CN105224792B - A kind of rolling bearing performance keeps the Forecasting Methodology of reliability - Google Patents

A kind of rolling bearing performance keeps the Forecasting Methodology of reliability Download PDF

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CN105224792B
CN105224792B CN201510603662.2A CN201510603662A CN105224792B CN 105224792 B CN105224792 B CN 105224792B CN 201510603662 A CN201510603662 A CN 201510603662A CN 105224792 B CN105224792 B CN 105224792B
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rolling bearing
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CN105224792A (en
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夏新涛
常振
李云飞
陈龙
南翔
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Henan University of Science and Technology
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Abstract

The invention discloses the Forecasting Methodology that a kind of rolling bearing performance keeps reliability, it is included in rolling bearing runnability best period, obtains performance data, builds performance sample rate function;The confidential interval of confidence level and performance stochastic variable is obtained according to Little Probability Event Princiole;According to Poisson counting process, acquisition performance data falls the frequency and rolling bearing performance holding reliability outside performance stochastic variable confidential interval, and then property retention Relative Reliability of the rolling bearing in future time is obtained, predicted roll bearing keeps the failure degree of optimum performance situation in future time accordingly.Any prior information of this method without Properties: Density function, without performance threshold is previously set, the failure degree of future time rolling bearing optimum performance situation can be predicted, prediction accuracy is high, the hidden danger that fails can be found in advance, to take intervening measure in time, avoiding generation severe safety accident from providing decision-making.

Description

A kind of rolling bearing performance keeps the Forecasting Methodology of reliability
Technical field
The invention belongs to rolling bearing military service performance failure to assess and performance reliability electric powder prediction, and in particular to one Kind rolling bearing performance keeps the Forecasting Methodology of reliability.
Background technology
Rolling bearing is that the sliding friction between axle and axle bed by operating is changed into rolling friction, so as to reduce friction loss A kind of precision mechanical components.Rolling bearing is typically made up of inner ring, outer ring, rolling element and retainer etc., and the effect of inner ring is Merge with axle matching and rotate together with the axis;The effect of outer ring is engaged with bearing block, is played a supportive role;Rolling element in inner ring and Rolled between outer ring, bear and transmit load;Retainer can be uniformly distributed rolling element, prevent rolling element from coming off with mutually touching Hit, the rotation of guiding rolling element and improvement Bearing inner lubrication.
Rolling bearing is one of most important part in machine driven system, is handed in Aero-Space, ship, automobile, track Logical field suffers from being widely applied.The part that rolling bearing is easily damaged simultaneously and in mechanical system is, it is necessary to periodically tie up Shield and replacing.The maintenance and replacing of rolling bearing usually require to dismount whole mechanical system, are consumed in disassembly process Time, manpower and materials cost are typically hundreds and thousands of times of bearing itself cost.However, that safeguards and change may cause not in time Whole system is failed to work because of bearing, cause bigger economic loss or even jeopardize the life peace of operating personnel Entirely.Therefore, according to specific operating mode and bearing parameter Accurate Prediction service life, excessive maintenance can be greatly reduced, reduces it Use cost and maintenance cost, there is highly important effect to industrial production and development in science and technology.
The performance of rolling bearing mainly includes vibration, noise, moment of friction, temperature rise, running accuracy etc., and these performances are to machine The runnability of tool system has a major impact.The performance failure of rolling bearing refers in running that rolling bearing is because of inside zero Part insufficient lubrication, friction and wear, damage, bonding, burn into deformation etc. failure and lose its runnability or cisco unity malfunction Phenomenon.Rolling bearing keeps the operation of optimum performance situation, is the basis that mechanical system realizes the operation of optimum performance situation.According to Theory of random processes, optimum performance situation reliability of operation is kept to change in future time rolling bearing, this can increase The big possibility for endangering mechanical system safe and reliable operation.Therefore, research rolling bearing performance keeps reliability to have important Application value.
