A vibration source identification method and device based on modal decomposition, a terminal and a medium

Abstract

The application provides a vibration source identification method and device based on modal decomposition, a terminal and a medium, the method comprising: decomposing an original signal by using a preset modal decomposition algorithm, calculating correlation coefficients between each modal component signal obtained by decomposition and the original signal, comparing the correlation coefficients corresponding to each modal component signal with the average of all the calculated correlation coefficients, and identifying and screening multiple target vibration signals tending to the same frequency based on the comparison result; performing cross-correlation operation on the multiple target vibration signals by using a preset cross-correlation algorithm, and positioning the vibration source based on the time difference corresponding to the target vibration signal calculated. The original signal is decomposed by the modal decomposition algorithm, and the target vibration signals tending to the same frequency are identified and screened from the modal component signals obtained by decomposition, then the time difference corresponding to the target vibration signal is calculated based on the cross-correlation algorithm, so that the identification and positioning of the vibration signals tending to the same frequency are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a vibration source identification method and device based on modal decomposition, a terminal and a medium. BACKGROUND

[0002] At present, it is of great significance to realize the measurement of vibration signals for improving the safety of the sea, the efficiency of resource development, the disaster warning ability and the structure health monitoring. The vibration signals are usually generated by factors such as tides, ocean currents, sea waves, seismic activities, biological groups and traffic shipping, which not only contain vibration signals of multiple complex frequencies, but also contain some vibration signals of the same frequency. However, the existing identification and positioning technology of vibration signals mainly includes time domain cross-correlation algorithm and frequency domain algorithm, which can only meet the measurement of vibration signals of different frequencies, wherein the frequency difference range of vibration signals of different frequencies is generally tens of hertz to hundreds of hertz, and cannot identify and position the vibration signals of the same frequency. Moreover, due to the slight difference in local density, multiple wave surfaces from the same vibration source are not likely to reach the sensor at the same frequency, but tend to be the same frequency, and the frequency difference range is generally very small, reaching a negligible degree. However, the existing identification and positioning technology of vibration signals cannot identify and position the vibration signals tending to the same frequency.

[0003] Therefore, the prior art has defects and needs to be improved and developed. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a vibration source identification method and device based on modal decomposition, a terminal and a medium, aiming at solving the problem that the existing technology cannot identify and position the vibration signals tending to the same frequency, and realizing the identification and positioning of the vibration signals tending to the same frequency.

[0005] The technical solution adopted by the present application to solve the technical problem is as follows:

[0006] A vibration source identification method based on modal decomposition, wherein the method comprises:

[0007] The original signal obtained is decomposed into multiple modal component signals by using a preset modal decomposition algorithm, and the correlation coefficients between each modal component signal and the original signal are calculated;

[0008] The average value of the correlation coefficients between all modal component signals and the original signal is calculated, and the correlation coefficients between each modal component signal and the original signal are compared with the average value to obtain the corresponding comparison result;

[0009] screening and identifying the modal component signals based on the comparison result to obtain a plurality of target vibration signals tending to be same frequency; wherein a frequency difference between the plurality of target vibration signals tending to be same frequency is not greater than a preset frequency threshold.

[0010] correlating the plurality of target vibration signals by using a preset cross-correlation algorithm to calculate time differences corresponding to the plurality of target vibration signals, and performing vibration source positioning based on the time differences to obtain a corresponding positioning result.

[0011] In an implementation manner, the original signal is a signal containing multi-point vibration information and noise collected by a data acquisition system according to a preset sampling rate and a preset number of sampling points.

[0012] In an implementation manner, before the correlation coefficients between each of the modal component signals and the original signal are calculated, the method further includes:

[0013] filtering zero-frequency noise signals in the plurality of modal component signals to obtain a plurality of filtered modal component signals;

[0014] The calculating of the correlation coefficients between each of the modal component signals and the original signal includes:

[0015] The calculating of the correlation coefficients between each of the modal component signals and the original signal includes:

[0016] In an implementation manner, before the original signal is decomposed by using the preset modal decomposition algorithm to obtain a plurality of modal component signals, the method further includes:

[0017] determining a best number of modal components for decomposing the original signal by using the preset modal decomposition algorithm based on permutation entropy; wherein the best number of modal components is an integer.

