Impedance tomography monitoring method for industrial electric heating equipment based on running electric heating signal source

Abstract

The application discloses an industrial electric heating equipment impedance tomography monitoring method based on an operating electric heating signal source. The method comprises the following steps: receiving a boundary response signal collected by an electrode array in a passive mode, sampling the signal according to a signal sampling rule, and obtaining corresponding boundary measurement data; pre-processing the boundary measurement data according to a pre-processing rule, extracting effective feature data from the pre-processing data according to a data extraction rule, and obtaining corresponding internal impedance distribution images by impedance inversion on the effective feature data according to an impedance inversion strategy and detection information collected by a detection circuit; and identifying abnormal state recognition results according to an abnormal state recognition model. The above method uses an existing operating electric heating signal source in the equipment operation process to realize boundary response collection and impedance inversion, thereby reducing the system structure complexity, improving the online deployment adaptability, and enhancing the application effect of the internal impedance distribution monitoring, the conductive state recognition and the abnormal state recognition of the industrial electric heating equipment.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment monitoring and impedance tomography technology, and in particular to an impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source. Background Technology

[0002] Industrial electrothermal equipment is widely used in industrial scenarios such as graphitization, metallurgy, calcination, heat treatment, and material sintering. The internal conductivity, impedance distribution, thermal field status, and local anomalies of the equipment directly affect the safety and stability of the production process. Therefore, online monitoring of the internal impedance distribution and abnormal status of industrial electrothermal equipment is of great significance.

[0003] Existing methods for monitoring the internal condition of industrial electrothermal equipment typically include current detection, voltage detection, furnace resistance detection, temperature detection, infrared thermography, and process tomography. Among these, impedance tomography (ETT) monitoring is increasingly being applied to the field of internal condition monitoring of industrial electrothermal equipment due to its non-invasive nature and ability to monitor cross-sectional distribution. Current ETT monitoring schemes typically employ an active excitation method, where an external excitation source applies an excitation current or voltage signal to the equipment under test or its internal materials. The response signal is then acquired by measuring electrodes placed at the equipment boundary, and the internal impedance distribution of the equipment is reconstructed based on the boundary measurement data.

[0004] However, existing active-excitation impedance tomography monitoring schemes still have the following problems: First, they require dedicated excitation sources and supporting sampling links, resulting in complex system structures. Second, when deployed online in industrial settings, active excitation devices are easily limited by installation conditions, high-temperature environments, and complex electromagnetic environments. Third, industrial electrothermal equipment typically generates existing operating electrothermal signals such as heating current signals, electrode power supply signals, busbar coupling signals, power frequency coupling signals, and background electromagnetic disturbances generated during equipment operation, but existing schemes do not adequately utilize these signal resources. Therefore, existing methods for monitoring the internal condition of industrial electrothermal equipment cannot perform passive impedance tomography monitoring. Summary of the Invention

[0005] This invention provides an impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source, aiming to solve the problem that existing methods for monitoring the internal state of industrial electrothermal equipment cannot perform passive impedance tomography monitoring.

[0006] In a first aspect, embodiments of the present invention provide an impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source. The method is applied to a monitoring terminal, which is communicatively connected to an electrode array and a detection circuit installed on the industrial electrothermal equipment. The electrode array is non-invasively disposed on the outside of the component under test (DUT) of the industrial electrothermal equipment. The electrode array includes multiple measuring electrodes and is used to acquire boundary response signals formed on the DUT side under the action of the operating electrothermal signal source. The method includes: The boundary response signal passively acquired by the electrode array is received, and the boundary response signal is sampled according to a preset signal sampling rule to obtain the corresponding boundary measurement data; The boundary measurement data is preprocessed according to preset preprocessing rules to obtain corresponding preprocessed data; According to the preset data extraction rules, the corresponding valid feature data is extracted from the preprocessed data; Based on the preset impedance inversion strategy and the detection information acquired by the detection circuit, impedance inversion is performed on the effective feature data to obtain the corresponding internal impedance distribution image. The internal impedance distribution image is identified based on a preset abnormal state identification model to obtain the corresponding abnormal state identification result.

[0007] Secondly, embodiments of the present invention also provide an impedance tomography monitoring device for industrial electrothermal equipment based on an operating electrothermal signal source. The device is configured in a monitoring terminal, which is communicatively connected to an electrode array and a detection circuit disposed on the industrial electrothermal equipment. The electrode array is non-invasively disposed on the outside of the component under test of the industrial electrothermal equipment. The electrode array includes multiple measuring electrodes and is used to acquire boundary response signals formed on the side of the component under test under the action of the operating electrothermal signal source. The device is used to execute the impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source as described in the first aspect above. The device includes: The boundary measurement data acquisition unit is used to receive the boundary response signal passively acquired by the electrode array, sample the boundary response signal according to the preset signal sampling rules, and obtain the corresponding boundary measurement data. The preprocessing unit is used to preprocess the boundary measurement data according to preset preprocessing rules to obtain corresponding preprocessed data. The effective feature data acquisition unit is used to extract corresponding effective feature data from the preprocessed data according to preset data extraction rules; Impedance inversion processing unit is used to perform impedance inversion on the effective feature data according to the preset impedance inversion strategy and the detection information acquired by the detection circuit, so as to obtain the corresponding internal impedance distribution image. The abnormal state identification result acquisition unit is used to identify the internal impedance distribution image according to the preset abnormal state identification model and obtain the corresponding abnormal state identification result.