In general, the performance failure experiment of rolling bearing refers to:According to some standard, such as national standard or industry mark Standard, certain amount sample is randomly selected from a collection of rolling bearing, then the sample of extraction is placed under identical experimental enviroment Reliable life complete trial is carried out, the life-span of each failure sample is obtained, finally according to standard GB/T24607-2009 to examination Test data to be handled, provide the reliability indexs such as form parameter b, L10, characteristics life and the average life span of rolling bearing, according to This makes reliability evaluation or analysis to this batch of bearing.But due to the raising of bearing quality, in reliability test process In to accomplish that each sample fails be unrealistic also unnecessary;The rolling bearing that some are expensive, quantity is few is finished Complete test is unpractical.
Existing performance reliability assessment and Forecasting Methodology, with assume in advance performance density function, performance degradation track with And performance failure threshold value is known as, according to performance reliability is obtained, having been achieved for certain effect.Existing reliability assessment side In method,《Aerospace journal》3rd phase in 2006 has delivered the article of entitled " reliability assessment based on Performance Degradation Data ", this article Chapter assumes the linear function that performance Degradation path is the time, considers performance degradation value Normal Distribution and given threshold value, can be with Carry out the reliability assessment of aerospace systems performance degradation.In existing rolling bearing performance reliability estimation method, CN104318043A discloses a kind of bearing vibration performance reliability mutation process detection method, and this method relies on time sequence The counting process of row, the very small amount raw information for the variation intensity that bear vibration is shown is obtained in Short Interval;By To the self-service resampling of variation intensity raw information, a large amount of generation information of variation intensity are simulated;Handled with Grey Prediction Model Information is generated, obtains variation intensity estimate;Reliability function, real-time estimate bear vibration dependable performance are characterized with Poisson process The mutation process of property.Time series of this method based on vibration information, the self-service principle of ash is incorporated into Poisson process, proposes that ash is self-service Poisson method, with the mutation process of predicted roll bearing performance reliability.But this method needs prior performance test availability Can threshold value, the threshold value is obtained by testing, different according to the damage location of selection and sensor, and corresponding threshold value is not yet Together.Therefore, it is unknown in Properties: Density function prior information and performance threshold is not previously set in existing reliability estimation method When, the mutation process of rolling bearing performance reliability can not be predicted.
The content of the invention
It is an object of the invention to provide the Forecasting Methodology that a kind of rolling bearing performance keeps reliability, in Properties: Density function Prior information is unknown and without in the case of performance threshold is previously set, solving the problems, such as rolling bearing performance holding reliability prediction.
In order to realize the above object the technical solution adopted in the present invention is:
A kind of rolling bearing performance keeps the Forecasting Methodology of reliability, comprises the following steps:
1) in rolling bearing runnability best period, rolling bearing performance is measured, obtains performance data;
2) according to principle of maximum entropy, performance data builds performance sample rate function obtained by step 1);
3) confidence level is obtained according to Little Probability Event Princiole, the confidence area of performance stochastic variable is obtained with quantile method Between;
4) frequency outside performance stochastic variable confidential interval is fallen according to Poisson counting process, acquisition performance data;
5) according to the no-failure probability of Poisson counting process, fallen by performance data outside performance stochastic variable confidential interval Frequency obtain rolling bearing performance keep reliability;
6) according to the relative error concept of measure theory, it is relatively reliable in the property retention of future time to obtain rolling bearing Degree, the failure degree of optimum performance situation is kept in future time according to the property retention Relative Reliability predicted roll bearing.
Performance data obtained by step 1) refers to the performance data that rolling bearing is run in assessment time interval, the assessment The rolling bearing running-in period that time interval refers to terminate after a time interval, assess end time of time interval for it is current when Between, t=1;Assess the time interval after time interval, referred to as predicted time section, t>1, each predicted time section is with commenting Estimating time interval has identical time span, and the end time in predicted time section is the future time described in step 6). Often increase by 1 predicted time section, future time t adds 1;Time t unit is consistent with the unit for assessing time interval.