[0018] In an implementation manner, the decomposing of the original signal by using the preset modal decomposition algorithm to obtain a plurality of modal component signals includes:

[0019] decomposing the original signal by using the preset modal decomposition algorithm and according to the best number of modal components to obtain a plurality of modal component signals corresponding to the best number of modal components; wherein the preset modal decomposition algorithm is a preset variational modal decomposition algorithm or a preset empirical modal decomposition algorithm.

[0020] In an implementation manner, the preset frequency threshold is 0.1 hertz.

[0021] In an implementation manner, the identifying and screening of the modal component signals based on the comparison result obtains multiple target vibration signals tending to be same frequency, and the method comprises the following steps of:

[0022] When the comparison result shows that the correlation coefficient between the modal component signal and the original signal is not greater than the average value, the modal component signal is filtered.

[0023] When the comparison result shows that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained to obtain multiple target vibration signals tending to be same frequency.

[0024] The application further discloses a vibration source identification device based on modal decomposition, wherein the device comprises:

[0025] A signal decomposition module is configured to decompose the obtained original signal by using a preset modal decomposition algorithm to obtain multiple modal component signals.

[0026] A correlation coefficient calculation module is configured to calculate the correlation coefficient between each of the modal component signals and the original signal.

[0027] An average value calculation module is configured to calculate a corresponding average value according to the correlation coefficient between all the modal component signals and the original signal.

[0028] A comparison module is configured to compare the correlation coefficient between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result.

[0029] A signal screening module is configured to identify and screen the modal component signals based on the comparison result to obtain multiple target vibration signals tending to be same frequency, and the frequency difference between the multiple target vibration signals tending to be same frequency is not greater than a preset frequency threshold.

[0030] A time difference calculation module is configured to perform cross-correlation operation on the multiple target vibration signals by using a preset cross-correlation algorithm to calculate the time difference corresponding to the multiple target vibration signals.

[0031] A vibration source positioning module is configured to perform vibration source positioning based on the time difference to obtain a corresponding positioning result.

[0032] The application further discloses a terminal, which comprises a memory, a processor, and a vibration source identification program based on modal decomposition stored in the memory and capable of running on the processor, and the vibration source identification program based on modal decomposition implements the steps of the vibration source identification method based on modal decomposition when executed by the processor.

[0033] The application further discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program which can be executed to implement steps of the modal decomposition based vibration source identification method.

[0034] The application provides a modal decomposition based vibration source identification method, device, terminal and medium, the modal decomposition based vibration source identification method comprises the following steps: decomposing an original signal obtained through a preset modal decomposition algorithm to obtain a plurality of modal component signals, and calculating correlation coefficients between the modal component signals and the original signal; calculating average values of the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficients between the modal component signals and the original signal with the average values to obtain corresponding comparison results; identifying and screening the modal component signals based on the comparison results to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold; performing cross-correlation operation on the plurality of target vibration signals through a preset cross-correlation algorithm to calculate time differences corresponding to the plurality of target vibration signals, and performing vibration source positioning based on the time differences to obtain corresponding positioning results. Therefore, the original signal is decomposed through the modal decomposition algorithm, and the target vibration signals tending to be the same frequency are identified and screened from the decomposed modal component signals, then the time differences corresponding to the target vibration signals are calculated through the cross-correlation algorithm, and the vibration source positioning is performed based on the time differences, so that the problem that the vibration signals tending to be the same frequency cannot be identified and positioned in the prior art can be solved, and the identification and positioning of the vibration signals tending to be the same frequency can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a flow chart of a preferred embodiment of the modal decomposition based vibration source identification method in the application;

[0036] Figure 2 is a flow chart of a specific modal decomposition based vibration source identification method disclosed by the application;

[0037] Figure 3 is a flow chart of a specific modal decomposition based vibration source identification method disclosed by the application;

[0038] Figure 4 is a structural schematic diagram of a forward transmission distributed sensing system based on a double-end structure disclosed by the application;

[0039] Figure 5 is a frequency domain schematic diagram of an original signal disclosed by the application;

[0040] Figure 6is a frequency domain diagram of a decomposed signal disclosed by the present application;

[0041] Figure 7 is a functional principle block diagram of a preferable embodiment of the vibration source recognition device based on modal decomposition in the present application;

[0042] Figure 8 is a functional principle block diagram of a preferable embodiment of the terminal in the present application. DETAILED DESCRIPTION

[0043] In order to make the objects, technical solutions and advantages of the present application clearer and more explicit, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0044] Please refer to Figure 1 , Figure 1 is a flow chart of the vibration source recognition method based on modal decomposition in the present application. As shown in Figure 1 , the vibration source recognition method based on modal decomposition in the present application comprises:

[0045] Step S11, decomposing the acquired original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals, and calculating the correlation coefficients between each of the modal component signals and the original signal.

