and acoustic signature characteristics of the acoustic signal of the heat exchanger during the operating time period based on the acoustic signal.

[0111] The data conversion module 20 is specifically used for:

[0112] The acoustic signal is processed by scaling to obtain multiple multi-scale signals;

[0113] Determine the RMS value of each multi-scale signal and form a data sequence;

[0114] The RMS scattering entropy value of the data sequence is calculated and used as the RMS scattering entropy of the acoustic signal.

[0115] The data conversion module 20 is also specifically used for:

[0116] Convert the acoustic emission signal into a time-domain signal;

[0117] Based on the time-domain signal, a linear spectrum is obtained using Fourier transform;

[0118] The linear spectrum is converted into a Mel spectrum using a Mel frequency filter bank.

[0119] The logarithmic energy and logarithmic spectrum of the Mel spectrum are taken, and the first derivative is performed to obtain the Mel frequency cepstral coefficients.

[0120] Based on the Mel frequency cepstral coefficients, the voiceprint features are obtained.

[0121] The operating status determination module 30 is used to determine the operating status of the heat exchanger during the operating time period based on the RMS dispersion entropy and the acoustic signature characteristics.

[0122] Furthermore, the operating status determination module is specifically used for:

[0123] The RMS dispersion entropy and the voiceprint features are used as inputs to the leakage identification model to obtain the leakage identification result;

[0124] Based on the leak identification results, the operating status of the heat exchanger during this operating period is determined.

[0125] Specifically, in this embodiment, the leakage identification model is obtained in the following manner:

[0126] Obtain a training dataset, which includes the corresponding RMS scattering entropy and voiceprint features obtained based on historical acoustic signals;

[0127] Based on the training dataset and convolutional neural network, a leak detection model is trained.

[0128] Specifically, before real-time monitoring, based on historical acoustic signals collected by acoustic sensors (including acoustic signals from normal operation and abnormal operation of the heat exchanger tube bundles), the corresponding RMS scattering entropy and acoustic signature features are obtained using the method described above. These RMS scattering entropy and acoustic signature features are then used as a training set to train a convolutional neural network, ultimately resulting in a leak detection model. During real-time monitoring, the corresponding RMS scattering entropy and acoustic signature features are first obtained based on the actual collected acoustic signals. These features are then used as input to the leak detection model, with the final output being either 0 or 1. 0 represents that the signal is not a normal acoustic signal indicating normal operation of the heat exchanger tube bundles, i.e., an abnormal signal; 1 represents that the signal is a normal acoustic signal indicating normal operation of the heat exchanger tube bundles.

[0129] Example 3

[0130] This embodiment provides an acoustic monitoring system for the operating status of a heat exchanger, such as... Figure 5 As shown, the system includes:

[0131] Multiple acoustic sensors 42 are disposed on the wall of the heat exchanger 41, and all acoustic sensors 42 are located at the ends of the tube bundle of the heat exchanger 41.

[0132] The aforementioned acoustic monitoring device 43 for heat exchanger operation status is electrically connected to the acoustic sensor 42.

[0133] Specifically, the number of acoustic sensors 42 can be determined based on the external volume of the heat exchanger. If the tower volume is large, more acoustic sensors 42 should be installed; if the tower volume is small, fewer acoustic sensors 42 should be installed. Furthermore, the acoustic sensors 42 can be symmetrically arranged or installed according to the relative position of the tube bundle within the heat exchanger, ensuring the closest possible relative distance between the acoustic sensors 42 and the heat exchanger tube bundle, thereby guaranteeing better quality acoustic signals. The acoustic sensors 42 are disposed on the outer surface of the heat exchanger. Specifically, four acoustic sensors can be installed at each end of the heat exchanger, with each end sensor having a rectangular structure and symmetrically distributed.

[0134] Example 4

[0135] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an acoustic monitoring method for the operating status of a heat exchanger.

[0136] Example 5

[0137] This embodiment provides a readable storage medium storing instructions for causing a machine to execute the above-described acoustic monitoring method for heat exchanger operating status.