[0008] Thirdly, embodiments of the present invention also provide a computer device, wherein the device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source as described in the first aspect above.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source as described in the first aspect above.

[0010] This invention provides an impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source. The method includes: receiving a boundary response signal passively acquired by an electrode array; sampling the boundary response signal according to a preset signal sampling rule to obtain corresponding boundary measurement data; preprocessing the boundary measurement data according to a preset preprocessing rule to obtain corresponding preprocessed data; extracting corresponding effective feature data from the preprocessed data according to a preset data extraction rule; performing impedance inversion on the effective feature data according to a preset impedance inversion strategy and detection information acquired by the detection circuit to obtain a corresponding internal impedance distribution image; and identifying the internal impedance distribution image according to a preset abnormal state identification model to obtain a corresponding abnormal state identification result. This method utilizes an existing operating electrothermal signal source during equipment operation to achieve boundary response acquisition and impedance inversion, thereby reducing system structural complexity, improving online deployment adaptability, and enhancing the application effects of monitoring internal impedance distribution, identifying conductivity states, and identifying abnormal states in industrial electrothermal equipment. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart of the impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source provided in an embodiment of the present invention; Figure 2 Another flowchart of the impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source provided in this embodiment of the invention; Figure 3 A schematic diagram illustrating an application scenario of the impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating another application scenario of the impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source provided in this embodiment of the invention. Figure 5 A schematic block diagram of an impedance tomography monitoring device for industrial electrothermal equipment based on a running electrothermal signal source, provided for an embodiment of the present invention; Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0015] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] This invention application provides an impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source. This method is applied in a monitoring terminal 10, which executes a stored software program to implement the aforementioned impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source. Figure 3 and Figure 4 As shown, the monitoring terminal 10 is communicatively connected to the electrode array 30 and the detection circuit 21 installed on the industrial electrothermal equipment 20. The electrode array 30 is non-invasively installed on the outside of the component under test in the industrial electrothermal equipment 20. The electrode array 30 includes multiple measuring electrodes and is used to collect the boundary response signal formed on the side of the component under test under the action of the operating electrothermal signal source. The monitoring terminal 10 is the terminal component used for signal acquisition and monitoring analysis, such as an edge computing chip (FPGA chip or high-performance DSP chip). During the operation of the industrial electrothermal equipment 20, there is an operating electrothermal signal source. The operating electrothermal signal source is an electrical or electromagnetic signal that is not actively applied by the monitoring terminal 10 or the electrode array 30. The operating electrothermal signal source may include heating current signal, electrode power supply signal, busbar coupling signal, power frequency coupling signal, rectifier power supply ripple signal, background electromagnetic disturbance signal generated by transformer operation, or other existing operating signals that can form a measurable response at the boundary of the industrial electrothermal equipment. The internal material 23 of the component 22 under test in the industrial electrothermal equipment is the object of measurement. Under the action of the operating electrothermal signal source, the boundary of the component 22 under test in the industrial electrothermal equipment 20 forms a measurable boundary response. An electrode array 30 disposed on the outer wall of the component under test passively acquires the boundary response and transmits the acquired boundary response signal to the monitoring terminal 10. The monitoring terminal 10 then processes the signal to obtain the abnormal state identification result inside the industrial electrothermal equipment. Figure 1 As shown, the method includes steps S110 to S150.

[0018] S110. Receive the boundary response signal passively acquired by the electrode array, sample the boundary response signal according to the preset signal sampling rules, and obtain the corresponding boundary measurement data.

[0019] In this embodiment, the cross-sectional area to be measured of the industrial electrothermal equipment is denoted as Ω, and the boundary region is denoted as... The electrical properties of materials or conductive regions inside industrial electrothermal equipment are characterized using complex admittance, and its expression is: (1); Where r is the position coordinate within the cross-sectional area of ​​the component under test, ω represents the angular frequency, σ(r) represents the conductivity at position r, ε(r) represents the dielectric constant at position r, and j is the imaginary unit.

[0020] Under the influence of an operating electrothermal signal source, the potential distribution of the component under test and its internal materials can be represented by the following equivalent forward model: (2); Where u(r,ω) represents the electric potential distribution, J run (r,ω) represents the equivalent excitation item formed by the operating electrothermal signal source in the component under test and its internal materials. Unlike traditional active excitation impedance tomography, the passive monitoring excitation in this embodiment is not actively applied by the monitoring system, but is equivalently formed by the existing operating electrothermal signal source during the operation of the industrial electrothermal equipment; during the power supply interruption window, the monitoring system can also apply a preset detection signal as auxiliary detection.

