CN107525671A - A kind of wind-powered electricity generation driving-chain combined failure character separation and discrimination method - Google Patents
A kind of wind-powered electricity generation driving-chain combined failure character separation and discrimination method Download PDFInfo
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Abstract
A kind of wind-powered electricity generation driving-chain combined failure character separation and discrimination method, step are as follows：Vibration signal is obtained using acceleration transducer；Using multi-wavelet packets transform, will be decomposed in the vibration signal whole frequency range of collection；To arrange entropy as evaluation index, select satisfactory single supported signal to be reconstructed respectively, complete noise reduction and the combined failure separation of signal；Using energy operator demodulation method to reconstructing signal transacting, the identification of fault message is completed.
Description
Technical field
The present invention relates to a kind of wind-powered electricity generation driving-chain combined failure character separation method.
Background technology
With the fast development of wind generating technology, the function of wind power equipment becomes strong, volume becomes big, degree of intelligence uprises,
Corresponding operation and maintenance cost is also continuously increased, and is increased with Wind turbines active time, and fault rate dramatically increases, data statistics
Proving, failure is concentrated mainly on the parts such as wind-powered electricity generation electric control system, transmission system, blade, once breaking down can cause to stop
Machine, huge economic loss is brought, wherein, driving unit fault causes fault power time most long, it is therefore necessary to wind-powered electricity generation
The running status of transmission chain system is monitored and diagnosed.
In actual motion, wind power equipment is often due to a variety of causes causes various faults while occurred, it is also possible to a kind of
The operation conditions of the wind power equipment that changes of failure, and then cause associated components to break down.Gear-box is as wind-power electricity generation
The important transmission parts of equipment, nonserviceable lower operation when, the signal that collects is mostly the fault-signal modulated, this be by
In when a failure occurs it, fault-signal is mainly shown as：Frequency and its multiple-frequency modulation are turned by axle around the meshing frequency of gear；Axle
Hold when failing, the vibration signal collected can be by periodic temporary impact pulse signal modulation.Therefore, how accurately,
Quickly fault signature is extracted, is the key to the diagnosis of wind power equipment running status, energy operator demodulation is as a kind of letter
Single quick demodulation method, can effectively solve the problem that the modulation phenomenon of signal.But due to energy operator demodulation method, it is vulnerable to noise
Influence, and the actual vibration signal obtained of engineering is mostly complicated amplitude-modulation frequency-modulation signal, and contain strong ambient noise, directly
Energy operator demodulation is carried out, it is difficult to extract whole fault signatures.
Chinese invention patent CN103900816A " a kind of wind power generating set Method for Bearing Fault Diagnosis " uses wavelet packet
Method is decomposed to vibration signal, is carried out noise reduction to the high frequency coefficient after decomposition using soft-threshold, is then carried out signal reconstruction,
Carry out three layers of decomposition of wavelet packet again to the signal after reconstruct, obtain the energy of each frequency band of third layer, and feature is used as using the energy
Vector input BP neural network, realizes Wind turbines bearing failure diagnosis.The patent is realized to signal using method of wavelet packet
Noise reduction and failure whether accurate judgement, but can not really realize the separation and identification of the combined failure of modulation.Chinese invention
Patent CN102937522A " a kind of gear-box combined failure diagnosis and system " uses dual-tree complex wavelet transform method decomposed signal,
Multiple subbands are obtained, each subband signal is analyzed using energy operator method respectively, extracts fault signature, though the patent can identify again
Failure is closed, but because the subband signal after decomposition is without relevant evaluation index, each subband signal need to be handled successively, cause to count
The result that evaluation time is long and accuracy is not high.Document " is based on LMD self-adapting multi-dimensions morphology and Teager energy operator methods
Application in bearing failure diagnosis " proposes to be decomposed into multiple subband signals using LMD (part mean decomposition method), and utilizes
Multi-scale morphology filtering is handled each subband, is demodulated after processing using energy operator, obtains bearing fault characteristics frequency,
This method can effectively extract fault message, but equally exist to the subband of decomposition without relevant evaluation On Index, need to be right successively
Each subband signal is handled, and causes to calculate the problem of time is long and precision is not high.
