CN118009903A - Non-uniform strain fiber Bragg grating demodulation method based on spectrum shape - Google Patents
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Abstract
The invention provides a demodulation method of a non-uniform strain fiber Bragg grating based on a spectrum shape, which comprises the following steps: the FBGs are segmented into a plurality of sub-FBGs, the period of the periodic variation of the refractive index of each sub-FBG has a significant difference, and the wavelengths of the plurality of sub-FBGs are output as a demodulation result by a deep learning or fitting method. The traditional FBG demodulation algorithm only outputs a peak finding result, the real situation of the non-uniform strain FBG cannot be accurately reflected, the novel method provided by the patent takes the whole shape of the FBG reflection spectrum as a data analysis object, a novel demodulation dimension is increased, the real strain of the FBG can be accurately reflected, and the method can be applied to the sensing fields of optical fiber shape sensors and the like.
Description
Technical Field
The invention relates to a demodulation method of a non-uniform strain fiber Bragg grating based on a spectrum shape, which is particularly suitable for demodulation of the non-uniform strain of the Bragg grating in optical fiber shape sensing.
Background
Fiber Bragg Gratings (FBGs) are widely used in various sensing fields. The change in the FBG center wavelength is used to feed back a change in external physical quantity, such as a change in temperature, strain, shape, etc. However, in a practical use scenario, the FBG may generate non-uniform strain, and it is difficult to accurately reflect the real variation of the FBG simply depending on the variation of the FBG center wavelength. For example, patent 202110805580.1 describes a shape sensor consisting of three single-mode fibers and an elastic alloy substrate, for sensing the length of FBG of the fiber strain to a length of 1 cm. When bending, the off-axis optical fiber is stretched or compressed, and different axially-oriented areas of the FBG in the optical fiber have different curvatures in most cases, while the conventional peak-finding algorithm can only give one peak value, which can cause a larger error in the reconstructed shape of the shape sensor.
In recent years, a series of novel FBG demodulation methods have emerged, including innovations of demodulation hardware and demodulation algorithms. Such as demodulation hardware based on arrayed waveguide gratings, FBGs, long period gratings, and further such as new approaches averaging algorithms, edge filtering algorithms, and bragg wavelength estimation algorithms. Patent 2022100242772 provides a method of converting the spectrum of FBG into light intensity demodulation, which makes the reflected light of FBG incident into 1×n arrayed waveguide grating, then passes through two mach-zehnder interferometers with fixed phase difference pi/2, and finally makes the measurement of light power by four photodetectors. This method, while avoiding demodulation of the center wavelength, does not give the correct demodulation result when the reflection spectrum of the FBG does not have symmetry with respect to the maximum light intensity position. Xuefeng Mao et al describe a near-average algorithm that uses a scanning laser method as hardware to calculate the weighted average of the maximum output voltage and the sampling points near the next maximum output voltage to form a method that quickly approximates the center value of the reflection peak, and the wavelength of the weighted average is output as the Bragg wavelength. This weighted average approach is applicable to any shape of reflection peak, but only one output, still cannot reflect the true strain of the FBG.
Therefore, for demodulation of the non-uniformly strained FBG, the amount of data contained in one reflection spectrum peak is too small to accurately reflect the true strain of the FBG. A new demodulation method needs to be explored, the characteristic dimension of the FBG is increased, and the real strain situation of the FBG is reflected more comprehensively. This technique is particularly effective for large scale FBG strains, such as fiber optic shape sensors.
Disclosure of Invention
The invention aims to provide a demodulation method of a non-uniform strain Fiber Bragg Grating (FBG) based on a spectrum shape, which is characterized in that the FBG is segmented into a plurality of sub-FBGs, the period of the periodic variation of the refractive index of each sub-FBG is obviously different, and the wavelengths of the plurality of sub-FBGs are output as a demodulation result by a deep learning or fitting method.