[0021] Specifically, the electrode array can be uniformly arranged circumferentially along the cross-section of the component under test, or a non-uniform arrangement can be adopted according to the size of the component under test, installation space, electrode distribution, and monitoring accuracy requirements. The measuring electrodes can be arranged in the form of a ring array, a segmented array, or an axial multi-section array. With the above arrangement, the boundary response of the industrial electrothermal equipment can be collected without changing the original operating state of the industrial electrothermal equipment. The total number of measuring electrodes is denoted as M.

[0022] The electrical signals measured by the M measuring electrodes can then be acquired, and these signals are the boundary response signals. Further, the boundary response signals are sampled according to the signal sampling rules to obtain the corresponding boundary measurement data. In this embodiment, the first... l The boundary region corresponding to each measuring electrode is denoted as e l Then the boundary response of the measuring electrode can be expressed as: (3); Among them, | e l | indicates the first l The length or area measure of the boundary region corresponding to each measuring electrode, V l (ω) represents the first... l The boundary response values ​​of each measuring electrode at angular frequency ω l ∈[1,M].

[0023] In a specific embodiment, step S110 includes the following sub-steps: adjusting the boundary response signal according to the adjustment model configured in the signal sampling rules to obtain a corresponding adjustment signal; polling the signal values ​​of each measurement electrode in the adjustment signal according to the polling sampling order configured in the signal sampling rules to obtain corresponding polling sampling data; and discretizing the polling sampling data according to the discrete measurement model configured in the signal sampling rules to obtain corresponding boundary measurement data.

[0024] Specifically, the boundary response signal can first be adjusted according to the adjustment model. This involves obtaining the distance between the measurement electrode location and the center point of the measured cross-section region in the component under test, and then amplifying or reducing the corresponding electrical signal of the boundary response signal based on this distance. For example, the adjustment function configured in the adjustment model could be: (4); y(r,ω)' is the adjusted electrical signal, y(r,ω) is the initial point signal, and D r,ω D0 is the reference distance between the location of the measuring electrode and the center point of the cross-sectional area to be measured in the component under test. This adjustment method can amplify the signal in the boundary response signal that is weakened by the detection distance.

[0025] Further, the signal values ​​of each measuring electrode in the adjustment signal are polled and sampled according to the polling sampling order to obtain corresponding polling sampling data. Here, sampling is performed by sequentially scanning the adjustment signals of each measuring electrode in a set polling sampling order to obtain corresponding polling sampling data. The sampling process involves converting the analog boundary response signal into digital boundary measurement data. Furthermore, to improve the reliability of the obtained polling sampling data, the signal values ​​in the obtained polling sampling data can be time-aligned; for example, the signal values ​​of each measuring electrode within a unified polling period are time-aligned to ensure the consistency of the sampling timing of each signal value in the polling sampling data.

[0026] Furthermore, the polling sampling data is discretized according to the configured discrete measurement model to obtain the corresponding boundary measurement data. In this embodiment, the finite element method can be used to discretize the cross-sectional area to be measured of the industrial electrothermal equipment, resulting in the following discretized forward model: (5); in, Indicated by the complex admittance distribution γ The system stiffness matrix is ​​determined by the matrix, where u represents the nodal potential vector and f is the system stiffness matrix. run This represents the equivalent excitation vector corresponding to the operating electrothermal signal source. Further, the configured discrete measurement model can be expressed as: (6); Where H represents the measurement mapping matrix and n represents the noise term. By obtaining the discrete boundary measurement data of all measuring electrodes through equation (6), the discrete boundary measurement data of the industrial electrothermal equipment can be obtained, that is, the boundary response of all measuring electrodes constitutes the boundary measurement vector, specifically expressed as: (7); y represents the boundary measurement data of the industrial electric heating equipment at the current moment, and T is the transpose symbol.

[0027] In a specific embodiment, the electrode array further includes at least one auxiliary excitation electrode; such as Figure 2 As shown, before sampling the boundary response signal according to the preset signal sampling rules to obtain the corresponding boundary measurement data, the method further includes: S101, determining whether the detection information meets the preset power supply pause window conditions; S102, if the power supply pause window conditions are met, generating a corresponding excitation command according to the preset active detection rules and sending it to the electrode array to apply the corresponding DC detection signal or AC detection signal to the industrial electrothermal equipment through the measuring electrode or auxiliary excitation electrode in the electrode array; S103, receiving the active boundary response data actively collected by the electrode array as the corresponding boundary measurement data; if the power supply pause window conditions are not met, performing the step of sampling the boundary response signal according to the preset signal sampling rules.

[0028] Specific embodiments of this application also include auxiliary active detection steps under power supply interruption windows. Specifically, a power supply interruption window refers to the time period during which the main power supply of the industrial electric heating equipment is in a phased stop, power supply intermittent, commutation gap, low current operation window, or preset power supply interruption detection window. Within the power supply interruption window, the main heating current or main power supply of the industrial electric heating equipment is lower than a preset threshold, or the industrial electric heating equipment control system outputs a detection-allowed status signal, thereby allowing the monitoring system to apply a detection signal with limited amplitude to the measured area.

