System and method for identifying vibration signal type based on POTDR

Abstract

The invention discloses a system and a method for identifying a vibration signal type based on POTDR, and relates to the technical field of optical fiber sensing. The system comprises a laser, a circulator, an optical fiber to be measured, a polarization beam splitter, a photoelectric converter assembly, a signal collector and a signal processing unit. The method comprises the following steps: when a vibration signal generated by a surrounding environment affects an optical fiber to be measured, obtaining a measurement result of vibration signal characteristics based on a POTDR demodulation technology; vibration signals at any position are collected, and a data relation library of the soil environment, the soil depth and the vibration signals is established; and based on the established data relationship library, identifying vibration signal characteristics in different soil environments and different depths, and further identifying the signal type of vibration. According to the invention, based on the IQ demodulation technology of POTDR, the soil medium characteristics and the burial depth are fused, and the accuracy of vibration signal type identification is improved.

Description

Technical Field

[0001] The present invention relates to the field of optical fiber sensing technology, and in particular to a system and method for identifying vibration signal types based on POTDR. Background Art

[0002] Fiber optic sensing technology is widely used in vibration measurement, structural health monitoring and other fields due to its advantages such as anti-electromagnetic interference, corrosion resistance and long transmission distance. Existing methods for identifying vibration categories using fiber optic sensing technology include support vector machines, deep learning models, and unsupervised learning methods. These methods usually directly establish a model library for the collected data, train the model, and obtain the identification type of the vibration signal. However, the establishment of a model between the vibration signal and the identification type lacks effective use of environmental information. Therefore, it is necessary to design a system and method for identifying vibration signal types based on POTDR, which can make full use of environmental information and improve the accuracy of vibration identification types. Summary of the invention

[0003] The present invention aims at the problems existing in the prior art and provides a system and method for identifying the type of vibration signal based on POTDR.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] A system for identifying vibration signal types based on POTDR comprises a laser, a circulator, an optical fiber to be tested, a polarization beam splitter, a photoelectric converter component, a signal collector and a signal processing unit. The pulse light emitted by the laser enters the circulator and is modulated by the optical fiber to be tested. When the optical signal scattered back by the optical fiber to be tested is output by the circulator, it reaches the polarization beam splitter and is transmitted to the photoelectric converter component. The photoelectric converter component converts the optical signal into an electrical signal and sends it to the signal collector and finally to the signal processing unit.

[0006] Based on the above technical solution, further, the photoelectric converter assembly includes a first photoelectric converter and a second photoelectric converter, wherein the first photoelectric converter converts the received optical signal of the X vector into an electrical signal in the X-axis direction, and the second photoelectric converter converts the received optical signal of the Y vector into an electrical signal in the Y-axis direction.

[0007] Based on the above technical solution, further, the polarization beam splitter decomposes the received signal into an optical signal of an X vector and an optical signal of a Y vector, and transmits the optical signal of the X vector to the first photoelectric converter, and transmits the optical signal of the Y vector to the second photoelectric converter.

[0008] Based on the above technical solution, further, the signal collector uses a digital coherent IQ demodulation algorithm to respectively calculate the angle of polarized light from the collected polarization signal at each position of the sensing optical fiber, and sends the result to the signal processing unit.

[0009] A method for identifying vibration signal types based on POTDR comprises the following steps: step S1, when the vibration signal generated by the surrounding environment affects the optical fiber to be tested, based on the demodulation technology of POTDR, obtaining the measurement result of the vibration signal characteristics; step S2, collecting the vibration signal at any position, and establishing a data relationship library of soil environment, soil depth and vibration signal; step S3, based on the established data relationship library, identifying the vibration signal characteristics in different soil environments and different depths, and then identifying the vibration signal type.

[0010] Based on the above technical solution, further, in step S1, the acquisition process is: the parameter signal of the polarized light carried by the received backscattered Rayleigh echo of each section of optical fiber is solved by the signal processing unit, and finally the measurement result of the vibration signal characteristic is obtained.

[0011] Based on the above technical solution, further, in step S2, the soil environment is set to a general soil environment and a high water content environment, wherein the general soil environment is set to a low water content soil with a water content between 11% and 13%, and the high water content environment is set to a high water content soil with a water content between 18% and 20%; the soil depth is set to 2 meters, 5 meters, 10 meters, 15 meters, and 20 meters; the parameters of the vibration signal include amplitude, frequency, and phase.

