CN109799090A - Using the bearing features frequency extraction method of the experience wavelet transformation of 3 subregion of frequency band - Google Patents
Using the bearing features frequency extraction method of the experience wavelet transformation of 3 subregion of frequency band Download PDFInfo
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Abstract
The invention discloses a kind of bearing features frequency extraction methods of experience wavelet transformation using 3 subregion of frequency band, structural parameters and bearing shaft working speed to monitored bearing, calculate the fault characteristic frequency of bearing all parts, then time-frequency conversion is carried out to bearing monitoring signal, using 3 times of maximum frequency as bandwidth, the time-frequency conversion space of signal is divided into the time-frequency region of several equibands；Kurtosis sequence is calculated in the signal component for acquiring them；It is mark with the kurtosis maximum value on kurtosis curve, finds the corresponding frequency f of its left and right adjacent minimumLAnd fR, it is intermediate point to analysis frequency band segmentation using the two frequencies, forms 3 interconnections and non-overlapping sub-band, on this basis, tectonic scale filter and wavelet filter carry out wavelet transformation to signal, obtain the signal component of frequency band.Envelope Analysis is done to the signal component of frequency band where kurtosis maximum value, obtains bearing fault characteristics frequency and its harmonic component.
Description
Technical field
The invention belongs to mechanical equipment state monitoring and fault diagnosis fields, are related to extracting bearing from bearing Dynamic Signal
The signal processing method of characteristic frequency.More particularly to a kind of bearing features frequency of experience wavelet transformation using 3 subregion of frequency band
Extracting method.
Background technique
Bearing is using most one of components in electromechanical equipment, its health status is most important to electromechanical equipment,
Monitor and identify that bearing state is one of the key content of device status monitoring and fault diagnosis on-line in equipment operation.Using
It is common method that effective signal processing technology, which extracts bearing features frequency by the Dynamic Signal of equipment,.Experience small echo becomes
Changing is a kind of signal adaptive processing method, it combine wavelet analysis categorical theory and empirical mode decomposition it is adaptive
Property, it is a series of with FM amplitude modulation characteristic component signal decomposition.It is in decomposed signal, with the local maximum of signal spectrum
Amplitude determines the boundary of sub-band, and analysis frequency band is divided into multiple non-overlapping sub-bands.On this basis, for each
The filter group of sub-band construction carries out wavelet transformation to signal, signal is had mapped on each frequency band.Document [1] is using warp
It tests wavelet transformation to decompose the vibration signal of locomotive axle bearing, Envelope Analysis is carried out to its component and extracts bearing fault spy
Levy frequency.Document [2] is directed to the characteristics of bearing fault excitation resonance phenomenon, proposes a kind of based on Hilbert envelope demodulation
Experience wavelet transformation frequency band dividing method, to overcome noise on band shared influence.Document [3] proposes a kind of based on envelope
Compose the method for diagnosing faults of the multilayer experience wavelet transformation High-speed Train Bearing of kurtosis.For experience wavelet transformation analysis vulnerable to making an uproar
The deficiency of acoustic jamming, document [4] have studied the Rolling Bearing Fault Character extraction side using Probabilistic Principal Component Analysis combination EWT
Method, document [5] are split its frequency spectrum using the sliding frequency window of an adaptive-bandwidth, then with water loop optimization algorithm to wrap
It is that index adaptively determines sliding frequency window position that network, which composes harmonic to noise ratio,.
In the electromechanical equipment course of work, the Dynamic Signal from equipment contains stronger noise, on its frequency spectrum, makes an uproar
The spectral peak of sound component will affect the peak Distribution of signal, the son frequency determined using local maximum amplitude method that document [6] proposes
Band boundary often loses reasonability, so that being not effectively separated shaft using the signal component that experience wavelet transformation decomposes
Hold the fault signature of failure.
It is the bibliography relevant to the application of applicant's retrieval below:
[1] Hongrui Cao, Fei Fan, Kai Zhou, Zhengjia He.Wheel-bearing fault
Diagnosis of trains using empirical wavelet transform [J], 2016, Measurement 82:
439–449。
[2] Dong Wang, Yang Zhao, Cai Yi, Kwok-Leung Tsui, Jianhui Lin. Sparsity
guided empirical wavelet transform for fault diagnosis of rolling element
Bearings [J] .2018, Mechanical Systems and Signal Processing, 101:292-308.
[3] Jianming Ding, Chengcheng Ding.Automatic detection of a wheelset
bearing fault using a multi-level empirical wavelet transform[J].Measurement,
2019,134:179–192。
[4] Hu Aijun, Nan Bing, Ren Yonghui, rolling bearing minor failure diagnosis [J] the vibration test based on PPCA-EWT
With diagnosis, 2018,2:365-370.
