CN109298447A - A kind of then pick-up method of low signal-to-noise ratio acoustic emission signal - Google Patents
A kind of then pick-up method of low signal-to-noise ratio acoustic emission signal Download PDFInfo
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Abstract
The invention discloses a kind of low signal-to-noise ratio acoustic emission signal then automatic pick methods, pick up the unstable technical problem of low signal-to-noise ratio, tail portion oscillator signal for solving current AIC method.The present invention substantially arrives time point k based on MER method pickoff signals when picking up has the acoustic emission signal of tail portion noise1, and the number of winning the confidence initial time is to substantially arriving time point k1Period as updated low signal-to-noise ratio acoustic emission signal, so as to exclude the influence of acoustic emission signal tail portion concussion；The coarse time series for resolving into multiple scale factors to the acoustic emission signal after excluding tail portion concussion using MS theory coarse signal, makes signal smoothing under different scale factors, to exclude the influence of low signal-to-noise ratio.Thus the influence for effectively eliminating low signal-to-noise ratio, tail portion concussion, greatly improves acoustic emission signal and then picks up precision, so that the strong applicability of this method, accuracy are high.
Description
Technical field
The invention belongs to signal processing technology fields, more particularly, to a kind of then pickup of low signal-to-noise ratio acoustic emission signal
Method.
Background technique
In recent years, microseism or sound emission monitoring technology have been widely used rock sample indoor test and all kinds of rock engineerings
In, for example hydrofracturing exploits oil gas, large water conservancy hydroelectric engineering underground rock cavern STABILITY MONITORING, and deep rock mass is opened
Digging and rock burst early warning survey etc..Microseism or sound emission monitoring technology include waveform recognition, sound wave then pick up, rock rupture positioning,
Rupture mechanism explanation and hazard prediction etc., it is that state event location and hazard prediction are the most basic and important that wherein sound wave, which then picks up,
One step picks up precision and directly influences the precision of rock rupture positioning and generate important shadow to rupture mechanism and hazard prediction
It rings.
Currently, sound emission then relies primarily on artificial pickup, but artificial pick up depends on the experience for picking up taker, vulnerable to
Individual factor influences and picks up low efficiency, and more seriously acoustic emission signal is then difficult precision pick under noisy environment.Cause
This domestic and foreign scholars proposes a series of then automatic pick methods containing noise signal.Gaci (2014) is proposed based on discrete
Waveform converts energy ratio (MER) method and long short time-window average ratio (STA/LTA) of the modification of (DWT), is 10 to 15 in signal-to-noise ratio
When, the MER method based on DWT has accurate picked up seismic signal then, and precision is higher than the STA/LTA method based on DWT,
But when signal-to-noise ratio is below 3, the precision that former approach picks up but is not so good as later approach.Li and Shang etc.
(2017) the improvement kurtosis method based on DWT and STA/LTA has picked up the p phases of microseismic signals then, and this method is in various noises
Than under conditions of can precision pick signal initial time, but STA/LTA method is used as time point, valve using threshold values trigger point
When being worth smaller, acoustic emission signal is easy to be triggered in advance by noise；When threshold values is larger, acoustic emission signal may not be triggered or trigger
It is later, cause error larger, is thus difficult to find the threshold values of a suitable different wave.Zhang etc. (2003) proposes one kind
The p wave of low signal-to-noise ratio is picked up based on the method for the red pond information criterion (AIC) of DWT then, this method overcomes DWT-
The problem of STA/LTA method threshold values is chosen, but DWT-AIC method picks up precision not as good as Li et al. under conditions of certain signal-to-noise ratio
(2017) method of the AIC based on empirical mode decomposition (EMD) proposed, however EMD is in the same of removal acoustic emission signal noise
When can lose a large amount of useful informations, cause pick up result precision reduce.
It can be seen that then pick-up method needs to study there are great limitation for existing low signal-to-noise ratio sound emission or microseismic signals
It is a kind of to pick up the automatic pick method that precision is high, stability is good.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of then pick-up method of low signal-to-noise ratio acoustic emission signal, solutions
Certainly AIC method picks up the unstable technical problem of low signal-to-noise ratio, tail portion oscillator signal, and then pick-up method is applicable in the acoustic emission signal
Property is strong, accuracy is high.
