CN106246164A - Coal bed gas well level monitoring system based on Fibre Optical Sensor - Google Patents

Abstract

The invention provides coal bed gas well level monitoring system based on Fibre Optical Sensor, including Fibre Optical Sensor level monitoring module, risk judgment module and alarm module；Described Fibre Optical Sensor level monitoring module carries out the level monitoring of coal bed gas well by liquid level fiber-optic grating sensor；Whether described risk judgment module exceedes defined threshold for the liquid level judging the coal bed gas well surveyed；Described alarm module is for performing warning when the liquid level of the coal bed gas well surveyed exceedes defined threshold.The invention have the benefit that and can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, there is the advantage such as compact conformation, long-time stability good, good endurance, electromagnetism interference, and when whether the liquid level of the coal bed gas well surveyed exceedes defined threshold can report to the police.

Description

Coal bed gas well level monitoring system based on Fibre Optical Sensor

Technical field

The present invention relates to coalbed gas logging field, be specifically related to coal bed gas well level monitoring system based on Fibre Optical Sensor.

Background technology

In correlation technique, coal bed gas well substantially carries out multiple seam conjunction and adopts, with echometer Timing measurement liquid level.But work as Liquid level drop to main product water layer upper and lower time, echometer does not detects real level, measured by be certain main product water of ullage The degree of depth of layer, this brings the biggest difficulty to Instructing manufacture.

Summary of the invention

For solving the problems referred to above, it is desirable to provide coal bed gas well level monitoring system based on Fibre Optical Sensor.

The purpose of the present invention realizes by the following technical solutions:

Coal bed gas well level monitoring system based on Fibre Optical Sensor, including Fibre Optical Sensor level monitoring module, risk judgment Module and alarm module；Described Fibre Optical Sensor level monitoring module carries out the liquid of coal bed gas well by liquid level fiber-optic grating sensor Position monitoring；Whether described risk judgment module exceedes defined threshold for the liquid level judging the coal bed gas well surveyed；Described warning Module is for performing warning when the liquid level of the coal bed gas well surveyed exceedes defined threshold.

The invention have the benefit that and can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, tool There are the advantages such as compact conformation, long-time stability good, good endurance, electromagnetism interference, and can be at the liquid of the coal bed gas well surveyed Reporting to the police when whether exceeding defined threshold in position, thus solves above-mentioned technical problem.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

Fig. 1 is present configuration connection diagram；

Fig. 2 is the schematic diagram of inventive sensor fault diagnosis module.

Reference:

The event of Fibre Optical Sensor level monitoring module 1, risk judgment module 2, alarm module 3, liquid level display module 4, sensor Barrier diagnostic system 5, signals collecting filter unit 51, fault signature extraction unit 52, online feature extraction unit 53, characteristic vector Preferred cell 54, failure modes recognition unit 55, failure mode updating block 56, health records unit 57.

Detailed description of the invention

The invention will be further described with the following Examples.

Application scenarios 1

See Fig. 1, Fig. 2, coal bed gas well of based on Fibre Optical Sensor the level monitoring system of an embodiment of this application scene System, including Fibre Optical Sensor level monitoring module 1, risk judgment module 2 and alarm module 3；Described Fibre Optical Sensor level monitoring mould Block 1 carries out the level monitoring of coal bed gas well by liquid level fiber-optic grating sensor；Described risk judgment module 2 is used for judging to be surveyed The liquid level of coal bed gas well whether exceed defined threshold；Described alarm module 3 exceedes for the liquid level at the coal bed gas well surveyed Warning is performed during defined threshold.

Preferably, described Fibre Optical Sensor level monitoring module 1 includes: liquid level fiber-optic grating sensor, it is laid in oil pipe Outer surface, makes the centre wavelength of fiber grating change by experiencing oil pipe external pressure；Laser instrument, it is used for exporting light Bundle；Bonder, its input receives the output beam of described laser instrument, and is passed along described liquid level optical fiber grating sensing Device；Fiber Bragg grating (FBG) demodulator, its signal input part is connected with the light signal output end of described bonder, is used for monitoring described liquid The change of position fiber-optic grating sensor centre wavelength, and then record coal bed gas well liquid level.

