CN109948286A - Based on the signal decomposition method for improving experience wavelet decomposition - Google Patents
Based on the signal decomposition method for improving experience wavelet decomposition Download PDFInfo
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Abstract
The present invention relates to a kind of based on the signal decomposition method for improving experience wavelet decomposition, it include: to carry out Fourier power spectral density spectral line to fault-signal to seek, it chooses several different size threshold values and rejecting processing is carried out to above-mentioned interference component spectrum, and fault-signal is subjected to resolution process based on New cultivars, utilize fault signature energy ratio information screening optimal threshold, optimal Decomposition result and modal components containing abundant fault characteristic information.The present invention is under jamming pattern, effectively optimal Decomposition is remained to go out the modal components for containing abundant fault characteristic information, eliminate interference, inhibit modal overlap and excessive decomposition phenomenon, desirable broken-down is obtained as a result, compensating for the deficiency of EWT, enriches the theoretical method of mode decomposition.
Description
Technical field
It is specifically a kind of based on improving experience wavelet decomposition the present invention relates to the fault-signal processing method of rotating machinery
Signal decomposition method.
Background technique
The rotating machineries such as hydraulic pump, hydraulic motor, motor, bearing, gear and rotor are widely used in each important
Industrial circle, the working environment that they are faced also tend to the bad working environments such as high temperature, high pressure, high speed, heavy duty, accelerate rotation
The deterioration for tool health status of making a connection, therefore the intelligent trouble diagnosis of rotating machinery is had a very important significance.Rotating machinery
The vibration mode and transmission path of failure are sufficiently complex, fault-signal have the characteristics that it is non-linear and non-stationary, and be easy quilt
Equipment introduces the interference such as noise, ambient noise and electromagnetism.Therefore, how to efficiently extract containing fault characteristic information mould abundant
State component inhibits interference just to become a critical issue.
The shortcomings that wavelet transformation are as follows: none principle that can be followed and criterion come select suitable wavelet basis function with
Signal aspect feature carries out accurate match；Once it is determined that basic function, during decomposition it will not be according to the shape of signal
State feature and change, result in it and do not have adaptivity truly；Again, although small echo has " school microscop "
Function, once it is determined that its resolution ratio also just determines therewith after basic function and scale factor；Finally, with Fourier in short-term
The limitation of transformation is the same, although wavelet transformation has multiple dimensioned more resolution capabilities, requires the signal in small echo window necessary
It is approximate steady (pseudo- steady).
Traditional empirical mode decomposition disadvantage are as follows: such as modal overlap, it cannot effectively be based on signal aspect
Feature separates the mode function of specific time scale, so that different modalities component appears in same decomposition result, either
The same modal components are decomposed in multiple decomposition results；For example end effect, the data edges of modal components will appear hair
Phenomenon is dissipated, and in an iterative process, this edge diverging gradually can inwardly pollute, and with the increase of the number of iterations, can make
Data sequence serious distortion is obtained, modal overlap and chaff component occurs.
In view of the above-mentioned problems, Gilles proposed in paper " Empirical Wavelet Transform " it is a kind of non-thread
The New Method for Processing of property and non-stationary signal, i.e. experience wavelet decomposition (Empirical Wavelet Transform, EWT), together
When its advantages of having merged both wavelet transformation and empirical mode decomposition.EWT passes through segmentation Fourier amplitude spectrum, and each
Wavelet orthogonal basis is established in segmentation section, can be several amplitude modulation-with compactly support frequency spectrum by a multi-modal signal decomposition
The sum of FM signal.But under jamming pattern, it can not effectively be decomposited containing abundant fault characteristic information modal components, pole
It is possible that there is modal overlap and excessive decomposition phenomenon.
Summary of the invention
The present invention is directed under jamming pattern, can not effectively be decomposed through EWT containing abundant fault characteristic information mode
Problem provides a kind of based on improvement experience wavelet decomposition (Improved Empirical Wavelet Transform, IEWT)
Signal decomposition method, this method use condition is broader, and discomposing effect becomes apparent from.
