CN116989913A - Cable line temperature monitoring method and system based on optical fiber sensing - Google Patents
Cable line temperature monitoring method and system based on optical fiber sensing Download PDFInfo
- Publication number
- CN116989913A CN116989913A CN202310739653.0A CN202310739653A CN116989913A CN 116989913 A CN116989913 A CN 116989913A CN 202310739653 A CN202310739653 A CN 202310739653A CN 116989913 A CN116989913 A CN 116989913A
- Authority
- CN
- China
- Prior art keywords
- denoising
- information
- monitoring
- filtering
- temperature
- 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.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 69
- 239000013307 optical fiber Substances 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000001914 filtration Methods 0.000 claims abstract description 66
- 238000000354 decomposition reaction Methods 0.000 claims description 18
- 239000000835 fiber Substances 0.000 claims description 15
- 230000009466 transformation Effects 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 3
- 238000011426 transformation method Methods 0.000 claims description 3
- 230000001131 transforming effect Effects 0.000 claims description 3
- 238000001069 Raman spectroscopy Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 9
- 238000010276 construction Methods 0.000 description 8
- 230000003287 optical effect Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 238000009825 accumulation Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000009529 body temperature measurement Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
- G01K11/32—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres
- G01K11/324—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres using Raman scattering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/148—Wavelet transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0211—Frequency selective networks using specific transformation algorithms, e.g. WALSH functions, Fermat transforms, Mersenne transforms, polynomial transforms, Hilbert transforms
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Measuring Temperature Or Quantity Of Heat (AREA)
Abstract
The application provides a cable line temperature monitoring method and a system for optical fiber sensing, wherein the method comprises the following steps: monitoring the real-time temperature of the cable line to be monitored by using an optical fiber sensor to obtain monitoring information; filtering the monitoring information by using a Butterworth filter to obtain filtering information; denoising the filtering information to obtain denoising information; and determining the temperature information of the cable line to be monitored according to the denoising information. According to the application, the optical fiber sensor is used for collecting monitoring information, and the Butterworth filter is used for carrying out accurate filtering and denoising treatment, so that the temperature monitoring precision can be improved.
Description
Technical Field
The application relates to the technical field of cable detection, in particular to a cable line temperature monitoring method and system for optical fiber sensing.
Background
With urban construction, the number of power cables laid is rapidly increasing. The power cable is widely applied in urban construction, and once the power cable fails, the life and property safety of people is directly threatened. In recent years, various cities are in peak periods of municipal construction, and external force damage caused by municipal construction excavation is a main cause of power cable faults. Therefore, the prevention of external damage caused by rough construction is an important point in the operation and maintenance of power cables. In addition, pipeline lines with different laying types and numerous numbers in the cable passage, such as municipal facilities of power cables, communication optical cables, water supply and drainage pipelines, gas pipelines and the like, have higher fire risks, such as short circuits, overload and the like of the power cables, easily cause damage to internal circuits of the pipeline, and seriously influence the life and property safety and the power operation order of people.
At present, the power grid adopts main measures for preventing external damage, firstly, line inspection force is increased, once the construction condition exists at the position near the cable, the power grid is immediately communicated with a construction unit, and the risk of cable breakage and fire hazard during construction are reduced as much as possible; and secondly, a linkage mechanism with departments such as a city management department is established, and the coordination is purposefully carried out. However, the related units often have no report before the event, and have no report after the event in time, and are hidden. The cable operation and maintenance unit lacks an effective technical monitoring means for preventing the power cable from being broken outwards and preventing fire, so that urban cable accidents are frequent. The traditional monitoring means mainly depend on a video monitoring technology and a temperature sensing monitoring technology, can only monitor fixed points, and cannot monitor external damage events and fire hazards with strong randomness and high sporadic performance in real time.