The technical scheme adopted by the invention is as follows:
A method for demodulating a non-uniformly strained fiber bragg grating based on a spectral shape, comprising:
Setting a wavelength threshold delta lambda B for judging that the FBG is segmented into sub FBGs;
Obtaining a reflection/transmission spectrum signal of the FBG;
calculating an operator FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segmentation number;
The sub-FBG center wavelength data is combined and then ordered based on the wavelength threshold Δλ B as sensed data.
Further, the calculating operation FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number specifically includes:
inputting the reflected/transmitted spectrum signals into a trained deep neural network model, and outputting to obtain a plurality of central wavelength values corresponding to each sub FBG;
the number of output results of the deep neural network model is FBG segmentation number; the deep neural network model is obtained through training of the collected data set.
Further, the architecture of the deep neural network model includes an input layer with 64 nodes of the ReLU activation function, two hidden layers with 64 nodes of the ReLU activation function, and an output layer containing 9 nodes without activation function. This design aims at achieving scalar regression, trained by Mean Square Error (MSE) loss function, and the optimizer selects Adam.
Further, in the training process, K-fold verification is adopted, training data is divided into 10 partitions, one of the partitions is sequentially selected as a verification set, and the rest is used for training. After training the model on each partition, the Mean Absolute Error (MAE) for each training round is recorded.
Finally, the deep neural network model can be trained on the entire training dataset, outputting multiple wavelength data corresponding to each sub-FBG. In practical applications, such as demodulating the FBG spectrum of non-uniform strain, the model can provide multiple wavelength information, thereby providing a more accurate tool for strain measurement in the field of optical fiber sensing.
Further, in order to improve the accuracy of the demodulation result, a threshold screening mechanism is introduced. In this mechanism, a threshold is set for assigning a score to each demodulated wavelength data, the score being related to the confidence of its associated DNN output value.
Further, the calculating operation FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number specifically includes:
First, the FBGs are equivalent to N sub-FBGs according to the number of FBG segments. The N sub FBGs are provided with lengths of L 1、L2、…、LN, respectively, which are added to the total length of the FBGs. Any N-1 lengths are the variables to be fitted. The minimum unit lengths of the refractive index periodic variations of the N sub-FBGs are set to be l 1、l2、…、lN, respectively.
Next, by using a transmission matrix method, the output light of the sub FBG i is used as the input light (i=1, 2, … N-1) of the sub FBG i+1, and a final simulation spectrum is obtained, and the error is calculated with the actual measurement spectrum by using a least square method. By scanning the parameters to be fitted, the parameter combination with the smallest error can be obtained. The output parameters include FBG length and wavelength information (L 1,L2,…,LN,λB1,λB2,λB3, … …).
Further, the number of FBG segments is 2-6.
A spectral shape-based non-uniformly strained fiber bragg grating demodulation apparatus comprising:
the data acquisition module is used for setting and judging a wavelength threshold delta lambda B for segmenting the FBG into sub-FBGs and acquiring reflection/transmission spectrum signals of the FBGs;
a center wavelength data calculation module for calculating an operator FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number;
and the output module is used for merging and sequencing the sub-FBG center wavelength data based on the wavelength threshold delta lambda B to serve as sensing data.
Further, in the center wavelength data calculation module, the operator FBG center wavelength data is calculated based on the reflection/transmission spectrum signal and the FBG segmentation number, specifically:
inputting the reflected/transmitted spectrum signals into a trained deep neural network model, and outputting to obtain a plurality of central wavelength values corresponding to each sub FBG;
the number of output results of the deep neural network model is FBG segmentation number; the deep neural network model is obtained through training of the collected data set.
