CN108180152A - A kind of wind turbine Weak fault detection method based on vibration signal cyclo-stationary - Google Patents
A kind of wind turbine Weak fault detection method based on vibration signal cyclo-stationary Download PDFInfo
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- CN108180152A CN108180152A CN201711086022.4A CN201711086022A CN108180152A CN 108180152 A CN108180152 A CN 108180152A CN 201711086022 A CN201711086022 A CN 201711086022A CN 108180152 A CN108180152 A CN 108180152A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D17/00—Radial-flow pumps, e.g. centrifugal pumps; Helico-centrifugal pumps
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
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- General Engineering & Computer Science (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
A kind of wind turbine Weak fault detection method based on vibration signal cyclo-stationary, includes the following steps：Step 1, acceleration signal is acquired；Step 2, collected signal is imported into program to be run, with the correlative character function check based on cyclostationary characteristic, obtains circulating density spectrum；Step 3, circulating density is composed and normalized, obtain cycle Correlated Spectroscopy；Step 4, it is strengthened envelope spectrum according to cycle Correlated Spectroscopy, preserves the ordinate data for strengthening envelope spectrum, as handle obtained data in real time；Step 5, using relevance function, calculating handles obtained data and the correlation of database data in real time；Step 6, according to relative coefficient failure resolution standard, judge whether this kind of failure be normal or belongs to which kind of failure, and will determine that result adds in database.It can detect and judge the fault type of wind turbine in real time using the present invention, detection is more accurate, has powerful practicability.
Description
Technical field
The invention belongs to signal processing and field of fault detection more particularly to one kind to be based on vibration signal cyclo-stationary
Wind turbine Weak fault detection method.
Background technology
Cyclo-stationary signal processing is a kind of emerging technology of mechanical signal processing risen recently.Cyclo-stationary signal is
The signal of hiding cycle information is included in signal.Cyclo-stationary signal is one kind of non-stationary signal, is examined compared to tradition
Survey mode, the signal generated closer to actual signal, especially rotating machinery.
The common rotating machinery fault detection method of field of signal processing mainly has Fourier transform, Short-time Fourier at present
Transformation, wavelet transformation, Second Generation Wavelet Transformation and multi-wavelet transformation etc., it may be said that be all based on the signature waveform base of inner product principle
Function signal decomposes, it is intended to flexibly with going preferably to handle signal with the basic function that signature waveform matches, extraction failure spy
Sign, so as to fulfill fault diagnosis.
But following shortcoming and defect exists in the prior art：Fourier transform, short time discrete Fourier transform, wavelet transformation,
The method of the fault detects such as Second Generation Wavelet Transformation and multi-wavelet transformation is built upon assume that signal is stationary signal on the basis of,
And in reality it is often non-stationary signal, so as to which these detection methods have unreasonable place, do not conform to reality.Meanwhile these
Traditional detection method is due to theoretic limitation, it is difficult to detect some important features of rotating machinery, such as blade passing frequency
BPF, blade have significant limitation than frequency BRF etc..
Invention content
The present invention provides a kind of wind turbine Weak fault detection methods based on vibration signal cyclo-stationary, can be real-time
Ground detects and judges the fault type of wind turbine, and detection is more accurate, has powerful practicability.
A kind of wind turbine Weak fault detection method based on vibration signal cyclo-stationary, includes the following steps：
Step 1, using the vibration acceleration signal of acceleration transducer acquisition wind turbine；
Step 2, collected acceleration signal is imported into program to be run, with the phase based on cyclostationary characteristic
The characteristic function detection of closing property, obtains circulating density spectrum；
Step 3, circulating density is composed and normalized, obtain cycle Correlated Spectroscopy；
Step 4, it is strengthened envelope spectrum according to cycle Correlated Spectroscopy, preserves the ordinate data for strengthening envelope spectrum, as in fact
When the data that handle；
Step 5, using relevance function, calculating handles obtained data and the correlation of database data in real time；
Step 6, according to relative coefficient failure resolution standard, judge whether this kind of failure be normal or belongs to which kind of failure,
And it will determine that result adds in database.
