CN103185635B - Extraction method and related system of transient components in signals - Google Patents

Abstract

The invention discloses an extraction method and a related system of transient components in signals. The extraction method and the related system are used for extracting the transient components from the signals. The method includes: a sensing device is utilized to input and carry out analog-digital conversion, and the signals are obtained; a parameterized model of the transient components is set up; the model is used to approximate the transient components in the signals, and model parameters are identified; and the transient components in the signals are extracted in an iteration mode.

Description

The extracting method of transient components and related system in a kind of signal

Technical field

The analysis that the present invention relates to signal is extracted, and specifically, relates to extracting method and the related system of transient components in a kind of signal.

Background technology

For extraction and the detection of the transient components in signal, especially there is extraction and the detection of periodic transient state characteristic, have a wide range of applications in the field such as detection of the fault diagnosis of plant equipment and status monitoring, biomedicine signals.Owing to there is the noise that a variety of causes causes in the signal that obtains in testing process, thus transient components wherein also can by noise pollution.

At present, the most direct transient components detection method directly judges whether there is transient components in time-domain signal exactly, but is often mixed in noise due to the transient components in signal, and directly judge that in signal, the accuracy of transient components is lower, efficiency is also lower.

By a kind of conventional method that the power Spectral Estimation of signal is also periodic characteristic in analytic signal.But for the period transient state characteristic that the duration in signal is short, less amplitude is shown as in power spectrum, often flooded by noise, simultaneously, transient state characteristic itself is radio-frequency component, so power spectrum is to the span of frequency very large at high band, is detected by power spectrum and often can not obtain significant feature.

Summary of the invention

Embodiments provide extracting method and the related system of transient components in a kind of signal, for extracting transient components from signal.

An extracting method for transient components in signal, comprising:

Utilize sensing device to input and carry out analog/digital conversion, obtaining signal;

Set up transient components parameterized model;

By the transient components in signal described in described Model approximation, and pick out model parameter;

Transient components in signal described in iterative extraction.

An extraction system for transient components in signal, comprising:

Sensing device, for inputting and carrying out analog/digital conversion, obtains signal;

Model apparatus for establishing, for setting up transient components parameterized model;

Device for identifying, for by the transient components in signal described in described Model approximation, and picks out model parameter;

Extraction element, for the transient components in signal described in iterative extraction.

As can be seen from the above technical solutions, the embodiment of the present invention has the following advantages:

First the present invention sets up parameterized model, then passes through Levenbery-Marquardt method iterative extraction transient components and its parameter of identification, the transient components then in iterative extraction signal; This extracting method, be applied to the detection of equipment failure, sensor installation on the appropriate location of equipment to be detected, the vibration signal of checkout equipment, as detection signal, adopt the transient components in this extracting method extraction detection signal, if the fault signature cycle of the transient components time interval of extracting and this equipment part coincide, then corresponding with this cycle in determining apparatus part position has fault.

Accompanying drawing explanation

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

The process flow diagram of the extracting method of transient components in the signal that Fig. 1 provides for the embodiment of the present invention;

The time-frequency representation schematic diagram of result is extracted when Fig. 2 is embodiment of the present invention centre bearer outer ring local fault;

The time-frequency representation schematic diagram of result is extracted when Fig. 3 is embodiment of the present invention centre bearer inner ring local fault;

Fig. 4 is the inner drive mechanism schematic diagram of embodiment of the present invention middle gear case;

The time-frequency representation schematic diagram of result is extracted when Fig. 5 is embodiment of the present invention middle gear case third gear broken teeth fault;

The structural representation of the extraction system of transient components in the signal that Fig. 6 provides for the embodiment of the present invention.

Embodiment

Embodiments provide extracting method and the related system of transient components in a kind of signal, for extracting transient components from signal.

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

Below in conjunction with accompanying drawing, the extracting method of transient components in signal is described in detail:

In a kind of signal that the embodiment of the present invention provides, the extracting method of transient components, please refer to Fig. 1; The method comprises:

S10, utilize sensing device to input and carry out analog/digital conversion, obtaining signal;

In some embodiments, can sensing device be utilized, the simulating signal of input be become voltage or current signal, namely converts digital signal to, just be sent to computing machine and process; For ease of illustrating, below this signal post is designated as

S20, set up transient components parameterized model;

Set up transient components parameterized model, be designated as:

Wherein, A represents the amplitude of model, and f represents the oscillation frequency of model, ζ _lwith ζ _rrepresent the left decay factor of model and the right decay factor of model respectively, τ represents delay time, model parameter vectors x=(A, f, ζ _l, ζ _r, τ).Parameter W _sLwith W _sRrepresent the two-part length in the interval left and right of model support respectively.