[0012] Based on the above technical solution, further, in step S3, the recognition process includes: step S31, first use the support vector machine to train the relationship model A between the vibration signal characteristics and the soil environment and soil depth; step S32, then use the LSTM model in deep learning to establish the category model B of the time domain characteristics of the vibration signal and the signal category; step S33, when a new vibration signal is collected, the trained relationship model A is used to determine which environmental category the vibration signal belongs to; the category model B determines which environmental condition the category model B is selected under according to the environmental category, thereby identifying the signal type.

[0013] Based on the above technical solution, further, in step S31, the process is: step A1, setting the vibration signal characteristics to [amplitude, frequency, phase]; step A2, combining the soil environment and the soil depth; step A3, cleaning and filtering the collected vibration signal data, and using a support vector machine to establish a relationship model A between the vibration signal characteristic data and the environmental category.

[0014] Based on the above technical solution, further, in step S32, the process is: step B1, determine the environmental conditions according to the combination of soil environment and soil depth; step B2, use the LSTM model to train the category model B of time domain features and signal types according to the environmental conditions.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention uses POTDR technology to accurately identify the type of vibration signal by matching the soil medium characteristics. It mainly constructs a set of IQ demodulation technology based on POTDR, and integrates the soil medium characteristics and burial depth to improve the accuracy of vibration signal type identification. And by making full use of environmental information and accurately selecting the model corresponding to the environmental information, the identification type of vibration signal can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of the system of the present invention;

[0018] Figure 2 It is a demodulation flow chart of the digital coherent IQ demodulator in the present invention;

[0019] Figure numerals: 111, laser; 112, circulator; 113, optical fiber to be tested; 114, polarization beam splitter; 115, first photoelectric converter; 116, second photoelectric converter; 117, signal collector; 118, signal processing unit; 119, multiplier; 120, low-pass filter; 121, divider. DETAILED DESCRIPTION

[0020] The present invention is further described and illustrated below in conjunction with the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0021] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0022] In the description of the present invention, it is to be understood that when an element is considered to be "connected" to another element, it may be directly connected to the other element or indirectly connected, that is, there are intermediate elements. On the contrary, when an element is said to be "directly" connected to another element, there are no intermediate elements.

[0023] Example 1

[0024] Combination Figure 1 As shown, this embodiment provides a system for identifying vibration signal types based on POTDR, including a laser 111, a circulator 112, an optical fiber to be tested 113, a polarization beam splitter 114, a photoelectric converter assembly, a signal collector 117, and a signal processing unit 118. The photoelectric converter assembly includes a first photoelectric converter 115 and a second photoelectric converter 116.

[0025] In this embodiment, the entire workflow of the system is as follows: the pulse light emitted by the laser 111 enters the circulator 112 and is modulated by the optical fiber to be tested 113. When the surrounding environment changes, such as when an earthquake occurs, the optical fiber to be tested 113 is modulated by external physical quantities, and the polarization state of the light in the optical fiber will change. Since the scattered light does not change the polarization characteristics of the incident light at that point, the optical signal scattered back by the optical fiber to be tested 113 carries the information of the polarization state, and is output by the circulator 112 and reaches the polarization beam splitter 114. The polarization beam splitter 114 is decomposed into two orthogonal optical signals of the X vector and the Y vector, which are correspondingly transmitted to the first photoelectric converter 115 and the second photoelectric converter 116. After the two converters convert the optical signal into an electrical signal, it is sent to the signal collector 117. The polarization signal of each position of the collected sensing optical fiber is respectively solved using a digital coherent IQ demodulation algorithm to calculate the angle of the polarized light and sent to the signal processing unit 118. The signal processing unit 118 solves the relevant parameters of the polarized light carried by the back Rayleigh scattered echo of each section of the optical fiber.

[0026] Reference Figure 2 The schematic diagram of digital coherent IQ demodulation is shown in FIG. 1 ; wherein IQ demodulation is to construct two orthogonal signals of the same frequency to simulate local oscillator signals LI and LQ according to the carrier frequency of the intermediate frequency signal: Figure 2 The input intermediate frequency IF is multiplied with LI and LQ by multiplier 119 respectively, and then the high-frequency component is filtered out by low-pass filter 120 to output two orthogonal components: I component and Q component, which are then passed through divider 121 and the inverse tangent is calculated based on the inverse tangent function to obtain the output Angle angle.