Preferably, described liquid level fiber-optic grating sensor includes:

Housing, it arranges a cover plate, and above-mentioned cover plate upper end is provided with circular flat diaphragm, and a pressure is implemented on described lid On the circular flat diaphragm of plate, described cover plate lower end connects a dowel steel；

Equi intensity cantilever, it is arranged in described housing, and is meshed with described dowel steel, described equal strength cantilever Beam upper surface has a groove, and described groove is provided with fiber grating, and described fiber grating draws optical fiber to institute Stating outside housing, described extraction optical fiber connects described bonder.

The above embodiment of the present invention can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, has knot The advantages such as structure is compact, long-time stability good, good endurance, electromagnetism interference, thus solve above-mentioned technical problem.

Preferably, described coal bed gas well level monitoring system also includes showing for the liquid level of the level condition of display monitoring in real time Showing module 4, monitoring liquid level data is sent to risk judgment module 2 He by wireless network by described liquid level fiber-optic grating sensor Liquid level display module 4.

This preferred embodiment can help staff to be preferably monitored coal bed gas well liquid level.

Preferably, described coal bed gas well level monitoring system also includes the biography diagnosing liquid level fiber-optic grating sensor Sensor fault diagnosis system 5, described sensor fault diagnosis system 5 includes that signals collecting filter unit 51, fault signature extract Unit 52, online feature extraction unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode update Unit 56 and health records unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis system 5 and achieves sensor fault diagnosis system 5 Fast construction, is conducive to monitoring liquid level fiber-optic grating sensor, it is ensured that the monitoring of liquid level fiber-optic grating sensor effectively performs.

Preferably, described signals collecting filter unit 51 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.96, the monitoring velocity phase of sensor fault diagnosis system 5 To improve 10%, the monitoring accuracy of sensor fault diagnosis system 5 improves 12% relatively.

Application scenarios 2

See Fig. 1, Fig. 2, coal bed gas well of based on Fibre Optical Sensor the level monitoring system of an embodiment of this application scene System, including Fibre Optical Sensor level monitoring module 1, risk judgment module 2 and alarm module 3；Described Fibre Optical Sensor level monitoring mould Block 1 carries out the level monitoring of coal bed gas well by liquid level fiber-optic grating sensor；Described risk judgment module 2 is used for judging to be surveyed The liquid level of coal bed gas well whether exceed defined threshold；Described alarm module 3 exceedes for the liquid level at the coal bed gas well surveyed Warning is performed during defined threshold.

Preferably, described Fibre Optical Sensor level monitoring module 1 includes: liquid level fiber-optic grating sensor, it is laid in oil pipe Outer surface, makes the centre wavelength of fiber grating change by experiencing oil pipe external pressure；Laser instrument, it is used for exporting light Bundle；Bonder, its input receives the output beam of described laser instrument, and is passed along described liquid level optical fiber grating sensing Device；Fiber Bragg grating (FBG) demodulator, its signal input part is connected with the light signal output end of described bonder, is used for monitoring described liquid The change of position fiber-optic grating sensor centre wavelength, and then record coal bed gas well liquid level.

Preferably, described liquid level fiber-optic grating sensor includes:

Housing, it arranges a cover plate, and above-mentioned cover plate upper end is provided with circular flat diaphragm, and a pressure is implemented on described lid On the circular flat diaphragm of plate, described cover plate lower end connects a dowel steel；

Equi intensity cantilever, it is arranged in described housing, and is meshed with described dowel steel, described equal strength cantilever Beam upper surface has a groove, and described groove is provided with fiber grating, and described fiber grating draws optical fiber to institute Stating outside housing, described extraction optical fiber connects described bonder.

The above embodiment of the present invention can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, has knot The advantages such as structure is compact, long-time stability good, good endurance, electromagnetism interference, thus solve above-mentioned technical problem.

Preferably, described coal bed gas well level monitoring system also includes showing for the liquid level of the level condition of display monitoring in real time Showing module 4, monitoring liquid level data is sent to risk judgment module 2 He by wireless network by described liquid level fiber-optic grating sensor Liquid level display module 4.

This preferred embodiment can help staff to be preferably monitored coal bed gas well liquid level.