The technical solution used to solve the technical problems of the present invention is that:
A kind of signal decomposition method based on improvement experience wavelet decomposition, includes the following steps:
(1) power density spectrum is sought
The calculating of power density spectrum is carried out to fault-signal；
(2) threshold value based on power density spectrum is rejected
The spectrum for being less than the threshold value in the power density spectrum of fault-signal is rejected using different threshold values, obtains new spectrum sequence
Column；
(3) fault-signal resolution process
N number of continuum is divided into [0 π] range to power density spectrum, establishes wavelet orthogonal basis in each section, it will
Fault-signal is decomposed into the sum of N number of modal components；
(4) optimal Decomposition result is screened
The characteristic energy ratio of each component in decomposition result corresponding to each threshold value is solved, and will be in each decomposition result
Maximum characteristic energy ratio compared and analyzed with time big characteristic energy ratio, and the maximum contrast value based on all decomposition results
In, maximum contrast is filtered out as a result, decomposition result corresponding to the maximum contrast value is optimal Decomposition as a result, corresponding threshold
Value is then known as optimal Decomposition threshold value, and maximum characteristic energy contains most abundant fault characteristic information than corresponding modal components.
The present invention by adopting the above technical scheme, compared with prior art EWT, beneficial effect is:
Under jamming pattern, remains to effectively optimal Decomposition and go out the modal components for containing abundant fault characteristic information, eliminate
Interference inhibits modal overlap and excessive decomposition phenomenon, obtains desirable broken-down result.The deficiency for compensating for EWT enriches mode point
The theoretical method of solution.
Detailed description of the invention
Fig. 1 is piston shoes wear-out failure signal time-domain diagram；
Fig. 2 is in the piston shoes wear-out failure signal decomposition result obtained based on IEWT method containing most abundant fault signature letter
Cease component time-domain diagram；
Fig. 3 is in the piston shoes wear-out failure signal decomposition result obtained based on IEWT method containing most abundant fault signature letter
Cease component power spectrum density figure；
Fig. 4 is in the piston shoes wear-out failure signal decomposition result obtained based on EWT method containing most abundant fault signature letter
Cease component time-domain diagram；
Fig. 5 is in the piston shoes wear-out failure signal decomposition result obtained based on EWT method containing most abundant fault signature letter
Cease component power spectrum density figure；
Fig. 6 is flow chart of the invention.
Specific embodiment
The present invention is further described with reference to the accompanying drawings and embodiments.
The present embodiment illustrates by taking piston shoes wear-out failure as an example.
Referring to Fig. 6, one kind being based on improvement experience wavelet decomposition (Improved Empirical Wavelet
Transform, IEWT) signal decomposition method, the specific steps are as follows:
Step (1), power density spectrum is sought
The calculating of power density spectrum is carried out to collected discrete state fault-signal f (n), spectrum distribution series are denoted as
P。
Step (2), the threshold value based on power density spectrum are rejected
The spectrum for being less than the threshold value in P is picked respectively using L different size of threshold value coefficient × mean (P)
It removes, obtains L new spectrum distribution series Pcoefficient, wherein coefficient=1, integer, the mean (P) of 2 ..., L is flat
Equal spectrum.Since interference spectrum is generally little, so the step largely eliminates interference component spectrum information to boundary
The adverse effect of adaptivenon-uniform sampling, to avoid modal overlap and excessive decomposition phenomenon on as far as possible.
Step (3), fault-signal resolution process
For PcoefficientIt is handled as follows: N number of continuum is divided into [0 π] range to power spectral density spectrum,
Thus N+1 boundary line is generated, it is assumed that signal has N number of mode to constitute, the maximum in spectrogram is screened, by M maximum
Descending arrangement is carried out, there can be two following situations:
1) M >=N illustrates that this method has filtered out sufficient maximum, and retains top n maximum；
2) M < N illustrates that the single mode quantity in signal is less than expected number of components N, retains all maximum, lay equal stress on
Set N.
Either segment is represented by Λn=[ωn-1,ωn],Wherein ωnIt is two
Midpoint (the ω of a continuous maximumn=0, ωN=π).Therefore by center ωn, width Tn=2 τnForm a changeover portion.
To ΛnWindowing process is carried out, is based on Meyer wavelet method, experience scaling function and experience wavelet function are respectively such as
Shown in lower.
Wherein, γ < minn[ωn+1-ωn/ωn+1+ωn], β (x)=35x4-8x5+70x6-20x7。
According to wavelet theory, formula (1) and (2), the wavelet transformation detail coefficients W of signalx(n, t) and Coefficients of Approximation Wx(0,
T) formula (3) and formula (4) be may be defined as:
Signal is reconstructed, as a result as shown in formula (5).
