CN108708871A - A kind of axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform - Google Patents
A kind of axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform Download PDFInfo
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- CN108708871A CN108708871A CN201810351851.9A CN201810351851A CN108708871A CN 108708871 A CN108708871 A CN 108708871A CN 201810351851 A CN201810351851 A CN 201810351851A CN 108708871 A CN108708871 A CN 108708871A
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Classifications
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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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- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform that the present invention provides a kind of, including：Short Time Fourier Transform is carried out to axial flow blower vibration signal with a window function, make axial flow blower vibration signal original waveform figure, frequency domain figure and carries out the time-frequency figure after Short Time Fourier Transform, and obtains relevant parameter；Build null vector Z and be used as reconstruction signal, calculating window function number coln, amplitude correction coefficient B, null vector Z length length (Z)；Inverse transformation is carried out to Short Time Fourier Transform matrix S, the reconstruction signal Z returned reconstructs time domain waveform by time-frequency figure；The figure of output time t and the reconstruction signal Z of return obtain axial flow blower vibration signal subconstiuent.The present invention can effectively extract the subconstiuent of axial flow blower vibration signal, and can operate with and carry out fault detect to axial flow blower.
Description
Technical field
The present invention relates to field of signal processing more particularly to a kind of axial flow blower vibrations based on inverse Short Time Fourier Transform
Signal subspace component extracting method.
Background technology
Wind turbine is a kind of mechanical energy by input, improves gas pressure and supplies gas side by side the machinery of body, wherein axial flow blower
When impeller rotates, gas, axially into impeller, is made the energy of gas increase, so from air inlet by the pushing of impeller blade
After flow into guide vane.Guide vane becomes axial flowing by air-flow is deflected, while introducing gas into diffuser pipe, further turns gas kinetic energy
It is changed to pressure energy, is finally introducing working line.The vibration very little of bearing portion when just starting to work, but with the duration of runs
It lengthens, dust non-uniform can be attached on impeller in wind turbine, gradually destroys the dynamic balancing of wind turbine, bear vibration is made gradually to add
Greatly, when vibration reaches the maximum value of wind turbine permission, wind turbine must shut down repairing.
Field of signal processing has Fourier transformation (FT), in short-term Fu for the processing method of axial flow blower vibration signal at present
In leaf transformation (STFT) and wavelet transformation (WT) etc., to extract the spectrum signature and T/F evolution Feature of signal.For
Above-mentioned processing method, MATLAB develop the works such as inverse Fourier transform function (IFFT) and inverse wavelet transform function (ICWTFT)
Tool.
Inverse Short Time Fourier Transform function (TFRISTFT) can only be directed to Short Time Fourier Transform function as inverse transformation
(TFRSTFT), do not applied to analyze the vibration signal of axial flow blower in the prior art, and detect axial flow blower failure.
The Chinese invention patent of 105548595 A of Publication No. CN discloses a kind of axis at different levels of extraction wind turbine gearbox
Refer to obtain vibration signal Short Time Fourier Transform the time-frequency of reflection vibration signal time-frequency characteristics in Rotating speed measring method
Spectrum；The frequency content Time-frequency Filter most outstanding of peak value in time-frequency spectrum and burial are handled；To burying treated time-frequency spectrum in short-term
Inverse Fourier transform obtains the signal that this feature frequency content changes over time, and seeks the time-varying instantaneous frequency of the signal.Although should
Fourier transformation and its inverse transformation are applied to the rotating speeds at different levels of detection wind turbine gearbox by patent, but in the prior art not
Applied to extract the subconstiuent of axial flow blower vibration signal, and detects axial flow blower failure, and obtained in inverse transformation
Signal accuracy rate is unable to get guarantee.
Invention content
The axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform that the present invention provides a kind of,
Time frequency signal of the axial flow blower vibration signal after Short Time Fourier Transform can be accurately reconstructed, and checks any time period
Waveform.