The content of the invention
The purpose of the present invention is to overcome the disadvantage mentioned above of prior art, proposes a kind of wind-powered electricity generation driving-chain combined failure feature point
From with discrimination method.
Present invention introduces multi-wavelet packets algorithm, multi-wavelet packets decomposition method can by signal in whole frequency ranges it is fine
Change and decompose.The characteristics of present invention contains multiple time-frequency characteristics basic functions using multi-wavelet packets, pass through of basic function and fault signature
Match somebody with somebody, obtain comprehensive fault message, realize the identification and diagnosis of failure.The present invention is using multi-wavelet packets transform to containing compound event
The modulated signal of barrier is decomposed, and feature reconstruction is carried out as evaluation index, the qualified single supported signal of selection to arrange entropy,
The fault-signal reconstructed with energy operator demodulation method, demodulation analysis, demodulation frequency spectrum is obtained, extracts fault signature, realizes event
Hinder the separation and diagnosis of feature.
The present invention includes the feature using multi-wavelet packets method separation signal, the single supported signal of selection, reconstruction signal, energy and calculated
Subsolution is adjusted with identifying the steps such as fault signature, specific as follows：
1. using vibration acceleration sensor collection wind-powered electricity generation driving-chain vibration signal x (t), wherein t represents collection signal institute
The corresponding time；
2. the vibration signal x (t) that step 1 collects is decomposed using multi-wavelet packets method, it is specific as follows：
(1) multi-wavelet pretreatment vibration signal x (t)；
Because the vibration signal x (t) is one-dimensional signal, and m ultiwavelet generally comprises multiple scaling functions and wavelet function, because
This carries out multi-wavelet pretreatment using repeated sampling method to vibration signal x (t), obtains two-dimentional multi-wavelet pretreatment signal x1
(t), t represents the time corresponding to collection signal.
(2) to two-dimentional multi-wavelet pretreatment signal x1(t) multi-wavelet packets decomposition is carried out；
M ultiwavelet decomposition formula is as follows：
In formula, sj,kRepresent the scale coefficient of jth layer decomposition, dj,kRepresent the wavelet coefficient of jth layer decomposition, Hn-2kFor signal
Carry out m ultiwavelet and decompose low-pass filter coefficients, Gn-2kM ultiwavelet is carried out for signal and decomposes high-pass filter coefficient, and j is small wavelength-division
Solve the number of plies, sj-1,nRepresent the scale coefficient of -1 layer of decomposition of jth, dj-1,nThe wavelet coefficient of -1 layer of decomposition of jth is represented, n is sampled point
Number, k are the sampling number k=0,1 ..., n-1 that m ultiwavelet decomposes jth layer coefficients；
Scaling function and wavelet function are decomposed simultaneously, wherein third layer decomposes to obtain 4 groups of scaling functions and 4 groups of small echo letters
Count, totally 16 single supported signals.
3. to arrange entropy as evaluation index, single supported signal of the acquisition of analytical procedure 2, chooses and is enriched containing characteristic information respectively
Single supported signal carry out the reconstruct of multi-wavelet packets list branch, obtain multi-wavelet packets list branch reconstruction signal；
(1) arrangement entropy is solved first, is comprised the following steps that：
1) phase space of reconstruction signal
It is assumed that discrete-time series x (t), t=1,2 ... and .., N }, phase space reconfiguration is carried out to it, obtained：
In formula：Y is the phase space of reconstruct
N is discrete time data length
M is Embedded dimensions
τ is time delay
K be reconstruct component number, k=N-m+1
X (j) is restructuring matrix jth row component
J be reconstruct phase space any row component, j=1,2 ... k
2) primary signal is reconfigured
In matrix row component Y (j,:) it is considered as a reconstruct component, share k reconstruct component, k=N-m+1, to Y
(j,:) in each element [x (j) x (j+ τ) ... x (j+ (m-1) τ)] according to rearranging from small to large, i1 i2...inTable
Show the index position for rearranging rear element column, there is unique group code sequence after rearrangement：S (l)={ i1
i2...id, in formula, l=1,2 ..., k, and k≤m！, the probability by the way that every kind of sequence is calculated is p1 p2…pk；
3) value of arrangement entropy is solved
The arrangement entropy H of Discrete Stochastic time series { x (t), t=1,2 ... .., N }pDefined by formula：J=1,2 ..., k, pjFor the probability of j-th of sequence；
4) normalization arrangement entropy
Data are normalized：HP=HP(m)/ln(m！).
