CN116026449A - Vibration positioning monitoring system based on single-core optical fiber sensing - Google Patents
Vibration positioning monitoring system based on single-core optical fiber sensing Download PDFInfo
- Publication number
- CN116026449A CN116026449A CN202310323931.4A CN202310323931A CN116026449A CN 116026449 A CN116026449 A CN 116026449A CN 202310323931 A CN202310323931 A CN 202310323931A CN 116026449 A CN116026449 A CN 116026449A
- Authority
- CN
- China
- Prior art keywords
- vibration
- module
- signal
- optical fiber
- core optical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention provides a vibration positioning monitoring system and method based on single-core optical fiber sensing, comprising a signal receiving module, a signal processing module, a signal analysis module, a feature extraction module, a signal recording module, a vibration identification module, a positioning module, a display module and an early warning module; the denoising treatment and the time-frequency analysis treatment are carried out on the vibration signals, so that the resolution ratio of a time spectrum is greatly improved, and the characteristics of the vibration signals can be effectively and accurately extracted; by constructing a characteristic recognition neural network model, a vibration cause recognition algorithm is established, and the purposes of early warning, no missing report and low false alarm are achieved, so that accurate positioning alarm is carried out, and an entering source is locked; the invention effectively solves the problems of slow detection, larger error and the like in the prior art, improves the resolution of the vibration signal, effectively extracts the characteristics of the vibration signal, accurately diagnoses the vibration reason and the vibration position, and improves the early warning and monitoring efficiency of the fiber vibration.
Description
Technical Field
The invention relates to an optical fiber vibration sensing technology, in particular to a vibration positioning monitoring system based on single-core optical fiber sensing.
Background
The optical fiber vibration sensor uses the optical fiber as a sensor for vibration sensing, and has wide application prospect in the engineering fields of structural health monitoring, oil and gas pipeline leakage monitoring, perimeter protection, earthquake monitoring and the like due to the advantages of high sensitivity, quick response, simple structure, uniform distribution and the like. The technology of using optical fibers as sensors to collect and transmit information and applying the optical fibers to the field of security protection is not developed in a large amount.
Most of the existing optical fiber vibration sensing technologies are distributed optical fiber vibration positioning systems based on double Mach-Zehnder interference structures, and have the advantages of long detection distance, no electromagnetic interference and the like. Many researchers develop this technology and have achieved certain results, but the problems of slow detection, large error and the like still exist. The dual MZI technology commonly used abroad utilizes three optical fibers for monitoring, and the data are asynchronous and greatly influenced by temperature, noise and other environments.
In the prior art, there is a distributed optical fiber vibration sensing positioning method based on FFT, by collecting backward Rayleigh scattering signals of a plurality of pulses, FFT is carried out on signals accumulated at each position, the frequency of the vibration signal at the position is determined according to the amplitude-frequency characteristic of the position, the frequency is used as a judgment condition for screening, the frequency result meeting the judgment condition is processed by the position difference value corresponding to adjacent moments, and the real effective vibration position is accurately identified under the influence of external environment on the optical cable. The prior art also provides a distributed optical fiber vibration sensing positioning device based on FFT, which is used for executing the positioning method, judging the vibration position by combining the vibration frequency characteristics of different vibration modes and the characteristics of real vibration burst, and has simple algorithm realization and good engineering practicability.
However, the above-mentioned technique has at least the following problems: the problems of slow detection, large error and the like exist in the prior art, and the type of interference cannot be clearly distinguished on a perceived vibration signal, so that vibration reasons cannot be accurately diagnosed, early warning accuracy is affected, and vibration positioning monitoring of optical fiber sensing is not intelligent enough.
Disclosure of Invention
According to the vibration positioning monitoring system based on single-core optical fiber sensing, the problems that detection is slow, errors are large and the like in the prior art are solved, the interference type cannot be clearly distinguished on a sensed vibration signal, therefore vibration reasons cannot be accurately diagnosed, early warning accuracy is affected, and vibration positioning monitoring of optical fiber sensing is not intelligent enough. The vibration signal resolution is improved, the vibration signal characteristics are effectively extracted, the vibration reasons and the vibration positions are accurately diagnosed, and the early warning and monitoring efficiency of the optical fiber vibration is improved.