Modal components f is obtained by above-mentionedk, can be indicated by following formula (6) and (7)
Step (4), the screening of optimal Decomposition result
It is obtained in L decomposition result based on above-mentioned, each and every one several modal components is contained in every kind of decomposition result, to every kind point
Each modal components of solution result seek feature energy energy ratio FER (energy and and frequency band at fault characteristic frequency and its frequency multiplication
The ratio of gross energy), and by the maximum characteristic energy ratio FER of each decomposition resultcoefficient,maxWith secondary big characteristic energy ratio
FERcoefficient,secondmaxIt compares and analyzes, i.e. Acoefficient=(FERcoefficient,max-FERcoefficient,secondmax)/
FERcoefficient,secondmax, in certain larger FERcoefficient,maxMaximum A is found out in rangecoefficient, it is denoted as Amax, Amax
Corresponding decomposition result is optimal Decomposition as a result, corresponding threshold value is known as optimal threshold, the FER of the resultcoefficient,max
Corresponding component is the component for containing abundant fault characteristic information.
Referring to fig. 2, Fig. 3, Fig. 4, Fig. 5, the present embodiment are based on IEWT method and prior art EWT method and grind respectively to piston shoes
Damage fault-signal is decomposed, and 8 and 53 modal components are respectively obtained, and is chosen in optimal Decomposition result and is contained most abundant event
Hinder characteristic information modal components.Clearly it is in based on the obtained Fig. 2 containing most abundant fault characteristic information modal components of IEWT
Periodic impulse characteristic information is showed, although also clear based on Fig. 4 containing most abundant fault characteristic information modal components that EWT is obtained
Periodic impulse characteristic information is presented clearly, but its spectrum is much smaller compared with obtaining based on IEWT；Contained based on what IEWT was obtained
There is the piston shoes wear-out failure original signal in the information from objective pattern and Fig. 1 of Fig. 2 of most abundant fault characteristic information modal components
Information from objective pattern has very high similitude, and contains most abundant fault characteristic information modal components compared with what is obtained based on EWT
Fig. 4 information from objective pattern and Fig. 1 in piston shoes wear-out failure original signal information from objective pattern similitude it is much higher.Base
In the modal components result figure 3 that IEWT optimal Decomposition obtains clearly present piston shoes wear-out failure characteristic frequency 171.5Hz and
Spectral line at its frequency multiplication, although being also clearly in based on the obtained Fig. 5 containing most abundant fault characteristic information modal components of EWT
The spectral line at piston shoes wear-out failure characteristic frequency 171.5Hz and its frequency multiplication is showed, but its spectrum is small compared with obtaining based on IEWT
Very much.
The above is only a specific embodiments of the invention, it is noted that although making referring to the embodiment to the present invention
It is described in detail, for those of ordinary skill in the art, several improvement and profit can be carried out to technical solution of the present invention
Decorations, but the spirit and scope of technical solution of the present invention is not departed from, these improvements and modifications are also considered as the protection model of the invention patent
It encloses.
Claims (1)
1. a kind of based on the signal decomposition method for improving experience wavelet decomposition, which comprises the steps of:
(1) power density spectrum is sought
The calculating of power density spectrum is carried out to fault-signal；
(2) threshold value based on power density spectrum is rejected
The spectrum for being less than the threshold value in the power density spectrum of fault-signal is rejected using different threshold values, obtains new spectrum sequence；
(3) fault-signal resolution process
N number of continuum is divided into [0 π] range to power density spectrum, wavelet orthogonal basis is established in each section, by failure
Signal decomposition is the sum of N number of modal components；
(4) optimal Decomposition result is screened
Solve the characteristic energy ratio of each component in decomposition result corresponding to each threshold value, and by each decomposition result most
Big characteristic energy ratio is compared and analyzed with time big characteristic energy ratio, and in the maximum contrast value based on all decomposition results, sieve
Select maximum contrast as a result, decomposition result corresponding to the maximum contrast value be optimal Decomposition as a result, corresponding threshold value then
Referred to as optimal Decomposition threshold value, maximum characteristic energy contain most abundant fault characteristic information than corresponding modal components.
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| CN113554103A (en) * | 2021-07-28 | 2021-10-26 | 大连海天兴业科技有限公司 | Fault diagnosis algorithm for rolling bearing of train running gear |
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