(2) the single supported signal enriched containing characteristic information is chosen：
It is 0.79 to set the threshold value of arrangement entropy according to Chebyshev inequality, and arrangement entropy is more than 0.79 single supported signal conduct
Reconstruction signal, data of the present invention select single supported signal that two arrangement entropys are more than 0.79.
4. reconstruction signal, single supported signal is reconstructed using m ultiwavelet, obtains reconstruction signal ya(t), wherein, a represents reconstruct letter
Number number, t represents the time corresponding to reconstruction signal；
As the inverse transformation process decomposed, how small the abundant single supported signal of the characteristic information to selection carry out for m ultiwavelet reconstruct
Ripple bag reconstructs, and specific formula is as follows：
K=0,1 ..., N-1
In formula, sj,nRepresent scale coefficient, dj,nRepresent wavelet coefficient, Hk-2nChecked the number for letter and carry out m ultiwavelet reconstruction low pass
Ripple device coefficient, Gk-2nHigh-pass filter coefficient is reconstructed to carry out m ultiwavelet to signal, j is the wavelet reconstruction number of plies, and N is sampled point
Number, sj-1,nRepresent the scale coefficient of -1 layer of decomposition of jth, dj-1,nRepresent the wavelet coefficient of -1 layer of jth decomposition, n is sampling number, k
The sampling number k=0,1 of jth layer coefficients is decomposed for m ultiwavelet ..., n-1.
5. demodulation analysis, the reconstruction signal y obtained using energy operator method demodulation step 4a(t) solved corresponding to, obtaining
Adjust spectrum signature；
Energy operator demodulation method step is as follows：
(1) for reconstruction signal ya(t) its energy operator ψ, is definedCFor：
Wherein, t represents the time corresponding to reconstruction signal,For reconstruction signal ya(t) time t is asked single order,
Second-order differential obtains.
(2) instantaneous amplitude and instantaneous frequency of amplitude modulationfrequency modulation time signal are solved using energy operator：
Wherein, t represents the time corresponding to reconstruction signal, i.e., the time corresponding to wind-powered electricity generation driving-chain, and a (t) is instantaneous amplitude, wi
For instantaneous frequency.
6. demodulation spectra characteristic frequency and the fault characteristic frequency in wind-powered electricity generation driving-chain running that step 5 obtains are contrasted,
The running status of wind-powered electricity generation driving-chain is judged, wherein bearing fault frequency, gear distress frequency calculation formula are as follows：
(1) bearing fault frequency：
1) outer ring failure-frequency fW
2) inner ring failure-frequency fN
3) rolling element failure-frequency fG
4) retainer failure-frequency fB
In formula：d0For rolling element diameter；D is pitch diameter；α is the nominal contact angle of bearing；frFor the rotating speed frequency of bearing
Rate；Z is the rolling element number of bearing.
(2) gear distress frequency：
Meshing frequency fM
fM=f1Z1=f2Z2
In formula：, frFor the speed-frequency of gear；Z is number of gear teeth；N gear rotational speeds, unit r/min；f1For driving wheel
Speed-frequency；f2For secondary speed frequency；Z1For the number of teeth of driving wheel；Z2For the number of teeth of driven pulley.