The optical fiber sensing technology is a novel sensing technology, redundant optical fibers in the power communication optical cable passing through the cable duct are directly utilized as sensors, and the monitoring and early warning of external broken hidden danger and fire hidden danger of the whole power cable are realized by establishing the relation between the scattering parameter change of the communication optical fibers and vibration change and environmental temperature change caused by external damage events. The distributed optical fiber sensing technology has the capability of simultaneously acquiring the measured distribution information which changes along with time and space in a sensing optical fiber area, has high accuracy, electromagnetic interference resistance and corrosion resistance, and can realize remote distributed measurement. However, products based on optical fiber temperature measurement and optical fiber vibration in the current market are poor in detection precision, and false alarm and missing alarm often occur, so that cable external breakage accidents are caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the application aims to provide a cable line temperature monitoring method and system for optical fiber sensing.
In order to achieve the above object, the present application provides the following solutions:
a method for monitoring the temperature of a cable line by optical fiber sensing, comprising:
monitoring the real-time temperature of the cable line to be monitored by using an optical fiber sensor to obtain monitoring information;
filtering the monitoring information by using a Butterworth filter to obtain filtering information;
denoising the filtering information to obtain denoising information;
and determining the temperature information of the cable line to be monitored according to the denoising information.
Preferably, filtering the monitoring information by using a butterworth filter to obtain filtered information, including:
determining a Butterworth filter model according to the filter order, the low-pass frequency and the sampling frequency value:
performing Butterworth filtering on the monitoring information;
transforming the Butterworth filter model according to a bilinear transformation method to obtain a transformation model;
determining denominator and numerator in the transformation model;
substituting the monitoring information, the denominator and the numerator into an iterative equation to obtain filtering information after the Butterworth filter filters.
Preferably, denoising the filtering information to obtain denoising information, including:
performing multi-scale wavelet decomposition on the filtering information to obtain high-frequency wavelet coefficients;
determining a denoising threshold according to the decomposition scale of the filtering information and the median value of the high-frequency wavelet coefficient;
constructing a wavelet denoising function according to the denoising threshold value;
and denoising the filtering information by utilizing the wavelet denoising function to obtain the denoising information.
Preferably, the denoising threshold value is:
wherein ,w j represents the j-th high frequency wavelet coefficient, < ->Mean value of high-frequency wavelet coefficient, medium j The median value of the high-frequency wavelet coefficient is represented, lambda represents the denoising threshold value, M represents the number of matrix columns of the filtering information, N represents the number of matrix rows of the filtering information, and L represents the decomposition scale of the filtering information.
Preferably, constructing a wavelet denoising function according to the denoising threshold value includes:
determining a threshold value of a wavelet denoising function according to the denoising threshold value;
constructing a wavelet denoising function according to the threshold value and the denoising threshold value; wherein the wavelet denoising function is:
wherein sign is a sign function, lambda 0 =0.5λ, λ represents a denoising threshold, a is an adjustable parameter, s=nn, N represents the number of all high frequency wavelet coefficients at the L-th scale, and N represents the number of all high frequency wavelet coefficients less than the denoising threshold at the L-th scale.
Preferably, the wavelet basis of the multi-scale wavelet decomposition is db2 wavelet.
Preferably, after denoising the filtering information to obtain denoising information, the method further includes:
and carrying out signal reconstruction on the denoising information to obtain the reconstructed denoising information.
A fiber optic sensing cabling temperature monitoring system comprising:
the monitoring module is used for monitoring the real-time temperature of the cable line to be monitored by utilizing the optical fiber sensor to obtain monitoring information;
the filtering module is used for filtering the monitoring information by using a Butterworth filter to obtain filtering information;
the denoising module is used for denoising the filtering information to obtain denoising information;
and the temperature determining module is used for determining the temperature information of the cable line to be monitored according to the denoising information.