The assessment time interval is in rolling bearing runnability best period, refers to the axis of rolling in the time interval It is optimal to carry row performance situation;In the rolling bearing runnability best period, rolling bearing operation keeps optimum performance gesture State, refer to almost without the possibility of performance failure;The period is usually located at rolling bearing running-in period and terminates the rear neighbouring time Section.
Rolling bearing performance mainly includes vibration, noise, moment of friction, temperature rise, running accuracy etc..In step 1), rolling Dynamic bearing runnability best period, by measuring system periodic measurement rolling bearing performance, and then predicted roll bearing should Time history of the performance failure degree in future time.
The performance data for building performance sample rate function is K, and k-th of performance data is xk, k=1,2 ..., K;K≥ 1000;
The performance sample rate function is p (x):
In formula (1), x is the performance stochastic variable of description rolling bearing performance;M is highest moment of the orign order;I is moment of the orign Order;λ0, λ1..., λmFor Lagrange multiplier, and there is first Lagrange multiplier λ0For:
In formula (2), x is the performance stochastic variable of description rolling bearing performance;S1For under performance stochastic variable x feasible zones Dividing value;S2For the upper dividing value of performance stochastic variable x feasible zones;I is moment of the orign order;λiFor i-th of Lagrange multiplier, glug Bright day multiplier λ1, λ2..., λmObtained by m equation group of formula (3):
In formula (3), x is the performance stochastic variable of description rolling bearing performance;xkFor k-th of performance data, k represents performance The sequence number of data, K are performance data number;S1For the floor value of performance stochastic variable x feasible zones, S2Can for performance stochastic variable x The upper dividing value in row domain;I and j is moment of the orign order, and m is highest moment of the orign order, λiFor i-th of Lagrange multiplier.
In step 3), confidence level is to the overall intrinsic optimum performance situation probability of happening of rolling bearing performance Characterize, its acquisition methods is:According to statistical Little Probability Event Princiole, significance can be using value as 0~0.2, such as 0.01st, 0.05,0.1 etc., corresponding confidence level is 1~0.8, such as 0.99,0.95,0.9;The value of confidence level is with small probability thing Part principle is foundation, and prior result of performance test determines, symbolizes the overall intrinsic optimum performance of rolling bearing performance The probability of happening of situation.
The confidence level P is using Little Probability Event Princiole as foundation, and prior result of performance test determines, under specific method includes Row step:
I) select same rolling bearing to make a service test in advance, enter in rolling bearing runnability in best period Row detection, obtains K >=1000 performance data, K is performance data number;Performance sample rate letter is built with K performance data Number p (x);Select reliability estimating value PqBe followed successively by 1,0.999,0.99,0.95,0.9,0.85,0.8 etc. 7 value respectively, with point Digit method is obtained corresponding to PqQ-th of performance stochastic variable confidential interval [XLq, XUq], being recorded in K performance data has How many individual data fall in performance stochastic variable confidential interval [XLq, XUq] outside, and thus obtain performance data fall it is random in performance Variable confidential interval [XLq, XUq] outside q-th of frequency values λq, X hereLqWith XUqIt is floor value and upper dividing value respectively, sequence number q =1,2,3 ..., 7;
Ii rolling bearing performance experiment and detection) are continued, W when performance failure, acquisition performance failure >= 1000 performance fail datas, W are performance failure data amount check;Or obtained in rolling bearing runnability in best period After obtaining K performance data, pause experiment, the rolling bearing is taken out, when the rolling surface of its raceway constructs performance failure Failure, simulate failure during performance failure, then the rolling bearing of failure detects during to there is simulated performance failure, obtains W performance fail data during performance failure;
Iii) it is recorded in that how many data in W performance fail data fall in performance stochastic variable confidential interval [XLq, XUq] outside, and thus obtain performance fail data and fall in performance stochastic variable confidential interval [XLq, XUq] outside q-th of frequency Value βq, X hereLqWith XUqIt is floor value and upper dividing value respectively, sequence number q=1,2,3 ..., 7;
Iv formula d) is pressedq={ [exp (- βq)-exp(-λq)]/exp(-λq) × 100% calculate performance failure when rolling Bearing performance keeps Relative Reliability dq, obtain q-th of dqIt is worth, here λqFall for performance data in performance stochastic variable confidence area Between [XLq, XUq] outside q-th of frequency values, βqFall for performance failure data in performance stochastic variable confidential interval [XLq, XUq] it Q-th outer of frequency values, sequence number q=1,2,3 ..., 7;From 7 dqChoose in value be less than and near -10% that, mark Its subscript q is q*, corresponding reliability estimating value Pq*It is exactly the prior result of performance test using Little Probability Event Princiole as foundation The confidence level P of determination.
