CN113670430A - Distributed optical fiber vibration sensing intelligent disturbance identification method - Google Patents
Distributed optical fiber vibration sensing intelligent disturbance identification method Download PDFInfo
- Publication number
- CN113670430A CN113670430A CN202110935859.1A CN202110935859A CN113670430A CN 113670430 A CN113670430 A CN 113670430A CN 202110935859 A CN202110935859 A CN 202110935859A CN 113670430 A CN113670430 A CN 113670430A
- Authority
- CN
- China
- Prior art keywords
- matrix
- phase
- optical fiber
- disturbance
- phase difference
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
- G01H9/004—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Optical Transform (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a distributed optical fiber vibration sensing intelligent disturbance identification method, which comprises the following steps: collecting various disturbance invasion signals; obtaining a phase matrix of a sensing subarea; processing the phase matrix to obtain a phase difference signal matrix; performing label definition on the phase difference signal matrix; decomposing the phase difference signal matrix; constructing a characteristic two-dimensional matrix; taking all the feature data and the labels in one-to-one correspondence as sample data, and balancing the samples of different label types; the invention effectively solves the problem of high nuisance alarm rate caused by environmental noise, light path randomness, phase fading and other problems in the phase sensitive optical time domain reflection distributed optical fiber vibration sensing system, and can identify the system intrusion event with extremely high accuracy; the invention effectively improves the real-time performance of the phase sensitive optical time domain reflection distributed optical fiber vibration sensing system, and greatly reduces training data and input redundant data required by system identification.
Description
Technical Field
The invention relates to the technical field of optical fiber sensing, in particular to a distributed optical fiber vibration sensing intelligent disturbance identification method.
Background
The Phase-sensitive Optical Time Domain Reflectometry (abbreviated as phi-OTDR) Optical fiber distributed sensing system is used for monitoring external disturbance events, can realize accurate positioning and type identification of the external vibration disturbance events, has the characteristics of high spatial resolution, long real-Time monitoring communication distance, capability of forming an intelligent sensing network and the like, and has a general application prospect in distributed long-distance alarm monitoring aspects such as petroleum pipeline side leakage detection, communication line detection, building structure detection, border security, intrusion alarm and the like.
The traditional phi-OTDR distributed optical fiber sensing system realizes intrusion disturbance positioning, and usually utilizes amplitude signals of backward Rayleigh scattering light to analyze and process, so that disturbance generation site positioning, intrusion pattern recognition, intrusion early warning and the like can be realized. In the aspect of intrusion disturbance identification, Xu Chengjin et al, Zhejiang university, 2017 extracts a plurality of characteristic parameters of short-time energy ratio, short-time level-crossing rate, disturbance duration and energy spectrum density of disturbance signal amplitude, and finally, classification of the disturbance signals is realized by identifying multi-characteristic parameter characteristic vectors through an SVM. The disturbance recognition rate of 800 groups of disturbance signals in total using four modes (beating, knocking, shaking and squeezing) is higher than 90%, and the recognition time is less than 0.6s (Xu, J.Guan, M.Bao, J.Lu, and W.Ye, "Pattern recognition based on enhanced multiple disturbance parameters for the disturbance in
-OTDR distributed optical fiber sensing system, "micro w.opt.technol.lett., vol.59, No.12, pp.3134-3141,2017); 30 time-frequency characteristics of Rayleigh scattered light amplitude time-domain signals are extracted by Wang Xin et al, Beijing university of transportation in 2019, watering, pressing and treading events are classified by combining a random forest classifier, and the lowest classification error rate can reach 2% (X.Wang, Y.Liu, S.Liang, W.Zhang, and S.Lou)"Event identification based on random for class distributor for phi-OTDR fiber-optical distributed distribution sensor," associated Phys, technol., vol.97, No. January, pp.319-325,2019). Although the disturbance classification with higher precision can be realized by using the amplitude signal, a large number of signals are required to realize the intrusion disturbance identification on the whole link, and the amplitude signal and the disturbance signal are not in a linear relation, and the influence of the distribution of scattering points in the optical fiber on the amplitude signal is great.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The invention is provided in view of the problems of the existing distributed optical fiber vibration sensing intelligent disturbance identification method.
