CN109060945A - Steel rail defect checking method for width based on leakage magnetic detection device and neural network - Google Patents
Steel rail defect checking method for width based on leakage magnetic detection device and neural network Download PDFInfo
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- CN109060945A CN109060945A CN201811008117.9A CN201811008117A CN109060945A CN 109060945 A CN109060945 A CN 109060945A CN 201811008117 A CN201811008117 A CN 201811008117A CN 109060945 A CN109060945 A CN 109060945A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/83—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/02—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring length, width or thickness
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses a kind of steel rail defect checking method for width based on leakage magnetic detection device and neural network, leakage magnetic detection device is placed on rail to be detected first, the magnetic leakage signal of rail to be detected in the x direction and the y direction is measured, signal time domain distribution map, the signal time domain distribution map with Y-direction that X-direction is obtained after bandpass filtering, enhanced processing；Then X-direction, the signal peak characteristic value of Y-direction and sitgnal distancel characteristic value are extracted, by itself and lift-off value input Neural Network Toolbox, obtains the coefficient of the defect of X-direction and Y-direction；The defect width of X-direction, Y-direction is finally calculated.The present invention it is original be only able to detect magnetic leakage signal on the basis of quantitative analysis is carried out to the flaw indication of different length, can more accurately judge hurt serious conditions.
Description
Technical field
The present invention relates to measuring technique and instrument fields more particularly to a kind of based on leakage magnetic detection device and neural network
Steel rail defect checking method for width.
Background technique
At present, Magnetic Flux Leakage Inspecting is lossless has become one of the main method that steel rail defect width is detected.In non-destructive testing
The basic principle of Magnetic Flux Leakage Inspecting refers to, ferrimagnet is by after local magnetized, in local magnetized area, if the material have crackle or
The hurts such as person's pit, the Distribution of Magnetic Field at hurt can mutate, and have partial magnetic field that can leak out, and form magnetic flux leakage, pass through
Detect the changes of magnetic field of this part of ferrimagnet, it can be determined that this part of the material is with the presence or absence of damage
The leakage field field intensity of fault location and size, the power of measured workpiece internal magnetic field and the sensor of defect are away from tested
Distance, that is, lift-off value of workpiece surface etc. is closely related, these factors can influence the sensitivity and reliability of detection simultaneously.Rationally
Select above-mentioned parameter most important to optimal testing result can be obtained.And the measurement of defect length is to measured workpiece hurt
Assessment is also most important, and different defect width may will affect polishing and replacement to workpiece.Therefore, this method is a kind of true
Determine the detection method of steel rail defect width.
Summary of the invention
The technical problem to be solved by the present invention is to provide a kind of based on leakage field inspection for defect involved in background technique
Survey the steel rail defect checking method for width of device and neural network.
Steel rail defect checking method for width based on leakage magnetic detection device and neural network, the leakage magnetic detection device include
Shell, magnetic yoke, excitation coil, Hall sensor array, the first castor and the second castor；
First castor, the second castor are all made of wide wheel, are separately positioned on the lower end surface of the shell, so that the shell
Body can roll on rail to be detected；
The magnetic yoke, excitation coil, Hall sensor array are arranged in the shell, wherein the magnetic yoke is in
The U-shaped set, on it, the excitation coil is connected with external power supply, and the magnetic yoke is for motivating for the excitation coil winding
Magnetic signal is issued towards rail to be detected when coil is powered；The Hall sensor array setting is in the magnetic yoke and described
Rail is parallel, for collecting magnetic leakage signal；
Steel rail defect checking method for width based on leakage magnetic detection device and neural network comprises the steps of:
Leakage magnetic detection device is placed on rail to be detected by step 1), makes it along rail according to preset speed threshold
Value is at the uniform velocity advanced.Wherein, the spacing between Hall sensor array and rail, i.e. lift-off value g is equal to preset distance threshold；