Further, in the center wavelength data calculation module, the operator FBG center wavelength data is calculated based on the reflection/transmission spectrum signal and the FBG segmentation number, specifically:
Equivalent FBGs to N sub FBGs according to the number of FBG segments; setting the lengths of N sub FBGs to be L 1、L2、…、LN respectively, and adding the lengths to be the total length of the FBGs; any N-1 length is a variable to be fitted; setting the minimum unit lengths of the refractive index periodic variation of the N sub FBGs to be l 1、l2、…、lN respectively;
Using a transmission matrix method, taking the emergent light of the sub FBG i as the incident light of the sub FBG i+1, wherein i=1, 2 and … N-1 to obtain a final simulation spectrum, and calculating an error with the actual measurement spectrum by using a least square method; by scanning parameters to be fitted, a parameter combination with the minimum error can be obtained; the output parameters include FBG length and wavelength information (L 1,L2,…,LN,λB1,λB2,λB3, … …).
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of non-uniformly strained fiber bragg grating demodulation based on a spectral shape as described when the computer program is executed.
A storage medium containing computer executable instructions that when executed by a computer processor implement a method of non-uniformly strained fiber bragg grating demodulation based on a spectral shape as described.
The beneficial effects of the invention are as follows: the invention takes the whole shape of the FBG reflection spectrum as a data analysis object, adds a new demodulation dimension, can more accurately reflect the real strain of the FBG, and can be applied to the sensing fields of optical fiber shape sensors and the like.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic diagram of a non-uniformly strained FBG segmented into sub-FBGs according to an embodiment of the invention;
FIG. 2 is a reflection spectrum of a non-uniformly strained FBG according to an embodiment of the invention;
FIG. 3 is a schematic flow chart of a method for demodulating a non-uniformly strained fiber Bragg grating based on a spectral shape according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a uniformly strained FBG segmented into sub-FBGs according to an embodiment of the invention;
FIG. 5 is a reflection spectrum of a uniformly strained FBG according to an embodiment of the invention;
FIG. 6 is a schematic diagram of a deep neural network according to an embodiment of the present invention;
FIG. 7 is a graph of test results for demodulating FBG wavelengths using DNN according to an embodiment of the invention;
FIG. 8 is a schematic diagram of a device for demodulating a non-uniformly strained fiber Bragg grating based on a spectral shape according to an embodiment of the present invention;
Fig. 9 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The features of the following examples and embodiments may be combined with each other without any conflict.
FIG. 1 is a schematic diagram of a non-uniformly strained FBG segmented into sub-FBGs according to an embodiment of the invention. The FBG 12 is a region having a periodic variation of refractive index in the core 11 of the optical fiber, and its minimum unit 13 of the periodic variation of refractive index generally has a small refractive index difference in the entire FBG. When the optical fiber is unevenly axially stretched or compressed, the refractive index periodicity of the FBG 12 is destroyed and the minimum unit 13 of the periodic variation of the FBG refractive index is no longer uniform in different regions. In this patent, it is specified that, if the optical path nl of the minimum unit 13 of the periodic variation of the FBG refractive index is changed by less than a set value δ (nl) within a length along the axial direction of the fiber axis, the periodically changed region can be regarded as a new FBG, such as sub FBG 141, FBG 142 and FBG 143 in fig. 1.
In practice, it is not strictly reasonable to equate 1 FBG to a series of, say, 3 sub FBGs, with a non-uniform strain of the fiber to a continuous strain. But this equivalent allows for a finer grating structure, which is beneficial for the sensing of FBG.
FIG. 2 is a reflection spectrum of a non-uniformly strained FBG according to an embodiment of the invention. The center wavelength found using the conventional peak finding algorithm can no longer accurately reflect the periodicity of the FBG.
Fig. 3 is a schematic flow chart of a demodulation method of a non-uniform strain fiber bragg grating based on a spectral shape according to an embodiment of the present invention. As shown in fig. 3, a demodulation method for a non-uniform strain fiber bragg grating based on a spectral shape according to an embodiment of the present invention may include the following steps:
In step S101, a wavelength threshold Δλ B is set that determines to segment the FBG into sub-FBGs.