Preferably, described coal bed gas well level monitoring system also includes the biography diagnosing liquid level fiber-optic grating sensor Sensor fault diagnosis system 5, described sensor fault diagnosis system 5 includes that signals collecting filter unit 51, fault signature extract Unit 52, online feature extraction unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode update Unit 56 and health records unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis system 5 and achieves sensor fault diagnosis system 5 Fast construction, is conducive to monitoring liquid level fiber-optic grating sensor, it is ensured that the monitoring of liquid level fiber-optic grating sensor effectively performs.

Preferably, described signals collecting filter unit 51 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.95, the monitoring velocity phase of sensor fault diagnosis system 5 To improve 11%, the monitoring accuracy of sensor fault diagnosis system 5 improves 11% relatively.

Application scenarios 3

See Fig. 1, Fig. 2, coal bed gas well of based on Fibre Optical Sensor the level monitoring system of an embodiment of this application scene System, including Fibre Optical Sensor level monitoring module 1, risk judgment module 2 and alarm module 3；Described Fibre Optical Sensor level monitoring mould Block 1 carries out the level monitoring of coal bed gas well by liquid level fiber-optic grating sensor；Described risk judgment module 2 is used for judging to be surveyed The liquid level of coal bed gas well whether exceed defined threshold；Described alarm module 3 exceedes for the liquid level at the coal bed gas well surveyed Warning is performed during defined threshold.

Preferably, described Fibre Optical Sensor level monitoring module 1 includes: liquid level fiber-optic grating sensor, it is laid in oil pipe Outer surface, makes the centre wavelength of fiber grating change by experiencing oil pipe external pressure；Laser instrument, it is used for exporting light Bundle；Bonder, its input receives the output beam of described laser instrument, and is passed along described liquid level optical fiber grating sensing Device；Fiber Bragg grating (FBG) demodulator, its signal input part is connected with the light signal output end of described bonder, is used for monitoring described liquid The change of position fiber-optic grating sensor centre wavelength, and then record coal bed gas well liquid level.

Preferably, described liquid level fiber-optic grating sensor includes:

Housing, it arranges a cover plate, and above-mentioned cover plate upper end is provided with circular flat diaphragm, and a pressure is implemented on described lid On the circular flat diaphragm of plate, described cover plate lower end connects a dowel steel；

Equi intensity cantilever, it is arranged in described housing, and is meshed with described dowel steel, described equal strength cantilever Beam upper surface has a groove, and described groove is provided with fiber grating, and described fiber grating draws optical fiber to institute Stating outside housing, described extraction optical fiber connects described bonder.

The above embodiment of the present invention can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, has knot The advantages such as structure is compact, long-time stability good, good endurance, electromagnetism interference, thus solve above-mentioned technical problem.

Preferably, described coal bed gas well level monitoring system also includes showing for the liquid level of the level condition of display monitoring in real time Showing module 4, monitoring liquid level data is sent to risk judgment module 2 He by wireless network by described liquid level fiber-optic grating sensor Liquid level display module 4.

This preferred embodiment can help staff to be preferably monitored coal bed gas well liquid level.

Preferably, described coal bed gas well level monitoring system also includes the biography diagnosing liquid level fiber-optic grating sensor Sensor fault diagnosis system 5, described sensor fault diagnosis system 5 includes that signals collecting filter unit 51, fault signature extract Unit 52, online feature extraction unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode update Unit 56 and health records unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis system 5 and achieves sensor fault diagnosis system 5 Fast construction, is conducive to monitoring liquid level fiber-optic grating sensor, it is ensured that the monitoring of liquid level fiber-optic grating sensor effectively performs.

Preferably, described signals collecting filter unit 51 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.94, the monitoring velocity phase of sensor fault diagnosis system 5 To improve 12%, the monitoring accuracy of sensor fault diagnosis system 5 improves 10% relatively.

Application scenarios 4

See Fig. 1, Fig. 2, coal bed gas well of based on Fibre Optical Sensor the level monitoring system of an embodiment of this application scene System, including Fibre Optical Sensor level monitoring module 1, risk judgment module 2 and alarm module 3；Described Fibre Optical Sensor level monitoring mould Block 1 carries out the level monitoring of coal bed gas well by liquid level fiber-optic grating sensor；Described risk judgment module 2 is used for judging to be surveyed The liquid level of coal bed gas well whether exceed defined threshold；Described alarm module 3 exceedes for the liquid level at the coal bed gas well surveyed Warning is performed during defined threshold.