[0046] In the present embodiment, the acquired original signal is decomposed by using a preset modal decomposition algorithm, and then the correlation coefficients between each of the decomposed modal component signals and the original signal are calculated.

[0047] It should be pointed out that the original signal is a signal containing multi-point vibration information and noise collected by a data acquisition system according to a pre-set sampling rate and sampling point number. By setting the sampling rate and the sampling point number, the frequency resolution of the algorithm can be distinguished as 0.1 Hz, so that the vibration signals tending to the same frequency can be distinguished. For example, the original signal containing multi-point vibration information and noise collected from the optical fiber sensor by the data acquisition system is acquired, and the original signal is decomposed into a plurality of modal components with limited bandwidth.

[0048] In this embodiment, when the obtained original signal is decomposed by using the preset modal decomposition algorithm, the number of modal components of the preset modal decomposition algorithm can be optimized based on permutation entropy, and then the original signal is decomposed by using the modal decomposition algorithm optimized based on permutation entropy, that is, the optimal number of modal components when the original signal is decomposed by using the preset modal decomposition algorithm is determined based on permutation entropy; wherein the optimal number of modal components is an integer, and then the obtained original signal is decomposed by using the preset modal decomposition algorithm and according to the optimal number of modal components, to obtain a plurality of modal component signals corresponding to the number of optimal modal components; wherein the preset modal decomposition algorithm can be a preset variational modal decomposition algorithm or a preset empirical modal decomposition algorithm.

[0049] It should be noted that the variational modal decomposition algorithm (VMD) is a non-recursive adaptive signal processing method, and its main goal is to decompose the original signal containing multi-point vibration information and noise into an integer number of sub-signals, i.e. intrinsic mode function (IMF), each IMF corresponds to a vibration signal and a noise signal.

[0050] For example, when the original signal is composed of k signals with different center frequencies ω k , the minimum estimation of the bandwidth of each modal component signal in the frequency domain is performed to form a minimization problem, that is:

[0051]

[0052] Where {u k}={u1,..., u k} represents each modal component contained in the original vibration sensing signal detected by the sensor, {ω k}={ω1,..., ω k} represents the center frequency corresponding to each modal component, represents the partial derivative of t, δ(t) represents the unit impulse function, u k (t) represents the kth modal component at time t, and j represents the complex form.

[0053] The sum of each modal component signal is equal to the original sensing signal, which is used as a constraint condition of the above minimization problem, so that a minimization problem with a constraint condition is obtained, and the constraint condition is:

[0054]

[0055] Where f represents the original signal containing vibration information and noise, and K represents the number of different frequency signals contained in the original signal.

[0056] To solve the minimization problem with constraints, a Lagrange multiplier method can be introduced to convert it into a minimization problem without constraints, i.e., a convex optimization problem formed by an augmented Lagrangian function, which is:

[0057]

[0058] where λ represents a Lagrange multiplier, represents a Lagrangian function, α represents a quadratic penalty factor, f(t) represents an original signal at time t, λ(t) represents a Lagrange multiplier at time t, uk(t) represents a kth modal component at time t, and the Lagrange multiplier mainly represents the coefficients of each vector in the linear combination of the gradient in the constraint condition, and the main role of the quadratic penalty factor is to suppress the influence of noise on the signal and prevent distortion. k

[0059] Then, the convex optimization problem formed by the augmented Lagrangian function can be optimized and solved by an Alternating Direction Method of Multipliers (ADMM). The solving method mainly decomposes the original objective function into a minimum problem of each sub-component, then solves the optimal solution of each sub-minimization problem through parallel operation, and finally obtains the optimal solution in the original objective function according to the comparative analysis. Among them, the update mode of the corresponding variational modal function {uk(t)} can be represented as: k

[0060]

[0061] where represents a one-sided spectrum, represents a spectral residual, represents an original signal in the frequency domain, represents an i th modal component in the frequency domain, represents a Lagrange multiplier in the frequency domain, and ω represents a center frequency.