The application specifically comprises the following technical scheme:
a vibration positioning monitoring system based on single-core optical fiber sensing, comprising the following parts:
the system comprises a signal receiving module, a signal processing module, a signal analysis module, a characteristic extraction module, a signal recording module, a vibration identification module, a positioning module, a display module and an early warning module;
the signal receiving module is used for receiving the vibration signal transmitted by the single-core optical fiber and is connected with the signal processing module and the positioning module in a data transmission mode;
the signal processing module is used for determining a denoising coefficient, denoising the vibration signal and connecting with the signal analysis module in a data transmission mode;
the signal analysis module is used for carrying out time-frequency analysis on the denoised vibration signals to obtain a time-frequency distribution function of the vibration signals, and the signal analysis module is connected with the feature extraction module in a data transmission mode;
the feature extraction module is used for mapping the time-frequency distribution function to a high-order time-frequency domain, calculating a high-order time-frequency distribution factor, constructing an extraction operator based on the high-order time-frequency distribution factor, and extracting the features of the vibration signal based on the extraction operator; the feature extraction module is connected with the vibration identification module in a data transmission mode;
the signal recording module is used for recording characteristic information of different vibration signals and corresponding to the generation reason of each vibration signal, and is connected with the vibration identification module in a data transmission mode;
the vibration recognition module is used for constructing a characteristic recognition neural network model, inputting the characteristics of the vibration signals into the model, and finally accurately outputting the vibration generation reason type corresponding to the current vibration signals through deep learning of the model, and is connected with the signal recording module, the display module and the early warning module in a data transmission mode;
the positioning module is used for acquiring the position information of the single-core optical fiber sensor and is connected with the display module and the early warning module in a data transmission mode;
the display module is used for displaying the vibration position and the reason;
and the early warning module is used for sending out early warning of unknown reasons.
The vibration positioning monitoring method based on single-core optical fiber sensing comprises the following steps:
s1, denoising and time-frequency analysis are carried out on a vibration signal, a time-frequency distribution function is mapped to a high-order time-frequency domain, a high-order time-frequency distribution factor is obtained, an extraction operator is constructed based on the high-order time-frequency distribution factor, and characteristics of the vibration signal are extracted;
s2, constructing a characteristic recognition neural network model, matching the vibration signal characteristics with known vibration generation reasons, outputting the vibration generation reasons according to the vibration signal characteristics, acquiring sensor position information, and giving out unknown reason early warning and positioning information for the recorded vibration generation reasons.
Further, the step S1 specifically includes:
and obtaining a threshold value, determining a denoising coefficient of the vibration signal by the threshold value, and denoising the vibration signal according to the denoising coefficient.
Further, the step S1 specifically includes:
and carrying out time-frequency analysis on the denoised vibration signal to obtain a time-frequency distribution function of the vibration signal, mapping the time-frequency distribution function to a high-order time-frequency domain, and calculating a high-order time-frequency distribution factor.
Further, the step S1 specifically includes:
and constructing an extraction operator of the vibration signal, and extracting the characteristics of the vibration signal based on the extraction operator.
Further, the step S2 specifically includes:
the characteristic recognition neural network model is constructed, the characteristics of the vibration signals are input into the model, and finally the vibration generation reason type corresponding to the current vibration signals is accurately output through deep learning of the model, wherein the characteristic recognition neural network model comprises an input layer, N hidden layers and an output layer.
Further, the step S2 specifically includes:
the matching degree of the feature points is represented by distance, and the input layer performs Taylor expansion on each feature point by using a deep learning function.
Further, the step S2 specifically includes:
if the difference between the current characteristics of the vibration signal and the characteristics recorded in the signal recording module is large, the characteristic identification neural network model outputs a result 0, and gives an early warning of unknown reasons to the early warning module, the reasons are confirmed by checking a camera near the sensor position or going to the site by a worker, after the current occurrence reason of the vibration signal is obtained, the vibration signal is stored in the signal recording module, and the characteristic identification neural network model is subjected to learning training by the current characteristics and the occurrence reason of the vibration signal, so that the characteristic identification neural network model can timely identify all the known vibration reasons, and the intelligence and the high efficiency of the vibration positioning monitoring system are ensured.