Step 2), enabling leakage magnetic detection device position is origin O, and the direction of advance of leakage magnetic detection device is that X-axis is square
To vertical rail surface upwardly direction to be detected is Z axis positive direction, is that Y-axis is square perpendicular to the direction of XOY plane to the right
To；The magnetic leakage signal of rail to be detected in the x direction and the y direction is obtained by the Hall sensor array；
Step 3) carries out band logical filter to the magnetic leakage signal of rail to be detected in the x direction and the y direction respectively using blocking method
After wave, enhanced processing, the signal time domain distribution map, the signal time domain distribution map with Y-direction that obtain X-direction；
Step 4), the peak-to-peak value for extracting flaw indication in the signal time domain distribution map of X-direction is the signal peak spy of X-direction
Value indicative TX, extract the signal time domain distribution map flaw indication of Y-direction peak-to-peak value be Y-direction signal peak characteristic value TY, TX,
TY unit is V；
Step 5) extracts the peak-to-peak sitgnal distancel spy away from for X-direction of flaw indication in the signal time domain distribution map of X-direction
Value indicative GX, extract the signal time domain distribution map flaw indication of Y-direction peak-to-peak sitgnal distancel characteristic value GY, GX away from for Y-direction,
GY unit is t；
Step 6) handles characteristic value and inputs neural network:
TX, GX, g and firstorder filter are carried out convolution, generate three spies about X-direction in first layer by step 6.1)
After levying mapping signal, three Feature Mapping signals about X-direction that first layer is generated are by Sigmoid function the
Two layers generate three Feature Mapping signals about X-direction；
Three Feature Mapping signals about X-direction are generated to the second layer to quantify, and are connected into one and are included three members
Plain Ax、Bx、CxFeature vector Tx；
TY, GY, g and firstorder filter are carried out convolution, generate three spies about Y-direction in first layer by step 6.2)
After levying mapping signal, three Feature Mapping signals about Y-direction that first layer is generated are by Sigmoid function the
Two layers generate three Feature Mapping signals about Y-direction；
Three Feature Mapping signals about Y-direction are generated to the second layer to quantify, and are connected into one and are included three members
Plain Ay、By、CyFeature vector Ty；
Step 7), using Neural Network Toolbox, by Tx、TyThe nerve of Neural Network Toolbox is arranged in input tool case
It is trained after member, target error, frequency of training, acquisition and Ax、Bx、Cx、Ay、By、CyOne-to-one coefficient ax、bx、cx、ay、
by、cy；
Step 8), by ax、bx、cxSubstitute into the defect width S that X-direction is calculated in following formulax, SxUnit is mm:
Step 9), by ay、by、cySubstitute into the defect width S that Y-direction is calculated in following formulay, SyUnit is mm:
As the present invention is based on the steel rail defect checking method for width of leakage magnetic detection device and neural network is further excellent
Change scheme, the preset threshold speed are 15km/h.
As the present invention is based on the steel rail defect checking method for width of leakage magnetic detection device and neural network is further excellent
Change scheme, step 3) is middle to carry out 30 bandpass filterings for arriving 3000hz, carries out 80 times of enhanced processing.
As the present invention is based on the steel rail defect checking method for width of leakage magnetic detection device and neural network is further excellent
Change scheme, the g=0.25mm in step 1).
As the present invention is based on the steel rail defect checking method for width of leakage magnetic detection device and neural network is further excellent
Change scheme, sampling time when being quantified in step 6.1), step 6.2) is 0.002s.
As the present invention is based on the steel rail defect checking method for width of leakage magnetic detection device and neural network is further excellent
Change scheme, it is 40 that the middle setting tool box parameter of step 7), which is neuron, target error 0.02, frequency of training 800000
It is secondary.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
It is original be only able to detect magnetic leakage signal on the basis of quantitative analysis is carried out to the flaw indication of different in width, it is more smart
Hurt serious conditions are judged quasi-ly.
Detailed description of the invention
Fig. 1 is the structural schematic diagram that magnetic yoke in the present invention, excitation coil, sensor array match.
In figure, 1- Hall sensor array, 2- magnetic yoke, 3- excitation coil.
Specific embodiment
The invention discloses a kind of steel rail defect checking method for width based on leakage magnetic detection device and neural network is such as schemed
Shown in 1, the leakage magnetic detection device includes shell, magnetic yoke, excitation coil, Hall sensor array, the first castor and crus secunda
Wheel；
First castor, the second castor are all made of wide wheel, are separately positioned on the lower end surface of the shell, so that the shell
Body can roll on rail to be detected；
The magnetic yoke, excitation coil, Hall sensor array are arranged in the shell, wherein the magnetic yoke is in
The U-shaped set, on it, the excitation coil is connected with external power supply, and the magnetic yoke is for motivating for the excitation coil winding
Magnetic signal is issued towards rail to be detected when coil is powered；The Hall sensor array setting is in the magnetic yoke and described
Rail is parallel, for collecting magnetic leakage signal；
The detection method comprises the steps of:
Leakage magnetic detection device is placed on rail to be detected by step 1), makes its speed along rail according to 15km/h
At the uniform velocity advance, wherein spacing, the i.e. lift-off value g=0.25mm between Hall sensor array and rail；
Step 2), enabling leakage magnetic detection device position is origin O, and the direction of advance of leakage magnetic detection device is that X-axis is square
To vertical rail surface upwardly direction to be detected is Y-axis positive direction, is that Y-axis is square perpendicular to the direction of XOY plane to the right
To；The magnetic leakage signal of rail to be detected in the x direction and the y direction is obtained by the Hall sensor array；
Step 3) carries out 30 to the magnetic leakage signal of rail to be detected in the x direction and the y direction respectively using blocking method and arrives
The bandpass filtering of 3000hz, after then carrying out 80 times of enhanced processing, obtain X-direction signal time domain distribution map, with Y-direction
Signal time domain distribution map；
Step 4), the peak-to-peak value for extracting flaw indication in the signal time domain distribution map of X-direction is the signal peak spy of X-direction
Value indicative TX, extract the signal time domain distribution map flaw indication of Y-direction peak-to-peak value be Y-direction signal peak characteristic value TY, TX,
TY unit is V；
Step 5) extracts the peak-to-peak sitgnal distancel spy away from for X-direction of flaw indication in the signal time domain distribution map of X-direction
Value indicative GX, extract the signal time domain distribution map flaw indication of Y-direction peak-to-peak sitgnal distancel characteristic value GY, GX away from for Y-direction,
GY unit is t；
Step 6) handles characteristic value and inputs neural network:
TX, GX, g and firstorder filter are carried out convolution, generate three spies about X-direction in first layer by step 6.1)
After levying mapping signal, three Feature Mapping signals about X-direction that first layer is generated are by Sigmoid function the
Two layers generate three Feature Mapping signals about X-direction；
Three Feature Mapping signals about X-direction are generated to the second layer to quantify, and are connected into one and are included three members
Plain Ax、Bx、CxFeature vector Tx, sampling time when being quantified is 0.002s；
TY, GY, g and firstorder filter are carried out convolution, generate three spies about Y-direction in first layer by step 6.2)
After levying mapping signal, three Feature Mapping signals about Y-direction that first layer is generated are by Sigmoid function the
Two layers generate three Feature Mapping signals about Y-direction；
Three Feature Mapping signals about Y-direction are generated to the second layer to quantify, and are connected into one and are included three members
Plain Ay、By、CyFeature vector Ty, sampling time when being quantified is 0.002s；
Step 7), using Neural Network Toolbox, by Tx、TyInput tool case, setting Neural Network Toolbox neuron are
40, target error 0.02, frequency of training be trained after being 80000 times, obtain and Ax、Bx、Cx、Ay、By、CyIt corresponds
Coefficient ax、bx、cx、ay、by、cy；
Step 8), by ax、bx、cxSubstitute into the defect width S that X-direction is calculated in following formulax, SxUnit is mm:
Step 9), by ay、by、cySubstitute into the defect width S that Y-direction is calculated in following formulay, SyUnit is mm:
Those skilled in the art can understand that unless otherwise defined, all terms used herein (including skill
Art term and scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Also
It should be understood that those terms such as defined in the general dictionary should be understood that have in the context of the prior art
The consistent meaning of meaning will not be explained in an idealized or overly formal meaning and unless defined as here.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not limited to this hair the foregoing is merely a specific embodiment of the invention
Bright, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in the present invention
Protection scope within.
Claims (6)
1. the steel rail defect checking method for width based on leakage magnetic detection device and neural network, the leakage magnetic detection device includes shell
Body, magnetic yoke, excitation coil, Hall sensor array, the first castor and the second castor；
First castor, the second castor are all made of wide wheel, are separately positioned on the lower end surface of the shell, enable the shell
It is enough to be rolled on rail to be detected；
The magnetic yoke, excitation coil, Hall sensor array are arranged in the shell, wherein the magnetic yoke is in inverted U
Shape, on it, the excitation coil is connected with external power supply for the excitation coil winding, and the magnetic yoke is used for logical in excitation coil
Magnetic signal is issued towards rail to be detected when electric；The Hall sensor array is arranged in the magnetic yoke and the rail is flat
Row, for collecting magnetic leakage signal；
It is characterized in that, the steel rail defect checking method for width based on leakage magnetic detection device and neural network comprises the steps of:
Leakage magnetic detection device is placed on rail to be detected by step 1), keeps it even according to preset threshold speed along rail
Speed is advanced.Wherein, the spacing between Hall sensor array and rail, i.e. lift-off value g is equal to preset distance threshold；
Step 2), enabling leakage magnetic detection device position is origin O, and the direction of advance of leakage magnetic detection device is X-axis positive direction, is hung down
Straight rail surface upwardly direction to be detected is Z axis positive direction, is Y-axis positive direction perpendicular to the direction of XOY plane to the right；It is logical
It crosses the Hall sensor array and obtains the magnetic leakage signal of rail to be detected in the x direction and the y direction；
Step 3), using blocking method respectively to rail to be detected magnetic leakage signal in the x direction and the y direction carry out bandpass filtering,
After enhanced processing, the signal time domain distribution map, the signal time domain distribution map with Y-direction that obtain X-direction；
Step 4), the peak-to-peak value for extracting flaw indication in the signal time domain distribution map of X-direction is the signal peak characteristic value of X-direction
It is mono- for signal peak the characteristic value TY, TX, TY of Y-direction to extract the peak-to-peak value of the signal time domain distribution map flaw indication of Y-direction by TX
Position is V；
Step 5) extracts the peak-to-peak sitgnal distancel characteristic value away from for X-direction of flaw indication in the signal time domain distribution map of X-direction
GX, extract the signal time domain distribution map flaw indication of Y-direction it is peak-to-peak away from for sitgnal distancel the characteristic value GY, GX, GY of Y-direction it is mono-
Position is t；
Step 6) handles characteristic value and inputs neural network:
TX, GX, g and firstorder filter are carried out convolution, reflected in first layer generation three features about X-direction by step 6.1)
After penetrating signal, three Feature Mapping signals about X-direction that first layer is generated are by a Sigmoid function in the second layer
Generate three Feature Mapping signals about X-direction；
Three Feature Mapping signals about X-direction are generated to the second layer to quantify, and are connected into one and are included three elements Asx、
Bx、CxFeature vector Tx；
TY, GY, g and firstorder filter are carried out convolution, reflected in first layer generation three features about Y-direction by step 6.2)
After penetrating signal, three Feature Mapping signals about Y-direction that first layer is generated are by a Sigmoid function in the second layer
Generate three Feature Mapping signals about Y-direction；
Three Feature Mapping signals about Y-direction are generated to the second layer to quantify, and are connected into one and are included three elements Asy、
By、CyFeature vector Ty；
Step 7), using Neural Network Toolbox, by Tx、TyNeuron, the mesh of Neural Network Toolbox is arranged in input tool case
It is trained after mark error, frequency of training, acquisition and Ax、Bx、Cx、Ay、By、CyOne-to-one coefficient ax、bx、cx、ay、by、cy；
Step 8), by ax、bx、cxSubstitute into the defect width S that X-direction is calculated in following formulax, SxUnit is mm:
Step 9), by ay、by、cySubstitute into the defect width S that Y-direction is calculated in following formulay, SyUnit is mm:
2. the steel rail defect checking method for width according to claim 1 based on leakage magnetic detection device and neural network,
It is characterized in that, the preset threshold speed is 15km/h.
3. the steel rail defect checking method for width according to claim 1 based on leakage magnetic detection device and neural network,
It is characterized in that, 30 bandpass filterings for arriving 3000hz is carried out in step 3), carry out 80 times of enhanced processing.
4. the steel rail defect checking method for width according to claim 1 based on leakage magnetic detection device and neural network,
It is characterized in that, the g=0.25mm in step 1).
5. the steel rail defect checking method for width according to claim 1 based on leakage magnetic detection device and neural network,
It is characterized in that, sampling time when being quantified in step 6.1), step 6.2) is 0.002s.
6. the steel rail defect checking method for width according to claim 1 based on leakage magnetic detection device and neural network,
It is characterized in that, it is 40 that setting tool box parameter, which is neuron, in step 7), target error 0.02, frequency of training 80000
It is secondary.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110568059A (en) * | 2019-09-24 | 2019-12-13 | 哈尔滨工业大学（深圳） | Nondestructive testing method and device for steel wire rope |
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