A kind of axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform, including：
(1) Short Time Fourier Transform is carried out to axial flow blower vibration signal with a window function, made
Axial flow blower vibration signal original waveform figure, frequency domain figure and carry out Short Time Fourier Transform after when
Frequency is schemed, and obtains relevant parameter, including：Short Time Fourier Transform matrix S, frequency matrix F, time matrix T, energy
Spectrum density P；
(2) structure null vector Z calculates the length of window function number coln, mobile spacing h, null vector Z as reconstruction signal
Length (Z) and amplitude correction coefficient B：
Coln* (win-overlap)+overlap=L
H=win-overlap
Length (Z)=N+ (coln-1) * h
B=2*h/win
Wherein, N counts for frequency domain sample, and L is the length that axial flow blower vibrates sampled signal；
(3) inverse transformation is carried out to Short Time Fourier Transform matrix S, the reconstruction signal Z returned is reconstructed by time-frequency figure
Time domain waveform；
(4) figure of output time t and the reconstruction signal Z of return obtains axial flow blower vibration signal subconstiuent.The axis
Flow fan vibration signal subconstiuent includes the axial flow blower vibration signal waveforms figure of any time period.
Preferably, carrying out Fourier in short-term by spectrogram function pair axial flow blower vibration signals in step (1)
Transformation, and it is Hamming windows (Hamming window) to give tacit consent to the window function used.
Preferably, being carried out at wavelet de-noising to axial flow blower vibration signal before Short Time Fourier Transform in step (1)
Reason.
Preferably, step (3) specifically includes：
(3.1) window function used according to Short Time Fourier Transform, the time period t checked to provisioning request1~t2, when calculating
Carve t1、t2It is respectively in which section window function of Short Time Fourier Transform, determines reconstruction signal Z in Short Time Fourier Transform square
Respective column range in battle array S, and screen Short Time Fourier Transform matrix S and obtain matrix S '；
(3.2) from moment t1The window at place starts, and carries out inverse Fourier transform to each section of window in matrix S ', folds successively
Add to moment t2The window at place, and it is multiplied by amplitude correction coefficient B, the reconstruction signal Z returned.
As a further preference, the calculating moment t described in step (3.1)1、t2It is respectively at Short Time Fourier Transform
The process of which section window function be：
It obtains：
Wherein, t=t1、t2, x is the time slice where moment t, and h is mobile spacing, and Δ t is (to sample in the sampling interval
The inverse of frequency), L is signal length.
As a further preference, in step (3.1), after determining row range, the frequency band f that is checked to provisioning request1~f2,
Calculate frequency f1、f2Residing line number respectively, determines that reconstruction signal Z needs corresponding line range in matrix S, other than line range
Numerical value set to 0, that is, filter other frequency bands, obtain matrix S ', be achieved in achievable any time period in reconstruction signal
On the basis of achieve the effect that filtering.
As further preferred, described calculating frequency f1、f2Residing line number process is respectively：
It obtains：
Wherein, f=f1、f2, F is the frequency matrix that Short Time Fourier Transform returns, x1It is a certain frequency f in frequency matrix F
In line number, FmFor the maximum value of each element in frequency matrix F, (F is one-dimensional square to the length that length (F) is frequency matrix F
Battle array).
Preferably, the present invention carries out failure inspection according to obtained axial flow blower vibration signal subconstiuent to axial flow blower
It surveys.
The present invention is based on inverse Short Time Fourier Transform, realize that axial flow blower vibration signal is efficient from time-frequency figure to time-domain diagram
The similarity of conversion, reconstruction signal and original signal is high, and algorithm is simple, and the speed of service is fast, with a high credibility；It mathematically completes pair
The extraction of axial flow blower vibration signal subconstiuent can extract any time period, the signal in frequency band, in practical applications may be used
Accurately, rapidly to obtain the active ingredient of axial flow blower vibration signal, and then one kind is provided for the fault detect of wind turbine
New thinking and method.