[0027] Example 2

[0028] Based on the identification system steps disclosed in Example 1, this embodiment provides a method for identifying the type of vibration signal based on POTDR, including the following steps:

[0029] Step S1, obtaining a vibration signal, specifically, when the vibration signal generated by the surrounding environment affects the optical fiber 113 to be tested, a measurement result of the vibration signal characteristic is obtained based on the POTDR demodulation technology.

[0030] In this embodiment, the acquisition process is as follows: when the vibration signal affects the optical fiber 113 to be tested through physical contact or through medium transmission, the light wave in the optical fiber 113 to be tested is modulated, affecting the relevant parameters of the polarized light. The relevant parameters of the polarized light carried by the back Rayleigh scattered echo of each section of the optical fiber to be tested are solved by the signal processing unit 118, and finally the measurement results of the vibration signal characteristics are obtained. It should be noted that POTDR is a fully distributed optical fiber sensor based on optical time domain reflectometry technology, which performs sensing by detecting changes in the polarization state of scattered light waves in the optical fiber.

[0031] Further, the demodulation technology process based on POTDR is as follows: the optical signal scattered back by the optical fiber 113 to be tested is output through the circulator 112; the polarization beam splitter 114 decomposes the signal output by the circulator 112 into mutually orthogonal X-vector optical signals and Y-vector optical signals, and inputs them into the first photoelectric converter 115 and the second photoelectric converter 116; the first photoelectric converter 115 and the second photoelectric converter 116 convert the received optical signal into mutually orthogonal X-electrical signals and Y-electrical signals, and send them to the signal collector 117. It should be noted that the first photoelectric converter 115 is used to convert the X-vector optical signal into the corresponding electrical signal, and the second photoelectric converter 116 is used to convert the Y-vector optical signal into the corresponding electrical signal; the signal collector 117 inputs the obtained mutually orthogonal X-electrical signals and Y-electrical signals into a bandpass filter with a frequency of 20Hz-20kHz, and obtains the I component and Q component, and then based on Figure 2 The demodulation process of the digital coherent IQ demodulator obtains the angle and outputs it.

[0032] It should be noted that the demodulation technology process based on POTDR is a part of the acquisition process. The acquisition process can obtain the characteristics of the vibration signal through POTDR demodulation and some processing processes.

[0033] Step S2, collecting vibration signals at any position, and establishing a data relationship database of soil environment, soil depth and vibration signals;

[0034] In this embodiment, when the soil environment is a general soil environment or a high water content environment, the soil depth is 2 meters, 5 meters, 10 meters, 15 meters, and 20 meters, and the vibration signal of a certain position is collected to establish a soil, depth, and vibration signal relationship library. As shown in Table 1 below. It should be noted that the general soil environment refers to low water content soil, whose water content is about 12%, preferably 11%-13%; the high water content environment refers to high water content soil with a water content between 18% and 20%.

[0035] Table 1

[0036]

[0037] Step S3: Based on the established data relationship library, the vibration signal characteristics under different soil environments and different depths are identified, and then the vibration signal type is identified.

[0038] Specifically, the entire recognition process is as follows: Step S31, first use a support vector machine to train a relationship model A between vibration signal features and soil environment and soil depth, the specific method is:

[0039] Step A1, setting the vibration signal characteristics to [amplitude, frequency, phase];

[0040] Step A2, combining soil environment and soil depth, and setting the corresponding environment category to 10 categories, specifically including: -2 meters under general environment, -5 meters under general environment, -10 meters under general environment, -15 meters under general environment, -20 meters under general environment, -2 meters under high water content environment, -5 meters under high water content environment, -10 meters under high water content environment, -15 meters under high water content environment, -20 meters under high water content environment;

[0041] Step A3: clean and filter the collected vibration signal data, and then use a support vector machine to establish a relationship model A between the vibration signal feature data and the environment category.

[0042] Step A4: When new data is collected, the model in step A3 is input to determine the corresponding soil environment and soil depth.

[0043] Step S32: Use the LSTM model in deep learning to establish a category model B of the time domain features of the vibration signal and the signal category. The specific method is as follows:

[0044] Step B1, according to the combination of soil environment and soil depth, determine the environmental condition = [-2 meters under general environment, -5 meters under general environment, -10 meters under general environment, -15 meters under general environment, -20 meters under general environment, -2 meters under high water content environment, -5 meters under high water content environment, -10 meters under high water content environment, -15 meters under high water content environment, -20 meters under high water content environment];

[0045] Step B2: According to the environmental conditions, use the LSTM model to train the category model B of the time domain features and signal types, that is, train 10 models.