It is high that precision is picked up to high s/n ratio acoustic emission signal for AIC method, but to low signal-to-noise ratio and tail portion oscillator signal
Unstable technical problem is picked up, the present invention proposes that a kind of new method, this method can overcome tail portion to shake well by MER method
The influence swung and multiple dimensioned (MS) are theoretical smooth noise while the coarse of sound emission signal characteristic can preferably be kept to think
Think, greatly improves the accuracy of low signal-to-noise ratio acoustic emission signal then.
To realize the above-mentioned technical purpose, the present invention adopts the following technical scheme that realization:
A kind of then pick-up method of low signal-to-noise ratio acoustic emission signal, comprising the following steps:
Step 1: importing low signal-to-noise ratio acoustic emission signal；
Low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., N is imported, wherein sample frequency is 3-6MHz, first sampling point
For the initial time of acoustic emission signal, the number of sampled point is N；
Step 2: low signal-to-noise ratio acoustic emission signal x (t) t=1,2 is determined according to following formula according to MER algorithm ..., N's
Substantially arrive time point k1；
MER(i)=(ER(i)|x(i)|)3 (2)
Wherein, x (j) is the amplitude of j-th of sampled point of signal x (t) to be picked up, and ne is the length of energy window, ER(i)For
Energy ratio at i-th point, MER(i)For the energy ratio modified at the i-th point；
The energy ratio of the modification of whole sampled points is calculated, and using sampled point corresponding to maximum value as acoustic emission signal
Substantially arrive time point k1；
Time point k is substantially arrived based on MER method pickoff signals1, and the number of winning the confidence initial time is to substantially arriving time point k1Time
The updated low signal-to-noise ratio acoustic emission signal of Duan Zuowei, so as to exclude the influence of acoustic emission signal tail portion concussion；
Step 3: low signal-to-noise ratio acoustic emission signal x (t) t=1,2 is reselected ..., the length of N；
Time point k is substantially arrived in selection1As updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., k1Length；
Step 4: using multiple dimensioned coarse granulation method to updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ...,
k1It is handled, respectively obtains coarse time series x when scale factor q difference valueq, coarse formula are as follows:
Wherein, q indicates scale factor, xqIt (j) is the coarse time series x when scale factor is qqIn the coarse grain of jth point
Change amplitude；[k1/ q] it is near k1The integer of/q；
By using MS theory coarse signal to exclude tail portion concussion after acoustic emission signal resolve into multiple scales because
The coarse time series of son, makes signal smoothing under different scale factors, to exclude the influence of low signal-to-noise ratio.
Step 5: picking up coarse time series x according to following formula using AIC methodqIn scale factor q difference value
When arrive time point mq:
AIC (k)=klog (var { x (1, k) })+([k1/q]-k-1)log(var{x(k+1,[k1/q])})
(4)；
Wherein, var { x (1, k) } is the 1st point of variance to kth point of coarse time series, var { x (k+1, [k1/
Q]) } it is the kth+1 o'clock of coarse time series to [k1/ q] point variance, AIC (k) be kth point sound emission AIC value；
To the coarse time series x of each scale factor qq, the sound emission AIC value of each time point is calculated, and will most
Coarse time series x when time point corresponding to small value is as current scale factor q valueqArrive time point mq；
By utilizing coarse time series corresponding to each scale factor of AIC technique picks then, and all
Then with the minimum value in the product of the current scale factor, as signal finally be accurate to when.
Step 6: coarse time series x when according to scale factor q and scale factor q valueqArrive time point mq, press
Following formula determines l when being accurate to of acoustic emission signal:
lq=q × mq (5)
L=min [l1,l2,…lq] (6)
Wherein, lqFor be q in scale factor low signal-to-noise ratio acoustic emission signal x (t) arrive time point.
This programme is comprehensive to use MER, MS and AIC technology, effectively eliminates the influence of low signal-to-noise ratio, tail portion concussion, greatly
Ground improves acoustic emission signal and then picks up precision.
Further, the value size of the length ne of energy window are as follows: 2T≤ne≤3T, T are low signal-to-noise ratio sound emission letter
Number period.