Therefore, the invention aims to provide a distributed optical fiber vibration sensing intelligent disturbance identification method, and aims to provide a low-time-delay high-precision phase-sensitive optical time domain reflection distributed optical fiber vibration sensing intelligent identification system for accurately identifying a vibration intrusion signal on a sensing link in real time.
In order to solve the technical problems, the invention provides the following technical scheme: a distributed optical fiber vibration sensing intelligent disturbance identification method comprises the steps of collecting various disturbance invasion signals; obtaining a phase matrix of a sensing subarea; processing the phase matrix to obtain a phase difference signal matrix representing the sensing information of the area; performing label definition on the phase difference signal matrix; decomposing the phase difference signal matrix; constructing a characteristic two-dimensional matrix; and taking all the feature data and the labels in one-to-one correspondence as sample data, and balancing the samples of different label types.
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: collecting multiple types of disturbance invasionThe step of signaling comprises: collecting N times of optical pulses (N is more than 1) by using a data acquisition card; forming a signal time-space domain matrix data [ d ] by the beat frequency signal corresponding to the optical pulsei，j]N*D(ii) a Each disturbance intrusion signal is collected by more than 1 group, and the total number of the disturbance intrusion signals is M groups, di，jA j-th beat signal representing the ith light pulse, i representing time information, and j representing position information.
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: the step of obtaining the phase matrix of the sensing partition comprises: dividing the sensing optical fiber into R sections of sensing areas; and demodulating the beat frequency signals at two ends of the sensing area according to groups by adopting a digital coherent demodulation algorithm to obtain a phase matrix phi of the R-section sensing area.
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: the step of processing the phase matrix to obtain a phase difference signal matrix representing the area sensing information comprises: and sequentially differentiating the vectors in the phase matrix phi according to columns to obtain a phase differential signal matrix S [ [ S ] ] representing the regional sensing informationi]R*M(ii) a Each column of the vector comprises N time domain sampling points, and the mth column of the phase difference signal is represented as Sm＝φm-φm+1Each column vector in the phase difference signal matrix S contains the accumulation of all disturbance intrusion signals in the represented sensing area.
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: the step of label defining the phase differential signal matrix comprises: perturbed phase difference vector SiThe label is yi1 is ═ 1; undisturbed phase difference vector SjThe label is y j0; and obtaining a label vector y containing R M elements.
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: the step of decomposing the phase differential signal matrix comprises: carrying out four-layer decomposition on the phase difference signal matrix S by utilizing a wavelet packet decomposition algorithm; decomposing the original phase difference signal into a high-frequency signal and a low-frequency signal; and decomposing the high-frequency and low-frequency signals of the previous layer into the next layer, and decomposing the four layers.
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: the step of constructing a two-dimensional matrix of features comprises: decomposing the phase difference matrix S to obtain each time domain vector Si16 frequency domain features of (1); constructing a characteristic two-dimensional matrix T ═ T from the frequency domain characteristics1,…,T16]。
As a preferred scheme of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention, wherein: the step of balancing samples of different label types by using all the characteristic data and the labels in one-to-one correspondence as sample data also comprises the step of inputting the sample data after balancing as training data into a limit gradient adding classifier XGboost for training, and the obtained training model can successfully identify the disturbance information in the phase sensitive optical time domain distributed optical fiber sensing system.
The invention has the beneficial effects that:
the invention effectively solves the problem of high nuisance alarm rate caused by environmental noise, light path randomness, phase fading and other problems in the phase sensitive optical time domain reflection distributed optical fiber vibration sensing system, and can identify the system intrusion event with extremely high accuracy; the invention effectively improves the real-time performance of the phase sensitive optical time domain reflection distributed optical fiber vibration sensing system, and greatly reduces training data and input redundant data required by system identification.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic overall flow chart of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention.
Fig. 2 is a detailed flow diagram of the distributed optical fiber vibration sensing intelligent disturbance identification method of the present invention.
Fig. 3 is a diagram of phase difference information after quadrature demodulation and difference operation according to the present invention.
FIG. 4 is a schematic diagram of four-layer wavelet packet decomposition feature extraction according to the present invention.
