CN102175768A - Method and device for detecting defects and failures of high-speed rail based on vibration signals - Google Patents

Method and device for detecting defects and failures of high-speed rail based on vibration signals Download PDF

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CN102175768A
CN102175768A CN2011100428985A CN201110042898A CN102175768A CN 102175768 A CN102175768 A CN 102175768A CN 2011100428985 A CN2011100428985 A CN 2011100428985A CN 201110042898 A CN201110042898 A CN 201110042898A CN 102175768 A CN102175768 A CN 102175768A
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hurt
imf
rail
vibration signal
high ferro
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王艳
章欣
沈毅
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Harbin Institute of Technology
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Harbin Institute of Technology
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Abstract

The invention discloses a method and device for detecting defects and failures of a high-speed rail based on vibration signals, belonging to the technical field of railway safety monitoring and protection. The invention solves the problem that the operating efficiency of a train is seriously influenced when a current measurement device detects the defects and failures of the high-speed rail. The method comprises the following steps of: 1, acquiring a vibration signal of the high-speed rail by a vibration acceleration transducer, carrying out experience mode decomposition on the vibration signal, and acquiring n IMF (Intrinsic Mode Function) components and one residual error; 2, calculating a correlation coefficient ui (i=1, 2, ......, n) of the n IMF components acquired in the step 1 and the acquired vibration signal, and selecting the IMF component, the correlation coefficient ui of which is larger than a threshold lambda, as defect and failure feature information IMF; 3, respectively calculating power spectral density of the defect and failure feature information IMF acquired in the step 2; and 4, determining defect and failure parts of the high-speed rail according to the power spectral density of the defect and failure feature information IMF acquired in the step 3, and finishing the detection of the defects and failures of the high-speed rail.

Description

A kind of high ferro rail defects and failures detection method and device based on vibration signal
Technical field
The present invention relates to a kind of high ferro rail defects and failures detection method and device, belong to railway security monitoring and guard technology field based on vibration signal.
Background technology
Railway is the main artery of communications and transportation, and development and national economy is played crucial effect.Along with improving constantly and the develop rapidly of the increasing, particularly high ferro of density and load-carrying of train running speed, new challenge has been proposed existing railway track flaw detection mode.
The flaw detection of existing railway track is to be the flaw detection pattern of assisting based on large-scale rail-defect detector car, small-sized steel rail defectoscope.And the speed of present domestic rail-defect detector car very low (being generally 50-80 kilometer/hour), but the speed per hour of high ferro is between 250-350 kilometer/hour, when these equipment detect rail, will take the high ferro circuit, seriously influence the efficient of train operation.Simultaneously, the small-sized steel rail defectoscope mostly is the people and pushes away flaw detection, can only carry out limited low-speed detection in the subrange.Obviously, existing method of detection, defect-detecting equipment, inspection speed can not satisfy the needs of high ferro development far away.
Summary of the invention
The present invention seeks to the problem that can have a strong impact on train operation efficient when existing measuring equipment detects high ferro rail defects and failures situation, a kind of high ferro rail defects and failures detection method and device based on vibration signal is provided in order to solve.
A kind of high ferro rail defects and failures detection method based on vibration signal of the present invention may further comprise the steps:
Step 1, gather the vibration signal of high ferro rail, and described vibration signal is carried out empirical modal decompose, obtain n IMF component and a residual error by vibration acceleration sensor; IMF is interior solid model state function;
N IMF component that step 2, calculation procedure one are obtained and the relative coefficient u that decomposes the vibration signal that collects before i(i=1,2 ..., n), choose relative coefficient u iGreater than the IMF component of threshold value λ as hurt characteristic information IMF;
Step 3, the power spectrum density of the hurt characteristic information IMF that obtains of calculation procedure two respectively,
The power spectrum density of step 4, the hurt characteristic information IMF that obtains according to step 3 is determined high ferro rail defects and failures position, finishes the detection of high ferro rail defects and failures.
Device based on above-mentioned a kind of high ferro rail defects and failures detection method based on vibration signal:
Its involving vibrations acceleration transducer, signaling conversion circuit, the empirical modal decomposing module, the hurt characteristic information extracting module, the hurt position determination module of the power density acquisition module of hurt characteristic information and high ferro rail, be arranged on the hurt information of the vibration acceleration sensor detection high ferro rail on the high ferro rail, and export to signaling conversion circuit, signaling conversion circuit is that the input hurt vibration signal of digital form is x (t) and exports to the empirical modal decomposing module with the analog signal conversion that receives, it is that x (t) handles that the empirical modal decomposing module will be imported the hurt vibration signal, n the IMF component that obtains exported to the hurt characteristic information extracting module, the IMF component that the hurt characteristic information extracting module will satisfy the hurt condition is exported to the power density acquisition module of hurt characteristic information, the power density acquisition module of hurt characteristic information calculates each power density that comprises the IMF component of hurt feature, and export to the hurt position determination module of high ferro rail, the hurt position determination module of high ferro rail is judged the hurt position of tested high ferro rail according to the power density of each IMF component.
Advantage of the present invention:
1) at the lower situation of existing rail examination mode efficient, on basis based on railway vibration transducer along the line, effectively utilize real-time vibration signal and excavated characteristic information, propose a kind of detailed hurt determination methods and device, can realize the rail in whole highway section is carried out the purpose of real-time detection.
2) utilize the adaptive ability of EMD, handled non-stationary, nonlinear rail vibration signal preferably the signal time yardstick; And calculate by corresponding PSD, therefrom obtain the rail defects and failures characteristic frequency in the vibration signal exactly.Wherein, utilize correlation analysis to extract to comprise the method for the IMF of main hurt characteristic information, prevented that EMD from decomposing the interference of introducing irrelevant IMF in the medium and low frequency scope, with further Accurate Analysis hurt feature.
Description of drawings
Fig. 1 is a process flow diagram of the present invention;
Fig. 2 is a rail vibration signal supervisory instrument structural representation;
Fig. 3 decomposes (EMD) process flow diagram for empirical modal;
Fig. 4 is a rail vibration sensor measuring point arrangenent diagram;
Fig. 5 is three kinds of different hurts positions on rail;
Fig. 6 is each rank IMF and residual error of harmless rail vibration signal;
Fig. 7 is the first rank IMF and the corresponding PSD thereof of harmless rail vibration signal;
The first rank IMF and the corresponding PSD thereof of rail vibration signal when Fig. 8 is the rail head hurt;
The first rank IMF and the corresponding PSD thereof of rail vibration signal when Fig. 9 is flange of rail hurt;
The first rank IMF and the corresponding PSD thereof of rail vibration signal when Figure 10 is web of the rail hurt;
Figure 11 is PSD figure harmless and three kinds of diverse location damage vibration signals.
Embodiment
Embodiment one: present embodiment is described below in conjunction with Fig. 1, Fig. 2 and Fig. 5, the present invention is directed to the machine-processed flaw detection demand that can't satisfy high-speed railway of existing inspection technique and flaw detection, adopt a kind of new bullet train defect detecting method based on the vibration signal characteristics extraction.By setting up respective track hurt acquisition sensor network along the railway, measure the rail vibration signal, and signal is carried out pre-service, sends to inspection car or information specific center; After inspection car or information center receive signal, utilizing empirical modal to decompose (EMD) decomposes the vibration signal that receives, thereby obtain corresponding interior solid model state function (IMF), resulting each rank IMF component has comprised the characteristic information (this characteristic information comprises harmless and diminishes, and diminishes information such as comprising hurt position, degree) of rail defects and failures.In the EMD decomposable process, often part IMF component obviously reflects the hurt feature, and other IMF component does not reflect the hurt feature substantially or it is not obvious to reflect, the bigger IMF of signal correlation analyzes and extract corresponding feature to its feature before needing to choose for this reason and decomposing.Comprise the power spectrum density (PSD) of hurt characteristic information IMF by calculating, the relation of research PSD and rail failure therefrom draws the frequency range and the feature of hurt.At last, set up the existence of rail failure and the judgment criterion of position thereof.
The present invention proposes a kind of concrete grammar and device of the real-time detection at high ferro hurt rail by above method, just utilize railway vibration transducer along the line to obtain its vibration signal, calculate by the PSD that this signal is carried out EMD decomposition and relevant IMF, obtain corresponding rail defects and failures feature, then the rail defects and failures judgment criterion is analyzed and set up to the hurt feature.The present invention has changed traditional in the past railway track flaw detection pattern, has not only improved the efficient of flaw detection, and can accomplish the real-time detection to whole highway section rail.
The described a kind of high ferro rail defects and failures detection method based on vibration signal of present embodiment may further comprise the steps:
Step 1, gather the vibration signal of high ferro rail, and described vibration signal is carried out empirical modal decompose, obtain n IMF component and a residual error by vibration acceleration sensor;
N IMF component that step 2, calculation procedure one are obtained and the relative coefficient u that decomposes the vibration signal that collects before i(i=1,2 ..., n), choose relative coefficient u iThe IMF that comprises the hurt characteristic information greater than the IMF component conduct of threshold value λ;
Step 3, the power spectrum density of the hurt characteristic information IMF that obtains of calculation procedure two respectively, power spectrum density is abbreviated as PSD;
The power spectrum density of step 4, the hurt characteristic information IMF that obtains according to step 3 is determined high ferro rail defects and failures position, finishes the detection of high ferro rail defects and failures.
The hurt of high ferro rail can occur in different positions, such as rail head, the web of the rail and the flange of rail, specifically referring to shown in Figure 5.By its vibration signal of vibration acceleration sensor collection, analyze the abnormal conditions in these vibration signals in the step 1.
In the step 2 greater than the relative coefficient u of threshold value λ iCan be one or more, as be one, then with this IMF component as the IMF component that comprises the hurt characteristic information, as be a plurality of, then with these IMF components all as the IMF component that comprises the hurt characteristic information.
Select in the step 3 wherein to comprise the IMF of hurt characteristic information and calculate its PSD, then, study the relation of its PSD and rail failure, therefrom draw the frequency range and the feature of diverse location hurt, set up the existence of damage and the judgment criterion of position thereof.
Referring to shown in Figure 2, the vibrating sensing apparatus that links to each other with tested rail, the signaling conversion circuit that links to each other with this sensor, and the microcomputer that is connected in this signaling conversion circuit carry out the coupling of hurt then by computing machine, establish the hurt criterion.Wherein, the vibration signal of sensor measurement can also can send to inspection center by wireless network by near the computing machine of inspection center wired being directly connected to through after changing.
Embodiment two: present embodiment is described below in conjunction with Fig. 4, present embodiment is described further embodiment one: the vibration signal of gathering the high ferro rail in the step 1 by vibration acceleration sensor, vibration acceleration sensor is arranged on the flange of rail upper surface of high ferro rail, and apart from flange of rail outward flange d distance, the span of described d is 18mm～20mm.
Embodiment three: below in conjunction with Fig. 3 present embodiment is described, present embodiment is described further embodiment one: the process of obtaining a n IMF component and a residual error in the step 1 is:
Setting input hurt vibration signal is x (t), t=1, and 2 ..., N,
Step 11, IMF decomposable process initialization: n=1, and satisfy relational expression r N-1(t)=x (t) establishment, wherein r N-1(t) be trend function after (n-1) inferior decomposition;
Step 12, screening process initialization, k=1, and satisfy relational expression h N (k-1)(t)=r N-1(t) set up, wherein h N (k-1)(t) be through the survival function after (k-1) inferior screening during the n time empirical modal decomposes;
Step 13, according to screening sequence obtain through the k time the screening after survival function h Nk(t);
Step 14, the survival function h that adopts standard deviation criterion determining step 13 to obtain Nk(t) whether satisfy the condition of eigenmode state function IMF, promptly
Figure BDA0000047517000000041
Whether less than threshold value H SD, 0.2≤H SD≤ 0.3;
Judged result is for being, execution in step 15, judged result be not for, k=k+1 then, and execution in step 13 then,
Step 15, eigenmode state function component c of extraction n(t)=h Nk(t);
Step 16, obtain the trend function r of input hurt vibration signal x (t) through the decomposition of the n time empirical modal n(t)=r N-1(t)-c n(t);
Step 17, judgement trend function r n(t) whether be monotonic quantity;
Judged result is not for, n=n+1 then, and execution in step 12 then, and judged result is finished leaching process for being, obtains n IMF component { c 1(t), c 2(t) ... c n(t) }; With 1 residual error RES:r n(t).
Obtain survival function h in the step 13 Nk(t) process is:
Step 131, utilize cubic spline function to obtain survival function h after input hurt vibration signal x (t) screens through the k-1 time in decomposing through the n time empirical modal N (k-1)(t) upper and lower envelope,
Step 132, the described survival function h of calculating N (k-1)(t) upper and lower enveloping curve is in the average of each t
Figure BDA0000047517000000051
Step 133, obtain the survival function after input hurt vibration signal x (t) screens through the k time in decomposing through the n time empirical modal
Figure BDA0000047517000000052
Embodiment four: the difference of present embodiment and embodiment three is, the middle H of step 14 SD=0.25, other is identical with embodiment three.
Embodiment five: present embodiment is described further embodiment one: the relative coefficient u in the step 2 iObtain by following formula:
u i = IMF n x ( t ) ,
Wherein, IMF nDecompose n the IMF component that obtains for vibration signal carries out empirical modal, x (t) is the vibration signal before the decomposition that decomposites this n IMF.
Threshold value λ in the step 2=0.4～0.6.
Embodiment six: the difference of present embodiment and embodiment one is, threshold value λ=0.5 in the step 2, and other is identical with embodiment one.
Embodiment seven: present embodiment is described further embodiment one: the power spectrum density that comprises hurt characteristic information IMF in the step 3 is obtained by following formula:
S ( f ) = ∫ - ∞ ∞ R ( τ ) e - 2 πifτ dτ ,
Wherein, R (τ) is for comprising the autocorrelation function of hurt characteristic information IMF.
The judgment criterion of determining high ferro rail defects and failures position in the step 4 is:
As if having frequency in the power spectral density plot that comprises hurt characteristic information IMF is the Frequency point of 3880Hz ± 100Hz, and the amplitude that this Frequency point amplitude is in harmless rail characteristic frequency 3360Hz fluctuates in 5% scope, and then being judged to be high ferro rail defects and failures position is rail head;
As if having frequency in the power spectral density plot that comprises hurt characteristic information IMF is the Frequency point of 3880Hz ± 100Hz, and this Frequency point amplitude is in 300%～350% scope of the amplitude that can't harm rail characteristic frequency 3360Hz, and then being judged to be high ferro rail defects and failures position is the flange of rail;
As if there being frequency in the power spectral density plot that comprises hurt characteristic information IMF is that then being judged to be high ferro rail defects and failures position is the web of the rail in 1200%～1300% scope of the Frequency point of 3600Hz ± 100Hz and the amplitude that this Frequency point amplitude is in harmless rail characteristic frequency 3360Hz.
Embodiment eight: present embodiment is described below in conjunction with Fig. 2, realize the device of the described a kind of high ferro rail defects and failures detection method based on vibration signal of embodiment one, its involving vibrations acceleration transducer 1, signaling conversion circuit 2, empirical modal decomposing module 3, hurt characteristic information extracting module 4, the hurt position determination module 6 of the power density acquisition module 5 of hurt characteristic information and high ferro rail, be arranged on the hurt information of the vibration acceleration sensor 1 detection high ferro rail on the high ferro rail, and export to signaling conversion circuit 2, signaling conversion circuit 2 is that the input hurt vibration signal of digital form is x (t) and exports to empirical modal decomposing module 3 with the analog signal conversion that receives, it is that x (t) handles that empirical modal decomposing module 3 will be imported the hurt vibration signal, n the IMF component that obtains exported to hurt characteristic information extracting module 4, the IMF component that hurt characteristic information extracting module 4 will satisfy the hurt condition is exported to the power density acquisition module 5 of hurt characteristic information, the power density acquisition module 5 of hurt characteristic information calculates each power density that comprises the IMF component of hurt feature, and export to the hurt position determination module 6 of high ferro rail, the hurt position determination module 6 of high ferro rail is judged the hurt position of tested high ferro rail according to the power density of each IMF component.
Embodiment nine: below in conjunction with Fig. 2, Fig. 6 to Figure 11 present embodiment is described, present embodiment provides a specific embodiment:
The embodiment of the present invention proposes a kind of high ferro rail defects and failures sniffer based on vibration signal as shown in Figure 2, comprises following components: vibration acceleration sensor, signaling conversion circuit, and microcomputer and corresponding interface circuits thereof.Each several part specific implementation structure and principle of work are described as follows:
At first, rail structural vibration is the result of rolling stock to the rail dynamic action, adopts vibration acceleration sensor that it is measured.The model of sensor is: the ULT2003/v piezoelectric acceleration sensor of ULT20 series; The model of rail is: the 60Kg/m rail.In the present embodiment layout of sensor as shown in Figure 4, sensor is positioned at flange of rail upper surface apart from flange of rail outward flange 20mm place.Through experiment repeatedly, this point can well record the vibration signal (hurt lays respectively at rail head, the web of the rail and the flange of rail such as Fig. 5) at different rail defects and failureses position and distinguish hurt characteristic information between them.The vibration signal that occurs in rail under rail head, the web of the rail and three kinds of diverse location situations of the flange of rail at not damaged and hurt is measured, and has obtained 4 groups of measurement data and has been used for analyzing.
Secondly, the vibration signal after the measurement is sampled by signaling conversion circuit, and the model of conversion chip is: AD9071 is one 10, the 100MSPS modulus conversion chip.Adopt the 100M high-speed sampling, can obtain more detailed information.
At last, the signal after the conversion is sent to microcomputer by wired or wireless mode carry out extraction of hurt signal characteristic and hurt coupling, set up the hurt judgment criterion.The microcomputer model is: the ThinkpadR400 notebook.
Below in conjunction with embodiment and description of drawings detection method embodiment of the present invention:
Execution in step one: the vibration signal at the different hurt positions of the rail that records (hurt is positioned at rail head, the web of the rail and three kinds of positions of the flange of rail) and not damaged vibration signal x (t) are carried out EMD respectively decompose, obtain each rank IMF and residual error of each signal, be designated as: c 1, c 2... c nAnd r.Vibration signal with the not damaged rail is an example, and each rank IMF after the decomposition and residual error are as shown in Figure 6.In like manner, can obtain each rank IMF and residual error after other three kinds of signal decomposition.
Execution in step two: n IMF after calculating three kinds of hurt situations and lossless case vibration signal respectively and decomposing is with its undecomposed relative coefficient u of signal before separately iEqually, be example with the vibration signal of not damaged rail, its n IMF with it undecomposed before relative coefficient of signal see Table 1.In like manner, can obtain other three kinds of signal n separately IMF with its undecomposed separately relative coefficient of signal before.
Each rank IMF of vibration signal of the harmless rail of table 1 is with its undecomposed relative coefficient of signal before
IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7
Relative coefficient 0.8798 0.3866 0.0525 0.0202 0.0126 0.0104 0.0075
Choose λ=0.5, so just the IMF that can select correlativity to comprise the hurt characteristic information more by force analyzes.Satisfy u as can be seen from Table 1 i＞λ=0.5 be 0.8798, the correlativity of other IMF a little less than, therefore select IMF1 to analyze.In like manner, other three kinds of hurt signals are carried out the IMF correlation analysis and select the bigger IMF of correlativity.In this example by after selecting all being the IMF1 after the signal decomposition separately.
Execution in step three: utilize power spectrum density that the IMF1 of four kinds of different hurt situations of gained in the step 2 is calculated, obtain the PSD of (hurt of rail head, the web of the rail and the flange of rail and not damaged) vibration signal under the different degree of impairments, under different hurt situations, draw the frequency range and the feature of rail defects and failures the contrast of PSD figure, draw judgment criterion at last.
Rail vibration signal with no hurt is an example, IMF1 and PSD such as Fig. 7 after it decomposes.As can be seen from Figure 7, the frequency range of no hurt rail vibration signal is 0-4000Hz.The higher frequency of amplitude is 1840Hz and 3360Hz, and this is the natural vibration frequency of harmless rail.In like manner, the PSD of the IMF1 of rail head, three kinds of different hurts of the flange of rail and web of the rail position rail vibration signal is respectively as Fig. 8, Fig. 9 and Figure 10.At last, the PSD with four kinds of situations is incorporated among the width of cloth figure, as Figure 11.
In Figure 11, be respectively harmless from top to bottom, the PSD figure that hinders at rail head, the flange of rail and the web of the rail, the PSD contrast from figure has all comprised the natural vibration frequency of former harmless rail as can be seen among the PSD of these signals.Simultaneously, from hindering among the PSD of rail head, the flange of rail and web of the rail figure, the frequency range of finding out the hurt signal is 3580Hz-3920Hz.3880Hz is the characteristic frequency that wound is arranged of rail head, and its amplitude approaches the amplitude of harmless rail characteristic frequency 3360Hz.When hurt occurred in the flange of rail, its characteristic frequency equally also was to approach 3880Hz, occurred in 3 times of characteristic frequency of rail head but its amplitude is a hurt.3600Hz is the characteristic frequency of hurt when occurring in the web of the rail, and its amplitude is to hinder 4 times of the frequency that occurs in the flange of rail.From above rule, as can be seen, do not have to hinder and the characteristic frequency and the characteristic rule thereof of the signal of hurt when occurring in three kinds of diverse locations.Therefore, we can set up the existence of corresponding hurt and the judgment criterion of position thereof by above rule.Promptly by the EMD of vibration signal is decomposed, extract the IMF that comprises the hurt characteristic information and it is carried out PSD calculate, observe its characteristic frequency and determined whether to take place the position of hurt and generation thereof from PSD figure then.Scheme as can be seen from the PSD of this example, well identified the characteristic frequency of diverse location hurt, determined the position that hurt takes place.
To sum up, hurt flaw detection for the high ferro rail, the present invention has provided detailed method and the device thereof that utilizes vibration signal to set up the hurt judgment criterion, can realize real-time detection preferably to whole highway section, determine the existence and the position thereof of hurt, improved flaw detection efficient, to guarantee the normal operation of bullet train.

Claims (10)

1. high ferro rail defects and failures detection method based on vibration signal is characterized in that this method may further comprise the steps:
Step 1, gather the vibration signal of high ferro rail, and described vibration signal is carried out empirical modal decompose, obtain n IMF component and a residual error by vibration acceleration sensor; IMF is interior solid model state function;
N IMF component that step 2, calculation procedure one are obtained and the relative coefficient u that decomposes the vibration signal that collects before i(i=1,2 ..., n), choose relative coefficient u iGreater than the IMF component of threshold value λ as hurt characteristic information IMF;
Step 3, the power spectrum density of the hurt characteristic information IMF that obtains of calculation procedure two respectively,
The power spectrum density of step 4, the hurt characteristic information IMF that obtains according to step 3 is determined high ferro rail defects and failures position, finishes the detection of high ferro rail defects and failures.
2. a kind of high ferro rail defects and failures detection method according to claim 1 based on vibration signal, it is characterized in that, gather the vibration signal of high ferro rail in the step 1 by vibration acceleration sensor, vibration acceleration sensor is arranged on the flange of rail upper surface of high ferro rail, and apart from flange of rail outward flange d distance, the span of described d is 18mm～20mm.
3. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 1 is characterized in that the process of obtaining a n IMF component and a residual error in the step 1 is:
Setting input hurt vibration signal is x (t), t=1, and 2 ..., N,
Step 11, IMF decomposable process initialization: n=1, and satisfy relational expression r N-1(t)=x (t) establishment, wherein r N-1(t) be trend function after (n-1) inferior decomposition;
Step 12, screening process initialization, k=1, and satisfy relational expression h N (k-1)(t)=r N-1(t) set up, wherein h N (k-1)(t) be through the survival function after (k-1) inferior screening during the n time empirical modal decomposes;
Step 13, according to screening sequence obtain through the k time the screening after survival function h Nk(t);
Step 14, the survival function h that adopts standard deviation criterion determining step 13 to obtain Nk(t) whether satisfy the condition of eigenmode state function IMF, promptly
Figure FDA0000047516990000011
Whether less than threshold value H SD, 0.2≤H SD≤ 0.3;
Judged result is for being, execution in step 15, judged result be not for, k=k+1 then, and execution in step 13 then,
Step 15, eigenmode state function component c of extraction n(t)=h Nk(t);
Step 16, obtain the trend function r of input hurt vibration signal x (t) through the decomposition of the n time empirical modal n(t)=r N-1(t)-c n(t);
Step 17, judgement trend function r n(t) whether be monotonic quantity;
Judged result is not for, n=n+1 then, and execution in step 12 then, and judged result is finished leaching process for being, obtains n IMF component { c 1(t), c 2(t) ... c n(t) }; With 1 residual error RES:r n(t).
4. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 3 is characterized in that, obtains survival function h in the step 13 Nk(t) process is:
Step 131, utilize cubic spline function to obtain survival function h after input hurt vibration signal x (t) screens through the k-1 time in decomposing through the n time empirical modal N (k-1)(t) upper and lower envelope,
Step 132, the described survival function h of calculating N (k-1)(t) upper and lower enveloping curve is in the average of each t
Step 133, obtain the survival function after input hurt vibration signal x (t) screens through the k time in decomposing through the n time empirical modal
Figure FDA0000047516990000022
5. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 3 is characterized in that the middle H of step 14 SD=0.25.
6. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 1 is characterized in that the relative coefficient u in the step 2 iObtain by following formula:
u i = IMF n x ( t ) ,
Wherein, IMF nFor carrying out empirical modal, vibration signal decomposes n the IMF component that obtains.
7. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 1 is characterized in that the threshold value λ in the step 2=0.4～0.6.
8. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 1 is characterized in that the power spectrum density of the hurt characteristic information IMF in the step 3 is obtained by following formula:
S ( f ) = ∫ - ∞ ∞ R ( τ ) e - 2 πifτ dτ ,
Wherein, R (τ) is for comprising the autocorrelation function of hurt characteristic information IMF.
9. a kind of high ferro rail defects and failures detection method based on vibration signal according to claim 1 is characterized in that, determines in the step 4 that the judgment criterion at high ferro rail defects and failures position is:
As if having frequency in the power spectral density plot that comprises hurt characteristic information IMF is the Frequency point of 3880Hz ± 100Hz, and the amplitude that this Frequency point amplitude is in harmless rail characteristic frequency 3360Hz fluctuates in 5% scope, and then being judged to be high ferro rail defects and failures position is rail head;
As if having frequency in the power spectral density plot that comprises hurt characteristic information IMF is the Frequency point of 3880Hz ± 100Hz, and this Frequency point amplitude is in 300%～350% scope of the amplitude that can't harm rail characteristic frequency 3360Hz, and then being judged to be high ferro rail defects and failures position is the flange of rail;
As if there being frequency in the power spectral density plot that comprises hurt characteristic information IMF is that then being judged to be high ferro rail defects and failures position is the web of the rail in 1200%～1300% scope of the Frequency point of 3600Hz ± 100Hz and the amplitude that this Frequency point amplitude is in harmless rail characteristic frequency 3360Hz.
10. realize the device of the described a kind of high ferro rail defects and failures detection method based on vibration signal of claim 1, it is characterized in that, its involving vibrations acceleration transducer (1), signaling conversion circuit (2), empirical modal decomposing module (3), hurt characteristic information extracting module (4), the power density acquisition module (5) of hurt characteristic information and the hurt position determination module (6) of high ferro rail, be arranged on the hurt information of vibration acceleration sensor (1) the detection high ferro rail on the high ferro rail, and export to signaling conversion circuit (2), signaling conversion circuit (2) is that the input hurt vibration signal of digital form is x (t) and exports to empirical modal decomposing module (3) with the analog signal conversion that receives, it is that x (t) handles that empirical modal decomposing module (3) will be imported the hurt vibration signal, n the IMF component that obtains exported to hurt characteristic information extracting module (4), the IMF component that hurt characteristic information extracting module (4) will satisfy the hurt condition is exported to the power density acquisition module (5) of hurt characteristic information, the power density acquisition module (5) of hurt characteristic information calculates the power density that each comprises the IMF component of hurt feature, and export to the hurt position determination module (6) of high ferro rail, the hurt position determination module (6) of high ferro rail is judged the hurt position of tested high ferro rail according to the power density of each IMF component.
CN2011100428985A 2011-02-22 2011-02-22 Method and device for detecting defects and failures of high-speed rail based on vibration signals Pending CN102175768A (en)

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CN103197001A (en) * 2013-03-13 2013-07-10 西南交通大学 High speed turnout injury identification method based on vibration signal wavelet threshold value denoising
CN103226132A (en) * 2013-04-25 2013-07-31 哈尔滨工业大学 High speed railway steel rail flaw detection experiment platform and detection method
CN103335617A (en) * 2013-06-19 2013-10-02 清华大学 Vibrational signal based railway track geometric deformation detection method
CN103472134A (en) * 2013-08-05 2013-12-25 河北省自动化研究所 Method for detecting steel rail fracture point by using sound
CN103808801A (en) * 2014-03-14 2014-05-21 哈尔滨工业大学 Real-time detection method for high-speed rail injury based on vibration and audio composite signals
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CN104309642A (en) * 2014-11-21 2015-01-28 成都光大灵曦科技发展股份有限公司 High-speed turnout crack monitoring device and method based on vibration mode analysis technology
CN104634872A (en) * 2015-01-10 2015-05-20 哈尔滨工业大学（威海） Online high-speed railway steel rail damage monitoring method
CN104699948A (en) * 2015-01-15 2015-06-10 东风朝阳朝柴动力有限公司 EMD (empirical mode decomposition) based computation method for recognizing sensitivity of signal source
CN105203645A (en) * 2015-06-08 2015-12-30 中国铁路总公司 Intelligent detection method of high-speed turnout crack damage based on vibration signal fusion
CN105740840A (en) * 2016-02-29 2016-07-06 中南大学 Nonlinear identification method for rock fracture signal and blasting vibration signal
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CN106289781A (en) * 2016-10-19 2017-01-04 南京拓控信息科技股份有限公司 Railroad vehicle bearing fault on-line measuring device and method
CN106314477A (en) * 2016-09-28 2017-01-11 成都奥克特科技有限公司 Track state online monitoring method based on vibration monitoring
CN107230176A (en) * 2017-05-12 2017-10-03 东南大学 A kind of history resource points Relational Evaluation method based on envelope chart analysis
CN106052606B (en) * 2016-05-27 2018-06-12 南京理工大学 A kind of raceway surface recess detection method for the Wavelet Energy Spectrum that is averaged based on scale
CN109030628A (en) * 2018-06-25 2018-12-18 北京交通大学 Based on the matched track structure Evaluation Method of Mechanical Property of vibration noise spectrum signature
CN110084185A (en) * 2019-04-25 2019-08-02 西南交通大学 A kind of bullet train slightly crawls the rapid extracting method of operation characteristic
CN110412125A (en) * 2019-06-21 2019-11-05 中国铁道科学研究院集团有限公司铁道建筑研究所 A method of identification railway track structure modal characteristics
CN110514294A (en) * 2019-08-30 2019-11-29 鞍钢矿业爆破有限公司 A kind of blasting vibration signal noise-reduction method based on EMD and VMD
CN111368642A (en) * 2020-02-11 2020-07-03 北京交通大学 Method for identifying modal frequency of railway ballastless track steel rail based on wheel-rail excitation
CN111896625A (en) * 2020-08-17 2020-11-06 中南大学 Real-time monitoring method and monitoring system for rail damage
CN112394403A (en) * 2020-11-03 2021-02-23 华北电力大学 Microseismic event detection method and system by using edge detection
CN112762809A (en) * 2020-12-31 2021-05-07 西南交通大学 Method for detecting gap fault of seamed steel rail
CN113188651A (en) * 2021-04-20 2021-07-30 北京航空航天大学 Vibration signal feature extraction and tool wear value prediction method based on IMF-PSD spectral line
CN114166943A (en) * 2021-12-03 2022-03-11 四川西南交大铁路发展股份有限公司 Active rail damage monitoring method and terminal based on nonlinear ultrasonic waves
CN115384580A (en) * 2021-05-24 2022-11-25 北京全路通信信号研究设计院集团有限公司 Steel rail online detection method and system
CN117269701A (en) * 2023-11-21 2023-12-22 川力电气有限公司 High-voltage switch cabinet partial discharge positioning method based on artificial intelligence

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CN103847761B (en) * 2012-11-30 2016-06-29 建维科技（深圳）有限公司 A kind of system and method for monitoring rail crack and damage in real time
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CN103226132A (en) * 2013-04-25 2013-07-31 哈尔滨工业大学 High speed railway steel rail flaw detection experiment platform and detection method
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CN104309642A (en) * 2014-11-21 2015-01-28 成都光大灵曦科技发展股份有限公司 High-speed turnout crack monitoring device and method based on vibration mode analysis technology
CN104634872A (en) * 2015-01-10 2015-05-20 哈尔滨工业大学（威海） Online high-speed railway steel rail damage monitoring method
CN104699948A (en) * 2015-01-15 2015-06-10 东风朝阳朝柴动力有限公司 EMD (empirical mode decomposition) based computation method for recognizing sensitivity of signal source
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CN105740840A (en) * 2016-02-29 2016-07-06 中南大学 Nonlinear identification method for rock fracture signal and blasting vibration signal
CN106052606B (en) * 2016-05-27 2018-06-12 南京理工大学 A kind of raceway surface recess detection method for the Wavelet Energy Spectrum that is averaged based on scale
CN106248801A (en) * 2016-09-06 2016-12-21 哈尔滨工业大学 A kind of Rail crack detection method based on many acoustie emission events probability
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