According to the model parameter vectors x=(A, f, the ζ that obtain _l, ζ _r, τ), computation model support Interval length W _s, wherein, left support burst length is as follows:

$W_{\mathrm{sL}}=\sqrt{\frac{-\ln \mathrm{\η}}{{\left(2\mathrm{\πf}\right)}^2}\·\frac{\sqrt{1-{\mathrm{\ζ}}_L^2}}{{\mathrm{\ζ}}_L}}---\left(1\right);$

Right support burst length is as follows:

$W_{\mathrm{sR}}=\sqrt{\frac{-\ln \mathrm{\η}}{{\left(2\mathrm{\πf}\right)}^2}\·\frac{\sqrt{1-{\mathrm{\ζ}}_R^2}}{{\mathrm{\ζ}}_R}}---\left(2\right);$

In above-mentioned formula (1) and formula (2), η represents attenuation rate coefficient.

S30, the transient components used in Model approximation signal, and pick out model parameter;

In some embodiments, using the civilian Burger-Ma Kuaertefa Levenbery-Marquardt method of row, hereinafter referred to as L-M method, is a kind of algorithm in least square fitting.The transient components in the signal of S10 acquisition is approached with the parameterized model that S20 sets up.

Being understandable that, there is transient components in the signal of acquisition, can take signal with discrete time change as employing point, according to relative error quadratic sum minimum criteria within the scope of support Interval, pick out model parameter; The problem of Model approximation transient components is now utilized to be expressed as:

$\min F\left(x\right)=\frac{\underset{i=n_L}{\overset{n_R}{\mathrm{\Σ}}}{f}_i^2\left(x\right)}{\underset{i=n_L}{\overset{n_R}{\mathrm{\Σ}}}{y}_{t_i}^2}=\frac{\underset{i=n_L}{\overset{n_R}{\mathrm{\Σ}}}{[{\mathrm{\ψ}}_{t_i}\left(x\right)-y_{t_i}]}^2}{\underset{i=n_L}{\overset{n_R}{\mathrm{\Σ}}}{y}_{t_i}^2}---\left(3\right);$

In above-mentioned formula (3), F (x) represents relative error quadratic sum in support Interval, is meant to the error sum of squares of support Interval inner model and signal, with the ratio of signal quadratic sum in support Interval; F (x) represents the error between model and signal, and f _ix () then represents that i-th signal adopts the error of point; Sampled point n _lwith n _rthe corresponding time with be respectively support Interval [τ-W _sL, τ+W _sR] in closest to sampling time of left end point and right endpoint.

Wherein, L-M method is a kind of existing non-linear least square method, and it is as follows that it solves concrete steps:

S301, given model parameter vectors x=(A, f, ζ _l, ζ _r, τ), its initial value is designated as $x^{\left(0\right)}={\mathrm{\γ}}^{\left(0\right)}=(A^{\left(0\right)},f^{\left(0\right)},{\mathrm{\ζ}}_L^{\left(0\right)},{\mathrm{\ζ}}_R^{\left(0\right)},{\mathrm{\τ}}^{\left(0\right)}),$ Wherein A ⁽⁰⁾, f ⁽⁰⁾, and τ ⁽⁰⁾represent parameter A, f, ζ respectively _l, ζ _r, the initial value of τ, initial parameter μ ⁽⁰⁾> 0, zoom factor v > 1, permissible error ε > 0, calculates F (x ⁽⁰⁾), put μ=μ ⁽⁰⁾, k=1;

S302, mark amount flag=0 is set, the value J (x respectively during compute matrix J (x) and f kth time iteration ^(k)) and f ^(k), formula is as follows:

$J\left(x^{\left(k\right)}\right)=\frac{1}{\left|\right|y\left|\right|}\·\left[\begin{array}{c}{\left[{\▿f}_{n_L}\left(x^{\left(k\right)}\right)\right]}^T\\ {\left[{\▿f}_{n_L+1}\left(x^{\left(k\right)}\right)\right]}^T\\ \·\\ \·\\ \·\\ {\left[{\▿f}_{n_R}\left(x^{\left(k\right)}\right)\right]}^T\end{array}\right]=\frac{1}{\left|\right|y\left|\right|}\·\left[\begin{array}{cccc}\frac{{\∂f}_{n_L}\left(x^{\left(k\right)}\right)}{{\∂x}_1}& \frac{{\∂f}_{n_L}\left(x^{\left(k\right)}\right)}{{\∂x}_2}& \·\·\·& \frac{{\∂f}_{n_L}\left(x^{\left(k\right)}\right)}{{\∂x}_5}\\ \frac{{\∂f}_{n_L+1}\left(x^{\left(k\right)}\right)}{{\∂x}_1}& \frac{{\∂f}_{n_L+1}\left(x^{\left(k\right)}\right)}{{\∂x}_2}& \·\·\·& \frac{{\∂f}_{n_L+1}\left(x^{\left(k\right)}\right)}{{\∂x}_5}\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \·& \·& \·& \·\\ \frac{{\∂f}_{n_R}\left(x^{\left(k\right)}\right)}{{\∂x}_1}& \frac{{\∂f}_{n_R}\left(x^{\left(k\right)}\right)}{{\∂x}_2}& \·\·\·& \frac{{\∂f}_{n_R}\left(x^{\left(k\right)}\right)}{{\∂x}_5}\end{array}\right]$

$f^{\left(k\right)}=\frac{{[f_{n_L}\left(x^{\left(k\right)}\right),f_{n_L+1}\left(x^{\left(k\right)}\right),\·\·\·,f_{n_R}\left(x^{\left(k\right)}\right)]}^T}{\left|\right|y\left|\right|}$

In above formula, [] ^tthe transposition of representing matrix [];

S303, according to formula x ^(k+1)=x ^(k)-[J ^t(x ^(k)) J (x ^(k))+μ ^(k)i] ^-1j ^t(x ^(k)) f ^(k), calculate kth+1 approximate value x ^(k+1), and calculate relative error quadratic sum F (x) kth+1 iterative computation value F (x according to formula (3) ^(k+1));

S304, judge relative error quadratic sum F (x) kth+1 iterative computation value F (x ^(k+1)) iterative computation value F (x secondary to kth ^(k)) magnitude relationship, judge that rule is as follows:

If F is (x ^(k+1)) < F (x ^(k)) and flag=0, then get μ=μ/v, go to step S303;

If F is (x ^(k+1)) < F (x ^(k)) and flag ≠ 0, go to step S305;

If F is (x ^(k+1)) > F (x ^(k)), then get μ=v μ, flag=1, go to step S303;

If S305 meets || J ^t(x ^(k)) f ^(k)||≤ε, then x ^(k+1)for minimal value x ^*approximate, stop; Otherwise make x ^(k)=x ^(k+1), k=k+1, goes to step S202, wherein, || J ^t(x ^(k)) f ^(k)|| be J ^t(x ^(k)) f ^(k)mould;

So far, pick out model parameter, be designated as wherein, represent the parameter such as amplitude, frequency, left decay factor, right decay factor, delay time that model is corresponding respectively. for approaching of transient components in signal under support Interval relative error quadratic sum minimum criteria.

Transient components in S40, iterative extraction signal;

According to S10, S20, S30, signal is the transient components model set up is utilization L-M method identified parameters result is corresponding transient components is without loss of generality, the m time identification result can be made to be designated as wherein, with represent the parameter identification result using L-M method to obtain the m time respectively;

Now, the transient components picked out is designated as residue signal after m-1 subtransient constituents extraction be designated as into residue signal after m subtransient constituents extraction be designated as into m ∈ Z.Then residual signals is expressed as:

$r_{{m,t}_i}=r_{m-1,t_i}-{\mathrm{\&psi;}}_{t_i}\left({\stackrel{\&OverBar;}{x}}_m\right);$

Suppose to perform this iterative process L time, then analyzed signal can be expressed as:

$y_{t_i}=\underset{m=1}{\overset{L}{\mathrm{\&Sigma;}}}{\mathrm{\&psi;}}_{t_i}\left({\stackrel{\&OverBar;}{x}}_m\right)+r_{{L,t}_i}=\underset{m=1}{\overset{L}{\mathrm{\&Sigma;}}}{\mathrm{\&psi;}}_{t_i}({\stackrel{\&OverBar;}{A}}_m,{\stackrel{\&OverBar;}{f}}_m,{\stackrel{\&OverBar;}{\mathrm{\&zeta;}}}_{\mathrm{Lm}},{\stackrel{\&OverBar;}{\mathrm{\&zeta;}}}_{\mathrm{Rm}},{\stackrel{\&OverBar;}{\mathrm{\&tau;}}}_m)+r_{L,t_i}$

In above formula, it is the residue signal after the L time iteration.