[0029] The detection circuit can detect the operating current, voltage, power, power supply control commands, commutation status, rectifier power supply working status, preset detection sequence, and other detection information of industrial electric heating equipment, and determine whether the detection information meets the power supply interruption window conditions. The power supply interruption window conditions are configured with corresponding detection judgment value ranges. If all values ​​in the detection information are within the corresponding detection judgment value range, it is determined that the power supply interruption window conditions are met. If a value is not within the corresponding detection judgment value range, it is determined that the power supply interruption window conditions are not met.

[0030] If the power supply interruption window condition is met, the system switches to auxiliary active detection mode. A corresponding excitation command is generated according to preset active detection rules and sent to the electrode array. This allows the application of a corresponding DC or AC detection signal to the industrial electrothermal equipment via the measuring electrodes or auxiliary excitation electrodes in the electrode array. Alternatively, auxiliary active detection can be initiated in response to a manual detection command input by the operator when the power supply interruption window condition is met. Before applying the preset DC or AC detection signal, it should be confirmed that the main power supply to the industrial electrothermal equipment is in an isolated state, or that the residual voltage and residual current are below a preset safety threshold.

[0031] In the assisted active detection mode, corresponding DC or AC detection signals can be applied to the industrial electrothermal equipment through at least one set of measuring electrodes in the electrode array; alternatively, corresponding DC or AC detection signals can be applied to the industrial electrothermal equipment through a separately configured auxiliary excitation electrode. The DC detection signal can be a constant current signal or a constant voltage signal; the AC detection signal can be a single-frequency AC signal, a multi-frequency AC signal, a swept-frequency AC signal, or a pulse detection signal. The amplitude, frequency, duration, and electrode combination of the detection signal can be preset according to the structural parameters of the industrial electrothermal equipment, the electrode placement, the electrical properties of the materials, and the safety threshold.

[0032] During the application of the preset DC or AC detection signal, active boundary response data is acquired through the electrode array. The monitoring terminal receives the active boundary response data actively acquired by the electrode array as the corresponding boundary measurement data. The active boundary response data includes one or more of the following: electrode potential, inter-electrode voltage, current response, impedance response, amplitude response, phase response, or frequency domain response data. The active boundary response data can be used independently for impedance inversion to obtain an internal impedance distribution image under a power supply interruption window; it can also be used to correct or verify boundary measurement data, preset equivalent excitation models, equivalent sensitivity matrices, or internal impedance distribution images obtained in passive monitoring mode. Specifically, the boundary measurement vector under the reference state can be updated based on the active boundary response data, the equivalent excitation field corresponding to the operating electrothermal signal source can be corrected, the equivalent sensitivity matrix can be reconstructed or dynamically corrected, or the impedance anomaly region, current deviation region, local overheating risk region, or material state anomaly region identified in passive monitoring mode can be verified.

[0033] If the power supply interruption window conditions are not met, the step of sampling the boundary response signal according to the preset signal sampling rules continues. That is, after power is restored, the step of passively acquiring the boundary response signal through the electrode array is switched to be executed.

[0034] S120. The boundary measurement data is preprocessed according to the preset preprocessing rules to obtain the corresponding preprocessed data.

[0035] Furthermore, the obtained boundary measurement data is preprocessed according to the preprocessing rules to obtain the corresponding preprocessed data.

[0036] In a specific embodiment, step S120 includes the following sub-steps: filtering the boundary measurement data according to the filtering parameters in the preprocessing rules to obtain corresponding filtered data; normalizing and correcting the filtered data according to the reference data configured in the preprocessing rules to obtain corresponding corrected data; and removing data outside the numerical range in the corrected data according to the numerical range in the preprocessing rules to obtain corresponding preprocessed data.

[0037] Specifically, the boundary measurement data can be filtered according to the filtering parameters set in the preprocessing rules to obtain the corresponding filtered data. The filtering parameters can be set to a specific frequency range. Specifically, a Fast Fourier Transform (FFT) can be performed on the boundary measurement data to convert the time-domain signal into a frequency-domain signal. Then, based on the filtering parameters, the energy peak value or root mean square value within the specific frequency range is extracted from the frequency-domain signal as the effective signal amplitude, thus obtaining the corresponding filtered data. Further, the filtered data is normalized and corrected according to the reference data configured in the preprocessing rules to obtain the corresponding corrected data. This normalization correction reduces the drift influence between different sampling times and different electrode channels. Then, data outside the numerical range in the corrected data is removed according to the numerical range in the preprocessing rules; this step is the process of removing outliers. After processing, the preprocessed data is obtained. The preprocessing rules may also include one or more of the following: noise reduction, baseline correction, power frequency suppression, synchronization correction, differential processing, and outlier removal.

[0038] In a specific embodiment, the step of normalizing and correcting the filtered data according to the reference data configured in the preprocessing rules to obtain the corresponding corrected data includes: obtaining the normalization coefficients corresponding to each measured value in the filtered data according to the normalization function in the preprocessing rules and the reference data; correcting each measured value according to the normalization coefficients and the reference data to obtain the differential boundary measured values ​​corresponding to each measured value as the corresponding corrected data.

