CN110146289A - A kind of rolling bearing Weak fault feature extracting method - Google Patents
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Abstract
The present invention relates to a kind of rolling bearing Weak fault feature extracting methods, and in particular to one kind decomposes the rolling bearing Weak fault feature extracting method of (ENCMD) based on integrated noise reconstruct combined modality, belongs to rotary machinery fault diagnosis field.This method mainly includes step S1: obtaining the vibration acceleration signal under housing washer failure, inner ring malfunction, obtains time-domain signal sample.Step S2: integrated noise reconstruct combined modality is carried out to obtained time-domain signal sample and decomposes (ENCMD), obtains combined modality function (CMF) component.Step S3: Fast Fourier Transform (FFT) is carried out to obtained combined modality function component, is converted by time domain to frequency domain and is analyzed.Step S4: by the frequency-domain analysis of combined modality, bearing feature information of weak faults is obtained, extracts fault signature.
Description
Technical field
The present invention relates to a kind of rolling bearing Weak fault feature extracting methods, and in particular to one kind is based on integrated noise weight
Structure combined modality decomposes the rolling bearing Weak fault feature extracting method of (ENCMD), belongs to rotary machinery fault diagnosis field.
Background technique
One of rolling bearing is one of most common components in rotating machinery, while being easiest to the part to break down.
According to incompletely statistics, in rotating machinery, about 30% failure is as caused by bearing fault.In engineering practice we
It is expected that accomplishing to check erroneous ideas at the outset to bearing fault, timely discovering device Weak fault simultaneously makes corresponding measure in time, can effectively reduce
The generation of economic loss and safety accident has important economic value and social value.And Weak fault typically refers to be in
The faint or incipient fault of early stage has the characteristics that symptom is unobvious, characteristic information is faint；Also refer to that fault signature is mechanical
System multi-jamming sources and very noisy are flooded, and cause signal-to-noise ratio low, it is difficult to identify.If bearing cannot be found accurately and in time
Weak fault, leaving, it continues to deteriorate development, once evolving into catastrophe failure, can cause the non-programmed halt of whole equipment, make
At economic loss, the even generation of safety accident.Therefore, how Weak fault is identified as early as possible, guaranteed safe production, prevent from pacifying
The generation of full accident, always is the project of primary study.
Summary of the invention
It is faint the technical problem to be solved by the present invention is to be directed to rolling bearing Weak fault signal fault feature, easily it is submerged in
Among ambient noise, the problem of causing its fault signature to be difficult to, a kind of rolling bearing Weak fault feature extraction side is provided
Method extracts Weak fault feature, can find rolling bearing fault information in time by the analysis to bearing vibration signal, improves
Failure judge accuracy and reliability and timeliness, for rolling bearing it is long-term, it is safe and efficient operation escort.
The technical solution adopted by the present invention is that: a kind of rolling bearing Weak fault feature extracting method, comprising the following steps:
Step1, noise component(s) is obtained using noise estimation techniques to the collected original signal x (t) of institute
Step1.1, original signal x (t) progress EMD is decomposed to obtain several IMF components, set is denoted as { ck(t), k=
1,2 ..., n } and a residual components, and calculate each IMF energy { Ek(t), k=1,2 ..., n }.
Step1.2, assume first IMF component c1It (t) is white noise, i.e. its white noise energy
Step1.3, calculate separately to obtain each IMF in confidence interval 95% according to IMF energy estimation formulas (3) under white noise
White noise energy under (parameter beta=0.719, ρ=2.449) and 99% (parameter beta=0.719, ρ=1.919)With
Step1.4, the energy { E by each IMFkAnd it is correspondingWithComparison.
If 1)Then by ck(t) as noisy IMF to be processed.
If 2)OrSimilarly by ck(t) as to be processed noisy
IMF, wherein α is a given tolerance, takes α=1.
3) otherwise, ck(t) the fault signature component as estimation.
Step1.5, the above noisy IMF to be processed is formed into { cl(t), l=1,2 ... }.
Step1.6, it is based on adjacent coefficient principle of noise reduction, to { cl(t), l=1,2 ... } in i-th sample carry out such as formula
(4) threshold process obtains purification noise component(s)
σ in formulal—cl(t) standard noise is poor；N—cl(t) data length；C-is threshold value regulatory factor,For the adjacent sample quadratic sum of i-th of sample point；- be i-th of sample point square；- be
Square of (i-1)-th sample point；- be i+1 sample point square；Tl- it is threshold value constant,- it is threshold value
Square of constant.
Step1.7, merge all purification noise component(s)sObtain estimated noise component in signal x (t)
It is Step2, rightResampling is carried out using random rearrangement sampling point mode, j-th (j=1,2 ...) is obtained and makes an uproar
Sound sample sequenceThen corresponding input signal is reconstructed using formula (1)
In formula,For fault-signal, wherein
Step3, to reconstruction signalCMD is carried out to decompose to obtain the decomposable least CMF component { c of quantityj,k(t), k=
1,2,...,n}。
Step3.1, to reconstruction signalEMD is carried out to decompose to obtain n IMF component and a residual components.
Step3.2, formula (8) computation sequence combined modality function (CMF) is utilized
CMFj(i)=CMFj(i-1)+IMFj(i) (8)
In formula, CMFj(0)=0, i=1,2 ..., n, CMFjFor reconstruction signalSequence combined modality function.
Step3.3, Fast Fourier Transform (FFT) is carried out to sequence combined modality function, obtains combined modality frequency spectrum, and to group
It molds state frequency spectrum and carries out kernel density function estimation, obtain the spectral density function estimation of combined modality function.
Step3.4, the cross-correlation coefficient for calculating k-th CMF spectral density function and+1 CMF spectral density function of kth
Dk,k+1。
Step3.5, according to Dk,k+1In each peak value represent a potential dimensional variation, by sequence CMF be decomposed into compared with
The CMF of small number, is denoted as cj,s(t)。
Step3.6, according to Cluster Validity standard, using formula (9) formula (10) calculate K* (K*=1,2 ..., k) a
cj,s(t) inside quadratic sum SSW (K*) and cj,s(t) quadratic sum SSB (K*) between
In formula,It is the K* cj,s(t) average spectral density function,It is the averag density letter of original decomposition
Number,It is cj,s(t) density function of i-th of element in, K* are the final numbers for decomposing CMF, and n (k) is the K* cj,s
(t) element number for including in,It is the K* cj,s(t) average spectral density functionWith it is original
The averag density function of decompositionBetween Euclidean distance,It is the K* cj,s(t) i-th yuan in
The density function of elementWith the K* cj,s(t) average spectral density functionEuclidean distance.
Step3.7, judge whether SSW (K*) < K*SSB (K*), if being then finally decomposed to K* CMF, otherwise K*=K*
+ 1, it returns to Step3.5 and continues to execute, until SSW (K*) < K*SSB (K*)
Step4, Step2 and Step3 is repeated until meeting the stopping criterion of assigned error permissible value ε.
er≤ε (11)
WhereinWithRespectively represent the noise average of estimationWith estimation noise component(s)Energy, ε be miss
Poor permissible value, er are noise estimation error.
Step5, when meeting stopping criterion, to all CMF components being calculated in Step3 using formula (2) carry out it is flat
Equal calculation process obtains the CMF component that is finally averaged
In formula, r represents best CMF number, cj,kIt (t) is j-th of best CMF component.
Step6, to it is finally obtained most preferably averagely CMF componentCarry out Fast Fourier Transform (FFT).
Step7, frequency-domain analysis is carried out, obtains fault signature.
The beneficial effects of the present invention are: extracting Weak fault feature by the analysis to bearing vibration signal, capable of sending out immediately
Now rolling bearing fault information improves accuracy and reliability and instantaneity that failure is judged, is that rolling bearing is long-term, pacifies
Entirely, efficient operation escorts.
Detailed description of the invention
Fig. 1 is overall step flow chart of the present invention；
Fig. 2 is inventive algorithm flow chart；
Fig. 3 is housing washer fault vibration signal fault characteristic index curve of the present invention；
Fig. 4 is rolling bearing inner ring fault vibration signal fault characteristic index curve of the present invention.
Specific embodiment
With reference to the accompanying drawings and detailed description, the invention will be further described.
Embodiment 1: as shown in Figure 1, a kind of rolling bearing Weak fault feature extracting method, is broadly divided into 4 steps,
Respectively fault-signal acquisition, fault-signal processing, fault-signal frequency-domain analysis, extraction Weak fault feature.
As shown in Fig. 2, a kind of rolling bearing Weak fault feature extracting method, specific steps are as follows:
One kind decomposing the rolling bearing Weak fault feature extraction side of (ENCMD) based on integrated noise reconstruct combined modality
Method, the rolling bearing Weak fault feature extracting method the following steps are included:
Step1, noise component(s) is obtained using noise estimation techniques to the collected original signal x (t) of institute
Step1.1, original signal x (t) progress EMD is decomposed to obtain { ck(t), k=1,2 ..., n } and { rn(t) }, and
Calculate each IMF energy { Ek(t), k=1,2 ..., n }.
Step1.2, assume first IMF component c1It (t) is white noise, i.e. its white noise energy
Step1.3, calculate separately to obtain each IMF in confidence interval 95% according to IMF energy estimation formulas (3) under white noise
White noise energy under (parameter beta=0.719, ρ=2.449) and 99% (parameter beta=0.719, ρ=1.919)With
Step1.4, the energy { E by each IMFkAnd it is correspondingWithComparison.
If 1)Then by ck(t) as noisy IMF to be processed.
If 2)OrSimilarly by ck(t) as to be processed noisy
IMF, wherein α is a given tolerance, takes α=1.
3) otherwise, ck(t) the fault signature component as estimation.
Step1.5, the above noisy IMF to be processed is formed into { cl(t), l=1,2 ... }.
Step1.6, it is based on adjacent coefficient principle of noise reduction, to { cl(t), l=1,2 ... } in i-th sample carry out such as formula
(4) threshold process obtains purification noise component(s)
σ in formulal—cl(t) standard noise is poor；N—cl(t) data length；C-is threshold value regulatory factor,—
For the adjacent sample quadratic sum of i-th of sample point；- be i-th of sample point square；- it is (i-1)-th sample
Square of this point；- be i+1 sample point square；Tl- it is threshold value constant,- putting down for threshold value constant
Side.
Step1.7, merge all purification noise component(s)sObtain estimated noise component in signal x (t)
It is Step2, rightResampling is carried out using random rearrangement sampling point mode, j-th (j=1,2 ...) is obtained and makes an uproar
Sound sample sequenceThen corresponding input signal is reconstructed using formula (1)
In formula,For fault-signal, wherein
Step3, to reconstruction signalCMD is carried out to decompose to obtain the decomposable least CMF component { c of quantityj,k(t),k
=1,2 ..., n }.
Step3.1, to reconstruction signalEMD is carried out to decompose to obtain n IMF component and a residual components.
Step3.2, formula (8) computation sequence combined modality function (CMF) is utilized
CMFj(i)=CMFj(i-1)+IMFj(i) (8)
In formula, CMFj(0)=0, i=1,2 ..., n, CMFjFor reconstruction signalSequence combined modality function.
Step3.3, Fast Fourier Transform (FFT) is carried out to sequence combined modality function, obtains combined modality frequency spectrum, and to group
It molds state frequency spectrum and carries out kernel density function estimation, obtain the spectral density function estimation of combined modality function.
Step3.4, the cross-correlation coefficient for calculating k-th CMF spectral density function and+1 CMF spectral density function of kth
Dk,k+1。
Step3.5, according to Dk,k+1In each peak value represent a potential dimensional variation, by sequence CMF be decomposed into compared with
The CMF of small number, is denoted as cj,s(t)
Step3.6, according to Cluster Validity standard, using formula (9) formula (10) calculate K* (K*=1,2 ..., k) a
cj,s(t) inside quadratic sum SSW (K*) and cj,s(t) quadratic sum SSB (K*) between
In formula,It is the K* cj,s(t) average spectral density function,It is the averag density letter of original decomposition
Number,It is cj,s(t) density function of i-th of element in, K* are the final numbers for decomposing CMF, and n (k) is the K* cj,s
(t) element number for including in,It is the K* cj,s(t) average spectral density functionWith it is original
The averag density function of decompositionBetween Euclidean distance,It is the K* cj,s(t) i-th yuan in
The density function of elementWith the K* cj,s(t) average spectral density functionEuclidean distance.
Step3.7, judge whether SSW (K*) < K*SSB (K*), if being then finally decomposed to K* CMF, otherwise K*=K*
+ 1, it returns to Step3.5 and continues to execute, until SSW (K*) < K*SSB (K*)
Step4, Step2 and Step3 is repeated until meeting the stopping criterion of assigned error permissible value ε.
er≤ε (11)
WhereinWithRespectively represent the noise average of estimationWith estimation noise component(s)Energy, ε is
Error permissible value, er are noise estimation error.
Step5, when meeting stopping criterion, to all CMF components being calculated in Step3 using formula (2) carry out it is flat
Equal calculation process obtains the CMF component that is finally averaged
In formula, r represents best CMF number, cj,kIt (t) is j-th of best CMF component.
Step6, to it is finally obtained most preferably averagely CMF componentCarry out Fast Fourier Transform (FFT).
Step7, frequency-domain analysis is carried out, obtains fault signature.
It is as shown in Figure 3 to Figure 4 to the feature extraction result of housing washer, inner ring Weak fault.
Specific embodiment is illustrated below with reference to Fig. 3 to Fig. 4.
As shown in figure 3, according to the method for the present invention, it, will using U.S.'s Case Western Reserve University electrical engineering laboratory data
The bearing outer ring localized cracks failure of the minimum 0.1178mm of fault diameter is considered as Weak fault, and wherein bearing load is zero load,
Turning frequency is 1791r/min, sample frequency 12kHz, is according to bear vibration theory bearing outer ring failure fundamental frequency calculated value
Fo=107.3Hz.
As shown in figure 4, according to the method for the present invention, it, will using U.S.'s Case Western Reserve University electrical engineering laboratory data
The bearing inner race localized cracks failure of the minimum 0.1178mm of fault diameter is considered as Weak fault, and wherein bearing load is zero load,
Turning frequency is 1791r/min, sample frequency 12kHz, is according to bear vibration theory bearing inner race failure fundamental frequency calculated value
fi=162.2Hz.
In conjunction with attached drawing, the embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (4)
1. a kind of rolling bearing Weak fault feature extracting method, it is characterised in that: specific steps are as follows:
Step1, noise component(s) is obtained using noise estimation techniques to the collected original signal x (t) of institute
It is Step2, rightResampling is carried out using random rearrangement sampling point mode, obtains j-th of (j=1,2 ...) noise sample
SequenceThen corresponding input signal is reconstructed using formula (1)
In formula,For fault-signal, wherein
Step3, to reconstruction signalCMD is carried out to decompose to obtain the decomposable least best CMF component { c of quantityj,k(t), k=
1,2,...,n}；
Step4, Step2 and Step3 is repeated until meeting the stopping criterion of assigned error permissible value ε；
Step5, when meeting stopping criterion, to all best CMF components being calculated in Step3 using formula (2) carry out it is flat
Equal calculation process obtains the CMF component that is most preferably averaged
In formula, r represents best CMF number, cj,kIt (t) is j-th of best CMF component；
Step6, to it is finally obtained most preferably averagely CMF componentCarry out Fast Fourier Transform (FFT)；
Step7, frequency-domain analysis is carried out, obtains fault signature.
2. a kind of rolling bearing Weak fault feature extracting method according to claim 1, it is characterised in that:
The Step1 specific steps are as follows:
Step1.1, original signal x (t) progress EMD is decomposed to obtain several IMF component { ck(t), k=1,2 ..., n } and it is remaining
Component { rn(t) }, and each IMF component energy { E is calculatedk(t), k=1,2 ..., n }；
Step1.2, assume first IMF component c1It (t) is white noise, white noise energy
Step1.3, calculate separately to obtain each IMF in 95% He of confidence interval according to IMF energy estimation formulas (3) under white noise
White noise energy under 99%WithGinseng of the confidence area at 95%
Number is β=0.719, and ρ=2.449, parameter of the confidence area at 99% is β=0.719, ρ=1.919；
Step1.4, the energy { E by each IMFkAnd it is correspondingWithComparison；
If 1)Then by ck(t) as noisy IMF to be processed；
If 2)OrSimilarly by ck(t) as noisy IMF to be processed,
Middle α is a given tolerance, takes α=1；
3) otherwise, ck(t) the fault signature component as estimation；
Step1.5, the above noisy IMF to be processed is formed into { cl(t), l=1,2 ... }；
Step1.6, it is based on adjacent coefficient principle of noise reduction, to { cl(t), l=1,2 ... } in i-th sample carry out such as formula (4)
Threshold process obtains purification noise component(s)
σ in formulal—cl(t) standard noise is poor；N—cl(t) data length；C-is threshold value regulatory factor,- it is the
The adjacent sample quadratic sum of i sample point；- be i-th of sample point square；- it is (i-1)-th sample point
Square；- be i+1 sample point square；Tl- it is threshold value constant,- be threshold value constant square；
Step1.7, merge all purification noise component(s)sObtain the noise component(s) estimated in signal x (t)
3. a kind of rolling bearing Weak fault feature extracting method according to claim 1, it is characterised in that: described
Step3 includes the following steps:
Step3.1, to reconstruction signalEMD is carried out to decompose to obtain n IMF component and a residual components；
Step3.2, formula (8) computation sequence combined modality function (CMF) is utilized
CMFj(i)=CMFj(i-1)+IMFj(i) (8)
In formula, CMFj(0)=0, i=1,2 ..., n, CMFjFor reconstruction signalSequence combined modality function；
Step3.3, to sequence combined modality function CMFjFast Fourier Transform (FFT) is carried out, obtains combined modality frequency spectrum, and to combination
Mode frequency spectrum carries out kernel density function estimation, obtains the spectral density function estimation of combined modality function；
Step3.4, k-th of CMF is calculatedjSpectral density function and+1 CMF of kthjThe cross-correlation coefficient of spectral density function
Dk,k+1；
Step3.5, according to Dk,k+1In each peak value represent a potential dimensional variation, by CMFjIt is decomposed into small number of
CMF is denoted as cj,s(t)；
Step3.6, according to Cluster Validity standard, utilize formula (9) and formula (10) to calculate K*(K*=1,2 ..., k) a cj,s
(t) inside quadratic sum SSW (K*) and cj,s(t) quadratic sum SSB (K between*)
In formula,It is K*A cj,s(t) average spectral density function,It is the averag density function of original decomposition,It is cj,s(t) density function of i-th of element, K in*It is the final number for decomposing CMF, n (k) is K*A cj,s(t) in
The element number for including,It is K*A cj,s(t) average spectral density functionWith original decomposition
Averag density functionBetween Euclidean distance,It is K*A cj,s(t) density of i-th of element in
FunctionWith K*A cj,s(t) average spectral density functionEuclidean distance；
Step3.7, judge whether SSW (K*) < K*SSB(K*), if being then finally decomposed to K*A CMF, otherwise K*=K*+ 1, it returns
It returns Step3.5 to continue to execute, until SSW (K*) < K*SSB(K*)。
4. a kind of rolling bearing Weak fault feature extracting method according to claim 1, it is characterised in that: described
Stopping criterion is provided by formula (11) in Step4
er≤ε (11)
WhereinWithRespectively represent the noise average of estimationWith estimation noise component(s)Energy, ε for error permit
Perhaps it is worth, er is noise estimation error.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110716228A (en) * | 2019-11-18 | 2020-01-21 | 上海理工大学 | Method for extracting characteristic signal for rotary type redundancy detection |
| CN111238808A (en) * | 2020-02-04 | 2020-06-05 | 沈阳理工大学 | Compound fault diagnosis method for gearbox based on empirical mode decomposition and improved variational mode decomposition |
| CN112326017A (en) * | 2020-09-28 | 2021-02-05 | 南京航空航天大学 | Weak signal detection method based on improved semi-classical signal analysis |
| CN113281617A (en) * | 2021-06-08 | 2021-08-20 | 中国民航大学 | Weak fault diagnosis method for airplane cable |
| CN114018581A (en) * | 2021-11-08 | 2022-02-08 | 中国航发哈尔滨轴承有限公司 | CEEMDAN-based rolling bearing vibration signal decomposition method |
| CN114593917A (en) * | 2022-03-08 | 2022-06-07 | 安徽理工大学 | Small sample bearing fault diagnosis method based on triple model |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893773A (en) * | 2016-04-20 | 2016-08-24 | 辽宁工业大学 | Vibration signal frequency characteristic extraction method based on EEMD technology, CMF technology and WPT technology |
| CN107505135A (en) * | 2017-08-15 | 2017-12-22 | 河北建设集团卓诚路桥工程有限公司 | A kind of rolling bearing combined failure extracting method and system |
-
2019
- 2019-05-28 CN CN201910450081.8A patent/CN110146289A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893773A (en) * | 2016-04-20 | 2016-08-24 | 辽宁工业大学 | Vibration signal frequency characteristic extraction method based on EEMD technology, CMF technology and WPT technology |
| CN107505135A (en) * | 2017-08-15 | 2017-12-22 | 河北建设集团卓诚路桥工程有限公司 | A kind of rolling bearing combined failure extracting method and system |
Non-Patent Citations (2)
| Title |
|---|
| M. GRASSO等: "A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis", 《MECHANICAL SYSTEMS AND SIGNAL PROCESSING》 * |
| 袁静 等: "改进集成噪声重构经验模式分解的微弱时频特征增强方法及应用", 《机械工程学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110716228A (en) * | 2019-11-18 | 2020-01-21 | 上海理工大学 | Method for extracting characteristic signal for rotary type redundancy detection |
| CN111238808A (en) * | 2020-02-04 | 2020-06-05 | 沈阳理工大学 | Compound fault diagnosis method for gearbox based on empirical mode decomposition and improved variational mode decomposition |
| CN111238808B (en) * | 2020-02-04 | 2021-08-17 | 沈阳理工大学 | Compound fault diagnosis method for gearbox based on empirical mode decomposition and improved variational mode decomposition |
| CN112326017A (en) * | 2020-09-28 | 2021-02-05 | 南京航空航天大学 | Weak signal detection method based on improved semi-classical signal analysis |
| CN113281617A (en) * | 2021-06-08 | 2021-08-20 | 中国民航大学 | Weak fault diagnosis method for airplane cable |
| CN114018581A (en) * | 2021-11-08 | 2022-02-08 | 中国航发哈尔滨轴承有限公司 | CEEMDAN-based rolling bearing vibration signal decomposition method |
| CN114018581B (en) * | 2021-11-08 | 2024-04-16 | 中国航发哈尔滨轴承有限公司 | Rolling bearing vibration signal decomposition method based on CEEMDAN |
| CN114593917A (en) * | 2022-03-08 | 2022-06-07 | 安徽理工大学 | Small sample bearing fault diagnosis method based on triple model |
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Application publication date: 20190820 |