It should be noted that, along with L increases, residual signals energy || r _l(t) || ²to exponentially form decay, and speed of decaying is relevant with the degree of closeness of transient components in signal with model.If in the model set up and signal, the transient components that is extracted is more close, then residual signals energy || r _l(t) || ²decay faster, otherwise decay is slower;

The end condition of iterative process is relevant with the application of method.Usually, can by the method for setting threshold value, when certain iteration, the energy of residue signal and the energy ratio of analyzed signal reach fixing threshold value, then iterative process stops.

Further, in some embodiments, step can be comprised after the step s 40: represent on time-frequency plane by the transient components of extraction;

The transient components of extraction is represented on time-frequency plane, the time-frequency representation of the transient components namely extracted, due to for multicomponent data processing, cross term is serious, and in this embodiment, transient components is extracted one by one, and each transient components is simple component composition, use wigner-ville distribution, hereinafter referred to as WVD, represent, because the time frequency resolution of WVD is high, therefore there is not cross term problem in WVD.

Then the WVD of continuous signal y (t) is:

${\mathrm{WVD}}_y(t,\mathrm{\&omega;})={\&Integral;}_{-\&infin;}^{+\&infin;}y(t+\frac{\mathrm{\&tau;}}{2})y^{*}(t-\frac{\mathrm{\&tau;}}{2})\exp (-\mathrm{j\&omega;\&tau;})\mathrm{d\&tau;}$

In above formula, () ^*the conjugation of representing matrix (),

The transient components extracted is simple component composition, is designated as ${\mathrm{\&psi;}}_{{\stackrel{\&OverBar;}{x}}_m}\left(t\right)={\mathrm{\&psi;}}_{t_i}\left({\stackrel{\&OverBar;}{x}}_m\right)={\mathrm{\&psi;}}_{t_i}({\stackrel{\&OverBar;}{A}}_m,{\stackrel{\&OverBar;}{f}}_m,{\stackrel{\&OverBar;}{\mathrm{\&zeta;}}}_{\mathrm{Lm}},{\stackrel{\&OverBar;}{\mathrm{\&zeta;}}}_{\mathrm{Rm}},{\stackrel{\&OverBar;}{\mathrm{\&tau;}}}_m),$ Its WVD is expressed as:

${\mathrm{WVD}}_m(t,\mathrm{\&omega;})={\&Integral;}_{-\&infin;}^{+\&infin;}{\mathrm{\&psi;}}_{{\stackrel{\&OverBar;}{x}}_m}(t+\frac{\mathrm{\&tau;}}{2}){\mathrm{\&psi;}}_{{\stackrel{\&OverBar;}{x}}_m}^{*}(t-\frac{\mathrm{\&tau;}}{2})\exp (-\mathrm{j\&omega;\&tau;})\mathrm{d\&tau;}$

In above formula, WVD _mitself there is not cross term in (t, ω);

By WVD sum corresponding for each transient components extracted superposition, be expressed as:

$\mathrm{WVD}(t,\mathrm{\&omega;})=\underset{m=1}{\overset{L}{\mathrm{\&Sigma;}}}{\mathrm{WVD}}_m(t,\mathrm{\&omega;}).$

Can obtain thus, WVD (t, ω) just represents for extracting transient components time-frequency plane.

The extracting method of transient components in the signal provided in the present embodiment, first parameterized model is set up, pass through Levenbery-Marquardt method iterative extraction transient components again and its parameter of identification, then the transient components in iterative extraction signal, transient components in the signal extracted can be applied to the detection of equipment failure, if the transient components time interval of extracting and the fault signature cycle of this equipment part coincide, then corresponding with this cycle in determining apparatus part position has fault.

In addition, first use WVD to represent on time-frequency plane each transient components extracted in the present embodiment, then superposed by the WVD of each transient components, acquired results is as the time-frequency representation of transient state characteristic in signal, both can obtain the high-resolution time-frequency representation of feature, cross term interference can have been avoided completely again.

It should be noted that, in the signal that the present embodiment provides, the extracting method of transient components, can be applicable to the detection in equipment failure, i.e. sensor installation on the appropriate location of equipment to be detected, and the vibration signal of checkout equipment, as detection signal this extracting method is adopted to extract detection signal in transient components, if the transient components time interval of extracting and fault signature cycle of this equipment part coincide, then corresponding with this cycle in determining apparatus part position has fault.

Below in conjunction with the detection that accompanying drawing can be applicable in equipment failure to the inventive method, tell about in detail.