In step 2, the correlative character function check process based on cyclostationary characteristic is：
Relevant parameter is set in a program, calculates the parameter of cyclo-stationary：
Wherein：T, T is the time；△ f go to zero, and the sequence that T tends to be just infinite cannot exchange；f1、f2Represent two calculated
Frequency；xΔf(t；f1) represent filtering；Represent xΔf(t,f2) conjugate complex number；J represents imaginary unit.
In step 4, the process for the envelope spectrum that strengthened is：The three-dimensional matrice of cycle Correlated Spectroscopy will be obtained, withTable
Show, then keeping cycle frequency α constant, frequency f being integrated, strengthened envelope spectrum after normalizing.
In step 5, the relevance function is
Wherein, A is to handle obtained data in real time, and B is database data；Cov (A, B) is contrast signal and fitted signal
Covariance, calculation formula is as follows：
Cov (A, B)=E (A × B)-E (A) * E (B)
Wherein, E (A × B) is the mathematic expectaion of the product of A and B；E (A) is the mathematic expectaion of A；E (B) is the mathematic expectaion of B.
In step 5, the process for obtaining database data is：Under the conditions of wind turbine normal work and various typical faults, with
Acceleration transducer acquires the acceleration signal of fan vibration, and inputs in line program to be shipped, normalized by what is be calculated
The ordinate output for strengthening envelope spectrum is data file, is stored in Database Folder.
The present invention solves the problems, such as to be assumed to be stationary signal to a certain extent, since wind turbine is as rotating machinery, production
Raw signal is largely cyclo-stationary signal, and the result of detection is closer to practical, more reliable.Meanwhile cyclo-stationary
Handling result overcome traditional detection method, blade passing frequency and blade ratio frequency detecting less than or unconspicuous difficulty, energy
The more features of wind turbine is showed.
In detection process, the time of program operation is short, can achieve the purpose that monitor in real time, according to relative coefficient and one
Fixed experience judges which kind of failure wind turbine belongs to, and by establishing the file of normal condition and fault condition, needs to compare to enrich
Compared with database so that later detection judgement more accurate, even entirely without experience is the detection of wind turbine Weak fault
Provide a kind of good mode.
Description of the drawings
Fig. 1 is the flow chart of the wind turbine Weak fault detection method the present invention is based on vibration signal cyclo-stationary；
Fig. 2 is the reinforcement envelope spectrogram of ideal signal simulation result；
Fig. 3 is the Fast Fourier Transform figure of ideal signal simulation result；
Fig. 4 is the reinforcement envelope diagram after normal wind turbine data processing；
Fig. 5 is the Fast Fourier Transform figure after normal wind turbine data processing；
Fig. 6 is the reinforcement envelope diagram after the wind turbine data processing that a foundation bolt loosens；
Fig. 7 is the Fast Fourier Transform figure after the wind turbine data processing that a foundation bolt loosens.
Specific embodiment
In order to more specifically describe the present invention, below in conjunction with the accompanying drawings and specific embodiment is to technical scheme of the present invention
It is described in detail.
As shown in Figure 1, the wind turbine Weak fault detection method based on vibration signal cyclo-stationary, includes the following steps：
S01, with the vibration acceleration signal of acceleration transducer acquisition wind turbine.
S02 sets corresponding parameter in a program, and collected signal is imported into program, calculates cyclo-stationary
Parameter：
Wherein：T, T is the time；△ f go to zero, and the sequence that T tends to be just infinite cannot exchange；f1、f2Represent two calculated
Frequency；xΔf(t,f1) represent filtering；Represent xΔf(t,f2) conjugate complex number；J represents imaginary unit.
Fan vibration signal can be reduced to：
Wherein v (t) is random signal；AiFor real number, the mould of cosine signal is represented；αiFor the cycle frequency for needing to detect.
For random signal v (t), work as corrv(f1,f2) in f1=f2When corr values be not zero.
Enable α=f1-f2, whenOrOr ± αiWhen, above formula corrx(f1,f2) value be not zero, that is, examine
The cycle frequency for needing to detect is measured, by corr functions, we can detect existing cyclo-stationary information in signal.