According to the specific embodiment provided by the application, the application discloses the following technical effects:
the application provides a cable line temperature monitoring method and a system for optical fiber sensing, wherein the method comprises the following steps: monitoring the real-time temperature of the cable line to be monitored by using an optical fiber sensor to obtain monitoring information; filtering the monitoring information by using a Butterworth filter to obtain filtering information; denoising the filtering information to obtain denoising information; and determining the temperature information of the cable line to be monitored according to the denoising information. According to the application, the optical fiber sensor is used for collecting monitoring information, and the Butterworth filter is used for carrying out accurate filtering and denoising treatment, so that the temperature monitoring precision can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, inclusion of a list of steps, processes, methods, etc. is not limited to the listed steps but may alternatively include steps not listed or may alternatively include other steps inherent to such processes, methods, products, or apparatus.
The application aims to provide a cable line temperature monitoring method and system for optical fiber sensing, which can acquire monitoring information by utilizing an optical fiber sensor, and perform accurate filtering and denoising treatment by using a Butterworth filter so as to improve temperature monitoring precision.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method provided by an embodiment of the present application, and as shown in fig. 1, the present application provides a method for monitoring a cable line temperature by using optical fiber sensing, including:
step 100: monitoring the real-time temperature of the cable line to be monitored by using an optical fiber sensor to obtain monitoring information;
step 200: filtering the monitoring information by using a Butterworth filter to obtain filtering information;
step 300: denoising the filtering information to obtain denoising information;
step 400: and determining the temperature information of the cable line to be monitored according to the denoising information.
Specifically, the mechanism of fiber temperature measurement in this embodiment is based on the effect of backward Raman (Raman) scattering. The laser pulse interacts with the fiber molecules to scatter, and there are various types of scattering, such as: rayleigh (Rayleigh) scattering, brillouin (Brillouin) scattering, raman (Raman) scattering, and the like. Where raman scattering is due to thermal vibrations of the fiber molecules, it produces a light of a wavelength longer than the light source, called Stokes light, and a light of a wavelength shorter than the light source, called Anti-Stokes light. The optical fiber is modulated by external temperature to change the Anti-Stokes light intensity in the optical fiber, the ratio of Anti-Stokes to Stokes provides an absolute indication of temperature, and the principle can be used for realizing distributed measurement of the temperature field along the optical fiber. By combining high quality pulsed light sources with high speed signal acquisition and processing techniques, accurate temperature values at all points along the fiber can be obtained. Based on the principle, the distributed optical fiber temperature measurement technology is particularly suitable for temperature monitoring of various long distances, and can obtain temperature signals of each point along the measured object, so that false alarm and false alarm are greatly reduced.
Since raman scattering is inelastic, the scattered light frequency changes, proving that the energy level of the photon has been shifted. Incident light hv 0 Excitation at low energy level E 1 The molecules will transition upward to a relatively unstable virtual energy level E a And then return to a relatively stable high energy level E 2 The photon emitted at this time is hv s The light emitted by this process is Stokes light (Stokes). The raman scattered stokes light frequency is expressed as:
when incident photon hv 0 Excitation at a high energy level E 2 Is a molecule of (a) and (b),the molecules will transition upward to a relatively unstable virtual energy level E b And then returns to a stable low energy level and emits scattered photons hv as The light emitted by this process is Anti-Stokes light (Anti-Stokes). The raman scattering anti-stokes optical frequency can be expressed as:
the system calculates the distance between the incident light and the reflected light from the propagation speed of the light in the optical fiber by detecting the time difference between the incident time and the reflected time. The material of the fiber determines the wavelength shift of the two lights in the fiber, and the stokes and anti-stokes light intensities are related to temperature as follows:
stokes light intensity:
Anti-Stokes light intensity:
in the formula ,λs Is Stokes wavelength; lambda (lambda) as Is an anti-stokes wavelength; t is absolute temperature; c is the speed in vacuum; h is a Planck constant; k is a Boltzmann constant.