In the above method, X in step i)LqWith XUqComputational methods same X respectivelyLAnd XU;Step ii) in, in rolling surface Fault method when constructing performance failure be with acidic materials on the rolling surface of raceway along the circumferential direction with 120 degree of interval Angle corrode 3 small and macroscopic spot altogether, simulate failure during performance failure.
In step 3), the confidential interval of performance stochastic variable is [XL, XU], floor value XLObtained with formula (4):
Upper dividing value XUObtained with formula (5):
In formula (4), formula (5), x is the performance stochastic variable of description rolling bearing performance;S1It is feasible for performance stochastic variable x The floor value in domain;S2For the upper dividing value of performance stochastic variable x feasible zones;[XL, XU] be performance stochastic variable x confidential interval;p (x) it is performance sample rate function;P is confidence level.
According to Poisson counting process, it is recorded in that how many data in K performance data fall putting in performance stochastic variable x Believe section [XL, XU] outside, and thus obtain performance data and fall in performance stochastic variable x confidential intervals [XL, XU] outside frequency λ。
In step 4), the frequency that performance data falls outside performance stochastic variable confidential interval is λ:
In formula (6), λ is that performance data falls in performance stochastic variable x confidential intervals [XL, XU] outside frequency, n is performance Data fall in performance stochastic variable x confidential intervals [XL, XU] outside number, K is performance data number.
In the Forecasting Methodology of the present invention, rolling bearing performance keeps reliability, refers in experiment and rolls during one's term of military service Bearing runs the possibility that can keep optimum performance situation.Property retention reliability shows as a function, the tool of the function Body value is referred to as property retention reliability.I.e. step 5) is the no-failure probability according to Poisson counting process, is fallen by performance data Frequency outside performance stochastic variable confidential interval obtains rolling bearing performance and keeps reliability function, and the specific of the function takes It is worth for property retention reliability.
In step 5), it is R (t) that rolling bearing performance, which keeps reliability,:
R (t)=exp (- λ t);T >=1 (7),
In formula (7), t is the time, t >=1;Rolling bearing performance keeps reliability when R (t) is time t, for characterizing the time Rolling bearing operation can keep the possibility of optimum performance situation during t;λ is that performance data falls in performance stochastic variable x confidences Section [XL, XU] outside frequency.
In step 6), rolling bearing is d (t) in the property retention Relative Reliability of future time:
In formula (8), rolling bearing performance keeps reliability when R (1) is current time t=1;T is future time, t>1;R (t) rolling bearing performance keeps reliability when being future time t;D (t) is that rolling bearing performance keeps Relative Reliability, is used for Rolling bearing operation keeps the failure degree of optimum performance situation when characterizing future time t.
In step 6), optimum performance situation is kept in future time according to property retention Relative Reliability predicted roll bearing The method of failure degree include:According to significance tests principle and measure theory, rolling bearing runnability is classified; It is classified according to rolling bearing runnability, the time history of predicted roll bearing optimum performance situation failure degree.
The general principle of rolling bearing runnability classification is as follows:
A) according to significance tests principle, if rolling bearing performance keeps Relative Reliability to be not less than 0%, institute is represented The future time rolling bearing performance of prediction keeps reliability to keep reliability not less than current time rolling bearing performance, then not Rolling bearing runnability can be refused and reached optimal situation;Otherwise, rolling bearing runnability can be refused to have reached Optimal situation;
B) when rolling bearing performance keeps Relative Reliability to be less than 0%, according to measure theory, absolute relative error exists (0%, 5%] between when measured value relative to true value error very little, absolute relative error (5%, 10%] between when survey Value is becoming big relative to the error of true value, and measured value becomes relative to the error of true value when absolute relative error is more than 10% Greatly.