The application has at least the following technical effects or advantages:
1. through the single-core optical fiber sensing and the independently developed modularized analyzer, the functions of micro-vibration sensing, signal processing, signal analysis and recognition are realized, the characteristics of frequency, time domain and the like of vibration signals are detected and analyzed, the characteristics of target characteristic quantities such as the frequency domain, the time domain and the like are effectively optimized and analyzed, the resolution ratio of time spectrum is greatly improved, the characteristics of the vibration signals can be effectively and accurately extracted, and therefore the accurate diagnosis of vibration causes is realized.
2. The optical fiber single-core induction technology is adopted first, the large-temperature-difference and long-distance scene is broken through, long-time, dead-angle-free and uninterrupted prevention is realized, different types of interference are intelligently identified, and the early warning accuracy is improved: and a vibration cause identification algorithm is established through the characteristic identification neural network model, so that no false alarm and low false alarm are early-warned, and accurate positioning alarm and source entering locking are performed.
3. According to the technical scheme, the problems of slow detection, large error and the like in the prior art can be effectively solved, the interference type cannot be clearly distinguished on the perceived vibration signal, so that vibration reasons cannot be accurately diagnosed, early warning accuracy is affected, and vibration positioning monitoring of optical fiber sensing is not intelligent enough. Through a series of effect researches, the system or the method can improve the resolution of the vibration signal finally through verification, effectively extract the characteristics of the vibration signal, accurately diagnose the vibration reason and the vibration position and improve the early warning and monitoring efficiency of the optical fiber vibration.
Drawings
FIG. 1 is a diagram of a vibration positioning monitoring system based on single-core optical fiber sensing according to the present application;
fig. 2 is a flowchart of a vibration positioning monitoring method based on single-core optical fiber sensing.
Detailed Description
According to the vibration positioning monitoring system based on single-core optical fiber sensing, the problems that detection is slow, errors are large and the like in the prior art are solved, the interference type cannot be clearly distinguished on a sensed vibration signal, therefore vibration reasons cannot be accurately diagnosed, early warning accuracy is affected, and vibration positioning monitoring of optical fiber sensing is not intelligent enough.
The technical scheme in the embodiment of the application aims to solve the problems, and the overall thought is as follows:
the modularized analyzer which is sensed by a single-core optical fiber and independently developed realizes the functions of micro-vibration sensing, signal processing, signal analysis and recognition, detects and analyzes the characteristics of frequency, time domain and the like of vibration signals, effectively optimizes and analyzes the characteristics of target characteristic quantities such as the frequency domain, the time domain and the like, greatly improves the resolution of time spectrum, and can effectively and accurately extract the characteristics of the vibration signals, thereby realizing the accurate diagnosis of vibration reasons; the optical fiber single-core induction technology is adopted first, the large-temperature-difference and long-distance scene is broken through, long-time, dead-angle-free and uninterrupted prevention is realized, different types of interference are intelligently identified, and the early warning accuracy is improved: and a vibration cause identification algorithm is established through the characteristic identification neural network model, so that no false alarm and low false alarm are early-warned, and accurate positioning alarm and source entering locking are performed.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to fig. 1, a vibration positioning monitoring system based on single-core optical fiber sensing described in the application comprises the following parts:
the system comprises a signal receiving module 10, a signal processing module 20, a signal analysis module 30, a characteristic extraction module 40, a signal recording module 50, a vibration identification module 60, a positioning module 70, a display module 80 and an early warning module 90;
the signal receiving module 10 is configured to receive a vibration signal transmitted by a single-core optical fiber, and the signal receiving module 10 is connected to the signal processing module 20 and the positioning module 70 by way of data transmission;
the signal processing module 20 is configured to determine a denoising coefficient, denoise the vibration signal, and the signal processing module 20 is connected to the signal analysis module 30 by a data transmission manner;
the signal analysis module 30 is configured to perform time-frequency analysis on the denoised vibration signal to obtain a time-frequency distribution function of the vibration signal, where the signal analysis module 30 is connected to the feature extraction module 40 by a data transmission manner;
the feature extraction module 40 is configured to map the time-frequency distribution function to a high-order time-frequency domain, calculate a high-order time-frequency distribution factor, then construct an extraction operator based on the high-order time-frequency distribution factor, and extract features of the vibration signal based on the extraction operator; the feature extraction module 40 is connected with the vibration identification module 60 by means of data transmission;
the signal recording module 50 is configured to record characteristic information of different vibration signals, and correspond to a generation reason of each vibration signal, and the signal recording module 50 is connected to the vibration identification module 60 through a data transmission manner;
the vibration identification module 60 is configured to construct a feature identification neural network model, input the features of the vibration signal into the model, and finally accurately output the vibration generation cause type corresponding to the current vibration signal through deep learning of the model, where the vibration identification module 60 is connected with the signal recording module 50, the display module 80 and the early warning module 90 by means of data transmission;
the positioning module 70 is configured to obtain position information of the single-core optical fiber sensor, and the positioning module 70 is connected with the display module 80 and the early warning module 90 by means of data transmission;
the display module 80 is used for displaying the vibration position and the reason.