[0039] The reference data is the boundary measurement vector under the reference state. The boundary measurement vector under the reference state can be obtained from the cold calibration data of the industrial electric heating equipment, the normal operation data in the initial stage of production, the boundary measurement data within the stable operating time window, or the historical normal operating database. For continuously operating industrial electric heating equipment, a time window that satisfies the current, voltage, power, or temperature change rate within a preset range can be selected as the stable operating time window, and the boundary measurement data within this time window can be averaged or averaged to obtain the boundary measurement vector under the reference state.

[0040] To reduce the impact of amplitude fluctuations in the operating electrothermal signal source on the measurement results, the normalization coefficients corresponding to each measured value in the filtered data can be obtained according to the normalization function set in the preprocessing rules and the reference data. The normalization coefficients based on the reference state are expressed as follows: (8); Furthermore, based on the obtained normalization coefficients and benchmark data, each of the measured values ​​is corrected to obtain the corresponding difference boundary measurement values ​​as the corresponding correction data. The normalized difference boundary measurement data can be expressed as: (9); Among them, y k y represents the boundary measurement value at the current time k. ref Represents the baseline data, α k Represents the normalization coefficient, Δy k This represents the normalized differential boundary measurement data; by obtaining the differential boundary measurement values ​​of each measurement value through equations (8) and (9), the influence of the amplitude fluctuation of the operating electric heating signal source can be reduced under passive conditions, making the monitoring results more accurate.

[0041] S130. Extract the corresponding valid feature data from the preprocessed data according to the preset data extraction rules.

[0042] Furthermore, based on data extraction rules, corresponding effective feature data are extracted from the preprocessed data. Amplitude features, frequency domain features, time domain features, correlation features, or spatial distribution features in the preprocessed data can be extracted as corresponding effective feature data. That is, effective components related to the internal impedance distribution changes of industrial electrothermal equipment are extracted from the preprocessed data, while background interference components unrelated to imaging are suppressed. Effective feature data are used to characterize boundary response information related to changes in internal impedance distribution, conductivity state, current deviation state, local overheating risk state, or abnormal material state of industrial electrothermal equipment. Specifically, the effective feature data in the target frequency band obtained through frequency domain analysis can be represented as follows: (10); Where F{·} denotes the Fourier transform, ω m y represents the angular frequency corresponding to the target frequency band. eff (ω m ) represents the effective feature data extracted in the target frequency band, and t is the time parameter; through this step, the useful part of the running electrothermal signal can be extracted, providing a more stable input for subsequent impedance inversion.

[0043] S140. Based on the preset impedance inversion strategy and the detection information acquired by the detection circuit, impedance inversion is performed on the effective feature data to obtain the corresponding internal impedance distribution image.

[0044] Furthermore, based on the impedance inversion strategy and the detection information acquired by the detection circuit, impedance inversion can be performed on the effective feature data obtained in the above steps to obtain the corresponding internal impedance distribution image. This step is used to establish the mapping relationship between the impedance inversion input data and the internal impedance distribution of the industrial electrothermal equipment. Impedance inversion can be achieved using differential reconstruction, regularization solution, iterative optimization, data-driven reconstruction, or a combination thereof. For example, the extracted effective boundary response components can be constructed into an inversion input vector or frequency domain boundary feature data, and then combined with the forward model, sensitivity matrix, or preset reconstruction model of the cross-section to be measured of the industrial electrothermal equipment to obtain the internal impedance distribution image of the industrial electrothermal equipment.

[0045] Specifically, since the operating electrothermal signal source is not actively applied by the monitoring system, an equivalent excitation model of the operating electrothermal signal source can be established based on the structural parameters of the industrial electrothermal equipment, electrode placement, power supply method, boundary measurement data under reference conditions, and preset operating parameters. This equivalent excitation model can be constructed using multiphysics simulation, cold-state calibration, stable operating condition calibration, or historical normal operating data. Based on this equivalent excitation model, an equivalent sensitivity matrix can be further constructed or dynamically modified, so that the equivalent sensitivity matrix characterizes the correspondence between the boundary measurement change and the internal complex admittance change of the industrial electrothermal equipment.

[0046] In a specific embodiment, step S140 includes the following sub-steps: mapping the detection information according to the mapping dictionary in the impedance inversion strategy to obtain the corresponding equivalent sensitivity matrix; and inverting and solving the effective feature data according to the regularization objective function of the equivalent excitation model in the impedance inversion strategy and the equivalent sensitivity matrix to obtain the corresponding internal impedance distribution image.

[0047] Specifically, equivalent excitation field dictionaries and sensitivity matrix dictionaries for multiple typical operating conditions can be pre-established as mapping dictionaries. The detection information (current operating current, voltage, power, spectral characteristics, or boundary response characteristics) is mapped according to these mapping dictionaries, and the corresponding equivalent sensitivity matrix is ​​obtained by matching the mappings from the mapping dictionaries. Specifically, matching equivalent excitation fields and equivalent sensitivity matrices can be obtained from the mapping dictionaries, or the sensitivity matrices corresponding to adjacent operating conditions can be interpolated and corrected to obtain the equivalent sensitivity matrix for impedance inversion under the current operating condition.