Using above-mentioned significance tests principle and measure theory as foundation, rolling bearing runnability classification is carried out.
The rolling bearing runnability classification refers to rolling bearing runnability being divided into S1, S2, S3, S4 totally 4 levels Not:
S1:Rolling bearing performance keeps Relative Reliability d (t) >=0%, i.e. operation of the rolling bearing in future time t Performance reaches optimal, and optimum performance situation is almost without the possibility of failure;
S2:Rolling bearing performance holding Relative Reliability d (t) ∈ [- 5%, 0%), i.e., rolling bearing is in future time t Runnability it is normal, optimum performance situation failure possibility it is small;
S3:Rolling bearing performance holding Relative Reliability d (t) ∈ [- 10%, -5%), i.e., rolling bearing is in future time t When runnability be deteriorated, optimum performance situation failure possibility increase;
S4:Rolling bearing performance keeps Relative Reliability d (t) < -10%, i.e. fortune of the rolling bearing in future time t Row degradation, the possibility of optimum performance situation failure become big.
4 grades being classified according to above-mentioned rolling bearing runnability, the failure of predicted roll bearing optimum performance situation The time history of degree.It is actually the optimum performance situation relative to current time that rolling bearing performance, which keeps Relative Reliability, The attenuation degree of reliability is kept in future time rolling bearing performance, negative value represents decay, unattenuated on the occasion of expression.The axis of rolling It is smaller to hold property retention Relative Reliability d (t), rolling bearing runnability becomes poorer, the possibility of optimum performance situation failure Property becomes bigger.
Relative Reliability d (t)=- 10% future time t is kept corresponding to rolling bearing performance, is rolling bearing performance The crash time of variation, before the crash time arrives, intervening measure is taken, the rolling bearing is safeguarded or changed, For avoiding that the severe safety accident brought by the failure of rolling bearing optimum performance situation occurs.
It is in state natural sciences fund that the rolling bearing performance of the present invention, which keeps the Forecasting Methodology of reliability, (51475144) completed under subsidy.Confidence level that what the Forecasting Methodology included will be known as, confidential interval, property retention are reliable Degree, and property retention Relative Reliability.Wherein, confidence level is to the overall intrinsic optimum performance of rolling bearing performance The sign of situation probability of happening;Confidential interval, which can be characterized in experiment and the operation of rolling bearing during one's term of military service, can keep optimal Performance situation;Property retention reliability represents that rolling bearing operation can keep the possibility of optimum performance situation;Property retention Relative Reliability is used to characterize the failure degree that the operation of future time rolling bearing keeps optimum performance situation.
The rolling bearing performance of the present invention keeps the Forecasting Methodology of reliability, is when rolling bearing runnability is optimal Phase, performance data is obtained, build performance sample rate function, obtain the confidential interval of confidence level and performance stochastic variable;According to Poisson counting process, acquisition performance data fall the frequency and rolling bearing performance holding outside performance stochastic variable confidential interval Reliability, and then rolling bearing is obtained in the property retention Relative Reliability of future time, predicted roll bearing will be in future accordingly Time keeps the failure degree of optimum performance situation.Any prior information of this method without Properties: Density function, without thing Performance threshold is first set, the failure degree of future time rolling bearing optimum performance situation, prediction accuracy can be predicted Height, the hidden danger that fails can be found in advance, to take intervening measure in time, avoiding generation severe safety accident from providing decision-making.This method Be a kind of Properties: Density function prior information it is unknown and need not be previously set performance threshold rolling bearing experiment with during one's term of military service Property retention Reliability Prediction Method;Method according to the invention it is possible in the possibility of rolling bearing optimum performance situation failure Property become big before take intervening measure, rolling bearing is safeguarded or changed, avoids that serious security incident occurs.