The present embodiment relates to a kind of detection of bearing local fault, the outer ring of bearing, inner ring and rolling body are the main happening parts of bearing fault, the local fault (as the peeling off of local, corrosion etc.) occurring in these positions often causes occurring in bear vibration transient impact, when bearing invariablenes turning speed, in vibration signal, there is the transient impact composition in cycle.But the duration of the vibration caused due to local fault is short, and this transient impact is mixed in ground unrest often, shows not obvious simultaneously, the energy increase showing as time-domain signal is remarkable, and the frequency band in frequency domain is wider, not easily detects and extracts.

Experimental subjects is the deep-groove roller bearing being arranged on reductor axle head, and model is 6205-2RS JEMSKF.During test, piezoelectric acceleration sensor is arranged on the position close to bearing on reducer shell.Vibration acceleration signal is stored by computer acquisition after piezoelectric acceleration sensor, the charge amplifier.

This test carries out under the state arranging fault.The typical fault of bearing is set respectively: outer ring fault local and inner ring local fault.In this case, according to structural parameters and rotating speed 1797r/min during experiment of bearing, sample frequency is 48KHz, calculates various kinematics parameters, as table 1.Table 1 represents the kinematics parameters of bearing 6205, cycle in table shows, when there is local fault in the outer ring of bearing, there is the period transient state impact composition that generating period is 9.31ms in vibration signal, same when inner ring local fault, there is the shock characteristic that generating period is 6.16ms.

Table 1

	Frequency/Hz	Cycle/ms
			Rolling body is by any cycle on outer ring	107.4	9.31
Rolling body is by any cycle on inner ring	162.1	6.16

Gather vibration signal when bearing outer ring local fault and inner ring local fault, utilize self-adapting detecting method to its analyzing and processing respectively, the cycle that calculating fault features frequency is corresponding.

Analysis result schematic diagram when Fig. 2 is bearing outer ring local fault.The vibration signal of Fig. 2 (a) for gathering time domain waveform, Fig. 2 (b) is the spectrogram that 2 (a) is corresponding.Apply method step proposed by the invention, the transient components in iterative extraction signal also represents on time-frequency plane, and resolution is high and without cross term, as shown in Fig. 2 (c).Significantly the cycle is the transient components of 9.31ms as can be seen from Figure, coincide, successfully extract fault signature, tracing trouble with the outer ring fault signature cycle.

Analysis result schematic diagram when Fig. 3 is bearing inner race local fault, similar with Fig. 2, Fig. 3 (b) is the spectrogram that 3 (a) is corresponding, and the time-frequency representation result obtained is as shown in Fig. 3 (c).Represent that result resolution is high and without cross term, clearly find out that the cycle is the transient components of 6.16ms, corresponding with inner ring fault signature, effectively extract fault signature, tracing trouble.

The embodiment of the present invention also relates to the fault detect of gear tooth breakage, after certain tooth fracture of gear, can cause in vibration signal and there is transient impact composition, and this composition is carried in the meshing frequency of noise and gear, need by its feature extraction out.

The object of this embodiment is the detection of the third gear meshing gear fault of certain automotive transmission, and the power transmission arrangment of gear case as shown in Figure 4.Piezoelectricity is installed in process of the test on the housing of variator and accelerates dynamic sensor, for picking up vibration acceleration signal.Vibration acceleration signal is stored by computer acquisition after piezoelectric acceleration sensor, the charge amplifier.

For the third gear of this gear case, the cycle has 4: be the swing circle of driving gear, the swing circle of driven gear, the swing circle of normal engagement driving gear, the swing circle of normal engagement driven gear respectively.These four cycles are respectively: (a) T=0.050; (b) T=0.054; (c) T=0.040; D () T=0.030, carries out the detection of period transient state composition characteristics to the vibration signal under third gear engagement.

Fig. 5 is the analysis result schematic diagram of third gear when there is broken teeth fault.The vibration signal of Fig. 5 (a) for recording when third gear exists broken teeth fault time domain waveform, Fig. 5 (b) is the spectrogram that 5 (a) is corresponding.Apply method step proposed by the invention, the transient components in iterative extraction vibration signal also represents on time-frequency plane, and resolution is high and without cross term, as shown in Fig. 5 (c).Significantly the cycle is the transient components of 0.05s as can be seen from Figure, corresponding with the swing circle of third speed drive gear, thus successfully diagnoses position of being out of order.