For emulation signal x=[1+cos (60 π t)+cos (100 π t)+cos (130 π t)] × N (0,1) due to negative value portion
The information divided can not be shown on the image, then at α=15,20,30,35,50,60,65,80,95,100,115,130, cycle
There is larger peak value, but the cyclical information that image at this time can't be indicated a need for intuitively in density spectra.
S03, obtained amplitude differ greatly, and are 1 by setting maximum value, obtained circulating density is composed and is normalized, is obtained
It is intuitive to show cycle frequency information to cycle Correlated Spectroscopy image；
S04 obtains the three-dimensional matrice of cycle Correlated Spectroscopy, withIt represents, then keeps cycle frequency α constant, f is accumulated
Point, strengthened envelope spectrum s (α) after normalizing：
S (α) is proportional to
Obtained reinforcement envelope spectrum ordinate matrix is saved as data file by S05；
The data file handled in real time is done relative coefficient calculating by S06 with corresponding normal and fault data file：
Wherein, A is the data in the data file handled in real time, and B is that the correspondence preserved in database is normal or failure
Data in file；Cov (A, B) is the covariance of contrast signal and fitted signal, and calculation formula is as follows：
Cov (A, B)=E (A × B)-E (A) * E (B)
Wherein, E (A × B) is the mathematic expectaion of the product of A and B；E (A) is the mathematic expectaion of A；E (B) is the mathematic expectaion of B.
S07 is detected and is judged with the related coefficient breakdown judge standard and experience tentatively established, real-time condition whether failure
And fault type, and the result that will determine that is saved in data, and database is helped to improve the mark of relative coefficient breakdown judge
It is accurate.
In order to which specific manifestation this method is in the advantage and feature of wind turbine Weak fault process field, to ideal signal x=[1+
Cos (60 π t)+cos (100 π t)+cos (130 π t)] × N (0,1) emulated, wherein, N (0,1) represents width in the time domain
Degree obedience mean value is zero, the random signal that variance is 1, and also the vibration acceleration data tested on wind turbine are carried out
Processing and comparison, while have also been made respective Fast Fourier Transform.
The simulation result of ideal signal strengthens envelope spectrum image and Fast Fourier Transform image, as shown in Figures 2 and 3；
The handling result of wind turbine data that the normal wind turbine data of acquisition and a foundation bolt loosen, the reinforcement envelope spectrum finally obtained
Image and Fast Fourier Transform image.Reinforcement envelope spectrum image and Fast Fourier Transform figure after normal wind turbine data processing
As shown in Figure 4 and Figure 5, reinforcement envelope spectrum image and fast Flourier after the wind turbine data processing that a foundation bolt loosens
Changing image is as shown in Figure 6 and Figure 7.
It is not difficult to find out, the frequency and Amplitude Ration that simulation result medium wave peak occurs all meet theory deduction, simultaneously because
The error amount estimated and occurred is also visibly homogeneous in envelope spectrum is strengthened, and demonstrates the feasible part of our programs；Normal wind turbine
Real data processing, obtained image meet actual treatment as a result, detected basic frequency 10Hz and in wind turbine
Due to the frequency multiplication of fundamental frequency that blade rotation actually generates, 20Hz, 30Hz, 40Hz etc..And traditional Fast Fourier Transform, although
Basic frequency 10Hz is equally detected, but certain frequencys multiplication, such as 20Hz, 30Hz, detection result are bad.Strengthen envelope stave
More information is showed and has been easy to differentiate and read, while the datum line of fluctuation very little for strengthening occurring in envelope spectrum is also demonstrated by this
The dependable with function that method handles real data.Meanwhile the processing knot of the Weak fault loosened to a foundation bolt
Fruit also detected basic frequency 10Hz and in wind turbine due to the frequency multiplication of fundamental frequency that actually generates of blade rotation, 20Hz,
30Hz, 40Hz etc. have different in frequency distribution and amplitude size with reinforcement envelope spectrum under normal circumstances, calculate related
Property coefficient is about 0.82, it was demonstrated that the method is used for the practicability and reliability of detection blower fan Weak fault.
Technical scheme of the present invention and advantageous effect is described in detail in above-described specific embodiment, Ying Li
Solution is the foregoing is merely presently most preferred embodiment of the invention, is not intended to restrict the invention, all principle models in the present invention
Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.