[0138] Example 6

[0139] To verify the monitoring effect of the heat exchanger, four sensors were installed on the outer heat exchanger head of Qilu Petrochemical, collecting 800 sets of acoustic signals with different leakage orifice diameters. 200 sets of noise signals were mixed in for identification. The accuracy of conventional models such as the CNN model was 79.50%, the accuracy of the empirical database matching model was 74.60%, and the identification accuracy of the online acoustic monitoring and diagnosis model for heat exchanger tube bundle leakage was 97.3%.

[0140] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0141] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application.

[0142] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0143] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. An acoustic monitoring method for the operating status of a heat exchanger, characterized in that, The method includes: Acquire acoustic signals from the heat exchanger over a period of time. Based on the acoustic signal, determine the RMS scattering entropy and acoustic signature characteristics of the acoustic signal during the operating time period. Based on the RMS dispersion entropy and the acoustic signature, the operating status of the heat exchanger during this operating period is determined.

2. The method according to claim 1, characterized in that, Based on the acoustic signal, determine the RMS spread entropy of the acoustic signal during the specified operating time, including: The acoustic signal is processed by scaling to obtain multiple multi-scale signals; Determine the RMS value of each multi-scale signal and form a data sequence; The RMS scattering entropy value of the data sequence is calculated and used as the RMS scattering entropy of the acoustic signal.

3. The method according to claim 2, characterized in that, The RMS scatter entropy of the data sequence is calculated using the following formula: Where RMS is the RMS scattering entropy value; x(t) is the data sequence; and τ is the scaling factor.

4. The method according to claim 1, characterized in that, Based on the acoustic signal, the acoustic signature characteristics of the acoustic signal during this operating time period are determined, including: The acoustic signal is subjected to time-domain transformation, Fourier transform, filtering, and differentiation to obtain the voiceprint features.

5. The method according to claim 4, characterized in that, The acoustic signal is subjected to time-domain transformation, Fourier transform, filtering, and differentiation to obtain voiceprint features, including: Convert the acoustic emission signal into a time-domain signal; Based on the time-domain signal, a linear spectrum is obtained using Fourier transform; The linear spectrum is converted into a Mel spectrum using a Mel frequency filter bank. The logarithmic energy and logarithmic spectrum of the Mel spectrum are taken, and the first derivative is performed to obtain the Mel frequency cepstral coefficients. Based on the Mel frequency cepstral coefficients, the voiceprint features are obtained.

6. The method according to claim 1, characterized in that, Based on the RMS dispersion entropy and the acoustic signature characteristics, the operating status of the heat exchanger during this operating period is determined, including: The RMS dispersion entropy and the voiceprint features are used as inputs to the leakage identification model to obtain the leakage identification result; Based on the leak identification results, the operating status of the heat exchanger during this operating period is determined.

7. The method according to claim 6, characterized in that, The method further includes: Obtain a training dataset, which includes the corresponding RMS scattering entropy and voiceprint features obtained based on historical acoustic signals; Based on the training dataset and convolutional neural network, a leak detection model is trained.

8. An acoustic monitoring device for the operating status of a heat exchanger, characterized in that, The device includes: The data acquisition module is used to acquire acoustic signals from the heat exchanger over a certain operating period. The data conversion module is used to determine the RMS spread entropy and acoustic signature characteristics of the acoustic signal of the heat exchanger during the operating time period based on the acoustic signal. The operating status determination module is used to determine the operating status of the heat exchanger during the specified operating time period based on the RMS dispersion entropy and the acoustic signature characteristics.

9. The apparatus according to claim 8, characterized in that, The operating status determination module is specifically used for: The RMS dispersion entropy and the voiceprint features are used as inputs to the leakage identification model to obtain the leakage identification result; Based on the leak identification results, the operating status of the heat exchanger during this operating period is determined.

10. An acoustic monitoring system for the operating status of a heat exchanger, characterized in that, The system includes: Multiple acoustic sensors are mounted on the wall of the heat exchanger, and all the acoustic sensors are located at the ends of the tube bundle of the heat exchanger. The acoustic monitoring device for heat exchanger operating status according to any one of claims 8-9, wherein the acoustic monitoring device for heat exchanger operating status is electrically connected to the acoustic sensor.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the acoustic monitoring method for the operating status of the heat exchanger as described in any one of claims 1-7.

12. A readable storage medium storing instructions, characterized in that, This instruction is used to cause the machine to perform the acoustic monitoring method for the operating status of the heat exchanger as described in any one of claims 1-7.

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