CN106980761A - A kind of rolling bearing running status degradation trend Forecasting Methodology - Google Patents
A kind of rolling bearing running status degradation trend Forecasting Methodology Download PDFInfo
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Abstract
The invention discloses rolling bearing running status degradation trend Forecasting Methodology, it is related to trend prediction field.The present invention provides a kind of rolling bearing running status degradation trend Forecasting Methodology, including：Step 1: gathering and obtaining characterizing the original vibration signal of rolling bearing running status；Step 2: the original vibration signal obtained to the step one carries out denoising Processing, obtain being capable of the useful signal of embodiments rolling bearing running status；Step 3: extracting the temporal signatures and frequency domain character for the useful signal that the step 2 is obtained；Step 4: carrying out Fusion Features to the temporal signatures and the frequency domain character, obtain characterizing the characteristic index of rolling bearing operation trend；Step 5: building degradation trend forecast model, degradation trend prediction is carried out to the characteristic index.The rolling bearing running status degradation trend Forecasting Methodology that the present invention is provided can carry out good prediction to the running status of rolling bearing.
Description
Technical field
The present invention relates to performance trend field, in particular to rolling bearing running status degradation trend Forecasting Methodology.
Background technology
Plant equipment is applied to the every aspect of human lives, work and production, and plays wherein very important
Role.At present, plant equipment just develops towards maximization, high speed, precise treatment, systematization, serialization and automation direction, machine
The running environment of tool equipment becomes increasingly complex changeable, and the health control even more for equipment proposes new challenge.With equipment
Can operation, the problems such as part aging, reliability reduction, residual life are reduced, continue safely and efficiently to work, safeguard with equipment
Can timely and effectively it perform, it would be highly desirable to which the mankind solve.Bearing is the important element of plant equipment, is played in mechanical system
Highly important effect.The performance degradation trend and life-span prediction method of bearing are always what plant equipment health control was studied
Emphasis.In order to effectively prevent equipment operating accuracy from declining, the plant equipment key zero using bearing as representative is maximally utilised
The ability to work of part, saves material spending, reduces accident, is increasingly necessary to track the running of parts.It is right
Plant equipment key components and parts carry out degradation trend and life search have been turned into extremely important one in modern comfort health control
Ring.
The content of the invention
It is an object of the invention to provide a kind of rolling bearing running status degradation trend Forecasting Methodology, it, which is significantly reduced, sets
Standby maintenance cost, the global reliability for improving equipment.
The present invention provides a kind of technical scheme：
A kind of rolling bearing running status degradation trend Forecasting Methodology, including：Step 1: gathering and obtaining characterizing the axis of rolling
Hold the original vibration signal of running status；Step 2: the original vibration signal obtained to the step one is carried out at de-noising
Reason, obtains being capable of the useful signal of embodiments rolling bearing running status；Step 3: that extracts that the step 2 obtains is described
The temporal signatures and frequency domain character of useful signal；Step 4: Fusion Features are carried out to the temporal signatures and the frequency domain character,
Obtain characterizing the characteristic index of rolling bearing operation trend；Step 5: degradation trend forecast model is built, to the characteristic index
Carry out degradation trend prediction.
Further, the degradation trend model includes least square method supporting vector machine, particle cluster algorithm and deviation accumulation
And method, the most young waiter in a wineshop or an inn is into SVMs for building forecast model, and the particle cluster algorithm is for the prediction mould
The parameter of type is optimized, and the deviation accumulation and method are used to be controlled the predicated error of the forecast model.
Further, the method for the Fusion Features is PCA, the temporal signatures and the frequency domain character
Multidimensional characteristic matrix is constituted, the multidimensional characteristic matrix obtains the characteristic index by the PCA.
Further, the step 2 carries out de-noising using Methods for Wavelet Denoising Used to the original vibration signal, and obtains
The useful signal.
The beneficial effects of the present invention are：The vibration signal of the rolling bearing gathered by online monitoring vibration sensor,
Obtain useful information and extract the state feature of information in domains such as time domain, frequency domain, time-frequency domains, obtain that bearing running status can be characterized
Multidimensional characteristic collection, and carry out the performance degradation trend prediction and life prediction of rolling bearing running status, be that bearing is based on regarding
Feelings maintenance, the important foundation for improving reliability.But multidimensional characteristic concentrates the two or more spies that there may be mutual redundancy
Levy, it is also possible to there is the feature completely irrelevant with bearing runnability state, simultaneously because rolling bearing structure, working environment etc.