Compared with prior art, the present invention is taking into full account wind-powered electricity generation drive mechanism complexity, multiple faults frequency modulation(PFM) easily occurs
On the premise of problem, wind-powered electricity generation driving-chain combined failure is diagnosed with reference to multi-wavelet packets algorithm and energy operator demodulating algorithm.Wherein,
Energy operator demodulation method can effectively solve the problem that the modulation phenomenon of signal as a kind of simple and quick demodulation method, but due to
Energy operator demodulation method, is vulnerable to influence of noise.Multi-wavelet packets transform is used as a kind of efficient, accurate decomposition method, relatively
It can meet the characteristics such as orthogonality, compact sup-port, symmetry, high-order vanishing moment simultaneously in wavelet packet analysis, can effectively solve the problem that energy
The problem of amount operator demodulation method is easily influenceed by ambient noise, and the concept of arrangement entropy is introduced, evaluation of the arrangement entropy as signal
Index quantification, the quick single supported signal for determining to include fault message, the selection for single branch reconstruction signal provide scientific basis, therefore
The present invention is applied in wind-powered electricity generation driving-chain combined failure character separation and identification process, you can is precisely separating combined failure, again may be used
Accurate identification fault signature.Also, the present invention reconstructs single supported signal using multi-wavelet packets, and the reconstruct of multi-wavelet packets list branch can either be real
Existing signal de-noising and can enough completes the character separation of combined failure.
Brief description of the drawings
Fig. 1 is the overview flow chart of the present invention；
Fig. 2 is multi-wavelet packets character separation flow chart；
Fig. 3 is that multi-wavelet packets list branch reconstructs flow chart；
Fig. 4 is the result figure individually identified to combined failure using energy operator demodulating algorithm, and Fig. 4 a are time-domain diagram, Fig. 4 b
To demodulate spectrogram；
Fig. 5 is the result figure to combined failure identification using the present invention, and Fig. 5 a are single time-domain diagram of branch reconstruction signal 1, Fig. 5 b
Spectrogram is demodulated to be corresponding, Fig. 5 c are single time-domain diagram of branch reconstruction signal 2, and Fig. 5 d are corresponding demodulation spectrogram.
Embodiment
The present invention is further illustrated below in conjunction with the drawings and specific embodiments.
As shown in figure 1, combined failure character separation of the present invention and discrimination method step are as follows：
1. using vibration acceleration sensor collection wind-powered electricity generation driving-chain vibration signal x (t), wherein t represents collection signal institute
The corresponding time；
2. being decomposed using multi-wavelet packets method to described vibration signal x (t), flow is as shown in Fig. 2 specifically such as
Under：
(1) multi-wavelet pretreatment is carried out to vibration signal x (t)；
Because the signal x (t) is one-dimensional signal, and m ultiwavelet generally comprises multiple scaling functions and wavelet function, therefore adopts
Multi-wavelet pretreatment is carried out to vibration signal x (t) with repeated sampling method, obtains two-dimentional multi-wavelet pretreatment signal x1(t), t
Represent the time corresponding to collection signal；
(2) to x1(t) multi-wavelet packets decomposition is carried out；
M ultiwavelet decomposition formula is as follows：
K=0,1 ..., n-1
In formula, sj,kRepresent scale coefficient, dj,kRepresent wavelet coefficient, Hn-2kM ultiwavelet, which is carried out, for signal decomposes LPF
Device coefficient, Gn-2kM ultiwavelet is carried out for signal and decomposes high-pass filter coefficient, and j is the wavelet decomposition number of plies, and n is sampling number t generations
Time corresponding to table wind-powered electricity generation driving-chain vibration signal；In (implication that K please be illustrate) formula, sj,kRepresent the yardstick system of jth layer decomposition
Number, dj,kRepresent the wavelet coefficient of jth layer decomposition, Hn-2kM ultiwavelet, which is carried out, for signal decomposes low-pass filter coefficients, Gn-2kFor letter
Number carrying out m ultiwavelet decomposes high-pass filter coefficient, and j is the wavelet decomposition number of plies, sj-1,nThe scale coefficient of -1 layer of decomposition of jth is represented,
dj-1,nThe wavelet coefficient of -1 layer of decomposition of jth is represented, n is sampling number, and k is the sampling number k=that m ultiwavelet decomposes jth layer coefficients
0,1,…,n-1；
Scaling function and wavelet function are decomposed simultaneously, wherein third layer decomposes to obtain 4 groups of scaling functions and 4 groups of small echo letters
Count, totally 16 single supported signals.