I as /I s Can be measured after photoelectric conversion, and the temperature T can be obtained by the parameters 0 。
The system was tested for sensitivity to temperature based on raman scattered light. Assuming that a laser is arranged to emit pulse light into an optical fiber with a non-uniform refractive index of an optical fiber core at a certain frequency, different forms of scattered light can be generated, and among various scattered light, the backward Raman scattered light is taken as a required extreme quantity, and the value of the optical power can be measured by a photoelectric detector, so that the relation between the optical power and the temperature can be deduced. In practical systems, bending of the fiber, power variations of the light pulse source, etc. affect the anti-stokes light intensity. In order to effectively improve measurement accuracy, stokes light is used as a reference channel of the system, and the scattering ratio of anti-Stokes light and Stokes light intensity is used as a temperature factor. Wherein the Anti-Stokes to Stokes scattering ratio is:
wherein the absolute temperature T may be expressed as:
a known calibration temperature T 0 Can be expressed as
ObtainingFrom the above equation, it can be seen that the temperature of the fiber is related to the ratio of the anti-stokes light intensity to the stokes light intensity. After the system temperature of the optical fiber is calibrated, the temperature value of each region of the optical fiber in the optical fiber temperature measuring system can be obtained by measuring the above two light intensities, so that the function of distributed optical fiber temperature sensing is realized.
After the temperature calibration is set in this embodiment, the stokes light intensity and the anti-stokes light intensity ratio (temperature factor) of each area measured on the optical fiber are compared with the two light intensity ratios at the calibration temperature, so that the temperature value of each point on the optical fiber can be obtained. The Raman scattering light intensity is divided into 3 areas, namely a front end reflection area, a temperature measuring area and a tail end reflection area. In the 3 areas, the signals of the temperature measuring areas are stable, and the temperature conditions on the optical fibers can be truly reflected. The signals of the front reflecting area and the tail reflecting area are relatively unstable, so that the calibration area is selected in the temperature measuring area.
OTDR (Optical Time Domain Reflection) is an optical time domain reflection technology for short, which not only can detect the joint loss, but also can detect and locate the fault point in the optical fiber without loss. Pulse light from the laser enters the fiber through the coupler, wherein most of the pulse is transmitted to the tail end of the fiber, but the refractive index of the pulse light is different due to different fiber media, when the pulse light encounters the media with different refractive indexes, the pulse light is scattered, and a part of the pulse light is scattered back and transmitted to the transmitting end along the fiber. And carrying out real-time positioning according to the relation between the back scattering light intensity and time of each point in the optical fiber.
Preferably, filtering the monitoring information by using a butterworth filter to obtain filtered information, including:
determining a Butterworth filter model according to the filter order, the low-pass frequency and the sampling frequency value:
performing Butterworth filtering on the monitoring information;
transforming the Butterworth filter model according to a bilinear transformation method to obtain a transformation model;
determining denominator and numerator in the transformation model;
substituting the monitoring information, the denominator and the numerator into an iterative equation to obtain filtering information after the Butterworth filter filters.
Specifically, in this embodiment, after Butterworth filtering is performed on the monitoring information, preliminary filtering processing is performed on white noise of the system.
Preferably, denoising the filtering information to obtain denoising information, including:
performing multi-scale wavelet decomposition on the filtering information to obtain high-frequency wavelet coefficients;
determining a denoising threshold according to the decomposition scale of the filtering information and the median value of the high-frequency wavelet coefficient;
constructing a wavelet denoising function according to the denoising threshold value;
and denoising the filtering information by utilizing the wavelet denoising function to obtain the denoising information.
In general, when the conventional wavelet threshold function is filtered, some noise is amplified, so that the signal processing effect is affected. The application builds the wavelet denoising function based on the decomposition scale of the wavelet, can perform self-adaptive transformation aiming at the high-frequency wavelet coefficients under different scales, can inhibit noise under different scales, and can enhance the layering sense of signals.