The early warning module 90 is configured to send out an early warning of an unknown cause.
Referring to fig. 2, a vibration positioning monitoring method based on single-core optical fiber sensing described in the application includes the following steps:
s1, denoising and time-frequency analysis are carried out on a vibration signal, a time-frequency distribution function is mapped to a high-order time-frequency domain, a high-order time-frequency distribution factor is obtained, an extraction operator is constructed based on the high-order time-frequency distribution factor, and characteristics of the vibration signal are extracted;
the optical fiber vibration sensing uses the optical fiber as a sensor for vibration sensing, has the advantages of high sensitivity, quick response, simple structure, uniform distribution and the like, and the single-core optical fiber sensing vibration positioning monitoring system and the single-core optical fiber sensing vibration positioning monitoring method disclosed by the invention can be widely applied to detection of bridge tunnels, highway railways, mountain stress change, urban underground pipe networks and other areas; detecting water areas such as ports, water-bank facilities and the like; and border lines, monitoring heavy spot areas such as military bases and the like.
Paving a single-core optical fiber to a region to be monitored, wherein the single-core optical fiber is a sensing device, and when a single-core optical fiber sensor receives external interference influence, part of characteristics of transmitted light in the single-core optical fiber are changed, so that characteristics of detected light are changed, and the characteristics of the light comprise attenuation, phase, wavelength, polarization, mode field distribution and propagation time; the output waveform of the interference light changes, so that the single-core optical fiber sensor can sense the vibration signal of the area to be monitored, an advanced optical fiber mixing technology is adopted, the single-core optical fiber is utilized to realize integrated sensing, and the sensed vibration signal is transmitted to the independently developed modularized analyzer.
The signal receiving module 10 on the modularized analyzer receives the vibration signal transmitted by the single-core optical fiber, the signal processing module 20 preprocesses the vibration signal, and the vibration signal perceived by the single-core optical fiber is set as
，/>
Representing the nth set of vibration signals, ">
Representing transposition by +.>
Indicating (I)>
. Because the single-core optical fiber is inevitably noisy in the signal acquisition process, the vibration signal needs to be denoised.
Obtaining a threshold value according to expert experience or experiments
Determining a denoising coefficient of the vibration signal by a threshold value, wherein the denoising coefficient has a calculation formula as follows: />
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the denoising coefficient, ++>
Representing scale factor,/->
Representing a low pass filter +.>
Representing a high pass filter +.>
,/>
，/>
Indicating the number of degrees of resolution. Denoising the vibration signal according to the denoising coefficient, wherein the specific formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the denoised vibration signal, +.>
Representation->
Conjugation of->
Representation->
Is a conjugate of (c).
The signal analysis module 30 performs time-frequency analysis on the denoised vibration signal to obtain a time-frequency distribution function of the vibration signal:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a time-frequency distribution function, t representing time, < ->
Indicates the vibration frequency +.>
And->
Respectively representing the start time and the end time of the vibration signal, < >>
Representing vibration signal +.>
Representing the reference point position +.>
A time-position fix is indicated and,
representing a window function.