3. to arrange entropy as evaluation index, single supported signal of the acquisition of analytical procedure 2, chooses qualified Dan Zhixin respectively
Number the reconstruct of multi-wavelet packets list branch is carried out, obtain single branch reconstruction signal；
(1) arrangement entropy is solved first, is comprised the following steps that：
1) phase space of reconstruction signal
It is assumed that discrete-time series x (t), t=1,2 ... and .., N }, phase space reconfiguration is carried out to it, obtained：
In formula：Y is the phase space of reconstruct
N is discrete time data length
M is Embedded dimensions
τ is time delay
K be reconstruct component number, k=N-m+1
X (j) is restructuring matrix jth row component
J be reconstruct phase space any row component, j=1,2 ... k
2) primary signal is reconfigured
In matrix row component Y (j,:) it is considered as a reconstruct component, share k reconstruct component, k=N-m+1, to Y
(j,:) in each element [x (j) x (j+ τ) ... x (j+ (m-1) τ)] according to rearranging from small to large, i1 i2...inTable
Show the index position for rearranging rear element column, there is unique group code sequence after rearrangement：S (l)={ i1
i2...id, in formula, l=1,2 ..., k, and k≤m！, the probability by the way that every kind of sequence is calculated is p1 p2…pk。
3) value of arrangement entropy is solved
The arrangement entropy H of Discrete Stochastic time series { x (t), t=1,2 ... .., N }pDefined by formula：J=1,2 ..., k, pjFor the probability of j-th of sequence；
4) normalization arrangement entropy
Data are normalized：HP=HP(m)/ln(m！).
(2) the single supported signal enriched containing characteristic information is chosen：
It is 0.79 to set the threshold value of arrangement entropy according to Chebyshev inequality, and arrangement entropy is more than 0.79 single supported signal conduct
Reconstruction signal, data of the present invention select single supported signal that two arrangement entropys are more than 0.79；
It is reconstruction signal to determine LLH1, LHH2.
4. the single supported signal of reconstruct.LLH1, LHH2 are determined as reconstruction signal as evaluation index to arrange entropy, with multi-wavelet packets
Reconfiguration principle for instruct, single supported signal is reconstructed, complete reconstruct after, carry out m ultiwavelet post processing, flow as shown in figure 3,
Specific formula for calculation is as follows.
K=0,1 ..., N-1
In formula, sj,nRepresent scale coefficient, dj,nRepresent wavelet coefficient, Hk-2nChecked the number for letter and carry out m ultiwavelet reconstruction low pass
Ripple device coefficient, Gk-2nHigh-pass filter coefficient is reconstructed to carry out m ultiwavelet to signal, j is the wavelet reconstruction number of plies, and N is sampled point
Number, sj-1,nRepresent the scale coefficient of -1 layer of decomposition of jth, dj-1,nRepresent the wavelet coefficient of -1 layer of jth decomposition, n is sampling number, k
The sampling number k=0,1 of jth layer coefficients is decomposed for m ultiwavelet ..., n-1.
5. demodulation analysis, the reconstruction signal y obtained using energy operator demodulation method analytical procedure 4a(t), corresponded to
Demodulation spectrum signature；
Energy operator demodulation method step is as follows：
(1) for reconstruction signal ya(t) its energy operator ψ, is definedCFor：
Wherein, t represents the time corresponding to wind-powered electricity generation driving-chain vibration signal,For time signal yaPair (t) when
Between t ask single order, second-order differential to obtain.
(2) instantaneous amplitude and instantaneous frequency of amplitude modulationfrequency modulation time signal are solved using energy operator：
Wherein, t represents the time corresponding to wind-powered electricity generation driving-chain vibration signal, and a (t) is instantaneous amplitude, wiFor instantaneous frequency.