Preferably, the denoising threshold value is:
wherein ,w j represents the j-th high frequency wavelet coefficient, < ->Mean value of high-frequency wavelet coefficient is shown, mean|w j The I represents the median value of the high-frequency wavelet coefficient, the lambda represents the denoising threshold value, the M represents the number of matrix columns of the filtering information, the N represents the number of matrix rows of the filtering information, and the L represents the decomposition scale of the filtering information.
Preferably, constructing a wavelet denoising function according to the denoising threshold value includes:
determining a threshold value of a wavelet denoising function according to the denoising threshold value;
constructing a wavelet denoising function according to the threshold value and the denoising threshold value; wherein the wavelet denoising function is:
wherein sign is a sign function, lambda 0 =0.5λ, λ represents the denoising threshold, a is an adjustable parameter, s=nn, N represents the number of all high frequency wavelet coefficients at the L-th scale that are less than the denoising thresholdA number.
Preferably, the wavelet basis of the multi-scale wavelet decomposition is db2 wavelet.
Preferably, after denoising the filtering information to obtain denoising information, the method further includes:
and carrying out signal reconstruction on the denoising information to obtain the reconstructed denoising information.
Further, after selecting a suitable wavelet base, through several layers of decomposition, a desired wavelet coefficient threshold needs to be selected in the next step. The selection of the threshold value plays a decisive role in the transformation process and influences the quality of the finally obtained denoising effect. After the threshold value is selected, all wavelet coefficients larger than the threshold value are reserved, the reserved values can be reduced by the threshold value and then are valued, and all the remaining wavelet coefficients smaller than the threshold value are set to 0. The current threshold processing method comprises 2 kinds of soft threshold and hard threshold, and the difference between the two is that the processing methods for the reserved wavelet coefficients are different. Soft thresholds are chosen here. The soft thresholding process is to process the wavelet coefficient by using a soft threshold function, the wavelet coefficient values with absolute values smaller than the threshold are all set to 0, the wavelet coefficient values with absolute values larger than the threshold are not completely reserved, but the wavelet coefficient is reduced and reset by using the threshold function to perform shrinkage process, so that the wavelet coefficient is continuous.
Because of the complexity of the wavelet transform method, in order to keep the real effective information of the signal as much as possible and ensure the limited system operation processing time, the subject is to perform three-layer transform of db2 wavelet on the signal according to the above analysis after 800 times of accumulation on the original signal.
Because the backward scattered light signal in the temperature measurement process of the system is too weak and contains a large amount of noise, the signal to noise ratio of the system is reduced. After comparing the various processing methods, it was found that among the numerous methods for improving the signal-to-noise ratio, the cumulative average method is preferred. Firstly, the accumulation average algorithm is a comparison basic method, the most outstanding advantage of the method is that the method is simple and feasible, the signal to noise ratio can be greatly improved only by increasing the accumulation times, but when the accumulation times reach a certain value, the improvement speed of the signal to noise ratio can be obviously slowed down, which is a great disadvantage of the method, and at the moment, if the purpose of denoising is achieved by continuously increasing the accumulation times, the measurement time can be increased by a plurality of times even if the accumulation times are increased by a small amount. To overcome this problem. A wavelet transform algorithm is employed to further improve the system signal to noise ratio.
The wavelet transform algorithm is based on fourier transform, and can effectively remove noise because the wavelet transform can analyze any section of the optical signal. After the signal is transformed, the decomposition coefficient with larger amplitude in the signal is reserved by implementing relatively simple threshold judgment in the transformation domain, and white noise can be furthest suppressed. The collected signal is divided into a low-frequency part and a high-frequency part, the signal of the low-frequency part is stable, the signal belongs to the useful signal, and the signal of the high-frequency part is regarded as a noise signal. The signal denoising can be divided into the following 3 steps.
(1) Signal wavelet decomposition. The proper wavelet base and the decomposition layer number are selected first, and then the signal is decomposed by the corresponding layer number.
(2) Threshold quantization of high frequency coefficients. Each layer has high frequency components for which coefficients are decomposed, the most appropriate threshold is determined, and the coefficients are then processed.