Complexity, cause single simple feature index be difficult intactly performance of the reflection rolling bearing under arms in periodic process move back
Change trend, it is difficult to determine its residual life, it is very unfavorable for bearing maintenance, maintenance and raising reliability.And the present invention is carried
Go out a kind of rolling bearing running status performance degradation trend and method for predicting residual useful life by online monitoring vibration signal, it is first
First pass through wavelet noise to reject the noise in collection signal and retain useful information, then respectively in time domain, frequency domain, time-frequency domain etc.
Extract the state feature of information and form multidimensional characteristic collection in domain.With reference to PCA, multidimensional characteristic collection is merged,
On the basis of not reducing the information content that legacy data included and rejecting redundancy and invalid components in multidimensional characteristic information,
The optimal characteristic index of statistical significance upside deviation is obtained, complete reflection bearing under arms in the cycle comprehensively is then based on
The characteristic index of runnability degradation trend and using least square method supporting vector machine as trend prediction model, uses cross validation
Method, and the comprehensive new method for using particle cluster algorithm Optimal Parameters, obtaining being capable of degree of precision prediction bearing performance degradation trend.
The present invention can obtain the new method of an energy full forecast rolling bearing performance degradation trend, can tie up further to carry out regarding feelings
Solid foundation is established in maintenance strategy research, so as to significantly reduce expense, improve the global reliability of equipment.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be attached to what is used required in embodiment
Figure is briefly described.It should be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore it is not construed as pair
The restriction of scope.For those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
The flow signal for the rolling bearing running status degradation trend Forecasting Methodology that Fig. 1 provides for embodiments of the invention
Figure；
Deviation accumulation and the schematic flow sheet of method that Fig. 2 provides for embodiments of the invention.
Embodiment
Below in conjunction with the accompanying drawings, the embodiment to the present invention is described in detail.
Referring to Fig. 1, present embodiments providing a kind of rolling bearing running status degradation trend Forecasting Methodology.Below to this
The specific steps of method are illustrated.
Step (1)：On-line monitoring vibrating sensor collection bearing vibration signal (includes reflection bearing runnability
The useful signal and noise signal of state).
Step (2)：Wavelet noise function (the Main Basiss Matlab algorithms, wherein base letter carried using MATLAB softwares
It is crucial that number, Decomposition order, threshold method and threshold value etc., which are chosen) most of noise jamming in vibration signal is eliminated, and retain
The useful signal of bearing runnability state can be reflected.
Step (3)：Extract temporal signatures collection (including dimension feature and dimensionless feature, 1, table 2 is shown in Table in detail), frequency domain special
Collect (being shown in Table 3 in detail), time and frequency domain characteristics collection (empirical mode decomposition method (Empirical Model Decomposition,
EMD non-linear, the non-stationary signal feature in bearing condition monitoring signal) can be efficiently extracted, and possesses higher time-frequency and is differentiated
Rate, time and frequency domain characteristics collection herein is the nonlinear characteristic in the bearing condition monitoring signal that EMD is extracted) and Weibull parameter
(form parameter and scale parameter of Weibull distribution can be used as the characteristic quantity for reflecting bearing running status to feature set.Due to Xi Er
Bert becomes transducing and preferably extracts bearing initial failure information, therefore status monitoring signal first passes through Hilbert transform and obtained newly
Data, then using maximum likelihood method be the form parameter and scale parameter that can obtain Weibull distribution, i.e., Weibull join
Number feature set), obtain that the multidimensional characteristic collection of bearing runnability state can be characterized, and build multidimensional characteristic collection matrix.
The index of temporal signatures containing dimension of table 1
The non_dimensional time domain characteristic index of table 2
The frequency domain character of table 3
Non-linear, non-stationary information, empirical modal are generally comprised in the status monitoring signal of plant equipment key components and parts
Decomposition method (Empirical Mode Decomposition, EMD) is a kind of non-linear, Non-stationary Signal Analysis method, it
By signal decomposition into a series of intrinsic mode functions (Intrinsic Mode Function, IMF) sum.For nonlinear spy
Levy, widely used method also has wavelet method.Compared to wavelet method, empirical mode decomposition method is not only able to effectively
Extract signal in Weak characteristic, and without choose basic function, therefore, adaptivity is stronger, be especially suitable for handle non-thread
Property signal.Influence of the human factor to result, decomposition result dependency analysis signal sheet are eliminated by empirical mode decomposition method
Body, can efficiently extract non-linear, non-stationary signal Weak characteristic, and available higher time frequency resolution, with good
Time-frequency locality.So the present embodiment extracts the non-linear spy in parts monitoring signals using empirical modal analysis method
Levy.