[5] Deng Feiyue, Qiang Yawen, Yang Shaopu, Hao Rujiang, Liu Yongqiang, a kind of adaptive frequency window experience wavelet transformation
Fault Diagnosis of Roller Bearings [J] XI AN JIAOTONG UNIVERSITY Subject Index, 2018,8:22-29.
【6】J.Gilles.Empirical Wavelet Transform[J].IEEE Transactions on
Signal Processing, 2013,61 (16): 3999-4010.
Summary of the invention
Aiming at the problem that classical experience wavelet transform signal analysis method is vulnerable to noise jamming, it is an object of the present invention to
A kind of bearing features frequency extraction method of experience wavelet transformation using 3 subregion of frequency band is provided.
In order to realize that above-mentioned task, the present invention use following technical solution: specifically follow these steps to implement:
(1) according to the structural parameters and bearing shaft working speed for being monitored bearing, the event of bearing all parts is calculated
Hinder characteristic frequency, determines maximum frequency therein；
(2) time-frequency conversion is carried out to bearing monitoring signal, then based on the time-frequency conversion result, with 3 times of maximum frequency
Rate is bandwidth, the time-frequency conversion space of signal is divided into a series of time-frequency region of equibands；
(3) time-frequency inverse transformation is carried out to above-mentioned time-frequency region respectively, acquires their signal component, counts on this basis
The kurtosis for calculating each component obtains the kurtosis sequence of one group of bearing monitoring signal；
(4) monitoring signals are constructed as abscissa value, kurtosis as ordinate value using the centre frequency of each time-frequency region
Frequency-kurtosis curve；
(5) local minimum adjacent in frequency-kurtosis curve searching kurtosis maximum value and its left and right, determines left and right
Frequency f corresponding to adjacent minimumLAnd fR；
(6) dividing frequencyband, using 0Hz as the edge frequency of start frequency band, the corresponding maximum frequency of the analysis frequency band of monitoring signals
Rate fs/ 2 as the edge frequency for terminating frequency band, wherein fsFor the sample frequency of monitoring signals, with frequency fLAnd fRFor intermediate frequency
The edge frequency of band constitutes [0, fL]、[fL, fR] and [fR, fs/ 2] 3 are mutually linked and non-overlapping frequency band；
(7) it is mutually linked by 3 of step (6) and based on non-overlapping frequency band, constructs experience wavelet transformation respectively
Scaling filter and wavelet filter, and to monitoring signals carry out wavelet transformation, obtain signal be mutually linked at above-mentioned 3 and
The signal component of non-overlapping frequency band；
(8) to [fL, fR] frequency band signal component carry out Envelope Analysis, determine fault characteristic frequency and its harmonic frequency.
The bearing features frequency extraction method of experience wavelet transformation using 3 subregion of frequency band of the invention, overcomes tradition
Transform method determines influencing vulnerable to noise component(s) spectral peak for sub-band section to analyze signal spectrum local maximum respective frequencies
Deficiency, meanwhile, the fault signature of bearing has been efficiently extracted directly with the mode that 3 subregions convert, without as conventional method from
It is found in numerous components and contains fault signature component.
Detailed description of the invention
Fig. 1 is the bearing features frequency extraction method flow chart of the experience wavelet transformation of the invention using 3 subregion of frequency band.
Fig. 2 is the vibration signal figure of Locomotive Bearing.
Fig. 3 is kurtosis-frequency curve and its frequency band segmentation figure.
Fig. 4 is the signal component of 3 frequency band subregions, wherein (A) figure is the signal component of subregion 1, and (B) figure is subregion 2
Signal component, (C) figure is the signal component of subregion 3.
Fig. 5 is the envelope spectrum of the signal component of the 2nd subregion.
Below in conjunction with drawings and examples and concrete application, the present invention is described in further detail.
Specific embodiment
As shown in Figure 1, the bearing features frequency that the present embodiment provides a kind of experience wavelet transformation using 3 subregion of frequency band mentions
Method is taken, follows these steps to implement:
(1) maximum frequency is determined:
According to the structural parameters of monitored bearing and shaft revolving speed, the fault characteristic frequency of bearing all parts is calculated, really
Fixed maximum frequency therein.
(2) signal acquisition:
In bearing working, vibration signal is acquired from bearing bush or bearing block.
(3) time frequency space is divided:
Time-frequency conversion is carried out to the signal of acquisition, based on the time-frequency conversion result, using 3 times of maximum frequency as band
Width, the analysis frequency band of splitting signal, is divided into signal time frequency space a series of time-frequency region of equibands.
(4) kurtosis sequence is calculated:
Time-frequency inverse transformation is carried out to above-mentioned time-frequency region respectively, their signal component is acquired, calculates on this basis
The kurtosis of each component obtains the kurtosis sequence of one group of monitoring signals.