[0062] And the one-sided spectrum is the Wiener filter output of the current signal spectral residual , and the bilateral spectrum can be obtained according to the symmetry of the real-valued signal, and its time-domain signal can be obtained by using the inverse Fourier transform.

[0063] According to the above formula, it can be found that the center frequency corresponding to each sub-modal component signal mainly exists in the estimation term. Therefore, each sub-modal component signal can be updated and optimized by the following formula, i.e.:

[0064] ​​

[0065] wherein, denotes the n+1th update of the kth central frequency ω.

[0066] Similarly, transform it into the frequency domain, that is:

[0067]

[0068] wherein, denotes the kth modal component with a central frequency ω in the frequency domain.

[0069] According to the above formula, the setting of the number of IMF components has an important influence on the estimation of the central frequency. The setting of an inappropriate number of components will cause a deviation between the IMF components and the actual signal, resulting in modal aliasing. Generally, when the set decomposition number is less than the actual decomposition number, a single IMF component will contain multiple frequency band components, and when the set decomposition number is greater than the actual number, a single frequency band will be decomposed into multiple components. Therefore, selecting an appropriate decomposition number is crucial to the decomposition effect of the VMD algorithm.

[0070] It should also be pointed out that permutation entropy (PE) as a parameter for measuring the complexity of one-dimensional time series can be used to measure the degree of disorder inside the signal, that is, the greater the entropy value, the more complex and random the decomposed signal; the smaller the entropy value, the simpler and more regular the decomposed signal. Therefore, in order to determine the optimal number of modal components in the VMD algorithm, the permutation entropy value can be used as an effect index for the number of decomposed IMF.

[0071] Referring to Figure 2 As shown in the figure, the specific process of determining the optimal number of modal components when the original signal is decomposed by the preset modal decomposition algorithm based on permutation entropy can be: the initial number of modal components in the preset modal decomposition algorithm can be set to 2, that is, k = 2, and the setting range of the number of modal components in the algorithm can be 2 to 10, that is, k ∈ [2, 10], when k is 2, that is, according to the current number of modal components is 2, the original signal is decomposed by VMD to obtain 2 IMF components, then the permutation entropy (PE, Permutation Entropy) of the 2 IMF components is calculated, and then the average value of the permutation entropy of the 2 IMF components is calculated, when k is 3, the permutation entropy of the 3 IMF components is calculated, and then the average value of the permutation entropy of the 3 IMF components is calculated, and so on, until the calculation of k = 10 is completed, and finally the k value corresponding to the minimum permutation entropy average value is determined as the optimal number of modal components, that is, the optimal k value.

[0072] Step S12, calculating the average value of the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficients between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result.

[0073] It can be understood that the average value comparison method based on correlation analysis is used to select the IMF components, that is, the average correlation coefficient values corresponding to all the modal component signals are calculated, and the correlation coefficients corresponding to each of the modal component signals are compared with the average value to obtain a corresponding comparison result.

[0074] For example, after the original signal is decomposed, k IMF components are obtained, and the correlation coefficients between each of the IMF components and the original signal are r1, r2,..., r k The correlation coefficients of each of the IMF components are averaged to obtain:

[0075]

[0076] wherein, represents the average value, and r i represents the correlation coefficient corresponding to the i-th IMF component. The calculated average value (average correlation coefficient value) is used as a threshold for judging whether the IMF component needs to be filtered.

[0077] Step S13, identifying and screening the modal component signals based on the comparison result to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold.