Fig. 5 is a phase sensitive optical time domain reflection distributed optical fiber vibration sensing system of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Furthermore, the present invention is described in detail with reference to the drawings, and in the detailed description of the embodiments of the present invention, the cross-sectional view illustrating the structure of the device is not enlarged partially according to the general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Example 1
Referring to fig. 1 to 4, a first embodiment of the present invention provides a distributed optical fiber vibration sensing intelligent disturbance identification method, including:
s1: collecting various disturbance invasion signals;
s2: obtaining a phase matrix of a sensing subarea;
s3: processing the phase matrix to obtain a phase difference signal matrix representing the sensing information of the area;
s4: performing label definition on the phase difference signal matrix;
s5: decomposing the phase difference signal matrix;
s6: constructing a characteristic two-dimensional matrix;
s7: and taking all the feature data and the labels in one-to-one correspondence as sample data, and balancing the samples of different label types.
The step of collecting various disturbance intrusion signals comprises the following steps:
s11: collecting N light pulses (N > 1);
s12: forming a signal time-space domain matrix data [ d ] by the beat frequency signals corresponding to the acquired optical pulsesi，j]N*D；
S13: more than 1 group of disturbance invasion signals are collected, and M groups are totali，jA j-th beat signal representing the ith light pulse, i representing time information, and j representing position information.
Specifically, the intrusion signal disturbance comprises actions such as climbing, trampling, knocking and the like; in the example, the disturbance intrusion signal is a trample event, 500 Rayleigh scattering light beat frequency signals returned by the light pulse are collected every time, the number of the collected points of the Rayleigh scattering light beat frequency signals returned by the light pulse is 10000 points, all signals within the time of 0.17s of the 5km optical fiber space distance are represented, and 500 groups of signal matrixes containing different disturbance information are provided.
Further, the step of obtaining the phase matrix of the sensing sub-area comprises:
s21: dividing the sensing optical fiber into R sections of sensing areas;
the two ends of each sensing area are provided with beat frequency signals, the sensing optical fiber is divided into 49 sensing areas, the distance of each sensing area is 100m, and the sensing area comprises 200 time domain sampling points.
S22: demodulating beat frequency signals at two ends of all sensing areas according to groups by adopting a digital coherent demodulation algorithm to obtain a phase matrix phi of an R-section sensing area;
the phase matrix phi contains 500 sets of 49 segments of 2450 column vectors, each containing 500 time domain data.
Further, the step of processing the phase matrix to obtain the phase difference signal matrix includes:
s31: and sequentially differentiating vectors in the phase matrix phi according to columns to obtain a phase differential signal matrix S ═ S representing regional sensing informationi]R*M；
Calculating to obtain a phase difference signal matrix S ═ Si]24500；
S32: each column of vectors comprises N time domain sampling points, and the mth column of phase difference signals is represented as Sm＝φm-φm+1Each column vector in the phase difference signal matrix S contains the accumulation of all the perturbation intrusion signals in the represented sensing area.
Specifically, the step of defining the label for the phase difference signal matrix includes:
s41: perturbed phase difference vector SiThe label is yi1, undisturbed phase difference vector SjThe label is yj＝0；
S42: obtaining a label vector y containing R x M elements;
i.e. a tag vector y of 2450 elements.
Specifically, the step of decomposing the phase difference signal matrix includes:
s51: decomposing the phase difference signal matrix S by utilizing a wavelet packet decomposition algorithm, and decomposing an original phase difference signal into a high-frequency signal and a low-frequency signal by referring to FIG. 4;
s52: and decomposing the high-frequency and low-frequency signals of the previous layer into the next layer, and decomposing the four layers.
Further, the step of constructing a two-dimensional matrix of features includes:
s61: decomposing the phase difference matrix S to obtain each phaseA time domain vector Si16 frequency domain features of (1);
s62: constructing a characteristic two-dimensional matrix T ═ T from the frequency domain characteristics1,…,T16]。
The S7 further includes that the obtained model can identify disturbance information in the phase-sensitive optical time domain distributed optical fiber sensing system, referring to fig. 3, the false alarm rate is less than 1%, and the time required for identifying and judging 1000 sensing regions is less than 0.006S.
Example 2
Referring to fig. 5, a second embodiment of the present invention, which is different from the first embodiment, is: the distributed optical fiber vibration sensing intelligent disturbance identification method is carried out through a phase sensitive optical time domain reflection distributed optical fiber vibration sensing system, and the system comprises a transmitting unit 100, a modulation unit 200 and a transmission unit 300.