The feature extraction module 40 extracts features of the vibration signal based on time-frequency analysis, firstly maps a time-frequency distribution function to a high-order time-frequency domain, calculates a high-order time-frequency distribution factor, then constructs an extraction operator based on the high-order time-frequency distribution factor, and extracts features of the vibration signal based on the extraction operator, wherein the specific steps of feature extraction are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a higher order time-frequency distribution factor,/for>
Is a time-frequency distribution function->
In (a) t is the deviation of the direction of the flow of the blood>
Representing imaginary units. Constructing an extraction operator based on high-order time-frequency distribution factors>
Extracting characteristics of vibration signals:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the characteristics of the vibration signal.
S2, constructing a characteristic recognition neural network model, matching the vibration signal characteristics with known vibration generation reasons, outputting the vibration generation reasons according to the vibration signal characteristics, acquiring sensor position information, and giving out unknown reason early warning and positioning information for the recorded vibration generation reasons.
The signal recording module 50 records characteristic information of different vibration signals and corresponds to the generation reason of each vibration signal. The vibration signal is generated by different objects which generate the vibration signal when passing through the single-core optical fiber, such as signal fluctuation caused by vehicles, animals and natural environment interference, signal fluctuation caused by artificial damage and the like. After the signal recording module 50 records and stores all known vibration signal generation reasons and characteristic information, when a single-core optical fiber generates a new vibration signal, the signal recording module can match the characteristics of the recorded vibration signal, so as to obtain the reason for generating the current vibration signal, and the specific steps of the vibration signal characteristic matching method are as follows:
the vibration identification module 60 constructs a feature identification neural network model, inputs the features of the vibration signals into the model, and finally accurately outputs the vibration generation reason type corresponding to the current vibration signals through deep learning of the model. The feature recognition neural network model includes an input layer, N hidden layers, and an output layer.
The method comprises the steps of obtaining the vibration signal characteristics and the historical data corresponding to the generation reasons, taking the historical data as characteristic recognition neural network to carry out deep learning sample data, dividing the data into a training set and a testing set, wherein one group of training samples comprise input and output, the input is the vibration signal characteristics, and the output is the reason of the generation of the input vibration signals. The input representation of a set of training samples is selected as
M represents the number of feature points of the current vibration signal feature sample.
In order to improve the processing speed and the robustness of the feature recognition neural network, the matching degree of feature points is expressed by distance, and the input layer carries out Taylor expansion on each feature point by using a deep learning function, wherein the calculation formula of the deep learning function is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a deep learning function>
Representing the bias of the feature points ∈ ->
Indicating transpose,/->
Representing constrained depth gradient->
Representing a constant factor.
The calculation process of the hidden layer is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
output representing hidden layer,/->
To activate the function +.>
Representing the connection weight of the current hidden layer and the previous hidden layer, +.>
Representing the bias of the current hidden layer +.>
Representing the input of the current hidden layer, i.e. the output of the last hidden layer. Each hidden layer outputs the calculation result to the next hidden layer until the Nth hidden layer outputs the calculation result to the output layer, and the output layer outputs the calculation result of the Nth hidden layer.
And performing error calculation on the model output and the sample output, optimizing parameters in the feature recognition neural network according to a gradient descent method, and completing training of the feature recognition neural network model until the error between the model output and the sample output is smaller than a preset threshold value after the test set is input into the feature recognition neural network model.
When the single-core optical fiber senses the vibration signal, the sensor position and the vibration signal are simultaneously transmitted to the modularized analyzer, the positioning module 70 in the modularized analyzer acquires the sensor position information, the signal analysis module 30 and the feature extraction module 40 respectively perform preprocessing and feature extraction on the vibration signal, the extracted features are transmitted to the vibration identification module 60, the vibration identification module 60 outputs the reason of the current vibration signal, and the display module 80 displays the vibration reason.