[0046] Step S33: When a new vibration signal is collected, the trained relationship model A can be used to determine which environmental category the vibration signal belongs to; the category model B determines which environmental condition model to select based on the environmental category, thereby identifying the signal type and improving the recognition accuracy.

[0047] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A system for identifying vibration signal types based on POTDR, characterized in that: It includes laser, circulator, optical fiber to be tested, polarization beam splitter, photoelectric converter assembly, signal collector and signal processing unit. The pulse light emitted by the laser enters the circulator and is modulated by the optical fiber to be tested. When the optical signal scattered back by the optical fiber to be tested is output by the circulator, it reaches the polarization beam splitter and is transmitted to the photoelectric converter component; the photoelectric converter component converts the optical signal into an electrical signal, which is sent to the signal collector and finally to the signal processing unit.

2. A system for identifying vibration signal types based on POTDR according to claim 1, characterized in that, The photoelectric converter assembly includes a first photoelectric converter and a second photoelectric converter, wherein the first photoelectric converter converts the received X-vector optical signal into an electrical signal in the X-axis direction, and the second photoelectric converter converts the received Y-vector optical signal into an electrical signal in the Y-axis direction.

3. A system for identifying vibration signal types based on POTDR according to claim 2, characterized in that, The polarization beam splitter decomposes the received signal into an X-vector optical signal and a Y-vector optical signal, and transmits the X-vector optical signal to the first photoelectric converter and transmits the Y-vector optical signal to the second photoelectric converter.

4. A system for identifying vibration signal types based on POTDR according to claim 1, characterized in that, The signal collector uses a digital coherent IQ demodulation algorithm to calculate the angle of polarized light from the collected polarization signal at each position, and sends the calculated angle to the signal processing unit.

5. A method for identifying vibration signal types based on POTDR, characterized in that: A system for identifying vibration signal types based on POTDR according to any one of claims 1 to 4 comprises the following steps: Step S1, when the vibration signal generated by the surrounding environment affects the optical fiber to be tested, the measurement result of the vibration signal characteristic is obtained based on the POTDR demodulation technology; Step S2, collecting vibration signals at any position, and establishing a data relationship database of soil environment, soil depth and vibration signals; Step S3: Based on the established data relationship library, the vibration signal characteristics under different soil environments and different depths are identified, and then the vibration signal type is identified.

6. A method for identifying vibration signal types based on POTDR according to claim 5, characterized in that, In step S1, the acquisition process is: The signal processing unit calculates the parameter signal of the polarized light carried by the backscattered Rayleigh echo of each section of the optical fiber to be tested, and finally obtains the measurement result of the vibration signal characteristic.

7. A method for identifying vibration signal types based on POTDR according to claim 5, characterized in that, In step S2, The soil environment is set as a general soil environment and a high water content environment, wherein the general soil environment is set as a low water content soil with a water content between 11% and 13%, and the high water content environment is set as a high water content soil with a water content between 18% and 20%; Set the soil depth to 2m, 5m, 10m, 15m, 20m; The parameters of vibration signals include amplitude, frequency and phase.

8. A method for identifying vibration signal types based on POTDR according to claim 7, characterized in that, In step S3, the identification process includes: Step S31, first use a support vector machine to train a relationship model A between vibration signal characteristics and soil environment and soil depth; Step S32: Use the LSTM model in deep learning to establish a category model B of the time domain features of the vibration signal and the signal category; Step S33: When a new vibration signal is collected, the trained relationship model A is used to determine which environmental category the vibration signal belongs to; the category model B determines which environmental condition the category model B is selected under based on the environmental category, thereby identifying the signal type.

9. A method for identifying vibration signal types based on POTDR according to claim 8, characterized in that, In step S31, the process is: Step A1, setting the vibration signal characteristics to [amplitude, frequency, phase]; Step A2, combining soil environment and soil depth; Step A3: clean and filter the collected vibration signal data, and then use a support vector machine to establish a relationship model A between the vibration signal feature data and the environment category.

10. A method for identifying vibration signal types based on POTDR according to claim 9, characterized in that, In step S32, the process is: Step B1, determining environmental conditions according to a combination of soil environment and soil depth; Step B2: According to environmental conditions, use the LSTM model to train the category model B of time domain features and signal types.

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