Specifically, the transmitting unit 100 further includes a 1 '2 optical fiber coupler 102, where the splitting ratio of the 1' 2 optical fiber coupler 102 is 90:10, and the signal emitted by the narrow linewidth laser 101 is divided into a first optical path a and a second optical path B, where the first optical path a is used as probe light, and the second optical path B is used as local oscillator light. A modulation unit 200 including an acousto-optic modulator (AOM)201 and an erbium-doped fiber amplifier (EDFA)202, the modulation unit 200 being connected to the transmission unit 100; a transmission unit 300 comprising a fiber circulator 301 and a sensing fiber 302, the transmission unit 300 being connected to an Erbium Doped Fiber Amplifier (EDFA) 202.
Preferably, the sensing fiber 302 is a single mode fiber, and the length of the sensing fiber 302 is 5km, and the treading invasion signals are artificially manufactured at 0.2km, 0.4km, 0.6km and 1.1 km.
The rest of the structure is the same as that of embodiment 1.
In use, the acousto-optic modulator (AOM)201 modulates the first optical path a as probe light into an optical pulse signal, shifts the frequency of the optical pulse signal to a high frequency of 80MHz, amplifies the optical pulse signal by an erbium-doped fiber amplifier (EDFA)202, and transmits rayleigh scattered light generated by the action of the optical pulse signal in the sensing fiber 302 entering the sensing fiber 302 through the fiber circulator 301 to be transmitted back to the fiber circulator 301.
Example 3
Referring to fig. 5, a third embodiment of the present invention is different from the second embodiment in that: the system also comprises a detection unit 400, wherein the detection unit 400 comprises a 2' 2 optical fiber coupler 401, a Balanced Photoelectric Detector (BPD)402 and a data acquisition card (DAQ) 403; the 2 '2 optical fiber coupler 401 has a splitting ratio of 50:50, is connected with the narrow linewidth laser 101 and the optical fiber circulator 301, and is used for coupling Rayleigh scattering light with the second light path B at the 2' 2 optical fiber coupler 401; the Balanced Photoelectric Detector (BPD)402 is connected with the 2' 2 optical fiber coupler 401, and the coupled signal is divided into two parts to be transmitted into the Balanced Photoelectric Detector (BPD)402 and converted into a beat frequency electric signal; the data acquisition card (DAQ)403 is connected to the Balanced Photodetector (BPD)402, and the beat frequency electrical signal is acquired by the digital acquisition card (DAQ)403 to obtain a digital signal.
Compared with the embodiment 2, further, the system further comprises a computer (PC)500 connected with a data acquisition card (DAQ) 403.
The rest of the structure is the same as that of embodiment 2.
In the using process, after the data acquisition card 403 acquires various types of disturbance intrusion signals, the disturbance intrusion signals are transmitted to the computer 500, and the computer 500 is used for processing and identifying the signals acquired by the digital acquisition card 403.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A distributed optical fiber vibration sensing intelligent disturbance identification method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting various disturbance invasion signals;
obtaining a phase matrix of a sensing subarea;
processing the phase matrix to obtain a phase difference signal matrix;
performing label definition on the phase difference signal matrix;
decomposing the phase difference signal matrix;
constructing a characteristic two-dimensional matrix;
and taking all the feature data and the labels in one-to-one correspondence as sample data, and balancing the samples of different label types.
2. The distributed optical fiber vibration sensing intelligent disturbance identification method according to claim 1, characterized in that: the step of collecting various disturbance intrusion signals comprises the following steps:
collecting N times of optical pulses (N >1) by using a data acquisition card;
forming a signal time-space domain matrix data [ d ] by the beat frequency signal corresponding to the optical pulsei,j]N*D；
Each disturbance intrusion signal is collected by more than 1 group, and the total number of the disturbance intrusion signals is M groups, di,jA j-th beat signal representing the ith light pulse, i representing time information, and j representing position information.
3. The distributed optical fiber vibration sensing intelligent disturbance identification method according to claim 1, characterized in that: the step of obtaining the phase matrix of the sensing partition comprises:
dividing the sensing optical fiber into R sections of sensing areas;
and demodulating the beat frequency signals at two ends of the sensing area according to groups by adopting a digital coherent demodulation algorithm to obtain a phase matrix phi of the R-section sensing area.