If the difference between the characteristics of the current vibration signal and the characteristics recorded in the signal recording module 50 is large, the characteristic recognition neural network model outputs the result 0, and gives an early warning of unknown reasons to the early warning module 90, the reasons need to be confirmed by checking a camera near the sensor position or by a worker going to the site, after the occurrence reasons of the current vibration signal are obtained, the current vibration signal is stored in the signal recording module 50, and the characteristic recognition neural network model is learned and trained by the characteristics and the occurrence reasons of the current vibration signal, so that the characteristic recognition neural network model can timely recognize all the known vibration reasons, and the intelligence and the high efficiency of the vibration positioning monitoring system are ensured.
In summary, the vibration positioning monitoring system based on single-core optical fiber sensing is completed.
The present invention is described 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 flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. Vibration positioning monitoring system based on single core optical fiber sensing, which is characterized by comprising the following parts:
the system comprises a signal receiving module, a signal processing module, a signal analysis module, a characteristic extraction module, a signal recording module, a vibration identification module, a positioning module, a display module and an early warning module;
the signal receiving module is used for receiving the vibration signal transmitted by the single-core optical fiber and is connected with the signal processing module and the positioning module in a data transmission mode;
the signal processing module is used for determining a denoising coefficient, denoising the vibration signal and connecting with the signal analysis module in a data transmission mode;
the signal analysis module is used for carrying out time-frequency analysis on the denoised vibration signals to obtain a time-frequency distribution function of the vibration signals, and the signal analysis module is connected with the feature extraction module in a data transmission mode;
the feature extraction module is used for mapping the time-frequency distribution function to a high-order time-frequency domain, calculating a high-order time-frequency distribution factor, constructing an extraction operator based on the high-order time-frequency distribution factor, and extracting the features of the vibration signal based on the extraction operator; the feature extraction module is connected with the vibration identification module in a data transmission mode;
the signal recording module is used for recording characteristic information of different vibration signals and corresponding to the generation reason of each vibration signal, and is connected with the vibration identification module in a data transmission mode;
the vibration recognition module is used for constructing a characteristic recognition neural network model, inputting the characteristics of the vibration signals into the model, and finally accurately outputting the vibration generation reason type corresponding to the current vibration signals through deep learning of the model, and is connected with the signal recording module, the display module and the early warning module in a data transmission mode;
the positioning module is used for acquiring the position information of the single-core optical fiber sensor and is connected with the display module and the early warning module in a data transmission mode;
the display module is used for displaying the vibration position and the reason;
and the early warning module is used for sending out early warning of unknown reasons.
2. The vibration positioning monitoring method based on single-core optical fiber sensing is characterized by comprising the following steps of:
s1, denoising and time-frequency analysis are carried out on a vibration signal, a time-frequency distribution function is mapped to a high-order time-frequency domain, a high-order time-frequency distribution factor is obtained, an extraction operator is constructed based on the high-order time-frequency distribution factor, and characteristics of the vibration signal are extracted;
s2, constructing a characteristic recognition neural network model, matching the vibration signal characteristics with known vibration generation reasons, outputting the vibration generation reasons according to the vibration signal characteristics, acquiring sensor position information, and giving out unknown reason early warning and positioning information for the recorded vibration generation reasons.
3. The vibration positioning monitoring method based on single-core optical fiber sensing as claimed in claim 2, wherein the step S1 specifically includes:
and obtaining a threshold value, determining a denoising coefficient of the vibration signal by the threshold value, and denoising the vibration signal according to the denoising coefficient.
4. A vibration positioning monitoring method based on single-core optical fiber sensing as claimed in claim 3, wherein said step S1 specifically comprises:
and carrying out time-frequency analysis on the denoised vibration signal to obtain a time-frequency distribution function of the vibration signal, mapping the time-frequency distribution function to a high-order time-frequency domain, and calculating a high-order time-frequency distribution factor.
5. The vibration positioning monitoring method based on single-core optical fiber sensing as claimed in claim 2, wherein the step S1 specifically includes:
and constructing an extraction operator of the vibration signal, and extracting the characteristics of the vibration signal based on the extraction operator.
6. The vibration positioning monitoring method based on single-core optical fiber sensing as claimed in claim 2, wherein the step S2 specifically includes:
the characteristic recognition neural network model is constructed, the characteristics of the vibration signals are input into the model, and finally the vibration generation reason type corresponding to the current vibration signals is accurately output through deep learning of the model, wherein the characteristic recognition neural network model comprises an input layer, N hidden layers and an output layer.