[0078] It can be understood that when the comparison result shows that the correlation coefficient between the modal component signal and the original signal is not greater than the average value, the modal component signal is filtered; when the comparison result shows that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained, and a plurality of target vibration signals tending to be the same frequency are obtained, that is, the modal components with correlation coefficients less than the average value are filtered out, and the modal components with correlation coefficients greater than the average value are retained to determine the target vibration signal. That is, after the original signal containing multi-point vibration information and noise is decomposed by the VMD algorithm, each of the IMF components decomposed is identified and screened to filter out the noise signal and retain the signal containing the main approximate same frequency vibration information to obtain the target vibration signal. It should be noted that when the frequency difference between a plurality of signals is not greater than a preset frequency threshold, the plurality of signals are considered to tend to be the same frequency, and the preset frequency threshold can be set to 0.1 Hz, that is, when the frequency difference between a plurality of signals is not greater than 0.1 Hz, it is determined that the signals tend to be the same frequency.

[0079] Step S14, the preset cross-correlation algorithm is used for cross-correlation operation on the plurality of target vibration signals to calculate time differences corresponding to the plurality of target vibration signals, and vibration source positioning is performed based on the time differences to obtain a corresponding positioning result.

[0080] In the embodiment, the cross-correlation algorithm is used for cross-correlation operation on the target vibration signals that tend to be the same frequency to obtain time differences corresponding to the target vibration signals, so that multi-point vibration source positioning that tends to be the same frequency is realized.

[0081] It can be seen that in the embodiment, the original signal is decomposed by the modal decomposition algorithm, and the target vibration signal that tends to be the same frequency is identified and selected from the decomposed modal component signals, and then the time difference corresponding to the target vibration signal is calculated by the cross-correlation algorithm, and vibration source positioning is performed based on the time difference, so that the problem that the vibration signal that tends to be the same frequency cannot be identified and positioned in the prior art can be solved, and then the identification and positioning of the vibration signal that tends to be the same frequency are realized.

[0082] Referring to Figure 3 The embodiment discloses a specific vibration source identification method based on modal decomposition, and compared with the previous embodiment, the technical solution is further described and optimized.

[0083] Step S21, the original signal obtained is decomposed by using a preset modal decomposition algorithm to obtain a plurality of modal component signals.

[0084] Step S22, the zero-frequency noise signals in the plurality of modal component signals are filtered to obtain a plurality of filtered modal component signals.

[0085] In the embodiment, after the original signal obtained is decomposed by using a preset modal decomposition algorithm to obtain a plurality of modal component signals, the zero-frequency noise signals in the plurality of modal component signals can be directly filtered to obtain a plurality of filtered modal component signals. It should be pointed out that after the zero-frequency noise is removed, the change trend of the original signal tends to be horizontal, so that the quality and accuracy of signal processing are improved.

[0086] Step S23, the correlation coefficients between each of the plurality of filtered modal component signals and the original signal are calculated.

[0087] In the embodiment, after the zero-frequency noise signals are removed from the plurality of decomposed modal component signals, the correlation coefficients between the remaining modal component signals and the original signal are calculated.

[0088] Step S24, calculating the average value of the correlation coefficients between each modal component signal and the original signal, and comparing the correlation coefficients between each modal component signal and the original signal with the average value to obtain a corresponding comparison result.

[0089] Step S25, identifying and screening the modal component signals based on the comparison result to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold.

[0090] Step S26, performing cross-correlation operation on the plurality of target vibration signals by using a preset cross-correlation algorithm to calculate the time difference corresponding to the plurality of target vibration signals, and performing vibration source positioning based on the time difference to obtain a corresponding positioning result.

[0091] The specific content of the above step S21, and steps S24 to S26 can refer to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

[0092] It can be seen that, in the embodiment of the application, the original signal is decomposed by the modal decomposition algorithm, and the target vibration signal tending to be the same frequency is identified and screened from the decomposed modal component signals, and then the time difference corresponding to the target vibration signal is calculated by the cross-correlation algorithm, and the vibration source positioning is performed based on the time difference, so as to solve the problem that the vibration signal tending to be the same frequency cannot be identified and positioned in the prior art, and further realize the identification and positioning of the vibration signal tending to be the same frequency.

[0093] The technical scheme of the application for vibration source identification and positioning can be applied to a forward transmission distributed sensing system based on a double-end structure, as shown in Figure 4 The phase information (original signal) demodulated by the distributed sensing system is collected by the data acquisition system, and then the phase information is subjected to variational modal decomposition and cross-correlation operation based on the technical scheme of the application for vibration source identification and positioning, so as to realize the identification and positioning of the multi-point vibration signal tending to be the same frequency. The data acquisition system collection can be 3x3 structure, or other structure.