Further, in the step 4, the value of scale factor q is integer of 1 to 20 value.
Further, in the step 1, the sample frequency of low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., N are
3MHz, the value range of total sampling number N are as follows: 3000≤N≤20000.
Beneficial effect
The present invention provides a kind of low signal-to-noise ratio acoustic emission signal then automatic pick methods, include the following steps: to import
Low signal-to-noise ratio acoustic emission signal；Time point is substantially arrived according to what following formula determined low signal-to-noise ratio acoustic emission signal according to MER algorithm
k1；Time point k is substantially arrived in selection1Length as updated low signal-to-noise ratio acoustic emission signal；Using multiple dimensioned coarse granulation method pair
Updated low signal-to-noise ratio acoustic emission signal is handled, and coarse time sequence when scale factor q difference value is respectively obtained
Arrange xq；Coarse time series x when scale factor q difference value is picked up using AIC methodqArrive time point mq；Selecting scale because
Sub- q arrives time point m with the scale factorqProduct when being accurate to l of the minimum value as low signal-to-noise ratio acoustic emission information.This hair
The bright shadow when picking up has the acoustic emission signal of tail portion noise, shaken based on the tail portion that MER method can exclude acoustic emission signal
It rings；When picking up low signal-to-noise ratio acoustic emission signal, using MS theory coarse signal sequence, under different scales signal is obtained
To smooth, to exclude the influence of low signal-to-noise ratio.Thus the influence for effectively eliminating low signal-to-noise ratio, tail portion concussion, greatly mentions
High acoustic emission signal then picks up precision.
Detailed description of the invention
Fig. 1 is the method for the invention flow chart.
Fig. 2 is figure the step of pickup when the present invention proceeds to low signal-to-noise ratio acoustic emission signal, wherein (a) indicates step 1
The low signal-to-noise ratio acoustic emission signal of importing；(b) indicate that step 2 substantially arrives time point k1=3089 according to the determination of MER algorithm；(c) table
Show the obtained updated low signal-to-noise ratio acoustic emission signal after step 3 redefines length；(d) indicate that step 4 uses more rulers
It spends coarse granulation method and scale factor distinguishes the obtained one group of coarse time series x of value 1-20q；(e) step 5 benefit is indicated
Coarse time series x is picked up with AIC methodqThen m when scale factor distinguishes value 1-20q；(f) indicate that step 6 obtains
Scale factor q and each scale under then mqProduct minimum corresponding to coarse time series x18。
Fig. 3 is manual, AIC, the method for the present invention pickup acoustic emission signal typical case figure, and wherein solid line, arrow, numerical value are
The pickup result of every kind of method.(a) it indicates to manually pick up method case diagram；(b) AIC pick-up method case diagram is indicated；(c) it indicates
The method of the present invention case diagram, as q=19, q and the product that result 62 is picked up under the scale are minimum, and final pick up then is
1178。
Fig. 4 is that AIC method and the method for the present invention pick up Comparative result residual plot with the method that manually picks up respectively.(a) it indicates
AIC method and the pickup Comparative result residual plot for manually picking up method；(b) it indicates the method for the present invention and manually picks up picking up for method
Take Comparative result residual plot.
Specific embodiment
With reference to the accompanying drawing 1~4, a kind of then pick-up method of low signal-to-noise ratio acoustic emission signal proposed by the present invention is made
It further illustrates.
As shown in Figure 1, a kind of then pick-up method of low signal-to-noise ratio acoustic emission signal provided by the invention, including following step
It is rapid:
Step 1: importing low signal-to-noise ratio acoustic emission signal；
Low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., N is imported, wherein sample frequency is 3MHz, and first sampling point is
The initial time of acoustic emission signal, the number N=8000 of sampled point, as shown in Fig. 2 (a).