In principle, the value of Δλ B＝2δ(nl).ΔλB can be set to a larger value, such as 0.1nm or more.
In step S102, the reflection/transmission spectrum signal of the FBG is measured.
Step S103, calculating the FBG center wavelength data and the length data based on the reflection/transmission spectrum signal and the FBG segment number.
In a specific embodiment, a neural network can be built and trained using reflection/transmission spectra as input parameters, and machine learning algorithms can be used to implement sub-FBG center wavelength data calculations based on spectral shape.
The machine learning method comprises the following steps:
First, a dataset is constructed, spectral pattern data of FBGs are collected, and the spectrum of the non-uniformly strained FBGs is equivalent to 3 sub-FBGs, each corresponding to one peak in the input irregular spectral pattern, as shown in fig. 2, sub-FBGs 141, 142 and 143 correspond to three peaks from right to left, respectively. The lengths of these sub-FBGs are denoted L1, L2 and L3, respectively, and the sum equals the total length of the FBGs. The minimum unit length of the refractive index periodic variation of each sub-FBG is l 1、l2、l3, respectively. And constructing each sample according to the central wavelengths of the three peaks and the corresponding spectrograms, and finally obtaining a data set.
Subsequently, by constructing a Deep Neural Network (DNN), the spectral data under non-uniform strain of each sample is used as input, with the goal of outputting a plurality of wavelength values corresponding to each sub-FBG, and training is performed to obtain a trained deep neural network model. In this embodiment, the architecture of the DNN includes an input layer with 64 nodes of the ReLU activation function, two hidden layers with 64 nodes of the ReLU activation function, and an output layer containing 3 nodes without activation function. This design aims at achieving scalar regression, trained by Mean Square Error (MSE) loss function, and the optimizer selects Adam.
In a specific embodiment, in the training process, K-fold verification is adopted, training data is divided into 10 partitions, one of the partitions is sequentially selected as a verification set, and the rest is used for training. After training the model on each partition, the Mean Absolute Error (MAE) for each training round is recorded. Finally, the model can be trained on the entire training dataset, outputting multiple wavelength data corresponding to each sub-FBG.
In a specific embodiment, a threshold screening mechanism is introduced to improve the accuracy of the demodulation results. In this mechanism, a threshold is set for assigning a score to each demodulated wavelength data, the score being related to the confidence of its associated DNN output value.
Specifically, having the deep neural network model output a plurality of values, e.g., 10 wavelength data, each wavelength data is assigned a corresponding score reflecting the confidence or reliability of the wavelength data in the deep neural network model output. Comparing the wavelength data with a preset threshold value, screening out wavelength data with a score higher than the threshold value, and regarding the wavelength data as effective wavelength information; while those wavelength data that score below the threshold are considered noise or uncertainty.
By way of specific example, assume that the threshold is set to 0.8. If the score of one wavelength data output by DNN is greater than 0.8, it is regarded as a valid wavelength; conversely, if the score is less than 0.8, it is excluded from the demodulation result to reduce errors and improve the accuracy of demodulation. This threshold screening mechanism effectively optimizes the demodulation results to make them more reliable and practical.
In a specific embodiment, the sub-FBG center wavelength data calculation based on the spectral shape can also be performed by using a fitting method. The fitting method comprises the following steps:
First, the FBG 12 shown in fig. 2 is equivalent to N sub-FBGs, such as 3 sub-FBGs, and the sub-FBGs 141, 142 and 143 shown in fig. 2. The fitting method is generally not suitable for excessively large N, and 2-6N are generally selected, otherwise, the fitting method can cause incongruity with the actual situation. The lengths of the three sub-FBGs are set to L 1、L2 and L 3, respectively, which add up to the total length of the FBG 12. Any two lengths are the variables to be fitted. The lengths of the minimum units 13 where the refractive index of the three sub-FBGs periodically varies are set to l 1、l2 and l 3, respectively.