(3) Wavelet reconstruction of the signal. And carrying out signal reconstruction on the top-layer wavelet decomposition coefficient which is subjected to effective processing and the high-frequency coefficient which is subjected to thresholding by adopting inverse operation.
Corresponding to the above method, the embodiment further provides a cable line temperature monitoring system for optical fiber sensing, which includes:
the monitoring module is used for monitoring the real-time temperature of the cable line to be monitored by utilizing the optical fiber sensor to obtain monitoring information;
the filtering module is used for filtering the monitoring information by using a Butterworth filter to obtain filtering information;
the denoising module is used for denoising the filtering information to obtain denoising information;
and the temperature determining module is used for determining the temperature information of the cable line to be monitored according to the denoising information.
The beneficial effects of the application are as follows:
according to the application, the optical fiber sensor is used for collecting monitoring information, and the Butterworth filter is used for carrying out accurate filtering and denoising treatment, so that the temperature monitoring precision can be improved.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present application and the core ideas thereof; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.
Claims (8)
1. A method for monitoring the temperature of a cable line by optical fiber sensing, which is characterized by comprising the following steps:
monitoring the real-time temperature of the cable line to be monitored by using an optical fiber sensor to obtain monitoring information;
filtering the monitoring information by using a Butterworth filter to obtain filtering information;
denoising the filtering information to obtain denoising information;
and determining the temperature information of the cable line to be monitored according to the denoising information.
2. The method for monitoring the temperature of a cable line by sensing an optical fiber according to claim 1, wherein filtering the monitoring information by using a butterworth filter to obtain filtered information comprises:
determining a Butterworth filter model according to the filter order, the low-pass frequency and the sampling frequency value:
performing Butterworth filtering on the monitoring information;
transforming the Butterworth filter model according to a bilinear transformation method to obtain a transformation model;
determining denominator and numerator in the transformation model;
substituting the monitoring information, the denominator and the numerator into an iterative equation to obtain filtering information after the Butterworth filter filters.
3. The method for monitoring the temperature of the cable line by using the optical fiber sensing device according to claim 1, wherein denoising the filtering information to obtain denoising information comprises the following steps:
performing multi-scale wavelet decomposition on the filtering information to obtain high-frequency wavelet coefficients;
determining a denoising threshold according to the decomposition scale of the filtering information and the median value of the high-frequency wavelet coefficient;
constructing a wavelet denoising function according to the denoising threshold value;
and denoising the filtering information by utilizing the wavelet denoising function to obtain the denoising information.
4. A method of monitoring the temperature of a fiber optic sensing cable line according to claim 3, wherein the denoising threshold is:
wherein ,w j represents the j-th high frequency wavelet coefficient, < ->Mean value of high-frequency wavelet coefficient is shown, mean|w j The I represents the median value of the high-frequency wavelet coefficient, the lambda represents the denoising threshold value, the M represents the number of matrix columns of the filtering information, the N represents the number of matrix rows of the filtering information, and the L represents the decomposition scale of the filtering information.
5. The method of claim 4, wherein constructing a wavelet denoising function based on the denoising threshold comprises:
determining a threshold value of a wavelet denoising function according to the denoising threshold value;
constructing a wavelet denoising function according to the threshold value and the denoising threshold value; wherein the wavelet denoising function is:
wherein sign is a sign function, lambda 0 =0.5λ, λ represents a denoising threshold, a is an adjustable parameter, s=n/N, N represents the number of all high frequency wavelet coefficients at the L-th scale, and N represents the number of all high frequency wavelet coefficients less than the denoising threshold at the L-th scale.
6. A method of monitoring the temperature of a fiber optic sensing cable line according to claim 3, wherein the wavelet basis of the multiscale wavelet decomposition is db2 wavelet.
7. The method for monitoring the temperature of a cable line by using optical fiber sensing according to claim 1, wherein after denoising the filtering information, denoising information is obtained, further comprising:
and carrying out signal reconstruction on the denoising information to obtain the reconstructed denoising information.