Preferably, described Fibre Optical Sensor level monitoring module 1 includes: liquid level fiber-optic grating sensor, it is laid in oil pipe Outer surface, makes the centre wavelength of fiber grating change by experiencing oil pipe external pressure；Laser instrument, it is used for exporting light Bundle；Bonder, its input receives the output beam of described laser instrument, and is passed along described liquid level optical fiber grating sensing Device；Fiber Bragg grating (FBG) demodulator, its signal input part is connected with the light signal output end of described bonder, is used for monitoring described liquid The change of position fiber-optic grating sensor centre wavelength, and then record coal bed gas well liquid level.

Preferably, described liquid level fiber-optic grating sensor includes:

Housing, it arranges a cover plate, and above-mentioned cover plate upper end is provided with circular flat diaphragm, and a pressure is implemented on described lid On the circular flat diaphragm of plate, described cover plate lower end connects a dowel steel；

Equi intensity cantilever, it is arranged in described housing, and is meshed with described dowel steel, described equal strength cantilever Beam upper surface has a groove, and described groove is provided with fiber grating, and described fiber grating draws optical fiber to institute Stating outside housing, described extraction optical fiber connects described bonder.

The above embodiment of the present invention can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, has knot The advantages such as structure is compact, long-time stability good, good endurance, electromagnetism interference, thus solve above-mentioned technical problem.

Preferably, described coal bed gas well level monitoring system also includes showing for the liquid level of the level condition of display monitoring in real time Showing module 4, monitoring liquid level data is sent to risk judgment module 2 He by wireless network by described liquid level fiber-optic grating sensor Liquid level display module 4.

This preferred embodiment can help staff to be preferably monitored coal bed gas well liquid level.

Preferably, described coal bed gas well level monitoring system also includes the biography diagnosing liquid level fiber-optic grating sensor Sensor fault diagnosis system 5, described sensor fault diagnosis system 5 includes that signals collecting filter unit 51, fault signature extract Unit 52, online feature extraction unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode update Unit 56 and health records unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis system 5 and achieves sensor fault diagnosis system 5 Fast construction, is conducive to monitoring liquid level fiber-optic grating sensor, it is ensured that the monitoring of liquid level fiber-optic grating sensor effectively performs.

Preferably, described signals collecting filter unit 51 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.93, the monitoring velocity phase of sensor fault diagnosis system 5 To improve 13%, the monitoring accuracy of sensor fault diagnosis system 5 improves 9% relatively.

Application scenarios 5

See Fig. 1, Fig. 2, coal bed gas well of based on Fibre Optical Sensor the level monitoring system of an embodiment of this application scene System, including Fibre Optical Sensor level monitoring module 1, risk judgment module 2 and alarm module 3；Described Fibre Optical Sensor level monitoring mould Block 1 carries out the level monitoring of coal bed gas well by liquid level fiber-optic grating sensor；Described risk judgment module 2 is used for judging to be surveyed The liquid level of coal bed gas well whether exceed defined threshold；Described alarm module 3 exceedes for the liquid level at the coal bed gas well surveyed Warning is performed during defined threshold.

Preferably, described Fibre Optical Sensor level monitoring module 1 includes: liquid level fiber-optic grating sensor, it is laid in oil pipe Outer surface, makes the centre wavelength of fiber grating change by experiencing oil pipe external pressure；Laser instrument, it is used for exporting light Bundle；Bonder, its input receives the output beam of described laser instrument, and is passed along described liquid level optical fiber grating sensing Device；Fiber Bragg grating (FBG) demodulator, its signal input part is connected with the light signal output end of described bonder, is used for monitoring described liquid The change of position fiber-optic grating sensor centre wavelength, and then record coal bed gas well liquid level.

Preferably, described liquid level fiber-optic grating sensor includes:

Housing, it arranges a cover plate, and above-mentioned cover plate upper end is provided with circular flat diaphragm, and a pressure is implemented on described lid On the circular flat diaphragm of plate, described cover plate lower end connects a dowel steel；

Equi intensity cantilever, it is arranged in described housing, and is meshed with described dowel steel, described equal strength cantilever Beam upper surface has a groove, and described groove is provided with fiber grating, and described fiber grating draws optical fiber to institute Stating outside housing, described extraction optical fiber connects described bonder.

The above embodiment of the present invention can pass through liquid level fiber-optic grating sensor indirect monitoring coal bed gas well level value, has knot The advantages such as structure is compact, long-time stability good, good endurance, electromagnetism interference, thus solve above-mentioned technical problem.

Preferably, described coal bed gas well level monitoring system also includes showing for the liquid level of the level condition of display monitoring in real time Showing module 4, monitoring liquid level data is sent to risk judgment module 2 He by wireless network by described liquid level fiber-optic grating sensor Liquid level display module 4.

This preferred embodiment can help staff to be preferably monitored coal bed gas well liquid level.

Preferably, described coal bed gas well level monitoring system also includes the biography diagnosing liquid level fiber-optic grating sensor Sensor fault diagnosis system 5, described sensor fault diagnosis system 5 includes that signals collecting filter unit 51, fault signature extract Unit 52, online feature extraction unit 53, characteristic vector preferred cell 54, failure modes recognition unit 55, failure mode update Unit 56 and health records unit 57.

The above embodiment of the present invention arranges sensor fault diagnosis system 5 and achieves sensor fault diagnosis system 5 Fast construction, is conducive to monitoring liquid level fiber-optic grating sensor, it is ensured that the monitoring of liquid level fiber-optic grating sensor effectively performs.

Preferably, described signals collecting filter unit 51 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction unit 52 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online feature extraction unit 53 is for carrying out integrated Empirical Mode to filtered on-line sensor test signal State is decomposed (EEMD) and is processed, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred cell 54 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes recognition unit 55 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode updating block 56, for being updated training set, continues to optimize sensor fault Diagnostic cast, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode updating block 56, to improve adaptation ability and the range of application of model.

Preferably, described health records unit 57 includes sub module stored and secure access submodule, described storage submodule Block uses storage model based on cloud storage, specifically, is encrypted after being compressed by fault message, is uploaded to cloud storage Device, described secure access submodule, for conducting interviews information, specifically, corresponding to sub module stored, downloads data to This locality, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records unit 57, on the one hand ensure that information security, on the other hand can be at any time Fault is conducted interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.92, the monitoring velocity phase of sensor fault diagnosis system 5 To improve 14%, the monitoring accuracy of sensor fault diagnosis system 5 improves 8% relatively.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. coal bed gas well level monitoring system based on Fibre Optical Sensor, is characterized in that, including Fibre Optical Sensor level monitoring module, wind Danger judge module and alarm module；Described Fibre Optical Sensor level monitoring module carries out coal bed gas by liquid level fiber-optic grating sensor The level monitoring of well；Whether described risk judgment module exceedes defined threshold for the liquid level judging the coal bed gas well surveyed；Institute State alarm module for performing warning when the liquid level of the coal bed gas well surveyed exceedes defined threshold.

Coal bed gas well level monitoring system based on Fibre Optical Sensor the most according to claim 1, is characterized in that, described optical fiber Transmitter liquid level monitoring modular includes: liquid level fiber-optic grating sensor, and it is laid in oil pipe outer surface, by experiencing oil pipe external pressure And make the centre wavelength of fiber grating change；Laser instrument, it is used for output beam；Bonder, its input receives described The output beam of laser instrument, and it is passed along described liquid level fiber-optic grating sensor；Fiber Bragg grating (FBG) demodulator, its signal inputs End is connected with the light signal output end of described bonder, for monitoring the change of described liquid level fiber-optic grating sensor centre wavelength Change, and then record coal bed gas well liquid level.

Coal bed gas well level monitoring system based on Fibre Optical Sensor the most according to claim 2, is characterized in that, also includes using In the liquid level display module of the level condition of display monitoring in real time, described liquid level fiber-optic grating sensor will monitoring by wireless network Liquid level data is sent to risk judgment module and liquid level display module.