Next, by using a transmission matrix method, the output light of the sub FBG 141 is used as the input light of the sub FBG 142, the output light of the sub FBG 142 is used as the input light of the sub FBG 143, and a final simulation spectrum is obtained, and the error is calculated by using a least square method with the actual measurement spectrum. By scanning the parameters to be fitted, a parameter combination with the smallest error can be obtained:
(λB1,λB2,λB3，……)
Step S104, merging and sequencing sub-FBG center wavelength data based on the wavelength threshold Deltalambda B as sensing data.
The wavelengths with the difference value smaller than the wavelength threshold delta lambda B in the obtained sub-FBG center wavelength data are combined, and then the curvature of the optical fiber is continuous in the length of one FBG, so that no abrupt change exists, and the sub-FBG center wavelength data can be sequenced from large to small or from small to large. This ordering does not strictly coincide with the real situation, but provides a way to most closely reflect the real situation of the FBG than the single wavelength obtained by the peak-finding of the FBG.
FIG. 4 is a schematic diagram of a uniformly strained FBG segmented into sub-FBGs according to an embodiment of the invention. The FBG 12 can be divided into only one sub-FBG 141. The situation is that the method for demodulating the uniformly-strained FBG based on the spectrum-shaped nonuniform strained fiber Bragg grating approximates the uniformly-strained FBG, and the method provided by the invention can be used for demodulation through the merging mechanism of the step S104, namely whether the FBG is uniformly strained or unevenly strained.
FIG. 5 is a reflection spectrum of a uniformly strained FBG according to an embodiment of the invention. The method for demodulating the nonuniform strain fiber Bragg grating based on the spectrum shape can obtain the result consistent with the peak searching algorithm.
Fig. 6 is a schematic diagram of a deep neural network according to an embodiment of the present invention, including an input layer with 64 nodes of a ReLU activation function, two hidden layers with 64 nodes of a ReLU activation function, and an output layer with 9 nodes without an activation function.
FIG. 7 is a graph of test results for demodulating FBG (including three sub-FBGs) wavelengths using DNN, according to an embodiment of the invention. In the multiple sets of test data, the predicted values of the sub-FBGs are almost identical to the results of the true values.
Corresponding to the embodiment of the method for demodulating the non-uniformly strained fiber Bragg grating based on the spectrum shape, the invention also provides an embodiment of a device for demodulating the non-uniformly strained fiber Bragg grating based on the spectrum shape.
Referring to fig. 8, a device for demodulating a non-uniformly strained fiber bragg grating based on a spectral shape according to an embodiment of the present invention includes:
the data acquisition module is used for setting and judging a wavelength threshold delta lambda B for segmenting the FBG into sub-FBGs and acquiring reflection/transmission spectrum signals of the FBGs;
a center wavelength data calculation module for calculating an operator FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number;
and the output module is used for merging and sequencing the sub-FBG center wavelength data based on the wavelength threshold delta lambda B to serve as sensing data.
The embodiment of the non-uniform strain fiber Bragg grating demodulation device based on the spectrum shape can be applied to any device with data processing capability, such as a computer or the like.
Referring to fig. 9, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements a method for demodulating a non-uniformly strained fiber bragg grating based on a spectral shape as described above when the computer program is executed.
In addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 9, any device with data processing capability in the embodiment generally includes other hardware according to the actual function of the any device with data processing capability, which will not be described herein.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The embodiment of the present invention also provides a computer readable storage medium having a program stored thereon, which when executed by a processor, implements a method for demodulating a non-uniformly strained fiber bragg grating based on a spectral shape in the above embodiment.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (10)
1. A method for demodulating a non-uniformly strained fiber bragg grating based on a spectral shape, comprising:
Setting a wavelength threshold delta lambda B for judging that the FBG is segmented into sub FBGs;
Obtaining a reflection/transmission spectrum signal of the FBG;
calculating an operator FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segmentation number;
The sub-FBG center wavelength data is combined and then ordered based on the wavelength threshold Δλ B as sensed data.