Description of the drawings
Fig. 1 is the method flow of the axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform
Figure.
Fig. 2 to Fig. 6 is 1 stationary signal simulation result of embodiment；
Fig. 2 is emulation signal original waveform figure, frequency domain figure and Short Time Fourier Transform time-frequency figure；
Fig. 3 is the complete comparison for restoring signal waveforms and original waveform；
Fig. 4 is the comparison of the recovery signal waveforms and original waveform of set period of time；
Fig. 5 is the comparison of the recovery signal waveforms and original waveform of setpoint frequency section；
Fig. 6 is the comparison for restoring signal waveforms and original waveform of setting time, frequency band.
Fig. 7 to Fig. 9 is 2 linear FM signal simulation result of embodiment；
Fig. 7 is oscillogram, frequency domain figure and the Short Time Fourier Transform time-frequency figure of linear FM signal；
Fig. 8 is the complete comparison for restoring signal waveforms and original waveform；
Fig. 9 is the comparison of the recovery signal waveforms and original waveform of set period of time.
Figure 10 to Figure 12 is 3 wind turbine actual operating data handling result of embodiment；
Figure 10 is oscillogram, frequency domain figure and in short-term Fourier change of the real data of one section of wind turbine operating after noise reduction process
Change time-frequency figure；
Figure 11 is the complete comparison for restoring signal waveforms and original waveform；
Figure 12 is the comparison for restoring signal waveforms and original waveform of setting time, frequency band.
Specific implementation mode
In order to more specifically describe the present invention, below in conjunction with the accompanying drawings and specific implementation mode is to technical scheme of the present invention
It is described in detail：
Embodiment 1, stationary signal simulation scenarios
S01, setting emulation signal：X=80sin (2 π 60t)+50sin (2 π 100t) t is the time, and this example is from 0 to 1
Second, sample frequency 1000, length 1000, frequency domain sample points N is 1024；
S02 makes time domain, frequency domain and the time-frequency figure of signal, STFT parameters：The long win=100 of window, Duplication 0.5, overlapping
Length overlap=50, referring to Fig. 2；
S03 builds null vector Z, calculates amplitude correction coefficient h=win-overlap；
S04 inputs relevant parameter, completely restores original signal according to time-frequency figure, arithmetic result is as shown in Figure 3.Dotted line signal
For original signal, solid-line signals are reconstruction signal, and ρ is related coefficient；
For S05, it is specified that the period is between 0.2 to 0.6 second, frequency is unlimited, substitutes into parameter, arithmetic result is as shown in Figure 4.It is empty
Line signal is original signal, and solid-line signals are reconstruction signal, and ρ is related coefficient；
For S06, it is specified that frequency band is 40HZ to 80HZ, the time is unlimited, substitutes into parameter, arithmetic result is as shown in Figure 5.Dotted line is believed
Number be original signal, solid-line signals are reconstruction signal；
For S07, it is specified that the period is between 0.15 to 0.85 second, frequency band is 90 to 110HZ, substitutes into parameter, arithmetic result
As shown in Figure 6.Dotted line signal is original signal, and solid-line signals are reconstruction signal.
The mathematic(al) representation of simulation example is：
Inverse Short Time Fourier Transform：
More than, B represents amplitude correction coefficient；C represents time slice number, i.e., total window function number, in corresponding program
Coln variables；M is time segment variable, and value is;1,c]；T represents the discretization variable of time, and the discretization that f represents frequency becomes
Amount；N counts for frequency domain sample；R is window function；S is the Short Time Fourier Transform matrix S that spectrogram functions return.
Embodiment 2, frequency variation signal simulation scenarios
S01 sets linear FM signal,
X=80sin (2 π 60t+ π 10t2)
Wherein each parameter is identical as simulation scenarios one, makes the time-frequency after its time domain, frequency domain and Short Time Fourier Transform
Figure, as shown in Figure 7；
S02 inputs relevant parameter, according to the reconstruction signal in time-frequency figure recovery full time, frequency band, as shown in Figure 8；
Dotted line signal is original signal, and solid-line signals are reconstruction signal, and ρ is related coefficient；
S03 inputs relevant parameter, and setpoint frequency section is 100-150HZ, makes reconstruction signal figure, as shown in Figure 9；Dotted line
Signal is original signal, and solid-line signals are reconstruction signal, it is seen that signal substantially within 0.4 to 0.9s, meets linear FM signal
Expression formula, it was demonstrated that the accuracy of this method.
The vibration data of embodiment 3, practical wind turbine
S01 acquires the vibration data of one section of wind turbine actual motion, it is known that the rotational frequency of wind turbine is 20HZ, sampling interval
For 0.2ms；
S02, to signal carry out wavelet de-noising processing, make after its time-domain diagram, frequency domain figure and Short Time Fourier Transform when
Frequency is schemed, as shown in Figure 10；
S03 inputs relevant parameter, checks full time, the reconstruction signal in frequency band, according to time-frequency figure reconstruction signal,
As shown in figure 11；Dotted line signal is original signal, and solid-line signals are reconstruction signal, and ρ is related coefficient；
S04 inputs relevant parameter, sets and checks that the period arrives 1.6s as 0.6, frequency band is 10 to 25HZ, arithmetic result
As shown in figure 12；Dotted line signal is original signal, and solid-line signals are reconstruction signal.
It by above-mentioned simulation result and real data handling result, is not difficult to find out, the method for the present invention can effectively be restored in short-term
Waveform after Fourier transformation carries out ISTFT operations to signal any time period, frequency band, and accuracy rate is up to 98% or more.
Technical scheme of the present invention and advantageous effect is described in detail in above-described specific implementation mode, Ying Li
Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of 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 (8)
1. a kind of axial flow blower vibration signal subconstiuent extracting method based on inverse Short Time Fourier Transform, which is characterized in that packet
Include following steps：
(1) Short Time Fourier Transform is carried out to axial flow blower vibration signal with a window function, makes axial flow blower vibration signal
Time-frequency figure after original waveform figure, frequency domain figure and progress Short Time Fourier Transform, and relevant parameter is obtained, including：In short-term in Fu
Leaf transformation matrix S, frequency matrix F, time matrix T, energy spectral density P；
(2) structure null vector Z is used as reconstruction signal, calculating window function number coln, amplitude correction coefficient B, null vector Z length
length(Z)；
(3) inverse transformation is carried out to Short Time Fourier Transform matrix S, the reconstruction signal Z returned reconstructs time domain by time-frequency figure
Oscillogram；
(4) figure of output time t and the reconstruction signal Z of return obtains axial flow blower vibration signal subconstiuent.
2. the axial flow blower vibration signal subconstiuent extraction side according to claim 1 based on inverse Short Time Fourier Transform
Method, which is characterized in that Fourier in short-term is carried out by spectrogram function pair axial flow blower vibration signals in step (1) and is become
It changes, and it is Hamming windows to give tacit consent to the window function used.
3. the axial flow blower vibration signal subconstiuent extraction side according to claim 1 based on inverse Short Time Fourier Transform
Method, which is characterized in that wavelet de-noising processing is carried out to axial flow blower vibration signal before Short Time Fourier Transform in step (1).
4. the axial flow blower vibration signal subconstiuent extraction side according to claim 1 based on inverse Short Time Fourier Transform
Method, which is characterized in that step (3) specifically includes：
(3.1) window function used according to Short Time Fourier Transform, the time period t checked to provisioning request1~t2, calculate moment t1、
t2It is respectively in which section window function of Short Time Fourier Transform, determines reconstruction signal Z in Short Time Fourier Transform matrix S
Respective column range, and screen Short Time Fourier Transform matrix S and obtain matrix S '；
(3.2) from moment t1The window at place starts, and inverse Fourier transform is carried out to each section of window in matrix S ', be sequentially overlapped to
Moment t2The window at place, and it is multiplied by amplitude correction coefficient B, the reconstruction signal Z returned.