Claims (5)
1. a kind of wind turbine Weak fault detection method based on vibration signal cyclo-stationary, includes the following steps：
Step 1, using the vibration acceleration signal of acceleration transducer acquisition wind turbine；
Step 2, collected acceleration signal is imported into program to be run, with the correlation based on cyclostationary characteristic
Characteristic function detects, and obtains circulating density spectrum；
Step 3, circulating density program calculated composes normalization, obtains cycle Correlated Spectroscopy；
Step 4, it is strengthened envelope spectrum according to cycle Correlated Spectroscopy, preserves the ordinate data for strengthening envelope spectrum, as place in real time
Manage obtained data；
Step 5, using relevance function, calculating handles obtained data and the correlation of database data in real time；
Step 6, according to relative coefficient failure resolution standard, judge whether this kind of failure be normal or belongs to which kind of failure, and will
Judging result adds in database.
2. the wind turbine Weak fault detection method according to claim 1 based on vibration signal cyclo-stationary, feature
It is, in step 2, the correlative character function check method based on cyclostationary characteristic is：
Wherein：T, T is the time；△ f go to zero, and the sequence that T tends to be just infinite cannot exchange；f1、f2Represent two frequencies calculated
Rate；xΔf(t,f1) represent filtering；Represent xΔf(t,f2) conjugate complex number；J represents imaginary unit.
3. the wind turbine Weak fault detection method according to claim 1 based on vibration signal cyclo-stationary, feature
It is, in step 4, the process for the envelope spectrum that strengthened is：The three-dimensional matrice of cycle Correlated Spectroscopy will be obtained, withIt represents, so
It keeps cycle frequency α constant afterwards, frequency f is integrated, strengthened envelope spectrum after normalizing.
4. the wind turbine Weak fault detection method according to claim 1 based on vibration signal cyclo-stationary, feature
It is, in step 5, the relevance function is
Wherein, A is to handle obtained data in real time, and B is database data；Cov (A, B) is the association of contrast signal and fitted signal
Variance, calculation formula are as follows：
Cov (A, B)=E (A × B)-E (A) * E (B)
Wherein, E (A × B) is the mathematic expectaion of the product of A and B；E (A) is the mathematic expectaion of A；E (B) is the mathematic expectaion of B.
5. the wind turbine Weak fault detection method according to claim 1 based on vibration signal cyclo-stationary, feature
It is, in step 5, the process for obtaining database data is：Under the conditions of wind turbine normal work and various typical faults, to accelerate
The acceleration signal of sensor acquisition fan vibration is spent, and is inputted in line program to be shipped, the normalized reinforcement that will be calculated
The ordinate output of envelope spectrum is data file, is stored in Database Folder.
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| CN109085763A (en) * | 2018-06-11 | 2018-12-25 | 浙江上风高科专风实业有限公司 | Extraction of the cyclo-stationary method based on complicated amplitude modulation model to fan vibration feature |
| CN109323757A (en) * | 2018-10-29 | 2019-02-12 | 浙江大学 | A method of estimation bubble population is to propeller sound source characteristics frequency inhibiting effect |
| CN109341780A (en) * | 2018-11-29 | 2019-02-15 | 浙江省环境保护科学设计研究院 | A kind of more means low cost fan trouble monitoring methods |
| CN110320018A (en) * | 2019-07-12 | 2019-10-11 | 北京交通大学 | A kind of combined failure of rotating machinery diagnostic method based on second-order cyclostationary characteristic |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109085763A (en) * | 2018-06-11 | 2018-12-25 | 浙江上风高科专风实业有限公司 | Extraction of the cyclo-stationary method based on complicated amplitude modulation model to fan vibration feature |
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| CN109341780A (en) * | 2018-11-29 | 2019-02-15 | 浙江省环境保护科学设计研究院 | A kind of more means low cost fan trouble monitoring methods |
| CN110320018A (en) * | 2019-07-12 | 2019-10-11 | 北京交通大学 | A kind of combined failure of rotating machinery diagnostic method based on second-order cyclostationary characteristic |
| CN110320018B (en) * | 2019-07-12 | 2020-08-11 | 北京交通大学 | Rotary machine composite fault diagnosis method based on second-order cyclostationarity |
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