2. The method according to claim 1, characterized in that the calculation of FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number is specifically:
inputting the reflected/transmitted spectrum signals into a trained deep neural network model, and outputting to obtain a plurality of central wavelength values corresponding to each sub FBG;
the number of output results of the deep neural network model is FBG segmentation number; the deep neural network model is obtained through training of the collected data set.
3. The method of claim 2, wherein each sample of the dataset comprises a reflection/transmission spectrum signal and a corresponding plurality of center wavelength values for each sub-FBG.
4. The method according to claim 1, characterized in that the calculation of FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number is specifically:
Equivalent FBGs to N sub FBGs according to the number of FBG segments; setting the lengths of N sub FBGs to be L 1、L2、…、LN respectively, and adding the lengths to be the total length of the FBGs; any N-1 length is a variable to be fitted; setting the minimum unit lengths of the refractive index periodic variation of the N sub FBGs to be l 1、l2、…、lN respectively;
Using a transmission matrix method, taking emergent light of a sub-FBGi as incident light of a sub-FBGi+1, and obtaining a final simulation spectrum by using i=1, 2 and … N-1, and calculating an error with an actual measurement spectrum by using a least square method; by scanning parameters to be fitted, a parameter combination with the minimum error can be obtained; the output parameters include FBG length and wavelength information (L 1,L2,…,LN,λB1,λB2,λB3, … …).
5. The method of claim 1, wherein the number of FBG segments is 2-6.
6. A spectral shape-based non-uniformly strained fiber bragg grating demodulation device, comprising:
the data acquisition module is used for setting and judging a wavelength threshold delta lambda B for segmenting the FBG into sub-FBGs and acquiring reflection/transmission spectrum signals of the FBGs;
a center wavelength data calculation module for calculating an operator FBG center wavelength data based on the reflection/transmission spectrum signal and the FBG segment number;
and the output module is used for merging and sequencing the sub-FBG center wavelength data based on the wavelength threshold delta lambda B to serve as sensing data.
7. The apparatus of claim 6, wherein the central wavelength data calculation module calculates FBG central wavelength data based on the reflection/transmission spectrum signal and the FBG segment number, specifically:
inputting the reflected/transmitted spectrum signals into a trained deep neural network model, and outputting to obtain a plurality of central wavelength values corresponding to each sub FBG;
the number of output results of the deep neural network model is FBG segmentation number; the deep neural network model is obtained through training of the collected data set.
8. The apparatus of claim 6, wherein the central wavelength data calculation module calculates FBG central wavelength data based on the reflection/transmission spectrum signal and the FBG segment number, specifically:
Equivalent FBGs to N sub FBGs according to the number of FBG segments; setting the lengths of N sub FBGs to be L 1、L2、…、LN respectively, and adding the lengths to be the total length of the FBGs; any N-1 length is a variable to be fitted; setting the minimum unit lengths of the refractive index periodic variation of the N sub FBGs to be l 1、l2、…、lN respectively;
Using a transmission matrix method, taking emergent light of a sub-FBGi as incident light of a sub-FBGi+1, and obtaining a final simulation spectrum by using i=1, 2 and … N-1, and calculating an error with an actual measurement spectrum by using a least square method; by scanning parameters to be fitted, a parameter combination with the minimum error can be obtained; the output parameters include FBG length and wavelength information (L 1,L2,…,LN,λB1,λB2,λB3, … …).
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a method of spectrally shape-based non-uniform strain fiber bragg grating demodulation as claimed in any one of claims 1-5 when the computer program is executed by the processor.
10. A storage medium containing computer executable instructions which when executed by a computer processor implement a method of spectral shape-based non-uniform strain fiber bragg grating demodulation in accordance with any one of claims 1-5.
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