5. the axial flow blower vibration signal subconstiuent extraction side according to claim 4 based on inverse Short Time Fourier Transform
Method, which is characterized in that the calculating moment t described in step (3.1)1、t2Which section window letter of Short Time Fourier Transform be respectively at
Several processes are：
It obtains：
Wherein, t=t1、t2, x is the time slice where moment t, and Δ t is the sampling interval, and L is signal length, between h is mobile
Away from h=win-overlap, win are window length, overlap is overlap length.
6. the axial flow blower vibration signal subconstiuent extraction side according to claim 4 based on inverse Short Time Fourier Transform
Method, which is characterized in that in step (3.1), after determining row range, the frequency band f that is checked to provisioning request1~f2, calculate frequency f1、
f2Residing line number respectively, determines that reconstruction signal Z needs corresponding line range in matrix S, the numerical value other than line range is set to 0,
Obtain matrix S '.
7. the axial flow blower vibration signal subconstiuent extraction side according to claim 6 based on inverse Short Time Fourier Transform
Method, which is characterized in that the calculating frequency f1、f2Residing line number process is respectively：
It obtains：
Wherein, f=f1、f2, F is the frequency matrix that Short Time Fourier Transform obtains, x1It is a certain frequency f in frequency matrix F
Line number, FmFor the maximum value of each element in frequency matrix F, length (F) is the length of frequency matrix F.
8. the axial flow blower vibration signal subconstiuent extraction side according to claim 1 based on inverse Short Time Fourier Transform
Method, which is characterized in that fault detect is carried out to axial flow blower according to obtained axial flow blower vibration signal subconstiuent.
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| CN114060291A (en) * | 2021-10-27 | 2022-02-18 | 江苏大学 | Centrifugal pump multi-source signal parallel processing method based on coupling misalignment working condition |
| CN115982527A (en) * | 2023-03-21 | 2023-04-18 | 西安电子科技大学 | FPGA-based time-frequency domain transformation algorithm implementation method |
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| CN109827656A (en) * | 2019-02-21 | 2019-05-31 | 国网江苏省电力有限公司南京供电分公司 | Load ratio bridging switch signal de-noising method based on STFT time-frequency spectrum coefficients model |
| CN110296095A (en) * | 2019-05-21 | 2019-10-01 | 上海宝钢工业技术服务有限公司 | Thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method |
| CN110296095B (en) * | 2019-05-21 | 2022-08-09 | 上海宝钢工业技术服务有限公司 | Intelligent monitoring and diagnosing method for operation state of induced draft fan of power station boiler of thermal power plant |
| CN110135390A (en) * | 2019-05-24 | 2019-08-16 | 哈尔滨工业大学 | The specific emitter identification method inhibited based on main signal |
| CN110135390B (en) * | 2019-05-24 | 2020-12-01 | 哈尔滨工业大学 | Radiation source individual identification method based on main signal suppression |
| CN112629838A (en) * | 2020-11-13 | 2021-04-09 | 三峡大学 | Wind turbine blade fault monitoring method |
| CN112629838B (en) * | 2020-11-13 | 2022-03-08 | 三峡大学 | Wind turbine blade fault monitoring method |
| CN114060291A (en) * | 2021-10-27 | 2022-02-18 | 江苏大学 | Centrifugal pump multi-source signal parallel processing method based on coupling misalignment working condition |
| CN115982527A (en) * | 2023-03-21 | 2023-04-18 | 西安电子科技大学 | FPGA-based time-frequency domain transformation algorithm implementation method |
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Application publication date: 20181026 |