CN103542261B - Pipeline leakage acoustic emission signals processing method based on compressed sensing and mask signal method HHT - Google Patents
Pipeline leakage acoustic emission signals processing method based on compressed sensing and mask signal method HHT Download PDFInfo
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Abstract
The present invention relates to a kind of pipeline leakage acoustic emission signals processing method based on compressed sensing and mask signal method HHT.Mainly can apply in the leakage acoustic emission signal detection of pipeline, boiler, main processing steps: the first step: obtain acoustic emission primary signal, utilize digital filter to filter the high-frequency noise beyond leakage acoustic emission signal frequency domain；Second step: introduce compressive sensing theory and acoustic emission signal is compressed sampling；3rd step: utilize OMP algorithm that compressed signal is carried out Accurate Reconstruction；4th step: use the EMD of mask signal method to decompose acoustic emission signal, the component of different frequency in signal is separated from high frequency to low frequency one by one；5th step: each acoustic emission signal frequency component is done Hilbert conversion, determines the beginning and ending time of acoustic emission signal.
Description
Technical field
The present invention relates to a kind of pipeline leakage acoustic emission signals process side based on compressed sensing and mask signal method HHT
Method.
Background technology
Signals collecting mode based on tradition Nyqusit sampling thheorem, it is desirable to sample frequency is at least signal highest frequency
2 times.The frequency range of acoustic emission signal be a few KHz to hundreds of KHz, general in reality use multichannel data to adopt at a high speed
Collecting system, sample frequency is 5 ~ 10 times of signal highest frequency, necessarily leads to Pang when monitoring acoustic emission signal in real time
Big data so that hardware implementation cost is higher, for subsequent data transmission, store and process is exerted heavy pressures on, and this is to leakage
The long term monitoring of signal is the most disadvantageous.
Acoustic emission signal is the transient signal containing noisy non-stationary, to the transition acoustic emission containing noisy non-stationary
Signal decomposites acoustic emission signal, and determine the start-stop position of the generation of acoustic emission signal be sound emission signal characteristic extract and
The key identified.HHT generally can be used to analyze.
HHT (Hilbert-Huang Transform, Hilbert-Huang transform) is by empirical mode decomposition EMD
(Empirical Mode Decomposition, empirical mode decomposition) and Hilbert convert two parts composition, and wherein EMD is
Core.EMD is a kind of effective treating method for non-stationary signal, and it is from characteristic time scale, in signal
Each frequency component decomposes out according to the height of frequency one by one.But, when needing the signal decomposed to there is multiple close frequencies,
Modal overlap phenomenon will be there is in traditional E MD decomposition method.The EEMD method proposed afterwards, constantly adds during signal decomposition
Enter white noise, make the white noise that in whole time frequency space, uniform distribution is additional, then signal is carried out independent test, use foot
All averages of enough tests, noise will be eliminated, and EEMD can solve the modal overlap problem in EMD to a certain extent, but
For signal amplitude-frequency than undesirable situation, preferable Detection results still can not be reached.
Summary of the invention
The problem that the object of the invention exists for above classical signal processing method, propose a kind of by compressive sensing theory and
The EMD adding mask signal is applied to the processing method in pipeline leakage acoustic emission signals detection technique.
The technical scheme is that: a kind of based on compressed sensing with the pipeline leakage acoustic emission signals of mask signal method HHT
Processing method, it is characterised in that: compressive sensing theory is incorporated in acoustic emission signal detection technique, then utilizes and add mask
The EMD decomposition method of signal realizes the decomposition to acoustic emission signal, and component does Hilbert conversion, determines what leakage occurred
Beginning and ending time, concrete process step is as follows:
The first step: obtain acoustic emission primary signal, utilizes digital filter to filter beyond leakage acoustic emission signal frequency domain
High-frequency noise；
Second step: the acoustic emission that the first step is obtained by applied compression perception theory is launched signal and is compressed sampling, with one
Individual length, much smaller than the observing matrix perception acoustic emission original signal of primary signal, obtains a group observations；
3rd step: utilize OMP (Orthogonal Matching Pursuit, orthogonal matching pursuit) algorithm to second step
The compressed signal obtained is reconstructed, and obtains reconstructing acoustic emission signal；
4th step: use the EMD of mask signal method to decompose acoustic emission signal, by different in the 3rd step reconstruct acoustic emission signal
The component of frequency separates from high frequency to low frequency successively；
5th step: each acoustic emission signal frequency component separating the 4th step does Hilbert conversion, determines acoustic emission signal
Beginning and ending time.