As can be seen from this example, the transient components that in the signal announced in the present invention, fault can effectively cause by transient components extracting method and parameter detecting thereof and extract, thus efficient diagnosis is out of order, its feature determines the method effectively can be applied to rotary machinery fault diagnosis.

For ease of better implementing the technical scheme of the embodiment of the present invention, the embodiment of the present invention is also provided for the related system implementing such scheme.

The extraction system of transient components in a kind of signal that the embodiment of the present invention provides, for extracting transient components from signal.Please refer to Fig. 6, this system comprises:

Sensing device 10, for inputting and carrying out analog/digital conversion, obtains signal;

Model apparatus for establishing 20, for setting up transient components parameterized model;

Device for identifying 30, for by the transient components in Model approximation signal, and picks out model parameter;

Extraction element 40, for the transient components in iterative extraction signal.

Preferably, this device for identifying 30 can comprise:

Approximation unit, for using Levenbery-Marquardt method, by the transient components in the parameterized model approximation signal set up;

Parameter identification unit, for picking out model parameter according to relative error quadratic sum minimum criteria in support Interval.

In some embodiments, in the signal that provides of the present embodiment, the extraction system of transient components can also comprise:

Indication device, for representing the transient components of extraction at time-frequency plane.

Preferably, this indication device can comprise:

First represents unit, for each transient components extracted is used wigner-ville distribution method, represents on time-frequency plane;

Superpositing unit, for the superposition by each transient components;

Second represents unit, for using wigner-ville distribution to represent at time-frequency plane by the result of superpositing unit, as the time-frequency representation of transient components in signal.

The extraction system of transient components in the signal provided in the present embodiment, first parameterized model is set up, pass through Levenbery-Marquardt method iterative extraction transient components again and its parameter of identification, then the transient components in iterative extraction signal, similarly, the transient components in the signal extracted can be applied to the detection of equipment failure.

Be understandable that, in the signal that the present embodiment provides transient components extraction system in, the function of each functional module can according to the method specific implementation in above-mentioned related method embodiment, and its specific implementation process with reference to the associated description of said method embodiment, can repeat no more herein.

In a kind of signal provided the embodiment of the present invention above, the extracting method of transient components and related system are described in detail, apply specific case herein to set forth principle of the present invention and embodiment, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping; Meanwhile, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, to sum up, this description should not be construed as limitation of the present invention.

Claims (4)

1. the extracting method of transient components in signal, is characterized in that, comprising:

Utilize sensing device to input and carry out analog/digital conversion, obtaining signal;

Set up transient components parameterized model;

By the transient components in signal described in described Model approximation, and pick out model parameter;

Transient components in signal described in iterative extraction;

Described transient components parameterized model is

Wherein, A represents the amplitude of model, and f represents the oscillation frequency of model, ζ _lwith ζ _rrepresent the left decay factor of model and the right decay factor of model respectively, τ represents delay time, model parameter vectors x=(A, f, ζ _l, ζ _r, τ), parameter W _sLwith W _sRrepresent the two-part length in the interval left and right of model support respectively;

Wherein, η represents attenuation rate coefficient,

$W_{\mathrm{sL}}=\sqrt{\frac{-\ln \mathrm{\&eta;}}{{\left(2\mathrm{\&pi;f}\right)}^2}\&CenterDot;\frac{\sqrt{1-{\mathrm{\&zeta;}}_L^2}}{{\mathrm{\&zeta;}}_L}},W_{\mathrm{sR}}=\sqrt{\frac{-\ln \mathrm{\&eta;}}{{\left(2\mathrm{\&pi;f}\right)}^2}}\&CenterDot;\frac{\sqrt{1-{\mathrm{\&zeta;}}_R^2}}{{\mathrm{\&zeta;}}_R}.$

2. extracting method according to claim 1, is characterized in that, comprises further after the transient components in signal described in described iterative extraction:

The transient components of extraction is represented on time-frequency plane.

3. extracting method according to claim 1 and 2, is characterized in that, described by the transient components in signal described in described Model approximation, and picks out model parameter and comprise:

Use the civilian Burger-Ma Kuaertefa of row, by the transient components in the parameterized model approximation signal set up; Model parameter is picked out according to relative error quadratic sum minimum criteria in described model support interval.

4. extracting method according to claim 2, is characterized in that, the described transient components by extraction represents and to comprise on time-frequency plane:

The each transient components extracted is used wigner-ville distribution method, represents on time-frequency plane, then by the superposition of each transient components, and using wigner-ville distribution to be represented at time-frequency plane, acquired results is as the time-frequency representation of transient components in signal.