If by empirical mode decomposition, signal obtains multiple modal components fi(t) with remainder rn(t), rn(t) letter is regarded as
Number (n+1)th component fn+1(t), then i-th (i=1,2 ..., n+1) individual component fi(t) energy is：
In formula, N is IMF components fi(t) data length.
Rule of thumb mode decomposition Complete Orthogonal, it can be deduced that：
E [x (t)]=E [f1(t)]+E[f2(t)]+...+E[fn+1(t)]
Weibull distribution is one of conventional statistical distribution pattern in fail-safe analysis, can be divided into two parameter Weibull point
Cloth and three-parameter Weibull distribution.The form parameter and scale parameter of Weibull distribution can also be used as reflection parts running status
Characteristic quantity.The present embodiment considers two parameter Weibull distribution, and its probability density function is：
β is form parameter in formula, and η is scale parameter.
Method for parameter estimation is generally divided into diagram method and the major class of analytic method two, diagram method includes experience distribution map method, prestige
Boolean's probability graph method and relative risk statistic map method etc.；Analytic method includes Maximum Likelihood Estimation Method and regression estimates method etc..This implementation
Example solves parameter by Maximum Likelihood Estimation Method.Maximum-likelihood estimation function is：
In formula, N is data length.Because the extraction of Hilbert transform pairs parts initial failure information has good effect
Really, xiThe data for being state detection signal after Hilbert transform.
On above formula, local derviation is sought β and η respectively, and makes it be zero, is obtained：
Above formula is Nonlinear System of Equations, is solved using Newton iteration method and obtains parameter beta and η estimate.Due to passing through pole
The estimate that the maximum-likelihood estimation technique is obtained usually there will be larger error, and amendment form parameter β can reduce evaluated error, its
Correction formula is：
In formula, N is data length, βUIt is correction value, β is estimate.
Step (4)：Using PCA PCA (Principal Components Analysis) to rolling bearing
The multidimensional characteristic of vibration signal is merged after de-noising, is not being reduced on the basis of legacy data includes the information content, is being obtained
The characteristic index of rolling bearing running status performance degradation trend can be characterized comprehensively.
The performance degradation characteristic index in parts running is obtained using principal component analysis progress multidimensional characteristic fusion
It is, by certain Linear Mapping, to form the new multidimensional characteristic collection less than original dimension by original multidimensional characteristic collection.New
It is mutually orthogonal two-by-two between feature set, and vector sorted from big to small according to the otherness between feature, sequence first vector reflection
Maximum difference between feature.The new vector that maximum difference amount is constituted so between multidimensional characteristic can not only be covered in feature
Useful information, and the redundancy section of original multidimensional characteristic collection is eliminated, realize with the mode of principal component to describe high dimension
According to feature.
If having n data in certain data collected, each data have p characteristic variable, then constitute a n × p
The multidimensional characteristic matrix of rank：
X in formulai=(x1i,x2i,...,xni)T, xijIt is j-th of characteristic variable of i-th of data.
Obtained by principal component transform by x1,x2,...,xpThe linear combination of expression：
If coefficient lijMeet：1)li1 2+li2 2+...+lip 2=1, i=1,2 .., p；2) coefficient lijMake linear combination yi
With yj(i ≠ j) is independent of each other；3)yiAccording to x1,x2,...,xpThe sequence of all linear combination variance sizes, then former multidimensional characteristic
Integrate first, second, pth principal component is y1,y2,...,yp。
Principal component analysis process represented by above formula is exactly to ask characteristic value and spy by the covariance matrix to multidimensional characteristic
Levy vector, and the characteristic vector corresponding to the characteristic value after order by size is arranged is used as the coefficient of linear combination.Utilize master
Component analyzing method carries out merging the step of obtaining parts performance degradation characteristic index to multidimensional characteristic X：
(1) parts multidimensional characteristic matrix X mean vector is calculated:
By multidimensional characteristic centralization：
(2) covariance matrix of the special matrix of multidimensional is calculated：
The eigen vector of multidimensional characteristic matrix is calculated, using the characteristic vector corresponding to eigenvalue of maximum as right
The coefficient of multidimensional characteristic matrix linear change, obtains parts performance degradation characteristic index.