The described first step and second step are known calculation matrix, a length of N of acoustic emission signal X, and degree of rarefication is K, abides by
Follow compressive sensing theory, X is projected to, obtain the one group of length measured value Y much smaller than N, according to measured value and calculation matrix
Do inverse operation and just can obtain sparse bayesian learning with reconstruction signal X 。
Described second step makes signal sampling no longer be limited by Nyquist sampling thheorem, improves efficiency of data compression, reduces
Data acquisition, transmission and carrying cost.
3rd step uses OMP algorithm, substantially completes the optimization of first step norm, it is achieved to compression acoustic emission signal
Quick Accurate Reconstruction.
Described interpolation mask signal in the 4th step acoustic emission signal, is carried out the acoustic emission signal containing mask signal
HHT (Hilbert-Huang Transform, Hilbert-Huang transform).
The mask signal added in the 4th step acoustic emission signal is, i.e. the frequency values of mask signal be adjacent two
Altofrequency sum, amplitude is the amplitude of highest frequency.
Described 4th step, when acoustic emission signal comprises N number of near by frequency component, needs constantly to add in catabolic process
N-1 mask signal, until last single frequency component remaining.
The acoustic emission signal component decomposited is done Hilbert conversion by described 5th step, it may be determined that its start-stop occurred
Time.
A kind of pipeline leakage acoustic emission signals processing method based on compressed sensing and mask signal method HHT, i.e. compresses sense
Know that theory is applied to leak in acoustic emission signal detection technique.
The invention has the advantages that: (1) makes signal sampling no longer be limited by Nyquist sampling thheorem, reduces data acquisition
Collection cost, improves efficiency of data compression；(2) transmission and the storage problem of big data are solved；(3) effectively suppress in catabolic process
The modal overlap phenomenon existed, makes decomposition result more accurately effectively, it is simple to the feature extraction of acoustic emission signal；(4) it is a kind of
New and effective acoustic emission signal compression and processing method.
Accompanying drawing explanation
Fig. 1 is the flow chart that patent of the present invention processes acoustic emission signal.
Fig. 2 is that patent compressed sensing of the present invention is to signal sampling compression process figure.
Fig. 3 is patent OMP algorithm flow chart of the present invention.
Fig. 4 is the patent of the present invention mask signal decomposition process figure as a example by IMF1.
Fig. 5 is present invention signal reconstruction based on CS example.
Fig. 6 is that present invention EMD based on mask signal decomposes example.
Detailed description of the invention
Present invention aim at the hyperfrequency in solution acoustic emission signal detection technique and the transmission of big data and storage problem,
Obtain accurate decomposition result and be characterized extraction, classify and offer is provided possible.Acoustic emission will be incorporated into by compressive sensing theory
In signal detection technique, the EMD decomposition method adding mask signal is then utilized to realize the decomposition to acoustic emission signal, and to dividing
Amount does Hilbert conversion, determines the beginning and ending time that leakage occurs, compressive sensing theory；And add the Martin Hilb of mask signal
The signal analysis method of spy-Huang.
Concrete processing scheme is:
The first step: obtain the filtering of acoustic emission primary signal.Owing to leakage acoustic emission signal has certain frequency domain, can design
Butterworth low pass filter, filters the high-frequency noise beyond leakage acoustic emission signal frequency domain.
Second step: introduce compressive sensing theory.Analyze the openness and non-correlation of signal.Acoustic emission signal is pressed
Contracting sampling, by a length much smaller than the observing matrix perception original signal of primary signal, obtains a group observations；
3rd step: compressed signal is reconstructed.(Orthogonal Matching Pursuit, orthogonal coupling chases after OMP
Track) algorithm is a kind of modified hydrothermal process on the basis of MP algorithm.Utilize OMP algorithm that compressed signal is carried out Accurate Reconstruction,
To reconstruct acoustic emission signal；
4th step: decomposed and reconstituted acoustic emission signal.The EMD adding mask signal method is used to decompose acoustic emission signal, will
In signal, the component of different frequency separates with this from high frequency to low frequency.
5th step: each acoustic emission signal frequency component is done Hilbert conversion, determines the beginning and ending time of acoustic emission signal.