[0048] In this embodiment, based on the obtained equivalent sensitivity matrix, the boundary measurement change and the internal complex admittance change satisfy the following linearization relationship: (11); Where J represents the equivalent sensitivity matrix, Δ γ Δ represents the change in complex admittance inside industrial electrothermal equipment. y This represents the change in features corresponding to the valid feature data.

[0049] Furthermore, the preprocessed data (changes in internal complex admittance) are inverted and solved based on the regularized objective function and equivalent sensitivity matrix of the equivalent excitation model: (12); Where W represents the weighting matrix, L represents the regularization matrix, and λ represents the regularization parameter. The analytical solution obtained by inversion can be expressed as: (13); The internal impedance distribution image of industrial electrothermal equipment can be obtained by solving the above formula (13).

[0050] S150. The internal impedance distribution image is identified according to the preset abnormal state identification model to obtain the corresponding abnormal state identification result.

[0051] Furthermore, the internal impedance distribution image can be identified based on the abnormal state identification model to obtain the abnormal state identification result. The output abnormal state identification result includes abnormal state identification information and change state identification information; the abnormal state identification information includes impedance abnormal region, conductivity abnormal region, material state abnormal region, and electrode contact abnormal region; the change state identification information includes impedance abnormal region, flow deviation region, and local overheating risk region.

[0052] In a specific embodiment, step S150 includes the following sub-steps: performing local anomaly identification on the internal impedance distribution image according to the anomaly region identification rules in the anomaly state identification model to obtain corresponding anomaly state identification information; performing local change state identification on the internal impedance distribution image according to the anomaly change identification rules in the anomaly state identification model to obtain corresponding change state identification information; and combining the anomaly state identification information with the change state identification information to obtain the corresponding anomaly state identification result.

[0053] Specifically, the internal impedance distribution image can be identified using abnormal region identification rules to determine whether there are local high-resistance or local low-resistance regions in the image. When a local high-resistance or local low-resistance region exists, the corresponding impedance abnormal region can be obtained, and the type of impedance abnormal region can be identified, thereby obtaining the conductivity abnormal region, material abnormal region, or electrode contact abnormal region inside the industrial electrothermal equipment as abnormal state identification information.

[0054] Furthermore, the internal impedance distribution image is subjected to local change state identification according to the abnormal change identification rules. When the internal impedance distribution image shows obvious uneven changes over time (the rate of change of each region is significantly different), the corresponding impedance abnormal region can be obtained. Further identification of the internal flow deviation region or local overheating risk region of the industrial electric heating equipment is used as abnormal state identification information.

[0055] The rules for identifying abnormal regions and abnormal changes can be configured with thresholds for impedance change amount and impedance change rate, respectively. For example, to identify abnormal regions using the impedance change amount threshold, an abnormal region can be defined as: (14); Where |Δγ(r)| represents the magnitude of the change in complex admittance at position p, τ represents the threshold for judging the change in impedance, and Ω abn This represents an abnormal region. Further, the percentage of abnormal regions can be defined as: (15); Among them, |Ω abn | represents the area of ​​the abnormal region, and |Ω| represents the total area of ​​the cross-sectional area to be measured in the industrial electric heating equipment. When η is greater than the preset threshold η0, it can be determined that there is an abnormal impedance, abnormal conductivity, risk of current deviation, risk of local overheating, or abnormal material state inside the industrial electric heating equipment.

[0056] This invention discloses a method for impedance tomography monitoring of industrial electrothermal equipment based on an operating electrothermal signal source. The method includes: receiving a boundary response signal passively acquired by an electrode array; sampling the boundary response signal according to a preset signal sampling rule to obtain corresponding boundary measurement data; preprocessing the boundary measurement data according to a preset preprocessing rule to obtain corresponding preprocessed data; extracting corresponding effective feature data from the preprocessed data according to a preset data extraction rule; performing impedance inversion on the effective feature data according to a preset impedance inversion strategy and detection information acquired by the detection circuit to obtain a corresponding internal impedance distribution image; and identifying the internal impedance distribution image according to a preset abnormal state identification model to obtain a corresponding abnormal state identification result. This method utilizes an existing operating electrothermal signal source during equipment operation to achieve boundary response acquisition and impedance inversion, thereby reducing system structural complexity, improving online deployment adaptability, and enhancing the application effects of monitoring internal impedance distribution, identifying conductivity states, and identifying abnormal states in industrial electrothermal equipment.

[0057] This invention also provides an impedance tomography monitoring device for industrial electrothermal equipment based on an operating electrothermal signal source. This device can be configured in a monitoring terminal 10 and is used to execute any embodiment of the aforementioned impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source. Specifically, please refer to... Figure 5 , Figure 5 This is a schematic block diagram of an impedance tomography monitoring device for industrial electrothermal equipment based on a running electrothermal signal source, provided as an embodiment of the present invention.

[0058] like Figure 5 As shown, the impedance tomography monitoring device 100 for industrial electrothermal equipment based on the operating electrothermal signal source includes a boundary measurement data acquisition unit 110, a preprocessing unit 120, an effective feature data acquisition unit 130, an impedance inversion processing unit 140, and an abnormal state identification result acquisition unit 150.

[0059] The boundary measurement data acquisition unit 110 is used to receive the boundary response signal passively acquired by the electrode array, sample the boundary response signal according to the preset signal sampling rules, and obtain the corresponding boundary measurement data.

[0060] The preprocessing unit 120 is used to preprocess the boundary measurement data according to preset preprocessing rules to obtain corresponding preprocessed data.

[0061] The effective feature data acquisition unit 130 is used to extract the corresponding effective feature data from the preprocessed data according to the preset data extraction rules.

[0062] Impedance inversion processing unit 140 is used to perform impedance inversion on the effective feature data according to the preset impedance inversion strategy and the detection information acquired by the detection circuit, so as to obtain the corresponding internal impedance distribution image.

[0063] The abnormal state identification result acquisition unit 150 is used to identify the internal impedance distribution image according to the preset abnormal state identification model and obtain the corresponding abnormal state identification result.

[0064] The impedance tomography monitoring device for industrial electrothermal equipment based on an operating electrothermal signal source provided in this embodiment of the invention applies the aforementioned impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source. It receives the boundary response signal passively acquired by the electrode array, samples the boundary response signal according to a preset signal sampling rule to obtain corresponding boundary measurement data; preprocesses the boundary measurement data according to a preset preprocessing rule to obtain corresponding preprocessed data; extracts corresponding effective feature data from the preprocessed data according to a preset data extraction rule; performs impedance inversion on the effective feature data according to a preset impedance inversion strategy and the detection information acquired by the detection circuit to obtain a corresponding internal impedance distribution image; and identifies the internal impedance distribution image according to a preset abnormal state identification model to obtain a corresponding abnormal state identification result. This method utilizes the existing operating electrothermal signal source during equipment operation to achieve boundary response acquisition and impedance inversion, thereby reducing system structural complexity, improving online deployment adaptability, and enhancing the application effect of monitoring internal impedance distribution, identifying conductivity status, and identifying abnormal states in industrial electrothermal equipment.

[0065] The aforementioned impedance tomography monitoring device for industrial electrothermal equipment based on an operating electrothermal signal source can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0066] Please see Figure 6 , Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device can be a monitoring terminal used to execute an impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source to achieve signal acquisition and monitoring analysis.

[0067] See Figure 6 The computer device 500 includes a processor 502, a memory, and a communication interface 505 connected via a communication bus 501. The memory may include a storage medium 503 and internal memory 504.

[0068] The storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it enables the processor 502 to execute an impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source. The storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

[0069] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0070] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source.

[0071] This communication interface 505 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device 500 to which the present invention is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0072] The processor 502 is used to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned impedance tomography monitoring method for industrial electrothermal equipment based on the running electrothermal signal source.

[0073] Those skilled in the art will understand that Figure 6 The embodiments of the computer device shown do not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. For example, in some embodiments, the computer device may include only memory and a processor. In such embodiments, the structure and function of the memory and processor are different from those shown. Figure 6 The embodiments shown are consistent and will not be repeated here.

[0074] It should be understood that, in this embodiment of the invention, the processor 502 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0075] In another embodiment of the invention, a computer-readable storage medium is provided. This computer-readable storage medium may be volatile or non-volatile. The computer-readable storage medium stores a computer program, wherein when executed by a processor, the computer program implements the steps included in the above-described impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source.

[0076] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0077] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Units with the same function may be grouped into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0079] Furthermore, the functional units in the various embodiments of the present invention 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.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable 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 the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks.

[0081] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for impedance tomography monitoring of industrial electrothermal equipment based on an operating electrothermal signal source, characterized in that, The method is applied to a monitoring terminal, which is communicatively connected to an electrode array and a detection circuit installed on an industrial electrothermal equipment. The electrode array is non-invasively disposed on the outside of the component under test in the industrial electrothermal equipment. The electrode array includes multiple measuring electrodes and is used to acquire the boundary response signal formed on the side of the component under test under the action of an operating electrothermal signal source. The method includes: The boundary response signal passively acquired by the electrode array is received, and the boundary response signal is sampled according to a preset signal sampling rule to obtain the corresponding boundary measurement data; The boundary measurement data is preprocessed according to preset preprocessing rules to obtain corresponding preprocessed data; According to the preset data extraction rules, the corresponding valid feature data is extracted from the preprocessed data; Based on the preset impedance inversion strategy and the detection information acquired by the detection circuit, impedance inversion is performed on the effective feature data to obtain the corresponding internal impedance distribution image. The internal impedance distribution image is identified based on a preset abnormal state identification model to obtain the corresponding abnormal state identification result.