Step 2: low signal-to-noise ratio acoustic emission signal x (t) t=1,2 is determined according to following formula according to MER algorithm ..., N's
Substantially arrive time point k1；
MER(i)=(ER(i)|x(i)|)3 (2)
Wherein, x (j) is the amplitude of j-th of sampled point of signal x (t) to be picked up, and ne is the length of energy window, ER(i)For
Energy ratio at i-th point, MER(i)For the energy ratio modified at the i-th point；
The energy ratio of the modification of whole sampled points is calculated, and using sampled point corresponding to maximum value as acoustic emission signal
Substantially arrive time point k1, the k that is calculated1=3089, as shown in Fig. 2 (b).
Step 3: low signal-to-noise ratio acoustic emission signal x (t) t=1,2 is reselected ..., the length of N；
Time point k is substantially arrived in selection1=3089 are used as updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., k1's
Length, i.e., intercept signal starting point is to the length of interval substantially to from time point 3089 from the low signal-to-noise ratio acoustic emission signal of importing
Interior signal, as updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., k1, as shown in Fig. 2 (c).
Step 4: using multiple dimensioned coarse granulation method to updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ...,
k1It is handled, respectively obtains coarse time series x when scale factor q difference valueq, coarse formula are as follows:
Wherein, q indicates scale factor, xqIt (j) is the coarse time series x when scale factor is qqIn the coarse grain of jth point
Change amplitude；Scale factor q distinguishes value 1 to 20, obtains 20 coarse time series x as shown in Fig. 2 (d)1、x2、……、
x20。
Step 5: picking up coarse time series x according to following formula using AIC methodqIn scale factor q difference value
When arrive time point mq:
AIC (k)=klog (var { x (1, k) })+([k1/q]-k-1)log(var{x(k+1,[k1/q])}) (4)
Wherein, var { x (1, k) } is the 1st point of variance to kth point of coarse time series, var { x (k+1, [k1/
Q]) } it is the kth+1 o'clock of coarse time series to [k1/ q] point variance, AIC (k) be kth point sound emission AIC value；
To the coarse time series x of each scale factor qq, the sound emission AIC value of each time point is calculated, and will most
Coarse time series x when time point corresponding to small value is as current scale factor q valueqArrive time point mq.Scale because
Shown in then result such as Fig. 2 (e) of sub- value 1-20.
Step 6: coarse time series x when according to scale factor q and scale factor q valueqArrive time point mq, press
Following formula determines l when being accurate to of acoustic emission signal:
lq=q × mq (5)
L=min [l1,l2,…lq] (6)
Wherein, lqFor be q in scale factor low signal-to-noise ratio acoustic emission signal x (t) arrive time point.
It is found by calculating, as scale factor q=18, coarse time series x18Arrive time point m18=142, scale
The factor and the product minimum then under the scale, value 2556, therefore the value is selected to be accurate to as final signal
Time point.
Embodiment 1:
Fig. 3 (a)~(c) is manual, AIC, the method for the present invention pickup acoustic emission signal typical case figure respectively, can by figure
Know: when AIC picks up Low SNR signal, due to the interference shaken by tail portion, multiple local minimums (1227,2775 institutes occurs
In point), global maximum corresponding time point is much larger than and manually picks up then；In addition most in then neighbouring AIC time series
Pipe has local minimum, but its local minimum corresponding time point (1227) and manual then (1172) differ 55 points, bright
It shows by noise effect.Thus for acoustic emission signal, especially tail portion noise ratio is more serious, and it is poor that AIC method picks up precision；
And the method for the present invention is very high for Low SNR signal or the serious acoustic emission signal pickup precision of tail portion noise, successfully solves
AIC method of having determined Low SNR signal then picks up the unstable and poor technical problem of precision.
Embodiment 2:
Fig. 4 be AIC method and the method for the present invention respectively with manually pick up Comparative result residual plot.100 groups of acoustic emission signals
The rock rupture event recorded from acoustic emission monitoring system DS5-16C is randomly selected to obtain.The method of the present invention calculating parameter
As follows: acoustic emission signal sample frequency 3MHz, number of sampling points is between 8000-30000, the sliding window length of MER method
For 30 sampled points.
As shown in Figure 4 AIC method picking error within 10 sampled points, 10-20,20-30,30-40,40-50 and
The ratio that total event is accounted for greater than 50 is respectively 17%, 5%, 8%, 1%, 0%, 69%；The method of the present invention corresponds to above-mentioned pickup and misses
The accounting that difference divides is respectively 47%, 18%, 21%, 5%, 3%, 6%.It follows that the method for the present invention is for containing noise
The lower ratio of picking up signal precision it is extremely low, greatly improve the accurate of the then automatic Picking of noisy acoustic emission signal
Property.