Entirety is sent out by leakage Acoustic emission signal processing side based on compressive sensing theory and mask signal method empirical mode decomposition
Process is as shown in Figure 1.Leakage acoustic emission signal is during actual propagation, inevitably by various external interference, so that
The primary signal collected is mingled with a lot of noise.Owing to the frequency of acoustic emission signal has a range of frequency domain, for signal
The too high part of medium frequency can use wave filter to be filtered.The present invention designs a Butterworth wave filter and filters former
High-frequency noise in beginning signal.It is then based on compressive sensing theory, filtered signal is compressed sampling, in order to signal
Transmission and storage.At the receiving terminal of signal, combine OMP (Orthogonal Matching by less observation
Pursuit, orthogonal matching pursuit) the algorithm realization Accurate Reconstruction to signal.Finally reconstruction signal is carried out fast Fourier change
Change, analyze the amplitude-frequency characteristic of signal, it is simple to structure mask signal.Mask signal is constantly added, after superposition in reconstruction signal
Signal be EMD decompose, draw the single-frequency component after decomposition.Finally each acoustic emission signal frequency component is Hilbert
Conversion, determines the beginning and ending time that leakage acoustic emission signal occurs.
Below in conjunction with the accompanying drawings, handling principle and the process of the present invention are further illustrated.
It is illustrated in figure 2 Signal Compression process based on compressed sensing.For signal X, transformed toOn territory, its
Being expressed as on this territory:.This is because, the signal for compressed sensing must have openness, and the most certainly
So signal is not sparse, and these signals have rarefaction representation on certain special domain,Degree of rarefication be K.With known
Observing matrixGo perception, obtain one group of length much smaller than original signal measurement of length value Y, measured value Y is transmitted with
During storage, just can save processing cost.Receiving terminal at signal carries out inverse transformation to measured value, it is possible to reconstruction signal.
Fig. 3 is OMP algorithm flow chart.At known observing matrix, in the case of observation Y and degree of rarefication K, can be in order to
With OMP algorithm reconstruction signal, show that signal X's approaches value.During initialization, make residual error, sky indexed set and iteration are set
Counting.First find out the footmark that residual error is corresponding with observing matrix row inner product maximum, after finding out footmark, update indexed set, record weight
Build atom, show that one of signal is approached value, then updates residual values secondly by method of least square, until iterations is more than
Being iteration ends equal to degree of rarefication, circulation searching the most always approaches value.
Fig. 4 show the mask signal method EMD decomposition step as a example by IMF1.Need before decomposition signal X is done quickly
Fourier transformation, primarily determines that mask signal to be constructed according to its amplitude-frequency characteristic.Concrete catabolic process is as follows:
(1) for analyzed signal, construct mask signal.Wherein, the frequency of mask signal
Value is adjacent two highest frequency sums, and amplitude is the amplitude of highest frequency.
(2) rightCarry out EMD decomposition, take its first IMF, be designated as, then haveEMD decomposition takes it
First IMF is designated as。
(3) average is calculated, result is as the IMF1 of signal decomposition.
When needing the signal decomposed comprises N number of appropriate frequency components, need to add N-1 mask in catabolic process
Signal, constantly repeats three above step, until last single frequency component remaining.
According to above theoretical foundation, Fig. 5 is a signal reconstruction example based on CS, it can be seen that reconstruct letter
Number the least with the error of primary signal, it is achieved that the Accurate Reconstruction to signal.
Fig. 6 show an EMD based on mask signal and decomposes example.It can be seen that primary signal from decomposition result
Being modulated by the signal of three different frequencies and form, the signal of different frequency can substantially make a distinction, and well inhibits mode
Aliasing.
The present invention is limited by Nyquist sampling thheorem when having broken classical signal collection, reduces data acquisition cost,
The Accurate Reconstruction to acoustic emission signal is realized by restructing algorithm.The EMD resolution adding mask signal effectively suppresses to decompose
Modal overlap phenomenon present in journey, makes decomposition result more accurately effectively.