[0094] For example, in order to evaluate the performance of the sensing system, a 20-meter sensing optical fiber part wrapped and fixed around a disc-shaped piezoelectric transducer (PZT) is subjected to vibration, two PZTs are placed at the 50-kilometer ends of the sensing optical fiber, the length of the sensing optical fiber is about 101.03 kilometers, and the two PZTs generate continuous vibration based on 10Hz and 10.1Hz sine driving signals, which transmit the displacement to the bonded optical fiber in the form of strain, wherein the phase signals detected by the double-end structure are shown in Figure 5As shown, the Figure 5 The frequency domain signal of one of the two ends is shown, and the Figure 5 It can be found that the detected phase signal contains two vibration signals of 10Hz and 10.1Hz, and the amplitudes are 250v and 80v respectively, and then the VMD algorithm based on permutation entropy optimization is used for decomposition, and the decomposition result is as shown in Figure 6 As shown, the Figure 6 It can be seen from the above that the two vibration signals of 10Hz and 10.1Hz, i.e. IMF3 and IMF4, are well restored, and finally the time difference corresponding to the two vibration source frequencies is calculated by the cross-correlation algorithm, so as to realize the vibration source positioning based on the time difference. For details, see the above Figure 2 As shown, the average value comparison method based on correlation analysis is used to select the k IMF components, that is, the correlation coefficients between the IMF components and the original signal are calculated, and then the average value of the k correlation coefficients is calculated, and the IMF components with correlation coefficients greater than the average value are retained, and then the cross-correlation algorithm is used to calculate the time difference corresponding to the two vibration source frequencies, so as to realize the multi-point vibration source positioning of approximately the same frequency based on the time difference.

[0095] In one embodiment, as shown in Figure 7 Based on the above vibration source identification method based on modal decomposition, the application also correspondingly provides a vibration source identification device based on modal decomposition, which comprises:

[0096] The signal decomposition module 11 is used for decomposing the obtained original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals;

[0097] The correlation coefficient calculation module 12 is used for calculating the correlation coefficients between each of the modal component signals and the original signal;

[0098] The average value calculation module 13 is used for calculating the corresponding average value according to the correlation coefficients between all the modal component signals and the original signal;

[0099] The comparison module 14 is used for comparing the correlation coefficients between each of the modal component signals and the original signal with the average value to obtain the corresponding comparison result;

[0100] The signal screening module 15 is used for identifying and screening the modal component signals based on the comparison result to obtain a plurality of target vibration signals tending to the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to the same frequency is not greater than a preset frequency threshold;

[0101] The time difference calculation module 16 is used for performing cross-correlation operation on the plurality of target vibration signals by using a preset cross-correlation algorithm to calculate the time difference corresponding to the plurality of target vibration signals;

[0102] a vibration source positioning module 17 configured to perform vibration source positioning based on the time difference to obtain a corresponding positioning result.

[0103] Figure 8 A structure schematic diagram of a terminal is provided in the embodiments of the present application. The terminal can include:

[0104] a memory 501, a processor 502, and a computer program stored in the memory 501 and executable on the processor 502.

[0105] The processor 502 implements the electrical impedance tomography method based on sparse recovery provided in the above embodiments when executing the program.

[0106] Further, the terminal further includes:

[0107] a communication interface 503 configured to communicate between the memory 501 and the processor 502.

[0108] The memory 501 is configured to store the computer program executable on the processor 502.

[0109] The memory 501 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0110] If the memory 501, the processor 502 and the communication interface 503 are independently implemented, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and complete the communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, only one line is shown in the figure, but it does not mean that there is only one bus or only one type of bus.

[0111] Optionally, in specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can complete the communication between each other through an internal interface.

[0112] The processor 502 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the application.

[0113] The embodiment further provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the above-mentioned method for electrical impedance tomography based on sparse recovery.

[0114] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0115] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are contained in at least one embodiment or example of the application. The illustrative representation of the above terms in the specification does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples, without contradiction.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequence of executable instructions for implementing the logic functions, which can be specifically embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can read instructions from a computer-readable medium and execute the instructions, or in conjunction with such an instruction execution system, apparatus or device.

[0117] It should be understood that portions of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, and in another embodiment, the hardware can be implemented using any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0118] It is to be understood that the application is not limited to the above-described examples, and that modifications or changes can be made by those skilled in the art in light of the foregoing description. All such modifications and changes are intended to be included within the scope of the application as defined in the appended claims.

Claims

1. A method for identifying a vibration source based on modal decomposition, characterized in that, The method comprises: decomposing the obtained original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals, and calculating a correlation coefficient between each of the modal component signals and the original signal; calculating an average value of the correlation coefficients between all the modal component signals and the original signal, and comparing the correlation coefficient between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result; based on the comparison result, identifying and screening the modal component signals to obtain a plurality of target vibration signals tending to be the same frequency; wherein the frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold. performing cross-correlation operation on the plurality of target vibration signals by using a preset cross-correlation algorithm to calculate a time difference corresponding to the plurality of target vibration signals, and performing vibration source positioning based on the time difference to obtain a corresponding positioning result. The method comprises: when the comparison result indicates that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained to obtain a plurality of target vibration signals tending to be the same frequency.

2. The modal decomposition based source identification method of claim 1, wherein, The original signal is a signal containing multi-point vibration information and noise collected by a data acquisition system according to a preset sampling rate and sampling point number.

3. The modal decomposition based source identification method of claim 1, wherein, Before the correlation coefficient between each of the modal component signals and the original signal is calculated, the method further comprises: filtering zero-frequency noise signals in the plurality of modal component signals to obtain a plurality of filtered modal component signals. The correlation coefficient between each of the modal component signals and the original signal is calculated. Before the original signal is decomposed by using the preset modal decomposition algorithm to obtain a plurality of modal component signals, the method further comprises:

4. The modal decomposition based source identification method of claim 1, wherein, determining the best number of modal components when the original signal is decomposed by using the preset modal decomposition algorithm based on permutation entropy; wherein the best number of modal components is an integer. The original signal is decomposed by using the preset modal decomposition algorithm according to the best number of modal components to obtain a plurality of modal component signals corresponding to the best number of modal components; wherein the preset modal decomposition algorithm is a preset variational modal decomposition algorithm or a preset empirical modal decomposition algorithm.

5. The modal decomposition based source identification method of claim 4, wherein, The preset frequency threshold is 0.1 Hz. The correlation coefficient between each of the modal component signals and the original signal is calculated.

6. The modal decomposition based source identification method of claim 1, wherein, The device comprises:

7. The modal decomposition based source identification method according to any one of claims 1 to 6, characterized in that, ​ ​ 8. A modal decomposition based vibration source identification apparatus, characterized by, ​ The signal decomposition module is configured to decompose the acquired original signal by using a preset modal decomposition algorithm to obtain a plurality of modal component signals. The correlation coefficient calculation module is configured to calculate a correlation coefficient between each of the modal component signals and the original signal. The average value calculation module is configured to calculate a corresponding average value according to the correlation coefficients between all the modal component signals and the original signal. The comparison module is configured to compare the correlation coefficients between each of the modal component signals and the original signal with the average value to obtain a corresponding comparison result. The signal screening module is configured to identify and screen the modal component signals based on the comparison result to obtain a plurality of target vibration signals tending to be the same frequency, wherein a frequency difference between the plurality of target vibration signals tending to be the same frequency is not greater than a preset frequency threshold. The time difference calculation module is configured to perform cross-correlation operation on the plurality of target vibration signals by using a preset cross-correlation algorithm to calculate a plurality of time differences corresponding to the plurality of target vibration signals. The vibration source positioning module is configured to perform vibration source positioning based on the time differences to obtain a corresponding positioning result. The signal screening module is specifically configured to: When the comparison result indicates that the correlation coefficient between the modal component signal and the original signal is greater than the average value, the modal component signal is retained to obtain a plurality of target vibration signals tending to be the same frequency.

9. A terminal, characterized by comprising: The memory, the processor, and a modal decomposition-based vibration source identification program stored in the memory and executable on the processor, the modal decomposition-based vibration source identification program being executed by the processor to implement the steps of the modal decomposition-based vibration source identification method according to any one of claims 1 to 7. The computer readable storage medium stores a computer program, which can be executed to implement the steps of the modal decomposition-based vibration source identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, ​

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