8. A fiber-optic sensing cabling temperature monitoring system, comprising:
the monitoring module is used for monitoring the real-time temperature of the cable line to be monitored by utilizing the optical fiber sensor to obtain monitoring information;
the filtering module is used for filtering the monitoring information by using a Butterworth filter to obtain filtering information;
the denoising module is used for denoising the filtering information to obtain denoising information;
and the temperature determining module is used for determining the temperature information of the cable line to be monitored according to the denoising information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310739653.0A CN116989913A (en) | 2023-06-21 | 2023-06-21 | Cable line temperature monitoring method and system based on optical fiber sensing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310739653.0A CN116989913A (en) | 2023-06-21 | 2023-06-21 | Cable line temperature monitoring method and system based on optical fiber sensing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116989913A true CN116989913A (en) | 2023-11-03 |
Family
ID=88527418
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310739653.0A Pending CN116989913A (en) | 2023-06-21 | 2023-06-21 | Cable line temperature monitoring method and system based on optical fiber sensing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116989913A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786333A (en) * | 2024-01-08 | 2024-03-29 | 江苏省家禽科学研究所 | Broiler chicken breeding behavior data acquisition device |
-
2023
- 2023-06-21 CN CN202310739653.0A patent/CN116989913A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786333A (en) * | 2024-01-08 | 2024-03-29 | 江苏省家禽科学研究所 | Broiler chicken breeding behavior data acquisition device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN200979076Y (en) | Distributed optical fiber oil gas pipeline alarming and sensor device | |
| CN104217513B (en) | The method improving phase sensitive optical time domain reflectometer identification intrusion event accuracy rate | |
| CN116989913A (en) | Cable line temperature monitoring method and system based on optical fiber sensing | |
| Liehr et al. | Incoherent optical frequency domain reflectometry and distributed strain detection in polymer optical fibers | |
| CN102879081A (en) | Data processing method in distributed optical fiber vibration system | |
| CN102589593A (en) | Phase sensitive type optical time domain reflection sensing system and method | |
| CN1908505A (en) | Alarm sensing system for distributed optical fiber oil and gas pipeline | |
| CN109058054A (en) | A kind of the bolt on-line monitoring system and method for wind power generator group | |
| KR20140051495A (en) | Method for improving optical time domain reflectometer(otdr) performance | |
| CN105157874A (en) | Distributed boundary security monitoring system and method | |
| CN116305699B (en) | Pipeline supervision system based on omnibearing sensing | |
| CN111157097A (en) | Vibration signal detection method based on phase sensitive optical time domain reflection system | |
| CN113819401A (en) | Desert buried pipeline monitoring system and method based on optical fiber vibration and temperature test | |
| CN203147289U (en) | Double-Sagnac pipeline safety monitoring system | |
| CN109374067A (en) | Many reference amounts Fibre Optical Sensor highway plate-girder real-time monitoring system and monitoring method | |
| CN108538011A (en) | A kind of laser radar fire detection method | |
| CN109444895B (en) | Vibration information positioning method for eliminating interference fading of distributed vibration sensor | |
| CN110987151A (en) | Communication optical cable state real-time monitoring system | |
| WO2020054143A1 (en) | Vibration detection optical fiber sensor and vibration detection method | |
| CN107631814B (en) | Optical self-coherent sensing optical path structure, frequency shift change detection method and sensing device | |
| CN115615332A (en) | State on-line monitoring system of optical fiber composite overhead ground wire | |
| CN114088308B (en) | Vibration pickup and leakage detection method for transport pipeline based on low-reflection chirped grating array | |
| CN206818249U (en) | The monitoring system of subsoil drain | |
| CN110702267B (en) | System and method for testing occurrence state of soil layer medium around buried pipeline | |
| CN114719950A (en) | Step-by-step optical fiber multi-dimensional characteristic parameter acquisition and extraction method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |