CN110320039A - A kind of Fault Diagnosis of Roller Bearings based on IITD and broad sense difference shape filtering - Google Patents
A kind of Fault Diagnosis of Roller Bearings based on IITD and broad sense difference shape filtering Download PDFInfo
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Abstract
The present invention relates to a kind of Fault Diagnosis of Roller Bearings based on IITD and broad sense difference shape filtering, it decomposes rolling bearing acceleration signal by using IITD method, while noise reduction, eliminate signal edge effect, increase Decomposition Accuracy, any one complicated non-stationary signal is adaptively decomposed into the sum of multiple PRC components, and the PRC component after decomposition is sought into related coefficient and kurtosis value, effective PRC component is chosen based on related coefficient-kurtosis criterion to be reconstructed, then the signal after reconstruct is filtered using Generalized Morphological differential filtering method；Finally filtered vibration signal is analyzed using Teager energy operator, extracts the fault signature of vibration signal, keeps fault diagnosis more accurate.
Description
Technical field
The present invention relates to a kind of Fault Diagnosis of Roller Bearings based on IITD and broad sense difference shape filtering, belong to therefore
Hinder diagnostic techniques field.
Background technique
For rolling bearing as the machine components being most widely used in rotating machinery, its running quality is to determine whole set
An important factor for standby precision, stability and service life.Bearing vibration signal carries running state information abundant, works as rolling
When local damage failure occurs in dynamic bearing, impaired loci and bearing other elements surface will decay shock pulse power when contacting, from
And causing the intrinsic vibration of the high frequency periodic of bearing, these impact signals mostly show as non-linear, non-stationary feature.Punching
The frequecy characteristic for hitting signal is able to reflect the fault message of bearing, but due to the complexity of equipment working environment, actual measurement
To the fault signature of vibration signal can often drown out in strong background noise, it is difficult to directly extract.Therefore, from strong background noise
Extract the key point that fault characteristic information is rolling bearing fault diagnosis.Existing method ITD is avoided that in EMD method operation
Interative computation reduces computation complexity.But the method is linear change when defining baseline, so obtained intrinsic rotation
Turning component (PRC) may be distorted.Therefore, it is proposed after replacing the linear transformation method in ITD using cubic spline interpolation method
It improves intrinsic time Scale Decomposition method (IITD), can effectively solve the problems, such as end effect.Classical Morphological Filtering Algorithm is due to choosing
Take structural element excessively single, the method, which cannot be applied effectively, is inhibiting noise jamming, and Generalized Morphological differential filtering device can be with
This drawback of effective solution effectively inhibits noise and is conducive to extract bearing fault spy under strong noise background
Sign.
Summary of the invention
The rolling bearing fault diagnosis based on IITD and broad sense difference shape filtering that the purpose of the present invention is to provide a kind of
Method, for solving the above problems.
In order to achieve the above object, technical scheme is as follows:
Step 1: measuring rolling bearing device using acceleration transducer, obtains vibration acceleration signal；
Step 2: IITD decomposition is carried out to vibration acceleration signal, that is, original signal, obtains several PRC component signals；
Step 3: calculating separately the related coefficient and kurtosis value of each PRC component signal and original signal, chooses related coefficient
Maximum value and the corresponding PRC component signal of second largest value or kurtosis the corresponding PRC component signal of maximum value and second largest value, and
It is reconstructed；
Step 4: signal after reconstruct is subjected to broad sense difference shape filtering；
Step 5: filtered signal is subjected to energy spectrum analysis, and extracts fault signature.
In the step 2, IITD decomposition is carried out to original signal, the process for obtaining PRC component is as follows:
(1) original signal { X is determinedt, t >=0 } and all Local Extremum XKAnd its corresponding time instant τk, k=1,2,
... M, M are extreme point sum, define τ0=0, in continuous threshold point interval [τk, τk+1] on define piecewise linearity baseline extraction
Operator L is as follows:
In formula:
Wherein: 0 < α < 1, usual α take 0.5；
(2) each control line point L is extractedk, endpoint processing is carried out to time series signal using mirror-symmetric extension method, is obtained
Obtain left and right ends extreme point (τ0,X0), (τM+1,XM+1), enabling k is respectively 0 and M-1, finds out L1And LMValue, then using three times
Spline interpolation is fitted all Lk, obtain background signal L1(t)。
(3) baseline is separated from original signal, obtains h1(t), i.e. h1(t)=Xt-L1(t), it is desirable that h1(t) it is
One intrinsic rotational component, i.e. h1(t)=PRC1If h1(t) intrinsic rotational component condition, i.e. baseline L are unsatisfactory fork+1≠ 0, by h1
(t) step (1)-(3) are repeated as original signal, until it is intrinsic rotational component；
(4) by PRC1It is separated from original signal, obtains a new signal r1(t), i.e. r1(t)=Xt-PRC1；
(5) again by r1(t) step (1)~(4) are repeated as original signal, obtains XtSecond meet PRC condition
Component PRC2, repetitive cycling n-1 times obtains XtThe component PRC for meeting PRC condition for n-thn, until rnIt (t) is a dull letter
Until several or constant, so far original signal XtIt has been broken down into n intrinsic rotational component PRCnWith a monotonic function rnThe sum of (t),
I.e.
In the step 3, correlation coefficient ρxyExpression formula are as follows:
In formula: E is mathematic expectaion, and x and y respectively indicate the abscissa value and ordinate value of original signal, μxAnd μyIt respectively indicates
The mean value of x and y, σxAnd σyThe standard deviation of respectively x and y；Related coefficient is bigger, and correlation is bigger.
Kurtosis K is very sensitive to impact signal as dimensionless group, particularly suitable for the analysis of vibration signal, kurtosis K
Expression formula are as follows:
In formula: μ is the mean value of signal, and σ is the standard deviation of signal.
Wherein the related coefficient of PRC component signal is big, and kurtosis is also big, and the kurtosis of bearing normal signal is about 3 and approaches
Normal distribution, and when it local fault occurs, the impact signal probability density due to caused by failure increase, and kurtosis value also can be with
Increase.
In the step four, broad sense difference shape filtering is defined as follows:
Building Generalized Morphological is alternately closed alternately to be opened with Generalized Morphological, on this basis, constructs Generalized Morphological differential filtering
Device.
GFC (n)=(fg1οg2οg1·g2)(n)
GFO (n)=(f ο g1·g2·g1οg2)(n)
F (n)=GFC (n)-GFO (n)
The effect of shape filtering depends not only on version, additionally depends on structural element.The mathematics of structural element at present
Shape is relatively simple, including linear type, triangle, semicircle etc..In view of bearing vibration signal characteristic and calculation amount
It influences, in order to effective filter out the random noise disturbance in bearing vibration signal, what is selected herein is semi-circular structural element.
Beneficial effects of the present invention:
The present invention decomposes rolling bearing acceleration signal using IITD method, while noise reduction, eliminates signal
Edge effect increases Decomposition Accuracy, adaptively by any one complicated non-stationary signal be decomposed into multiple PRC components it
With, and the PRC component after decomposition sought into related coefficient and kurtosis value, effective PRC is chosen based on related coefficient-kurtosis criterion
Component is reconstructed, and is then filtered using Generalized Morphological differential filtering method to the signal after reconstruct；Finally utilize
Teager energy operator analyzes filtered vibration signal, extracts the fault signature of vibration signal, makes fault diagnosis more
Add accurate.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of IITD and the Fault Diagnosis of Roller Bearings of broad sense difference shape filtering；
Fig. 2 (a) is original bearing inner race acceleration signal and IITD decomposed signal；
Fig. 2 (b) is original bearing outer ring acceleration signal and IITD decomposed signal；
Fig. 3 (a) is signal time-domain diagram after inner ring reconstruct；
Fig. 3 (b) is signal time-domain diagram after the reconstruct of outer ring；
Fig. 4 (a) is the energy spectrum analysis result figure of signal after inner ring reconstruct；
Fig. 4 (b) is the energy spectrum analysis result figure of signal after the reconstruct of outer ring；
Fig. 5 (a) is the result of spectrum analysis figure that inner ring ITD is decomposed；
Fig. 5 (b) is the result of spectrum analysis figure that outer ring ITD is decomposed.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings and specific examples.