The above description is only an embodiment of the present invention, is not intended to limit the invention, all in spirit of that invention and original
It within then, changes, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (4)
1. a kind of then pick-up method of low signal-to-noise ratio acoustic emission signal, which comprises the following steps:
Step 1: importing low signal-to-noise ratio acoustic emission signal；
Low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., N is imported, wherein sample frequency is 3-6MHz, and first sampling point is sound
Emit the initial time of signal, the number of sampled point is N；
Step 2: low signal-to-noise ratio acoustic emission signal x (t) t=1,2 is determined according to following formula according to MER algorithm ..., N is substantially
To time point k1；
MER(i)=(ER(i)|x(i)|)3 (2)
Wherein, x (j) is the amplitude of j-th of sampled point of signal x (t) to be picked up, and ne is the length of energy window, ER(i)It is i-th point
The energy ratio at place, MER(i)For the energy ratio modified at the i-th point；
The energy ratio of the modification of whole sampled points is calculated, and substantially using sampled point corresponding to maximum value as acoustic emission signal
To time point k1；
Step 3: low signal-to-noise ratio acoustic emission signal x (t) t=1,2 is reselected ..., the length of N；
Time point k is substantially arrived in selection1As updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., k1Length；
Step 4: using multiple dimensioned coarse granulation method to updated low signal-to-noise ratio acoustic emission signal x (t) t=1,2 ..., k1It carries out
Processing, respectively obtains coarse time series x when scale factor q difference valueq, coarse formula are as follows:
Wherein, q indicates scale factor, xqIt (j) is the coarse time series x when scale factor is qqIt shakes in the coarse of jth point
Width；[k1/ q] it is near k1The integer of/q；
Step 5: picking up coarse time series x according to following formula using AIC methodqIn scale factor q difference value
To time point mq:
AIC (k)=klog (var { x (1, k) })+([k1/q]-k-1)log(var{x(k+1,[k1/q])})
(4)；
Wherein, var { x (1, k) } is the 1st point of variance to kth point of coarse time series, var { x (k+1, [k1/ q]) } be
The kth+1 o'clock of coarse time series is to [k1/ q] point variance, AIC (k) be kth point sound emission AIC value；
To the coarse time series x of each scale factor qq, calculate the sound emission AIC value of each time point, and by minimum value institute
Coarse time series x when corresponding time point is as current scale factor q valueqArrive time point mq；
Step 6: coarse time series x when according to scale factor q and scale factor q valueqArrive time point mq, by following
Formula determines l when being accurate to of acoustic emission signal:
lq=q × mq (5)
L=min [l1,l2,…lq] (6)
Wherein, lqFor be q in scale factor low signal-to-noise ratio acoustic emission signal x (t) arrive time point.
2. the method according to claim 1, wherein the value of the length ne of energy window is big in the step 2
It is small are as follows: 2T≤ne≤3T, T are the period of low signal-to-noise ratio acoustic emission signal.
3. the method according to claim 1, wherein the value of scale factor q is 1 to 20 in the step 4
Integer value.
4. the method according to claim 1, wherein in the step 1, low signal-to-noise ratio acoustic emission signal x (t) t
The sample frequency of=1,2 ..., N are 3MHz, the value range of total sampling number N are as follows: 3000≤N≤20000.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114324616A (en) * | 2022-03-15 | 2022-04-12 | 山东大学 | Acoustic emission signal intercepting method and system |
| CN114324616B (en) * | 2022-03-15 | 2022-06-07 | 山东大学 | Acoustic emission signal intercepting method and system |
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Application publication date: 20190201 Assignee: Hunan creative Blasting Engineering Co.,Ltd. Assignor: CENTRAL SOUTH University Contract record no.: X2022980003448 Denomination of invention: A time of arrival pickup method for acoustic emission signal with low signal-to-noise ratio Granted publication date: 20190726 License type: Common License Record date: 20220402 |