7. The vibration positioning monitoring method based on single-core optical fiber sensing as claimed in claim 6, wherein the step S2 specifically comprises:
the matching degree of the feature points is represented by distance, and the input layer performs Taylor expansion on each feature point by using a deep learning function.
8. The vibration positioning monitoring method based on single-core optical fiber sensing as claimed in claim 6, wherein the step S2 specifically comprises:
if the difference between the current vibration signal characteristics and the characteristics recorded in the signal recording module is large, the characteristic identification neural network model outputs a result 0, and gives an early warning of unknown reasons to the early warning module, the reasons need to be confirmed by checking a camera near the sensor position or going to the site by a worker, after the current vibration signal occurrence reasons are obtained, the vibration signal characteristics and the occurrence reasons are stored in the signal recording module, and learning and training are carried out on the characteristic identification neural network model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310323931.4A CN116026449B (en) | 2023-03-30 | 2023-03-30 | Vibration positioning monitoring system based on single-core optical fiber sensing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310323931.4A CN116026449B (en) | 2023-03-30 | 2023-03-30 | Vibration positioning monitoring system based on single-core optical fiber sensing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116026449A true CN116026449A (en) | 2023-04-28 |
| CN116026449B CN116026449B (en) | 2023-06-16 |
Family
ID=86074512
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310323931.4A Active CN116026449B (en) | 2023-03-30 | 2023-03-30 | Vibration positioning monitoring system based on single-core optical fiber sensing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116026449B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117370878A (en) * | 2023-12-07 | 2024-01-09 | 山东第一医科大学第一附属医院(山东省千佛山医院) | Epidermis extraction and positioning method and system based on spine joint vibration information |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107256635A (en) * | 2017-07-14 | 2017-10-17 | 浙江大学 | Vehicle identification method based on distributing optical fiber sensing in a kind of intelligent transportation |
| CN108509850A (en) * | 2018-02-24 | 2018-09-07 | 华南理工大学 | A kind of invasion signal Recognition Algorithm based on distribution type fiber-optic system |
| CN108709633A (en) * | 2018-08-29 | 2018-10-26 | 中国科学院上海光学精密机械研究所 | Distributed optical fiber vibration sensing intelligent and safe monitoring method based on deep learning |
| CN109117832A (en) * | 2018-10-12 | 2019-01-01 | 成都理工大学 | High-order is synchronous to extract transformation signal Time-Frequency Analysis Method |
| CN111157099A (en) * | 2020-01-02 | 2020-05-15 | 河海大学常州校区 | Distributed optical fiber sensor vibration signal classification method and identification classification system |
| CN114266271A (en) * | 2021-12-13 | 2022-04-01 | 长飞光纤光缆股份有限公司 | Distributed optical fiber vibration signal mode classification method and system based on neural network |
| US20220172611A1 (en) * | 2020-12-01 | 2022-06-02 | Verizon Patent And Licensing Inc. | Systems and methods for fiber optic based vehicle-direction detection |
| CN114623922A (en) * | 2022-05-11 | 2022-06-14 | 高勘(广州)技术有限公司 | Distributed optical fiber sensing early warning system, control method, device and storage medium thereof |
-
2023
- 2023-03-30 CN CN202310323931.4A patent/CN116026449B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107256635A (en) * | 2017-07-14 | 2017-10-17 | 浙江大学 | Vehicle identification method based on distributing optical fiber sensing in a kind of intelligent transportation |
| CN108509850A (en) * | 2018-02-24 | 2018-09-07 | 华南理工大学 | A kind of invasion signal Recognition Algorithm based on distribution type fiber-optic system |
| CN108709633A (en) * | 2018-08-29 | 2018-10-26 | 中国科学院上海光学精密机械研究所 | Distributed optical fiber vibration sensing intelligent and safe monitoring method based on deep learning |
| CN109117832A (en) * | 2018-10-12 | 2019-01-01 | 成都理工大学 | High-order is synchronous to extract transformation signal Time-Frequency Analysis Method |