4. The distributed optical fiber vibration sensing intelligent disturbance identification method according to claim 3, characterized in that: the step of processing the phase matrix to obtain a phase difference signal matrix comprises:
and sequentially differentiating the vectors in the phase matrix phi according to columns to obtain a phase differential signal matrix S [ [ S ] ] representing the regional sensing informationi]R*M；
Each column of the vectors comprises N time domain sampling points and the mth column of the phase differenceThe partial signal is denoted as Sm＝φm-φm+1Each column vector in the phase difference signal matrix S contains the accumulation of all disturbance intrusion signals in the represented sensing area.
5. The distributed optical fiber vibration sensing intelligent disturbance identification method according to any one of claims 1 to 4, characterized in that: the step of label defining the phase differential signal matrix comprises:
perturbed phase difference vector SiThe label is yi1, undisturbed phase difference vector SjThe label is yj＝0；
And obtaining a label vector y containing R M elements.
6. The distributed optical fiber vibration sensing intelligent disturbance identification method according to claim 5, characterized in that: the step of decomposing the phase differential signal matrix comprises:
decomposing the phase difference signal matrix S by utilizing a wavelet packet decomposition algorithm, and decomposing an original phase difference signal into a high-frequency signal and a low-frequency signal;
and decomposing the high-frequency and low-frequency signals of the previous layer into the next layer, and decomposing the four layers.
7. The distributed optical fiber vibration sensing intelligent disturbance identification method according to claim 6, characterized in that: the step of constructing a two-dimensional matrix of features comprises:
decomposing the phase difference matrix S to obtain each time domain vector Si16 frequency domain features of (1);
constructing a characteristic two-dimensional matrix T ═ T from the frequency domain characteristics1,…,T16]。
8. The distributed optical fiber vibration sensing intelligent disturbance identification method according to claim 7, characterized in that: the step of balancing samples of different label types by using all the feature data and the labels in one-to-one correspondence as sample data further comprises the steps of,
the balanced sample data is used as training data and input into a limit gradient adding classifier XGboost for training, and the obtained training model can successfully identify disturbance information in the phase sensitive optical time domain distributed optical fiber sensing system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110935859.1A CN113670430B (en) | 2021-08-16 | 2021-08-16 | Distributed optical fiber vibration sensing intelligent disturbance identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110935859.1A CN113670430B (en) | 2021-08-16 | 2021-08-16 | Distributed optical fiber vibration sensing intelligent disturbance identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113670430A true CN113670430A (en) | 2021-11-19 |
| CN113670430B CN113670430B (en) | 2022-10-18 |
Family
ID=78543172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110935859.1A Active CN113670430B (en) | 2021-08-16 | 2021-08-16 | Distributed optical fiber vibration sensing intelligent disturbance identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113670430B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115790815A (en) * | 2023-01-17 | 2023-03-14 | 常熟理工学院 | Method and system for rapidly identifying disturbance of distributed optical fiber sensing system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110320147A1 (en) * | 2010-06-25 | 2011-12-29 | Schlumberger Technology Corporation | Precision measurements in a fiber optic distributed sensor system |
| CN105466548A (en) * | 2015-12-16 | 2016-04-06 | 上海大学 | Phase sensitive optical time domain reflection fiber sensing system positioning method |
| CN105509868A (en) * | 2015-12-16 | 2016-04-20 | 上海大学 | Phase-sensitive optical time domain reflectometry fiber distributed sensing system phase calculation method |
| US20180080812A1 (en) * | 2017-07-25 | 2018-03-22 | University Of Electronic Science And Technology Of China | Distributed optical fiber sensing signal processing method for safety monitoring of underground pipe network |
| CN109391321A (en) * | 2018-11-20 | 2019-02-26 | 山东大学 | Disturbance positioning method in a kind of phase sensitive OTDR sensing |
| CN109961100A (en) * | 2019-03-26 | 2019-07-02 | 北京邮电大学 | WIFI indoor occupant intrusion detection method based on Principal Component Analysis |
| CN113049083A (en) * | 2021-03-09 | 2021-06-29 | 中国人民解放军国防科技大学 | Method for realizing high-frequency phase demodulation in distributed optical fiber sensing system |
-
2021