Step (5)：The conclusion obtained according to step (4), design forecast model and parameter optimization.With least square support to
Amount machine uses cross-validation method as forecast model in optimizing to parameter model, and synthesis uses particle swarm optimization algorithm, to carry
Accuracy rate and accuracy that height is trained every time.And using deviation accumulation and method (Cumulative Sum, CUSUM) to current mould
The predictive ability of type is tested, and further improves the accuracy of prediction.
Obtained forecast model is optimized by particle cluster algorithm, although parameter has reached in some sense optimal, but
It is but to lack further to examine the predictive ability of model, the present embodiment, which is utilized, uses error accumulation and method
(Cumulative Sum, CUSUM) tests to the predictive ability of "current" model, further improves the accuracy of prediction.
Referring to Fig. 2, the specific steps of deviation accumulation given below and method.
Assuming that the error Z=y of forecast modeli-yiIt is to obey average for μ, variance is σ2Normal distribution, to obey normal state
The array Z of distribution is obtained after being standardized：
Accumulation and method examine the predictive ability of "current" model using two indices：
UBi=max [0, (di-m)+UBi-1]
LBi=max [0, (- di-m)+LBi-1]
In above formula, UBiRepresent the overgauge of error, LBiRepresent the minus deviation of error.Original state UB0And LB0All it is
0, it is generally the case that parameter m takes 0.5, and threshold value h takes 3.
What the present embodiment was proposed is entered based on Least Square Support Vector Regression, particle cluster algorithm and error accumulation and method
The research step of row plant equipment key components and parts trend prediction is as follows：
(1) running status to plant equipment key components and parts is monitored on-line, and the vibration signal of acquisition is filtered
Ripple handles (Methods for Wavelet Denoising Used), to ensure that the characteristic information that vibration data is included is not flooded by noise；
(2) the multidimensional characteristic information of Monitoring Data is extracted, is obtained using the mode (principal component analytical method) of Fusion Features
The characteristic index of plant equipment key components and parts running status performance degradation trend；
(3) the trend prediction mould based on Least Square Support Vector Regression, particle cluster algorithm and accumulation and method is set up
Type；
(4) by characteristic index input prediction model, the degradation trend to plant equipment key components and parts running status is realized
Predict, and output predicts the outcome.
Step (6)：Trend prediction, using the characteristic index obtained in step 4 as trend prediction model input data, and
Determine training set and test set；Rolling bearing running status performance degradation trend is predicted.
In the present embodiment, training set length is 100, and institute's extracting method of the present invention and control methods carry out Single-step Prediction, its
Middle cross-validation method selects 5-Fold；The major parameter of particle cluster algorithm is set：Maximum evolution quantity is 200, planted
Group's maximum quantity is that 20, global and local search capability is respectively set to 1.7 and 1.5；The major parameter of genetic algorithm is set to：
Maximum evolutionary generation is 200, and population maximum quantity is 20.
Claims (4)
1. a kind of rolling bearing running status degradation trend Forecasting Methodology, it is characterised in that including：
Step 1: gathering and obtaining characterizing the original vibration signal of rolling bearing running status；
Step 2: the original vibration signal obtained to the step one carries out denoising Processing, obtaining being capable of embodiments rolling
The useful signal of dynamic bearing running status；
Step 3: extracting the temporal signatures and frequency domain character for the useful signal that the step 2 is obtained；
Step 4: carrying out Fusion Features to the temporal signatures and the frequency domain character, obtain characterizing rolling bearing operation trend
Characteristic index；
Step 5: building degradation trend forecast model, degradation trend prediction is carried out to the characteristic index.
2. rolling bearing running status degradation trend Forecasting Methodology according to claim 1, it is characterised in that the degeneration
Trend prediction model includes least square method supporting vector machine, particle cluster algorithm and deviation accumulation and method, and the most young waiter in a wineshop or an inn is into branch
Holding vector machine is used to build forecast model, and the particle cluster algorithm is used to optimize the parameter of the forecast model, described
Deviation accumulation and method are used to be controlled the predicated error of the forecast model.
3. rolling bearing running status degradation trend Forecasting Methodology according to claim 1, it is characterised in that the feature
The method of fusion is PCA, and the temporal signatures constitute multidimensional characteristic matrix, the multidimensional with the frequency domain character
Eigenmatrix obtains the characteristic index by the PCA.
4. rolling bearing running status degradation trend Forecasting Methodology according to claim 1, it is characterised in that the step
Two carry out de-noising using Methods for Wavelet Denoising Used to the original vibration signal, and obtain the useful signal.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108760256A (en) * | 2018-03-23 | 2018-11-06 | 佛山科学技术学院 | A kind of prediction technique of axle box remaining life |
| CN108898050A (en) * | 2018-05-17 | 2018-11-27 | 广东工业大学 | A kind of flexible material process equipment roll shaft performance index calculation method |
| CN109214097A (en) * | 2018-09-14 | 2019-01-15 | 上海工程技术大学 | A kind of long related failure trend prediction method of dimensionless group rolling bearing |
| CN109359791A (en) * | 2018-12-26 | 2019-02-19 | 湖南科技大学 | A kind of mechanical system degradation trend prediction technique and system |
| CN109460618A (en) * | 2018-11-13 | 2019-03-12 | 华中科技大学 | A kind of rolling bearing remaining life on-line prediction method and system |
| CN109827777A (en) * | 2019-04-01 | 2019-05-31 | 哈尔滨理工大学 | Rolling bearing fault prediction technique based on Partial Least Squares extreme learning machine |
| CN109855875A (en) * | 2019-01-15 | 2019-06-07 | 沈阳化工大学 | A kind of rolling bearing operational reliability prediction technique |
| CN113255591A (en) * | 2021-06-25 | 2021-08-13 | 四川九通智路科技有限公司 | Bearing fault diagnosis method based on random forest and fusion characteristics |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103954450A (en) * | 2014-05-19 | 2014-07-30 | 重庆交通大学 | Bearing life degradation performance evaluation index construction method based on main component analysis |
| CN104655423A (en) * | 2013-11-19 | 2015-05-27 | 北京交通大学 | Rolling bearing fault diagnosis method based on time-frequency domain multidimensional vibration feature fusion |
-
2017
- 2017-03-29 CN CN201710195149.3A patent/CN106980761A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104655423A (en) * | 2013-11-19 | 2015-05-27 | 北京交通大学 | Rolling bearing fault diagnosis method based on time-frequency domain multidimensional vibration feature fusion |
| CN103954450A (en) * | 2014-05-19 | 2014-07-30 | 重庆交通大学 | Bearing life degradation performance evaluation index construction method based on main component analysis |
Non-Patent Citations (1)
| Title |
|---|
| 杨鹏: "基于在线监测信息的机械设备关键零部件寿命预测方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108760256A (en) * | 2018-03-23 | 2018-11-06 | 佛山科学技术学院 | A kind of prediction technique of axle box remaining life |
| CN108898050A (en) * | 2018-05-17 | 2018-11-27 | 广东工业大学 | A kind of flexible material process equipment roll shaft performance index calculation method |
| CN109214097A (en) * | 2018-09-14 | 2019-01-15 | 上海工程技术大学 | A kind of long related failure trend prediction method of dimensionless group rolling bearing |
| CN109214097B (en) * | 2018-09-14 | 2021-09-10 | 上海工程技术大学 | Method for predicting long-related fault trend of rolling bearing with dimensionless parameters |
| CN109460618A (en) * | 2018-11-13 | 2019-03-12 | 华中科技大学 | A kind of rolling bearing remaining life on-line prediction method and system |
| CN109460618B (en) * | 2018-11-13 | 2023-02-10 | 华中科技大学 | Rolling bearing residual life online prediction method and system |
| CN109359791A (en) * | 2018-12-26 | 2019-02-19 | 湖南科技大学 | A kind of mechanical system degradation trend prediction technique and system |
| CN109359791B (en) * | 2018-12-26 | 2020-06-05 | 湖南科技大学 | Mechanical system degradation trend prediction method and system |
| CN109855875A (en) * | 2019-01-15 | 2019-06-07 | 沈阳化工大学 | A kind of rolling bearing operational reliability prediction technique |
| CN109827777A (en) * | 2019-04-01 | 2019-05-31 | 哈尔滨理工大学 | Rolling bearing fault prediction technique based on Partial Least Squares extreme learning machine |
| CN109827777B (en) * | 2019-04-01 | 2020-12-18 | 哈尔滨理工大学 | Rolling bearing fault prediction method based on partial least square method extreme learning machine |
| CN113255591A (en) * | 2021-06-25 | 2021-08-13 | 四川九通智路科技有限公司 | Bearing fault diagnosis method based on random forest and fusion characteristics |
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Application publication date: 20170725 |