| CN111157099A (en) * | 2020-01-02 | 2020-05-15 | 河海大学常州校区 | Distributed optical fiber sensor vibration signal classification method and identification classification system |
| US20220172611A1 (en) * | 2020-12-01 | 2022-06-02 | Verizon Patent And Licensing Inc. | Systems and methods for fiber optic based vehicle-direction detection |
| CN114266271A (en) * | 2021-12-13 | 2022-04-01 | 长飞光纤光缆股份有限公司 | Distributed optical fiber vibration signal mode classification method and system based on neural network |
| CN114623922A (en) * | 2022-05-11 | 2022-06-14 | 高勘(广州)技术有限公司 | Distributed optical fiber sensing early warning system, control method, device and storage medium thereof |
Non-Patent Citations (1)
| Title |
|---|
| 时银水;姬红兵;王磊;: "基于CWT的常规雷达编队目标架次自动识别", 现代雷达, no. 07, pages 34 - 37 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117370878A (en) * | 2023-12-07 | 2024-01-09 | 山东第一医科大学第一附属医院(山东省千佛山医院) | Epidermis extraction and positioning method and system based on spine joint vibration information |
| CN117370878B (en) * | 2023-12-07 | 2024-03-05 | 山东第一医科大学第一附属医院(山东省千佛山医院) | Epidermis extraction and positioning method and system based on spine joint vibration information |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116026449B (en) | 2023-06-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11562224B2 (en) | 1D-CNN-based distributed optical fiber sensing signal feature learning and classification method | |
| CN111222743B (en) | Method for judging vertical offset distance and threat level of optical fiber sensing event | |
| CN201191221Y (en) | Disturbance signal determination module of distributed optical fiber vibration sensor system | |
| CN103775832B (en) | Based on the device that the petroleum pipeline leakage of transient flow Inverse Problem Method detects | |
| CN107167168B (en) | Phase sensitive optical time domain reflection distributed optical fiber sensing system accurate positioning method | |
| RU2014128537A (en) | HYDRAULIC RUPTURE MONITORING | |
| CN103196465B (en) | Phase sensitive optical time-domain reflectometer (phi-OTDR) sensing signal noise separation and signal extraction method | |
| CN116026449B (en) | Vibration positioning monitoring system based on single-core optical fiber sensing | |
| CN111537056A (en) | Pipeline along-line third-party construction dynamic early warning method based on SVM and time-frequency domain characteristics | |
| Sun et al. | Target location method for pipeline pre-warning system based on HHT and time difference of arrival | |
| CN109470352A (en) | Distributed optical fiber pipeline safety monitoring algorithm based on adaptive threshold | |
| Fouda et al. | Pattern recognition of optical fiber vibration signal of the submarine cable for its safety | |
| CN105067101A (en) | Fundamental tone frequency characteristic extraction method based on vibration signal for vibration source identification | |
| CN112032577B (en) | Oil stealing and leakage monitoring device and method for optical cable in oil pipeline | |
| CN109120336A (en) | Anti- false alarm false dismissal method based on phase sensitive optical time domain reflection sensor | |
| CN115987386A (en) | Optical cable same-route detection device and method | |
| CN115265750A (en) | Optical fiber distributed acoustic wave sensing system and method | |
| CN111239842A (en) | Rainwater invasion optical cable monitoring system and method based on distributed optical fiber sensing technology | |
| Liu et al. | Turning telecommunication fiber‐optic cables into distributed acoustic sensors for vibration‐based bridge health monitoring | |
| CN108344432B (en) | The method for obtaining the heat transfer agent of Brillouin fiber optic distributed sensing system | |
| KR20200092503A (en) | Diagnosis method of sewage condition using Deep Learning based on acoustic in-out data | |
| CN116186642B (en) | Distributed optical fiber sensing event early warning method based on multidimensional feature fusion | |
| US20230024104A1 (en) | Identification of false transformer humming using machine learning | |
| JP2024516568A (en) | Mapping using optical fiber sensing | |
| CN115146677A (en) | Geological judgment method and device based on TBM cutter vibration signal and terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |