CN103994815A - Method for recognizing optical fiber vibration source - Google Patents

Abstract

The invention discloses a method for recognizing an optical fiber vibration source. The method includes the steps that vibration signals of optical fibers are detected, and vibration data of vibration signals are acquired; energy of the vibration signals and energy distribution are acquired from the vibration data; subintervals are partitioned according to energy distribution, and the probability of energy distribution of the subintervals is calculated; an energy distribution entropy model is built according to the probability of energy distribution of the subintervals, and a normalization entropy value is calculated; fundamental frequency variable coefficients are calculated, and the vibration source of the vibration signals are recognized according to the normalization entropy value and the fundamental frequency variable coefficients. By the adoption of the mode, the vibration source of the vibration signals can be accurately recognized.

Description

A kind of method of identifying optical fiber vibration source

Technical field

The present invention relates to a kind of method of identifying optical fiber vibration source, belong to fiber-optic vibration and measure the crossing domain of processing subject with random signal.

Background technology

People are increasing for the demand of the energy, and pipeline is carried becomes the major way of carrying the energy.Protection becomes with oil and gas pipes, optical cable near zone or the optical cable itself of optical cable companion row the key issue that current predispersed fiber alarm system need to solve.

Existing predispersed fiber alarm system generally adopts single-stage model to carry out vibration source and detects identification, be direct-detection method of identification, by analyzing the feature of one piece of data, the type that judges vibration source according to feature is (as machinery excavates, vehicle process), and then make early warning judgement according to recognition result.Existing predispersed fiber alarm system is identified by neural network, normalization kurtosis, intrinsic mode function or Chaotic Analysis Method, is mainly used in the situation of high s/n ratio.But the vibration signal that vibration source produces is non-stationary signal complicated and changeable, and pipeline distance and real time environment complexity, causes existing predispersed fiber alarm system cannot identify exactly vibration source.

Summary of the invention

The invention provides a kind of method of identifying optical fiber vibration source, to solve the problem that cannot identify exactly vibration source.

A kind of method of identifying optical fiber vibration source provided by the invention, it comprises: the vibration signal of detection fiber, and obtain the vibration data of vibration signal; Obtain energy and the energy distribution of vibration signal from vibration data; Divide sub-range according to energy distribution, and calculate the probability of each sub-range energy distribution; Set up the Distribution Entropy model of energy according to the probability of sub-range energy distribution, and calculate normalization entropy; Calculate the fundamental frequency coefficient of variation, according to the vibration source of normalization entropy and fundamental frequency coefficient of variation identification vibration signal.

Wherein, the energy of vibration signal is:

$E_s=10\log \left(\underset{i=1}{\overset{1024}{\mathrm{\Σ}}}{x}_i^2\right)$

Wherein, E _sfor the energy of every frame vibration signal, unit is dB; x _iit is the amplitude of the vibration signal of i sampled point.

Wherein, the Distribution Entropy model of energy is:

$H_E\left(X\right)=E\left(I\left(X\right)\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}P\left(x_i\right)I\left(x_i\right)=-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}P\left(x_i\right)\log p\left(x_i\right);$

Wherein, the energy domain that X is vibration signal, the quantity of information function that I (X) is X, p is the probability density function of energy x.

Wherein, vibration source generation vibration signal at the Distribution Entropy model of one minute vibration data energy is:

$H\left(X\right)=-\underset{i=1}{\overset{m\×n}{\mathrm{\Σ}}}p\left(x_i\right){\log }_2\left(p\left(x_i\right)\right).$

Wherein, set p (x _i) log _bp (x _i)=0 according to Shannon's theorems, occurs that maximum entropy is in the time that the sub-range of energy probability equates:

$H_{\max }=-\underset{i=1}{\overset{m\×n}{\mathrm{\Σ}}}p\left(x_i\right)\mathrm{lo}{g}_{b}p\left(x_i\right)={\log }_b\left(\mathrm{mn}\right).$

Wherein, normalization entropy is:

$\hat{H}\left(X\right)=\frac{H\left(X\right)}{H_{\max }}.$

Wherein, the method also comprises: set up the Recognition of Vibration Sources model of vibration signal, the Recognition of Vibration Sources model of vibration signal meets following relation:

$V\left(m\right)=\frac{E\left\{\right[X_n-E\left(X_n\right)\left]\right[X_{n-m}-E\left(X_{n-m}\right)\left]\right\}}{\sqrt{D\left(X_n\right)}*\sqrt{D\left(X_{n-m}\right)}};$

Wherein, X _nfor the data sequence of vibration signal, X _n-mfor sequence X _nthe data sequence of the vibration signal that time delay m is ordered, E{[X _n-E (X _n)] [X _n-m-E (X _n-m)] be sequence X _nwith sequence X _n-mcovariance, D (X _n) be sequence X _nvariance, D (X _n-m) be sequence X _n-mvariance.

Wherein, the fundamental frequency coefficient of variation is:

$\mathrm{cv}=\frac{\mathrm{\σ}}{\left|\mathrm{\μ}\right|};$

$\mathrm{\σ}=\sqrt{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_i-\mathrm{\μ})};$

$\mathrm{\μ}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i;$

Wherein, N is observed reading quantity, x _iit is the amplitude of the vibration signal of i sampled point; The fundamental frequency coefficient of variation is a dimensionless number, for describing significantly different overall discreteness of average, and the unit of elimination or the impact of average difference on the comparison of two or more data degree of variation, to add up the parameter of vibration source.

Wherein, comprise according to the vibration source of normalization entropy and fundamental frequency coefficient of variation identification vibration signal: judge whether the fundamental frequency coefficient of variation is less than the 3rd default threshold value; If not, the vibration source of vibration signal is non-rainy vibration source; If so, judge whether normalization entropy is greater than the 4th default threshold value; If so, the vibration source of vibration signal is the vibration source that rains; If not, the vibration source of vibration signal is non-rainy vibration source; Wherein, the 3rd threshold value is for setting coefficient of variation threshold value, and the 4th threshold value is for setting coefficient of variation threshold value.

By such scheme, the invention has the beneficial effects as follows: the present invention, by according to the vibration source of normalization entropy and fundamental frequency coefficient of variation identification vibration signal, can accurately identify the vibration source of vibration signal.

Brief description of the drawings

In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing of required use during embodiment is described is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.Wherein:

Fig. 1 is the process flow diagram of the method for the identification optical fiber vibration source of first embodiment of the invention;

Fig. 2 is the process flow diagram of the method for the identification optical fiber vibration source of second embodiment of the invention;

Fig. 3 is the process flow diagram of the method for the identification optical fiber vibration source of third embodiment of the invention;

Fig. 4 is the process flow diagram of the method for the identification optical fiber vibration source of fourth embodiment of the invention;

Fig. 5 is that vibration source is to touch the testing result of optic cable vibration data in 16 minutes;

Fig. 6 is that vibration source is the testing result of mechanical execution vibration data in 16 minutes;

Fig. 7 is that vibration source is to knock the testing result of optical cable well in 16 minutes;

Fig. 8 is that vibration source is that train is through the testing result in 16 minutes;

Fig. 9 is that vibration source is that rain signal is through the testing result in 16 minutes;

Figure 10 is that vibration source is the testing result of signal in 16 minutes of dripping.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtaining under performing creative labour prerequisite, belong to the scope of protection of the invention.

Shown in Figure 1, Fig. 1 is the process flow diagram of the method for the identification optical fiber vibration source of first embodiment of the invention.The method of the identification optical fiber vibration source that as shown in Figure 1, the present embodiment discloses comprises:

S101: the vibration signal of detection fiber;

S102: set up the Recognition of Vibration Sources model of vibration signal, and obtain the fundamental frequency coefficient of variation of Recognition of Vibration Sources model;

S103: according to the vibration source of fundamental frequency coefficient of variation identification vibration signal, accurately to identify the vibration source of vibration signal;

S104: extract the vibration data of vibration signal, and obtain the energy of vibration signal from vibration data;

S105: the Distribution Entropy model of setting up energy according to information entropy.

In S102, the Recognition of Vibration Sources model of vibration signal meets following relation:

$V\left(m\right)=\frac{E\left\{\right[X_n-E\left(X_n\right)\left]\right[X_{n-m}-E\left(X_{n-m}\right)\left]\right\}}{\sqrt{D\left(X_n\right)}*\sqrt{D\left(X_{n-m}\right)}}---\left(1\right)$

Wherein, X _nfor the data sequence of vibration signal, X _n-mfor sequence X _nthe data sequence of the vibration signal that time delay m is ordered, E{[X _n-E (X _n)] [X _n-m-E (X _n-m)] be sequence X _nwith sequence X _n-mcovariance, D (X _n) be sequence X _nvariance, D (X _n-m) be sequence X _n-mvariance.

The fundamental frequency coefficient of variation of Recognition of Vibration Sources model is:

$\mathrm{cv}=\frac{\mathrm{\σ}}{\left|\mathrm{\μ}\right|}---\left(2\right)$

$\mathrm{\σ}=\sqrt{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_i-\mathrm{\μ})}---\left(3\right)$

$\mathrm{\μ}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i---\left(4\right)$

Wherein, N is observed reading quantity, x _iit is the amplitude of the vibration signal of i sampled point; The fundamental frequency coefficient of variation is a dimensionless number, for describing significantly different overall discreteness of average, and the unit of elimination or the impact of average difference on the comparison of two or more data degree of variation, to add up the parameter of vibration source.

In S105, the Distribution Entropy model H of energy _e(X) be:

$H_E\left(X\right)=E\left(I\left(X\right)\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}P\left(x_i\right)I\left(x_i\right)=-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}P\left(x_i\right)\log p\left(x_i\right)---\left(5\right)$

Wherein, the energy domain that X is vibration signal, the quantity of information function that I (X) is X, p is the probability density function of energy x.

Vibration source produces vibration signal:

$H\left(X\right)=-\underset{i=1}{\overset{m\×n}{\mathrm{\Σ}}}p\left(x_i\right){\log }_2\left(p\left(x_i\right)\right)---\left(6\right)$

Set p (x _i) log _bp (x _i)=0 according to Shannon's theorems, occurs that maximum entropy is in the time that the sub-range of energy probability equates:

$H_{\max }=-\underset{i=1}{\overset{m\×n}{\mathrm{\Σ}}}p\left(x_i\right)\mathrm{lo}{g}_{b}p\left(x_i\right)={\log }_b\left(\mathrm{mn}\right)---\left(7\right)$

,, can obtain to facilitate calculating the entropy normalization of per minute according to maximum entropy:

$\hat{H}\left(X\right)=\frac{H\left(X\right)}{H_{\max }}---\left(8\right)$

The present embodiment passes through according to the vibration source of fundamental frequency coefficient of variation identification vibration signal, and sets up the Distribution Entropy model of energy according to information entropy, can accurately identify the vibration source of vibration signal.

Shown in Figure 2, Fig. 2 is the process flow diagram of the method for the identification optical fiber vibration source of second embodiment of the invention.The recognition methods that the present embodiment discloses comprises:

S201: the vibration signal of detection fiber, obtains the vibration data of vibration signal;

S202: vibration data is sampled and auto-correlation processing in short-term;

S203: coefficient of autocorrelation threshold value is set;

S204: calculate the number N of the extreme point that is greater than coefficient of autocorrelation threshold value, and record the time interval T of adjacent two extreme points;

S205: whether the number N that judges extreme point is greater than default first threshold N0; If so, enter S206; If not, enter S210;

S206: judge that whether the minimum time Tmin between the extreme point of coefficient of autocorrelation threshold value is greater than default Second Threshold T0, if so, enters S207; If not, return to S203, and increase coefficient of autocorrelation threshold value;

S207: computing time interval T fundamental frequency coefficient of variation cv;

S208: judge that whether fundamental frequency coefficient of variation cv is less than the 3rd default threshold value cv0, if so, enters S209; If not, enter S210;

S209: the vibration source of vibration signal is mechanical vibration source;

S210: the vibration source of vibration signal is on-mechanical vibration source, in the time that the duration is greater than 4s, the vibration source of vibration signal is train vibration source.

In S202, vibration data is carried out to sampling processing, extract fundamental frequency information from vibration data, get the vibration data that length is 20 frames, front 10 frame data territory remainder datas carry out relevant treatment, and the data length of at every turn participating in computing is 10 frames, and meet formula (1).Wherein, X _nfor length is the data sequence of the vibration signal of 10 frames; X _n-mfor sequence X _ntime delay m point, length is the data sequence of the vibration signal of 10 frames.

Vibration data is carried out to auto-correlation processing in short-term, vibration data is carried out to 10 times of down-sampled processing, down-sampled formula is:

x‘(n)＝x(m) (9)

Wherein, m=10n, n=1,2,3 ...Vibration data after down-sampled is carried out to auto-correlation processing in short-term, can obtain the coefficient of autocorrelation in short-term of vibration data.

In S203, the initial value of coefficient of autocorrelation threshold value is 0.3.

In S205, first threshold N0 is preferably setting value.

In S206, Second Threshold T0 is preferably Fixed Time Interval.

In S207, according to the fundamental frequency coefficient of variation cv of formula (2), (3), (4) interval T computing time.

In S208, the 3rd threshold value cv0 is preferably and sets coefficient of variation threshold value.

The vibration source that the present embodiment can accurately be identified vibration signal is mechanical vibration source or on-mechanical vibration source.

Shown in Figure 3, Fig. 3 is the process flow diagram of the method for the identification optical fiber vibration source of third embodiment of the invention.The recognition methods that the present embodiment discloses comprises:

S301: the vibration signal of detection fiber, obtains the vibration data of vibration signal;

S302: energy and the energy distribution of obtaining vibration signal from vibration data;

S303: divide sub-range according to energy distribution;

S304: the Probability p (x that calculates each sub-range energy distribution _i);

S305: calculate the normalization entropy of i minute

S306: the fundamental frequency coefficient of variation cv of T0 minute before calculating;

S307: judge that whether fundamental frequency coefficient of variation cv is less than the 3rd default threshold value cv0, if so, enters S308; If not, enter S310;

S308: before judgement, whether the normalization entropy of T0 minute is greater than the 4th default threshold value H0, if so, enters S309; Enter if not S310;

S309: the vibration source of vibration signal is the vibration source that rains;

S310: the vibration source of vibration signal is non-rainy vibration source.

In S302, obtain the energy of vibration signal from vibration data, calculate the energy of vibration signal, wherein the energy of every frame vibration signal is:

$E_s=10\log \left(\underset{i=1}{\overset{1024}{\mathrm{\Σ}}}{x}_i^2\right)---\left(10\right)$

Wherein, E _sfor the energy of every frame vibration signal, unit is dB; x _iit is the amplitude of the vibration signal of i sampled point.

In S305, calculate the normalization entropy of i minute according to formula (5), (6), (7), (8)

In S306, the fundamental frequency coefficient of variation cv of T0 minute before calculating according to formula (2), (3), (4), wherein the fundamental frequency coefficient of variation is less, illustrates that measured value degree of variation is less, also more stable.

In S308, the 4th threshold value H0 is preferably setting data energy average information entropy.

The vibration source that the present embodiment can accurately be identified vibration signal is vibration source or the non-rainy vibration source of raining.

Shown in Figure 4, Fig. 4 is the process flow diagram of the method for the identification optical fiber vibration source of fourth embodiment of the invention.The recognition methods that the present embodiment discloses comprises:

S401: the vibration signal of detection fiber;

S402: the Distribution Entropy model of setting up energy;

S403: whether the energy that judges vibration signal possesses three rainy features, if so, enters S404; If not, enter S405;

S404: vibration source is the vibration source that rains;

S405: judge whether to there is high-energy, if so, enter S406; If not, enter S407;

S406: vibration source is knocking;

S407: whether the duration that judges continuous signal is greater than 4s, if so, enters S408; If not, enter S411;

S408: judge whether fundamental frequency coefficient of variation cv is greater than 0.1, if so, enters S409; If not, enter S410;

S409: vibration source is train;

S410: vibration source Wei Po road signal;

S411: the vibration data of vibration signal is carried out to auto-correlation processing in short-term, and vibration source is for knocking road surface.

In S402, set up the Distribution Entropy model of energy according to formula (5), (6), (7), (8).

In S403, three that rain are characterized as: uniformly, when temporal evolution, stably, and exist for a long time.

In S408, obtain fundamental frequency coefficient of variation cv according to formula (2), (3), (4).

In S411, the vibration data of vibration signal is carried out to auto-correlation processing is identical with S202 in short-term.

Fig. 5-Figure 10 is the testing result obtaining according to disclosed recognition methods.

For current common vibration source type, system all can accurately be identified, and has improved the performance of optical fiber safety early-warning system.Simultaneously, threaten degree according to different vibration sources to safe early warning region, according to the priority of setting vibration source early warning, vibration source priority by height be followed successively by earth touch optical cable, mechanical execution, knock optical cable well, train passes through, rains, drips, represent by numbering 6～1 respectively, wherein 6 represent that warning level is the highest, i.e. the threaten degree maximum of such vibration source to system, and 1 expression is little to system threaten degree.

In sum, the present invention is by identifying the vibration source of vibration signal according to the fundamental frequency coefficient of variation, and the vibration source that can accurately identify vibration signal is mechanical vibration source or on-mechanical vibration source; And by set up the Distribution Entropy model of energy according to information entropy, the vibration source that can accurately identify vibration signal is vibration source or the non-rainy vibration source of raining, and then can accurately identify the vibration source of vibration signal.

These are only embodiments of the present invention; not thereby limit the scope of the claims of the present invention; every equivalent structure or conversion of equivalent flow process that utilizes instructions of the present invention and accompanying drawing content to do; or be directly or indirectly used in other relevant technical fields, be all in like manner included in scope of patent protection of the present invention.

Claims (9)

1. a method of identifying optical fiber vibration source, is characterized in that, described method comprises:

The vibration signal of detection fiber, and obtain the vibration data of described vibration signal;

Obtain energy and the energy distribution of described vibration signal from described vibration data;

Divide sub-range according to described energy distribution, and calculate the probability of sub-range energy distribution described in each;

Set up the Distribution Entropy model of described energy according to the probability of described sub-range energy distribution, and calculate normalization entropy;

Calculate the fundamental frequency coefficient of variation, identify the vibration source of described vibration signal according to described normalization entropy and the described fundamental frequency coefficient of variation.

2. method according to claim 1, is characterized in that, the energy of described vibration signal is:

$E_s=10\log \left(\underset{i=1}{\overset{1024}{\mathrm{\Σ}}}{x}_i^2\right)$

Wherein, E _sfor the energy of every frame vibration signal, unit is dB; x _iit is the amplitude of the vibration signal of i sampled point.

3. method according to claim 2, is characterized in that, the Distribution Entropy model of described energy is:

$H_E\left(X\right)=E\left(I\left(X\right)\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}P\left(x_i\right)I\left(x_i\right)=-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}P\left(x_i\right)\log p\left(x_i\right);$

Wherein, the energy domain that X is described vibration signal, the quantity of information function that I (X) is X, p is the probability density function of energy x.

4. method according to claim 3, is characterized in that, described vibration source produces described vibration signal and at the Distribution Entropy model of one minute vibration data energy is:

$H\left(X\right)=-\underset{i=1}{\overset{m\×n}{\mathrm{\Σ}}}p\left(x_i\right){\log }_2\left(p\left(x_i\right)\right).$

5. method according to claim 4, is characterized in that, sets p (x _i) log _bp (x _i)=0 according to Shannon's theorems, occurs that maximum entropy is in the time that the sub-range of described energy probability equates:

$H_{\max }=-\underset{i=1}{\overset{m\×n}{\mathrm{\Σ}}}p\left(x_i\right)\mathrm{lo}{g}_{b}p\left(x_i\right)={\log }_b\left(\mathrm{mn}\right).$

6. method according to claim 5, is characterized in that, described normalization entropy is:

$\hat{H}\left(X\right)=\frac{H\left(X\right)}{H_{\max }}.$

7. method according to claim 6, is characterized in that, described method also comprises:

Set up the Recognition of Vibration Sources model of described vibration signal, the Recognition of Vibration Sources model of described vibration signal meets following relation:

$V\left(m\right)=\frac{E\left\{\right[X_n-E\left(X_n\right)\left]\right[X_{n-m}-E\left(X_{n-m}\right)\left]\right\}}{\sqrt{D\left(X_n\right)}*\sqrt{D\left(X_{n-m}\right)}};$

Wherein, X _nfor the data sequence of described vibration signal, X _n-mfor described sequence X _nthe data sequence of the vibration signal that time delay m is ordered, E{[X _n-E (X _n)] [X _n-m-E (X _n-m)] be described sequence X _nwith described sequence X _n-mcovariance, D (X _n) be described sequence X _nvariance, D (X _n-m) be described sequence X _n-mvariance.

8. method according to claim 7, is characterized in that, the described fundamental frequency coefficient of variation is:

$\mathrm{cv}=\frac{\mathrm{\σ}}{\left|\mathrm{\μ}\right|};$

$\mathrm{\σ}=\sqrt{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_i-\mathrm{\μ})};$

$\mathrm{\μ}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i;$

Wherein, N is observed reading quantity, x _iit is the amplitude of the vibration signal of i sampled point; The described fundamental frequency coefficient of variation is a dimensionless number, for describing significantly different overall discreteness of average, and the unit of elimination or the impact of average difference on the comparison of two or more data degree of variation, to add up the parameter of described vibration source.

9. method according to claim 8, is characterized in that, the described vibration source of identifying described vibration signal according to described normalization entropy and the described fundamental frequency coefficient of variation comprises:

Judge whether the described fundamental frequency coefficient of variation is less than the 3rd default threshold value;

If not, the vibration source of described vibration signal is non-rainy vibration source;

If so, judge whether described normalization entropy is greater than the 4th default threshold value;

If so, the vibration source of described vibration signal is the vibration source that rains;

If not, the vibration source of described vibration signal is non-rainy vibration source;

Wherein, described the 3rd threshold value is for setting coefficient of variation threshold value, and described the 4th threshold value is for setting coefficient of variation threshold value.