2. The impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source according to claim 1, characterized in that, The step of sampling the boundary response signal according to a preset signal sampling rule to obtain the corresponding boundary measurement data includes: The boundary response signal is adjusted according to the adjustment model configured in the signal sampling rules to obtain the corresponding adjusted signal; According to the polling sampling order configured in the signal sampling rules, the signal values ​​of each measuring electrode in the adjustment signal are polled and sampled in turn to obtain the corresponding polling sampling data; The polled sampling data is discretized according to the discrete measurement model configured in the signal sampling rules to obtain the corresponding boundary measurement data.

3. The impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source according to claim 1, characterized in that, The step of preprocessing the boundary measurement data according to preset preprocessing rules to obtain corresponding preprocessed data includes: The boundary measurement data is filtered according to the filtering parameters in the preprocessing rules to obtain the corresponding filtered data. The filtered data is normalized and corrected according to the reference data configured in the preprocessing rules to obtain the corresponding corrected data; Data outside the numerical range in the correction data is removed according to the numerical range in the preprocessing rules to obtain the corresponding preprocessed data.

4. The impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source according to claim 3, characterized in that, The step of normalizing and correcting the filtered data according to the reference data configured in the preprocessing rules to obtain the corresponding corrected data includes: The normalization coefficients corresponding to each measurement value in the filtered data are obtained according to the normalization function in the preprocessing rules and the reference data. The measured values ​​are corrected according to the normalization coefficient and the reference data to obtain the differential boundary measured values ​​corresponding to each measured value as the corresponding correction data.

5. The impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source according to any one of claims 1-4, characterized in that, The step of performing impedance inversion on the effective feature data based on a preset impedance inversion strategy and the detection information acquired by the detection circuit to obtain the corresponding internal impedance distribution image includes: The detection information is mapped according to the mapping dictionary in the impedance inversion strategy to obtain the corresponding equivalent sensitivity matrix; The effective feature data are inverted and solved using the regularized objective function of the equivalent excitation model and the equivalent sensitivity matrix in the impedance inversion strategy to obtain the corresponding internal impedance distribution image.

6. The impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source according to claim 5, characterized in that, The electrode array further includes at least one auxiliary excitation electrode. Before sampling the boundary response signal according to a preset signal sampling rule to obtain the corresponding boundary measurement data, the method further includes: Determine whether the detected information meets the preset power outage window conditions; If the power supply interruption window condition is met, a corresponding excitation command is generated according to the preset active detection rules and sent to the electrode array to apply a corresponding DC detection signal or AC detection signal to the industrial electrothermal equipment through the measuring electrode or auxiliary excitation electrode in the electrode array. The active boundary response data actively acquired by the electrode array is received as the corresponding boundary measurement data. If the power supply interruption window conditions are not met, the step of sampling the boundary response signal according to the preset signal sampling rules is executed.

7. The impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source according to claim 6, characterized in that, The step of identifying the internal impedance distribution image based on a preset abnormal state identification model to obtain the corresponding abnormal state identification result includes: Based on the abnormal region identification rules in the abnormal state identification model, the internal impedance distribution image is subjected to local abnormal state identification to obtain the corresponding abnormal state identification information. Based on the abnormal change recognition rules in the abnormal state recognition model, the local change state recognition of the internal impedance distribution image is performed to obtain the corresponding change state recognition information. The abnormal state identification information is combined with the changed state identification information to obtain the corresponding abnormal state identification result.

8. An impedance tomography monitoring device for industrial electrothermal equipment based on an operating electrothermal signal source, characterized in that, The device is configured on a monitoring terminal, which is communicatively connected to an electrode array and a detection circuit on the industrial electrothermal equipment. The electrode array is non-invasively disposed on the outside of the component under test in the industrial electrothermal equipment. The electrode array includes multiple measuring electrodes and is used to acquire the boundary response signal formed on the side of the component under test under the action of a running electrothermal signal source. The device is used to perform the impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source as described in any one of claims 1-7. The device includes: The boundary measurement data acquisition unit is used to receive the boundary response signal passively acquired by the electrode array, sample the boundary response signal according to the preset signal sampling rules, and obtain the corresponding boundary measurement data. The preprocessing unit is used to preprocess the boundary measurement data according to preset preprocessing rules to obtain corresponding preprocessed data. The effective feature data acquisition unit is used to extract corresponding effective feature data from the preprocessed data according to preset data extraction rules; Impedance inversion processing unit is used to perform impedance inversion on the effective feature data according to the preset impedance inversion strategy and the detection information acquired by the detection circuit, so as to obtain the corresponding internal impedance distribution image. The abnormal state identification result acquisition unit is used to identify the internal impedance distribution image according to the preset abnormal state identification model and obtain the corresponding abnormal state identification result.

9. A computer device, characterized in that, The device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the steps of the impedance tomography monitoring method for industrial electrothermal equipment based on a running electrothermal signal source as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the impedance tomography monitoring method for industrial electrothermal equipment based on an operating electrothermal signal source as described in any one of claims 1-7.

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