6. judge failure
Contrast step 5 obtain demodulation spectra frequecy characteristic with wind-powered electricity generation driving-chain running fault characteristic frequency therefore
Hinder frequency, judge the running status of Wind turbines, the present invention is recognized with wind-powered electricity generation driving-chain bearing fault to its running status：
Bearing fault frequency：
1) outer ring failure-frequency fW
2) inner ring failure-frequency fN
3) rolling element failure-frequency fG
4) retainer failure-frequency fB
In formula：d0For rolling element diameter；D is pitch diameter；α is the nominal contact angle of bearing；frFor the rotating speed frequency of bearing
Rate；Z is the rolling element number of bearing.
The Rolling Bearing Fault Character frequency of table 1
Fig. 4 b are that Fig. 4 a are obtained using the processing of energy operator demodulation method, the failure-frequency run according to known device：It is interior
Circle failure-frequency is 121.9Hz, is determined as bearing inner race failure.Because energy operator demodulates self-defect, using present invention side
Method handles wind-powered electricity generation driving-chain vibration signal, as a result as shown in Fig. 5 a, Fig. 5 b, Fig. 5 c, Fig. 5 d：Outer ring failure-frequency 76.88Hz, says
There is inside and outside circle combined failure in the bright bearing, it was demonstrated that the present invention can realize the separation and identification of combined failure simultaneously.
Claims (6)
1. a kind of wind-powered electricity generation driving-chain combined failure character separation and discrimination method, it is characterised in that：How small described method use is
Ripple bag method separates the feature of signal, selects single supported signal, reconstruction signal, and energy operator demodulation and identification fault signature,
Comprise the following steps that：
(1) using vibration acceleration sensor collection wind-powered electricity generation driving-chain vibration signal x (t), wherein t is represented corresponding to collection signal
Time；
(2) decomposed in vibration signal x (t) whole frequency ranges collected using multi-wavelet packets method to step (1)；
(3) to arrange entropy as evaluation index, single supported signal of analytical procedure (2) acquisition, the list enriched containing characteristic information is chosen
Supported signal carries out the reconstruct of multi-wavelet packets list branch, obtains multi-wavelet packets list branch reconstruction signal；
(4) the selected single supported signal of reconstruct, single supported signal is reconstructed using multi-wavelet packets method respectively, obtains reconstruction signal ya(t),
Wherein a=1,2 ..., a represent the number of reconstruction signal, and t represents the time corresponding to reconstruction signal；
(5) demodulation analysis, the reconstruction signal y obtained using energy operator method demodulation step (4)a(t) demodulated corresponding to, obtaining
Spectrum signature；
(6) demodulation spectra characteristic frequency and the fault characteristic frequency in wind-powered electricity generation driving-chain running that step (5) obtains are contrasted, it is right
The running status of wind-powered electricity generation driving-chain judges.
2. wind-powered electricity generation driving-chain combined failure character separation according to claim 1 and discrimination method, it is characterised in that：It is described
The method that the vibration signal x (t) that step (2) is collected using multi-wavelet packets method to step (1) is decomposed is as follows：
(1) multi-wavelet pretreatment vibration signal x (t)；
Multi-wavelet pretreatment is carried out to vibration signal x (t) using repeated sampling method, obtains two-dimentional multi-wavelet pretreatment signal x1
(t), t represents the time corresponding to collection signal；
(2) to two-dimentional multi-wavelet pretreatment signal x1(t) multi-wavelet packets decomposition is carried out；
M ultiwavelet decomposition formula is as follows：
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In formula, sj,kRepresent the scale coefficient of jth layer decomposition, dj,kRepresent the wavelet coefficient of jth layer decomposition, Hn-2kCarried out for signal
M ultiwavelet decomposes low-pass filter coefficients, Gn-2kM ultiwavelet is carried out for signal and decomposes high-pass filter coefficient, and j is wavelet decomposition layer
Number, sj-1,nRepresent the scale coefficient of -1 layer of decomposition of jth, dj-1,nRepresent the wavelet coefficient of -1 layer of jth decomposition, n is sampling number, k
The sampling number k=0,1 of jth layer coefficients is decomposed for m ultiwavelet ..., n-1；
Scaling function and wavelet function are decomposed simultaneously, wherein third layer decomposes to obtain 4 groups of scaling functions and 4 groups of wavelet functions,
Totally 16 single supported signals.