Embodiment 1: the present invention is based on the rolling bearing fault diagnosis of IITD and broad sense difference shape filtering as shown in Figure 1:
Method includes the following steps that step 1: measuring rolling bearing device using acceleration transducer, obtains vibration acceleration
Signal；
Step 2: IITD decomposition is carried out to vibration acceleration signal, that is, original signal, obtains several PRC component signals；
(1) original signal { X is determinedt,T >=0 } all Local Extremum XKAnd its corresponding time instant τk, k=1,2,
... M, M are extreme point sum, define τ0=0, in continuous threshold point interval [τk, τk+1] on define piecewise linearity baseline extraction
Operator L is as follows:
In formula:
Wherein: 0 < α < 1, usual α take 0.5；
(2) each control line point L is extractedk, endpoint processing is carried out to time series signal using mirror-symmetric extension method, is obtained
Obtain left and right ends extreme point (τ0,X0), (τM+1,XM+1), enabling k is respectively 0 and M-1, finds out L1And LMValue, then using three times
Spline interpolation is fitted all Lk, obtain background signal L1(t)。
(3) baseline is separated from original signal, obtains h1(t), i.e. h1(t)=Xt-L1(t), it is desirable that h1(t) it is
One intrinsic rotational component, i.e. h1(t)=PRC1If h1(t) intrinsic rotational component condition, i.e. baseline L are unsatisfactory fork+1≠ 0, by h1
(t) step (1)-(3) are repeated as original signal, until it is intrinsic rotational component；
(4) by PRC1It is separated from original signal, obtains a new signal r1(t), i.e. r1(t)=Xt-PRC1；
(5) again by r1(t) step (1)~(4) are repeated as original signal, obtains XtSecond meet PRC condition
Component PRC2, repetitive cycling n-1 times obtains XtThe component PRC for meeting PRC condition for n-thn, until rnIt (t) is a dull letter
Until several or constant, so far original signal XtIt has been broken down into n intrinsic rotational component PRCnWith a monotonic function rnThe sum of (t),
I.e.
Step 3: calculating separately the related coefficient and kurtosis value of each PRC component signal and original signal, chooses related coefficient
Maximum value and the corresponding PRC component signal of second largest value or kurtosis the corresponding PRC component signal of maximum value and second largest value, and
It is reconstructed；
Correlation coefficient ρxyExpression formula are as follows:
In formula: E is mathematic expectaion, and x and y respectively indicate the abscissa value and ordinate value of original signal, μxAnd μyIt respectively indicates
The mean value of x and y, σxAnd σyThe standard deviation of respectively x and y；Related coefficient is bigger, and correlation is bigger.
Kurtosis K is very sensitive to impact signal as dimensionless group, particularly suitable for the analysis of vibration signal, kurtosis K
Expression formula are as follows:
In formula: μ is the mean value of signal, and σ is the standard deviation of signal.
Wherein the related coefficient of PRC component signal is big, and kurtosis is also big, and the kurtosis of bearing normal signal is about 3 and approaches
Normal distribution, and when it local fault occurs, the impact signal probability density due to caused by failure increase, and kurtosis value also can be with
Increase.
Step 4: signal after reconstruct is subjected to broad sense difference shape filtering；Building Generalized Morphological alternately closes and Generalized Morphological
Alternating is opened, and on this basis, constructs Generalized Morphological differential filtering device.
GFC (n)=(fg1οg2οg1·g2)(n)
GFO (n)=(f ο g1·g2·g1οg2)(n)
F (n)=GFC (n)-GFO (n)
The effect of shape filtering depends not only on version, additionally depends on structural element.The mathematics of structural element at present
Shape is relatively simple, including linear type, triangle, semicircle etc..In view of bearing vibration signal characteristic and calculation amount
It influences, in order to effective filter out the random noise disturbance in bearing vibration signal, what is selected herein is semi-circular structural element.
Step 5: filtered signal is subjected to energy spectrum analysis, and extracts fault signature.
Embodiment 2: choose in the present embodiment at 4800 points analyzed respectively fault-signal, first in rolling bearing
Circle fault-signal is analyzed, and Fig. 2 (a) is the time domain of 5 PRC component signals and original signal that inner ring fault-signal IITD is decomposed
Figure；As can be seen from the figure inner ring fault-signal has obtained effective decomposition.Calculate separately 5 PRC component signals and original signal
Related coefficient and kurtosis value, value is as shown in table 1；As can be seen from Table 1 the related coefficient of PRC1 and PRC2 and original signal and
Kurtosis value is larger, is reconstructed so choosing the two component signals, as shown in Fig. 3 (a)；Reconstruction signal is passed through into Generalized Morphological
Differential filtering removes noise jamming.Spectrum analysis, knot are carried out to the vibration signal after removal noise by Teager energy operator
Fruit such as Fig. 4 (a)；It should be apparent that inner ring failure fundamental frequency is 152.3Hz and the characteristic frequency of frequency multiplication is prominent from figure.Fig. 5
(a) for ITD decompose result of spectrum analysis figure, ITD method due to by ambient noise interference and end effect influenced, no
It can effective handling failure signal.By analyzing above it is found that in allowable range of error, bearing can be accurately judged to and belong to inner ring
Failure.
1 inner ring PRC related coefficient of table and kurtosis index
| Component signal | PRC1 | PRC2 | PRC3 | PRC4 | PRC5 |
| Kurtosis | 7.1494 | 4.4551 | 3.0272 | 3.4701 | 2.2144 |
| Related coefficient | 0.9338 | 0.6.013 | 0.2786 | 0.0582 | 0.0114 |
Housing washer fault-signal is analyzed first, Fig. 2 (b) is 5 that inner ring fault-signal IITD is decomposed
The time-domain diagram of PRC component signal and original signal；As can be seen from the figure inner ring fault-signal has obtained effective decomposition.It counts respectively
The related coefficient and kurtosis value of 5 PRC component signals and original signal are calculated, value is as shown in table 2；As can be seen from Table 2 PRC1 and
The related coefficient and kurtosis value of PRC2 and original signal are larger, are reconstructed so choosing the two component signals, such as Fig. 3 (b) institute
Show；By reconstruction signal by Generalized Morphological differential filtering, noise jamming is removed.By Teager energy operator to removal noise after
Vibration signal carry out spectrum analysis, as a result such as Fig. 4 (b)；It should be apparent that outer ring failure fundamental frequency is 105.5Hz from figure
And the characteristic frequency of frequency multiplication is prominent.Fig. 5 (b) is the result of spectrum analysis figure that ITD is decomposed, and ITD method is due to by ambient noise
Interference and end effect influence, cannot effective handling failure signal.By being analyzed above it is found that in allowable range of error
It is interior, bearing can be accurately judged to and belong to outer ring failure.
2 outer ring PRC related coefficient of table and kurtosis index
| Component signal | PRC1 | PRC2 | PRC3 | PRC4 | PRC5 |
| Kurtosis | 6.8924 | 17.4358 | 3.9506 | 3.0961 | 2.3689 |
| Related coefficient | 0.9727 | 0.3157 | 0.1277 | 0.0636 | 0.0121 |
Specific embodiments of the present invention are explained in detail above in conjunction with attached drawing, but the present invention is not limited to above-mentioned realities
Example is applied, it within the knowledge of a person skilled in the art, can also be without departing from the purpose of the present invention
Various changes can be made.
Claims (4)
1. a kind of Fault Diagnosis of Roller Bearings based on IITD and broad sense difference shape filtering, which is characterized in that including such as
Lower step:
Step 1: measuring rolling bearing device using acceleration transducer, obtains vibration acceleration signal；
Step 2: IITD decomposition is carried out to vibration acceleration signal, that is, original signal, obtains several PRC component signals；
Step 3: the related coefficient and kurtosis value of each PRC component signal and original signal are calculated separately, chooses related coefficient most
The big corresponding PRC component signal of maximum value and second largest value for being worth PRC component signal corresponding with second largest value or kurtosis, and to it
It is reconstructed；
Step 4: signal after reconstruct is subjected to broad sense difference shape filtering；
Step 5: filtered signal is subjected to energy spectrum analysis, and extracts fault signature.
2. the Fault Diagnosis of Roller Bearings according to claim 1 based on IITD and broad sense difference shape filtering,
It is characterized in that: in the step 2, IITD decomposition being carried out to original signal, the process for obtaining PRC component is as follows:
(1) original signal { X is determinedt, t >=0 } and all Local Extremum XKAnd its corresponding time instant τk, k=1,2 ... M, M are
Extreme point sum, defines τ0=0, in continuous threshold point interval [τk, τk+1] on to define piecewise linearity baseline extraction operator L as follows:
In formula:
Wherein: 0 < α < 1；
(2) each control line point L is extractedk, endpoint processing is carried out to time series signal using mirror-symmetric extension method, is obtained left
Right both ends extreme point (τ0,X0), (τM+1,XM+1), enabling k is respectively 0 and M-1, finds out L1And LMValue, then use cubic spline
Interpolation is fitted all Lk, obtain background signal L1(t)；
(3) baseline is separated from original signal, obtains h1(t), i.e. h1(t)=Xt-L1(t), it is desirable that h1It (t) is one
Intrinsic rotational component, i.e. h1(t)=PRC1If h1(t) intrinsic rotational component condition, i.e. baseline L are unsatisfactory fork+1≠ 0, by h1(t)
Step (1)-(3) are repeated as original signal, until it is intrinsic rotational component；
(4) by PRC1It is separated from original signal, obtains a new signal r1(t), i.e.,
r1(t)=Xt-PRC1；
(5) again by r1(t) step (1)~(4) are repeated as original signal, obtains XtSecond component for meeting PRC condition
PRC2, repetitive cycling n-1 times obtains XtThe component PRC for meeting PRC condition for n-thn, until rn(t) for a monotonic function or
Until constant, so far original signal XtIt has been broken down into n intrinsic rotational component PRCnWith a monotonic function rnThe sum of (t), i.e.,
3. the Fault Diagnosis of Roller Bearings according to claim 1 based on IITD and broad sense difference shape filtering,
It is characterized in that: in the step 3, correlation coefficient ρxyExpression formula are as follows:
In formula: E is mathematic expectaion, and x and y respectively indicate the abscissa value and ordinate value of original signal, μxAnd μyRespectively indicate x and y
Mean value, σxAnd σyThe standard deviation of respectively x and y；
The expression formula of kurtosis K are as follows:
In formula: μ is the mean value of signal, and σ is the standard deviation of signal.
4. the Fault Diagnosis of Roller Bearings according to claim 1 based on IITD and broad sense difference shape filtering,
Be characterized in that: the broad sense difference shape filtering selects semi-circular structural element.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108152037A (en) * | 2017-11-09 | 2018-06-12 | 同济大学 | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering |
| CN109187023A (en) * | 2018-09-04 | 2019-01-11 | 温州大学激光与光电智能制造研究院 | A kind of automobile current generator bearing method for diagnosing faults |
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2019
- 2019-06-21 CN CN201910541778.6A patent/CN110320039A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108152037A (en) * | 2017-11-09 | 2018-06-12 | 同济大学 | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering |
| CN109187023A (en) * | 2018-09-04 | 2019-01-11 | 温州大学激光与光电智能制造研究院 | A kind of automobile current generator bearing method for diagnosing faults |
Non-Patent Citations (3)
| Title |
|---|
| 向玲等: "基于改进ITD和峭度准则的滚动轴承故障诊断方法", 《机床与液压》 * |
| 黄刚劲等: "CEEMD与广义形态差值滤波结合的故障诊断方法研究", 《华中科技大学学报（自然科学版）》 * |
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Application publication date: 20191011 |