(5) frequency-kurtosis curve is drawn:
Using the centre frequency of each time-frequency region as abscissa value, kurtosis as ordinate value, monitoring signals are constructed
Frequency-kurtosis curve.
(6) edge frequency is determined:
Kurtosis maximum value, the local minimum of Zuo Xianglin, right adjacent local minimum are determined in frequency-kurtosis curve,
Determine frequency f corresponding to the adjacent minimum in left and rightLAnd fR, in this, as edge frequency value.
(7) wavelet division frequency band:
Using 0Hz as the edge frequency of start frequency band, the analysis frequency band of monitoring signals corresponds to maximum frequency fs/ 2 as eventually
The only edge frequency of frequency band, wherein fsFor the sample frequency of monitoring signals.
With frequency fLAnd fRFor the edge frequency of intermediate frequency band, [0, f is constitutedL]、[fL, fR] and [fR, fs/ 2] it mutually holds in the mouth for 3
It connects, non-overlapping frequency band.
(8) signal component is sought.
Be mutually linked by 3, based on non-overlapping frequency band, respectively construct experience wavelet transformation scaling filter and
Wavelet filter, and to monitoring signals carry out wavelet transformation, obtain signal be mutually linked at above-mentioned 3, non-overlapping frequency band
Signal component.
(9) fault characteristic frequency is extracted:
To [fL, fR] frequency band signal component carry out Envelope Analysis, determine fault characteristic frequency and its harmonic frequency.
In the present embodiment, the time-frequency conversion is a kind of signal analysis method with inverible transform, it believes time domain
Number it is mapped to time frequency space.
The kurtosis is to describe signal in a kind of dimensionless statistical indicator of time domain.
The frequency-kurtosis curve is a kind of kurtosis curve of approximation varying with frequency of simplification, which is one
The intermediate frequency in time-frequency section, kurtosis are the kurtosis values of signal component in the time frequency space.
3 subregion of frequency band refers to corresponding with the adjacent left minimum of frequency-kurtosis curve peak value and right minimum
Frequency is the segmentation that edge frequency analyzes signal frequency band, 3 frequency band sections of formation.
The signal reconstruction, which refers to, isolates the frequency band to the progress time-frequency inverse transformation of selected frequency band or inverse Fourier transform
Signal component process.
It is the concrete implementation and its calculating process that inventor provides below.
(1) model for checking detection bearing, determines the structural parameters size of bearing, including inner ring diameter, race diameter, rolling
The revolution revolving speed of the diameter of kinetoplast, the number of rolling element, contact angle etc. and bearing, (1)~(3) calculate event as follows
Hinder characteristic frequency:
Outer ring fault characteristic frequency:
Inner ring fault characteristic frequency:
Rolling element fault characteristic frequency:
Wherein, fnFor bearing gyrofrequency, d is rolling element diameter, and E is the pitch diameter of bearing, and Z is rolling element number, and α is to connect
Feeler, DiFor the diameter of bearing inner race, DoFor the diameter of bearing outer ring.Wherein fn=n/60, n are the revolution revolving speed (r/ of bearing
min)。
If Δ f is the bandwidth of frequency band subregion, Δ f=3 × max (f is enabledo,fi, fb)。
(2) monitoring signals are set as f (t), its Fu determines that leaf transformation is in band bandwidthA kind of its time-frequency becomes
It is changed to:
In formula, κ is normal number,For the Fourier transformation of p (t),ForConjugate function.
Due to the π f of ω=2, transformation results W (t, ω) can be written as W (t, f).It takesTo signal f (t)
Time-frequency conversion is carried out, obtains signal [0, ts, 0, fs/2], ts is the sampling time of signal.
(3) determine the sample frequency for setting signal as fs, then time-frequency interal separation number be
(4) according to the following steps, the time domain component and its kurtosis value, centre frequency of each time-frequency region are extracted.
1) time-frequency section serial number i is set, i=1 is enabled
2) the initial frequency f in time-frequency section is sought0With cutoff frequency f1
f0=(i-1) Δ f (7)
f1=i Δ f (8)
3) each time-frequency section [0, t is calculated by time-frequency inverse transformations,f0,f1] signal component yi (t).
4) the kurtosis K of the component is soughtr(i):
L=t in formulas/fs,It is yi(t) average value.
5) centre frequency in time-frequency section is calculated；
fc(i)=(i-1) Δ f+ Δ f/2 (11)
6) as i ≠ N, enable i=i+1, repeat 2)~5), continue the high and steep of the centre frequency for calculating sub-band and component signal
Degree.
(5) it is constructed using the centre frequency in time-frequency section as abscissa, its kurtosis for corresponding to signal component as ordinate
Frequency-kurtosis curve.
(6) frequency-kurtosis curve maximum value K is determinedrmax；The adjacent minimum in its left side is found, the minimum is corresponding
Frequency be denoted as fL；The adjacent minimum in its right side is found, the corresponding frequency of the minimum is denoted as fR。
(7) following segmentation is done to the analysis frequency band of signal:
1st frequency band: [0, fL]
2nd frequency band: [fL,fR]
3rd frequency band: [fR,fs/2]
(8) based on above-mentioned 3 frequency bands, empirically theory of wavelet transformation constructs orthogonal wavelet filter group.
It is [Ω with the small subband frequencies that angular frequency indicates due to the π f of Ω=2n-1,Ωn], in n-th small subband frequencies
[Ωn-1,Ωn] filter groupWithAre as follows:
In formula, β (x)=(- 20x3+70x2-84x+35)x4,0 < γ < 1.
(9) in sub-band [Ωn-1,Ωn], the low frequency component f that the decomposition of the experience wavelet transformation of signal f (t) obtains0(t)
With high fdrequency component fk(t) it is respectively as follows:
Wherein, the convolution algorithm of * representative function.
Wherein, F-1[] indicates inverse Fourier transform,It respectively indicates 's
Conjugation.
(10) frequency band [f is takenL,fR] component signal do envelope spectrum analysis, on envelope spectrum according to spectral peak identify bearing features
Frequency.
Specific application example:
43418 type locomotive of east wind, the bearing designation of running part are 52732QT, roller diameter 34mm, internal diameter 160mm,
Outer diameter is 290mm, and roller number is 17, contact angle 0.
The vibration signal of bearing bush, sample frequency 128000Hz are acquired using acceleration transducer, signal length is
8192。
Fig. 2 is the vibration signal of rolling element failure, and Fig. 3 is kurtosis-frequency curve and its frequency band point, and Fig. 4 is 3 subregions
Signal component, wherein (A) figure is the signal component of subregion 1, and (B) figure is the signal component of subregion 2, and (C) figure is subregion 3
Signal component.
At this point, load is 2001Kg, revolving speed is 531r/min (8.85Hz), is calculated and is learnt using formula (1)~(4), the axis
The fault characteristic frequency for holding inner ring, outer ring and rolling element is respectively 86.59Hz, 63.86Hz and 57.23Hz.
Fig. 5 is the component envelope spectrum of the 2nd subregion where kurtosis peak-peak, 26.56Hz and 57.81Hz points in figure
Not Wei bearing turn 3 frequencys multiplication and rolling element fault characteristic frequency of frequency.
Claims (1)
1. a kind of method that the experience wavelet transformation using 3 subregion of frequency band extracts bearing fault characteristics frequency, which is characterized in that tool
Body follows these steps to implement:
(1) according to the structural parameters and bearing shaft working speed for being monitored bearing, the failure for calculating bearing all parts is special
Frequency is levied, determines maximum frequency therein；
(2) time-frequency conversion is carried out to bearing monitoring signal, then based on the time-frequency conversion result, the maximum frequency with 3 times is
Bandwidth is divided into the time-frequency conversion space of signal a series of time-frequency region of equibands；
(3) time-frequency inverse transformation is carried out to above-mentioned time-frequency region respectively, acquires their signal component, calculated on this basis each
The kurtosis of a component obtains the kurtosis sequence of one group of bearing monitoring signal；
(4) frequency of monitoring signals is constructed as abscissa value, kurtosis as ordinate value using the centre frequency of each time-frequency region
Rate-kurtosis curve；
(5) local minimum adjacent in frequency-kurtosis curve searching kurtosis maximum value and its left and right, determines that left and right is adjacent
Frequency f corresponding to minimumLAnd fR；
(6) dividing frequencyband, using 0Hz as the edge frequency of start frequency band, the analysis frequency band of monitoring signals corresponds to maximum frequency fs/2
As the edge frequency for terminating frequency band, wherein fsFor the sample frequency of monitoring signals, with frequency fLAnd fRFor the side of intermediate frequency band
Boundary's frequency constitutes [0, fL]、[fL,fR] and [fR,fs/ 2] 3 are mutually linked and non-overlapping frequency band；
(7) it is mutually linked by 3 of step (6) and based on non-overlapping frequency band, constructs the ruler of experience wavelet transformation respectively
Filter and wavelet filter are spent, and wavelet transformation is carried out to monitoring signals, signal is obtained and is not mutually linked at above-mentioned 3 and mutually not
The signal component of overlapping frequency band；
(8) to [fL,fR] frequency band signal component carry out Envelope Analysis, determine fault characteristic frequency and its harmonic frequency.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111504635A (en) * | 2020-04-21 | 2020-08-07 | 哈尔滨理工大学 | Planetary gear fault diagnosis method based on differential evolution probability neural network |
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