Claims (9)
1. a pipeline leakage acoustic emission signals processing method based on compressed sensing and mask signal method HHT, it is characterised in that:
Compressive sensing theory is incorporated in acoustic emission signal detection technique, then utilizes the EMD decomposition method adding mask signal real
The now decomposition to acoustic emission signal, and component is done Hilbert conversion, determine the beginning and ending time that leakage occurs, specifically process step
Rapid as follows:
The first step: obtain acoustic emission primary signal, utilizes digital filter to filter the high frequency beyond leakage acoustic emission signal frequency domain
Noise；
Second step: applied compression perception theory is compressed sampling to acoustic emission signal X that the first step obtains, remote by a length
Less than the calculation matrix perception acoustic emission primary signal of primary signal, obtain a group observations；
3rd step: utilize OMP (Orthogonal Matching Pursuit, orthogonal matching pursuit) algorithm that second step is obtained
Compressed signal X ＇ be reconstructed, obtain reconstruct acoustic emission signal；
4th step: use the EMD of mask signal method to decompose acoustic emission signal, by different frequency in the 3rd step reconstruct acoustic emission signal
Component separate successively from high frequency to low frequency；
5th step: each acoustic emission signal frequency component separating the 4th step does Hilbert conversion, determines rising of acoustic emission signal
The only time.
Pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT the most according to claim 1 processes
Method, it is characterised in that: the first step and second step known calculation matrix Φ, the acoustic emission signal X length that the first step obtains
For N, degree of rarefication is K, it then follows compressive sensing theory, projects on calculation matrix Φ by acoustic emission signal X that the first step obtains,
To one group of length much smaller than the measured value Y of N, do inverse operation according to measured value and calculation matrix and just can reconstruct what second step obtained
Compressed signal X ＇, obtains sparse bayesian learning
At pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT the most according to claim 1 and 2
Reason method, it is characterised in that: described second step makes signal sampling no longer be limited by Nyquist sampling thheorem, improves data pressure
Contracting efficiency, reduces data acquisition, transmission and carrying cost.
Pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT the most according to claim 1 processes
Method, it is characterised in that: the 3rd step uses OMP algorithm, substantially completes the optimization of first step norm, it is achieved to second step
The compressed signal quick Accurate Reconstruction of X ＇ obtained.
Pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT the most according to claim 1 processes
Method, it is characterised in that: in the 4th step acoustic emission signal, add mask signal, the acoustic emission signal containing mask signal is entered
Row HHT Hilbert-Huang transform.
The most according to claim 1 or 5 at pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT
Reason method, it is characterised in that: the mask signal added in the 4th step acoustic emission signal is { S1(t)……Sn-1(t) }, i.e.
The frequency values of mask signal is adjacent two highest frequency sums, and amplitude is the amplitude of highest frequency.
Pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT the most according to claim 1 processes
Method, it is characterised in that: the 4th step, when acoustic emission signal comprises N number of near by frequency component, needs in catabolic process constantly
Add N-1 mask signal, until last single frequency component remaining.
Pipeline leakage acoustic emission signals based on compressed sensing and mask signal method HHT the most according to claim 1 processes
Method, it is characterised in that: the acoustic emission signal component decompositing the 5th step does Hilbert conversion, it may be determined that it occurs
Beginning and ending time.
9. according to the pipe leakage acoustic emission based on compressed sensing and mask signal method HHT described in any one of claim 1-8
Signal processing method is applied to pipeline leakage testing.
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| CN104035434A (en) * | 2014-06-13 | 2014-09-10 | 武汉理工大学 | Air leakage monitoring system for diesel engine air valve |
| CN104747912B (en) * | 2015-04-23 | 2017-04-12 | 重庆邮电大学 | Fluid conveying pipe leakage acoustic emission time-frequency positioning method |
| CN109140241B (en) * | 2018-08-21 | 2019-10-29 | 吉林大学 | A kind of compressed sensing based pipeline leakage positioning method |
| CN109813417A (en) * | 2019-01-18 | 2019-05-28 | 国网江苏省电力有限公司检修分公司 | A kind of shunt reactor method for diagnosing faults based on improvement EMD |
| CN110131595B (en) * | 2019-05-21 | 2020-09-04 | 北京化工大学 | Method, device and system for processing pipeline slow leakage signal |
| CN110553156B (en) * | 2019-08-30 | 2020-09-25 | 清华大学 | Pipeline ultrasonic guided wave compression sensing health monitoring method |
| CN112525999B (en) * | 2020-11-30 | 2023-05-23 | 北京强度环境研究所 | Acoustic emission signal correlation analysis method based on frequency components |
| CN114330455B (en) * | 2022-01-05 | 2022-10-11 | 哈尔滨工业大学 | Steel rail acoustic emission signal rapid high-precision reconstruction method based on compressed sensing |
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| RU2258865C1 (en) * | 2004-01-14 | 2005-08-20 | Ефремов Александр Иванович | Method of detecting location of leakage in pipelines |
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