3. wind-powered electricity generation driving-chain combined failure character separation according to claim 1 and discrimination method, it is characterised in that：It is described
The step of (3) to arrange entropy as evaluation index, analytical procedure (2) obtain single supported signal, choose containing characteristic information enrich
Single supported signal carries out the reconstruct of multi-wavelet packets list branch, and the method for obtaining multi-wavelet packets list branch reconstruction signal is as follows：
(1) arrangement entropy is solved first：
1) phase space of reconstruction signal
It is assumed that discrete-time series x (t), t=1,2 ... and .., N }, phase space reconfiguration is carried out to it, obtained：
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<mo>,</mo>
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<mo>,</mo>
<mn>...</mn>
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</mrow>
In formula：Y is the phase space of reconstruct, and N is discrete time data length, and m is Embedded dimensions, and τ is time delay, and k is reconstruct
The number of component, k=N-m+1, x (j) are restructuring matrix jth row component, and j is the phase space any row component of reconstruct, j=1,
2,...k；
2) primary signal is reconfigured
In matrix row component Y (j,:) it is considered as a reconstruct component, share k reconstruct component, k=N-m+1, to Y
(j,:) in each element [x (j) x (j+ τ) ... x (j+ (m-1) τ)] according to rearranging from small to large, i1 i2 ...
inExpression rearranges the index position of rear element column, there is unique group code sequence after rearrangement：S (l)={ i1 i2
... id, in formula, l=1,2 ..., k, and k≤m！, the probability by the way that every kind of sequence is calculated is p1 p2 … pk；
3) value of arrangement entropy is solved
The arrangement entropy H of Discrete Stochastic time series { x (t), t=1,2 ... .., N }pDefined by formula：
J=1,2 ..., k, pjFor the probability of j-th of sequence；
4) normalization arrangement entropy
Data are normalized：HP=HP(m)/ln(m！)；
(2) the single supported signal enriched containing characteristic information is chosen：
It is 0.79 to set the threshold value of arrangement entropy according to Chebyshev inequality, and single supported signal of the arrangement entropy more than 0.79 is as reconstruct
Signal, data of the present invention select single supported signal that two arrangement entropys are more than 0.79.
4. wind-powered electricity generation driving-chain combined failure character separation according to claim 1 and discrimination method, it is characterised in that：It is described
The step of the selected signal of (4) reconstruct, method is as follows：
The single supported signal enriched to the characteristic information that step (3) is chosen carries out multi-wavelet packets reconstruct, and specific formula is as follows：
<mrow>
<msub>
<mi>s</mi>
<mrow>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>,</mo>
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</msub>
<mo>=</mo>
<munder>
<mo>&Sigma;</mo>
<mi>n</mi>
</munder>
<mrow>
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<mi>H</mi>
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<mn>2</mn>
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<munder>
<mo>&Sigma;</mo>
<mi>n</mi>
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<mi>k</mi>
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<mn>2</mn>
<mi>n</mi>
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<mi>j</mi>
<mo>,</mo>
<mi>n</mi>
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<mo>,</mo>
</mrow>
K=0,1 ..., N-1；
In formula, sj,nRepresent scale coefficient, dj,nRepresent wavelet coefficient, Hk-2nChecked the number for letter and carry out m ultiwavelet reconstruction low pass filter
Coefficient, Gk-2nHigh-pass filter coefficient is reconstructed to carry out m ultiwavelet to signal, j is the wavelet reconstruction number of plies, and N is sampling number,
sj-1,nRepresent the scale coefficient of -1 layer of decomposition of jth, dj-1,nThe wavelet coefficient of -1 layer of decomposition of jth is represented, n is sampling number, and k is
M ultiwavelet decomposes the sampling number k=0,1 of jth layer coefficients ..., n-1.