- 2021-08-16 CN CN202110935859.1A patent/CN113670430B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110320147A1 (en) * | 2010-06-25 | 2011-12-29 | Schlumberger Technology Corporation | Precision measurements in a fiber optic distributed sensor system |
| CN105466548A (en) * | 2015-12-16 | 2016-04-06 | 上海大学 | Phase sensitive optical time domain reflection fiber sensing system positioning method |
| CN105509868A (en) * | 2015-12-16 | 2016-04-20 | 上海大学 | Phase-sensitive optical time domain reflectometry fiber distributed sensing system phase calculation method |
| US20180080812A1 (en) * | 2017-07-25 | 2018-03-22 | University Of Electronic Science And Technology Of China | Distributed optical fiber sensing signal processing method for safety monitoring of underground pipe network |
| CN109391321A (en) * | 2018-11-20 | 2019-02-26 | 山东大学 | Disturbance positioning method in a kind of phase sensitive OTDR sensing |
| CN109961100A (en) * | 2019-03-26 | 2019-07-02 | 北京邮电大学 | WIFI indoor occupant intrusion detection method based on Principal Component Analysis |
| CN113049083A (en) * | 2021-03-09 | 2021-06-29 | 中国人民解放军国防科技大学 | Method for realizing high-frequency phase demodulation in distributed optical fiber sensing system |
Non-Patent Citations (2)
| Title |
|---|
| 李常 等: "分布式光纤振动检测系统信号后处理算法研究", 《山东科学》 * |
| 秦祖军 等: "基于φ-OTDR的振动检测研究型综合实验设计", 《实验室研究与探索》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115790815A (en) * | 2023-01-17 | 2023-03-14 | 常熟理工学院 | Method and system for rapidly identifying disturbance of distributed optical fiber sensing system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113670430B (en) | 2022-10-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Vehicle detection and classification using distributed fiber optic acoustic sensing | |
| Jousset et al. | Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features | |
| Aktas et al. | Deep learning based multi-threat classification for phase-OTDR fiber optic distributed acoustic sensing applications | |
| CN105509868B (en) | Phase sensitive optical time domain reflection distributed fiber-optic sensor system phase calculation method | |
| CN101556724B (en) | Safety management system of optical fiber perimeter and pattern recognition method thereof | |
| Bai et al. | Detection and identification of external intrusion signals from 33 km optical fiber sensing system based on deep learning | |
| CN105371943B (en) | The demodulation method and device of a kind of distributed optical fiber vibration sensing system | |
| CN113049084B (en) | Attention mechanism-based Resnet distributed optical fiber sensing signal identification method | |
| Shang et al. | Research progress in distributed acoustic sensing techniques | |
| CN104034409B (en) | Distributed optical fiber vibration sensing method and system based on pulse code external modulation | |
| CN103226028B (en) | Method for identifying and detecting disturbance signals of phase-sensitive optical time domain reflectometer | |
| Shi et al. | An easy access method for event recognition of Φ-OTDR sensing system based on transfer learning | |
| Kotchenova et al. | Modeling Lidar waveforms with time‐dependent stochastic radiative transfer theory for remote estimations of forest structure | |
| Yang et al. | Pipeline safety early warning by multifeature-fusion CNN and LightGBM analysis of signals from distributed optical fiber sensors | |
| CN113670430B (en) | Distributed optical fiber vibration sensing intelligent disturbance identification method | |
| Li et al. | An anti-noise ϕ-OTDR based distributed acoustic sensing system for high-speed railway intrusion detection | |
| Kettridge et al. | Do peatland microforms move through time? Examining the developmental history of a patterned peatland using ground‐penetrating radar | |
| Wu et al. | Simultaneous extraction of multi-scale structural features and the sequential information with an end-to-end mCNN-HMM combined model for fiber distributed acoustic sensor | |
| Wu et al. | Monitoring of the transmission line galloping with a novel distributed optical fibre sensor and its statistical data analysis | |
| Zhu et al. | A comprehensive bibliometric analysis of signal processing and pattern recognition based on distributed optical fiber | |
| Shiloh et al. | Fiber-optic distributed seismic sensing data generator and its application for training classification nets | |
| Nag et al. | First versus subsequent return‐stroke current and field peaks in negative cloud‐to‐ground lightning discharges | |
| US20240085238A1 (en) | Joint communication and sensing for fallen tree localization on overhead lines | |
| Zhang et al. | Classification of interference-fading tolerant Φ-OTDR signal using optimal peak-seeking and machine learning | |
| Ghozzi et al. | FDTD‐based sensitivity analysis of GPR acquisition parameters for accurate detection of buried cylindrical objects |
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 |