5. wind-powered electricity generation driving-chain combined failure character separation according to claim 1 and discrimination method, it is characterised in that：It is described
The step of (5) use the obtained reconstruction signal y of energy operator method demodulation step (4)a(t) demodulation spectrum signature corresponding to, obtaining
Method it is as follows：
Energy operator demodulation method step is as follows：
(1) for reconstruction signal ya(t) its energy operator ψ, is definedCFor：
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<mi>&psi;</mi>
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<mn>2</mn>
</msup>
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<mo>=</mo>
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</mover>
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</mover>
<mi>a</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
Wherein, t represents the time corresponding to reconstruction signal,For reconstruction signal ya(t) single order, second order are asked to time t
Differential obtains；
(2) instantaneous amplitude and instantaneous frequency of amplitude modulationfrequency modulation time signal are solved using energy operator：
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<mrow>
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<mi>c</mi>
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<mi>c</mi>
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<mi>i</mi>
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<mfrac>
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<mi>c</mi>
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<mi>&psi;</mi>
<mi>c</mi>
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<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>)</mo>
</mrow>
</mfrac>
</msqrt>
</mrow>
Wherein, t represents the time corresponding to reconstruction signal, i.e., the time corresponding to wind-powered electricity generation driving-chain, and a (t) is instantaneous amplitude, wiFor wink
When frequency.
6. wind-powered electricity generation driving-chain combined failure character separation according to claim 1 and discrimination method, it is characterised in that：It is described
The step of (6) contrast step (5) demodulation spectra characteristic frequency and wind-powered electricity generation driving-chain running for obtaining in fault characteristic frequency,
Judge the running status of Wind turbines, wherein bearing fault frequency, gear distress frequency calculation formula is as follows：
(1) bearing fault frequency：
1) outer ring failure-frequency fW
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<mi>f</mi>
<mi>r</mi>
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<mi>Z</mi>
</mrow>
2) inner ring failure-frequency fN
<mrow>
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</mrow>
<msub>
<mi>f</mi>
<mi>r</mi>
</msub>
<mi>Z</mi>
</mrow>
3) rolling element failure-frequency fG
<mrow>
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</msub>
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</msub>
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<mi>d</mi>
<mn>0</mn>
</msub>
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</msup>
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<msub>
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</mrow>
4) retainer failure-frequency fB
<mrow>
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<mi>B</mi>
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<mo>=</mo>
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<mfrac>
<mrow>
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</msub>
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<mi>o</mi>
<mi>s</mi>
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</mrow>
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</mfrac>
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</mrow>
<msub>
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<mi>r</mi>
</msub>
</mrow>
In formula：d0For rolling element diameter；D is pitch diameter；α is the nominal contact angle of bearing；frFor the speed-frequency of bearing；Z is
The rolling element number of bearing；
(2) gear distress frequency：
Meshing frequency fM
<mrow>
<msub>
<mi>f</mi>
<mi>M</mi>
</msub>
<mo>=</mo>
<msub>
<mi>f</mi>
<mi>r</mi>
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<mo>=</mo>
<mfrac>
<mi>n</mi>
<mn>60</mn>
</mfrac>
<mi>Z</mi>
</mrow>
fM=f1Z1=f2Z2
In formula：, frFor the speed-frequency of gear；Z is number of gear teeth；N gear rotational speeds, unit r/min；f1For driving wheel rotating speed
Frequency；f2For secondary speed frequency；Z1For the number of teeth of driving wheel；Z2For the number of teeth of driven pulley.
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| CN110470462A (en) * | 2019-08-22 | 2019-11-19 | 苏州旋械感知信息科技有限公司 | One kind being based on C0The reconstructing method of the mechanical system fault features of complexity |
| CN110595751A (en) * | 2019-09-19 | 2019-12-20 | 华东理工大学 | Early fault characteristic wavelet reconstruction method guided by Gini index and application thereof |
| CN112146142A (en) * | 2019-06-27 | 2020-12-29 | 宁波方太厨具有限公司 | Method for recognizing sound fault of range hood |
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