Brief description of the drawings
Fig. 1 is Frictional Moment for Rolling Bearings data profile in embodiment 1;
Fig. 2 is Frictional Moment for Rolling Bearings sample rate function curve diagram in embodiment 1;
Fig. 3 is the time history diagram that Frictional Moment for Rolling Bearings keeps Relative Reliability in embodiment 1;
Fig. 4 is bearing vibration acceleration information distribution map in embodiment 2;
Fig. 5 is bearing vibration acceleration samples density function curve figure in embodiment 2;
Fig. 6 is the time history diagram that bearing vibration acceleration keeps Relative Reliability in embodiment 2.
Embodiment
With reference to embodiment, the present invention is further illustrated.
In embodiment, the time of periodic measurement rolling bearing performance is after rolling bearing running-in period terminates Carried out in one time interval;Rolling bearing runnability situation is optimal in the time interval, and the time interval is the assessment time Section, the end time for assessing time interval are current time, t=1.Future time during prediction refers to after assessment time interval Time interval, referred to as predicted time section often increases by 1 predicted time section, and t adds 1.Time t unit is with assessing the time The unit in section is consistent.For example, periodic measurement rolling bearing performance since 1 day January in 2015, was tied on June 30th, 2015 Beam, to predict on January 1st, 2017 to the possibility of the rolling bearing optimum performance situation failure between June 30.Here, comment It is 0.5 year (since 1 day January in 2015, to end of day June 30 in 2015) to estimate time interval, current time t=1, future Time is t=1+4=5, experienced 4 predicted time sections altogether, each predicted time section is 0.5 year, when predicting the 5th Between section (on January 1st, 2017 is between June 30) when the failure of rolling bearing optimum performance situation possibility.Here, 5 Time interval amounts to 2.5 years, and time t unit is 0.5 year.
Embodiment 1
The rolling bearing performance of the present embodiment 1 keeps the Forecasting Methodology of reliability, comprises the following steps:
1) in rolling bearing runnability best period, the rolling in time interval is assessed by measuring system periodic measurement The moment of friction of bearing, in current time t=1, obtain K performance data of moment of friction, K=20000, k-th of performance number According to for xk, k=1,2 ..., 20000, data unit Nm;Frictional Moment for Rolling Bearings data profile such as Fig. 1 institutes of acquisition Show;
In the present embodiment 1, it is 1 year to assess time interval, and each predicted time section is 1 year, and time t unit is Year;The Frictional Moment for Rolling Bearings performance is predicted using the Forecasting Methodology of the rolling bearing performance holding reliability of the present embodiment Time history of the failure degree in future time;
2) according to principle of maximum entropy, performance data builds performance sample rate function p (x) obtained by step 1):
In formula (1), x is the performance stochastic variable of description rolling bearing performance;M is highest moment of the orign order;I is moment of the orign Order;λ0, λ1..., λmFor Lagrange multiplier, and there is first Lagrange multiplier λ0For:
In formula (2), x is the performance stochastic variable of description rolling bearing performance;S1For under performance stochastic variable x feasible zones Dividing value;S2For the upper dividing value of performance stochastic variable x feasible zones;I is moment of the orign order;λiFor i-th of Lagrange multiplier, glug Bright day multiplier λ1, λ2..., λmObtained by m equation group of formula (3):
In formula (3), x is the performance stochastic variable of description rolling bearing performance;xkFor k-th of performance data, k represents performance The sequence number of data, K are performance data number;S1For the floor value of performance stochastic variable x feasible zones, S2Can for performance stochastic variable x The upper dividing value in row domain;I and j is moment of the orign order, and m is highest moment of the orign order, λiFor i-th of Lagrange multiplier;
With 20000 moment of friction performance datas structure moment of friction sample rate function p (x) obtained by step 1), knot Fruit is as shown in Figure 2;
3) confidence level is obtained according to Little Probability Event Princiole, the confidence area of performance stochastic variable is obtained with quantile method Between;
According to Little Probability Event Princiole, obtain confidence level P=0.99 by testing in advance, can calculate moment of friction with Machine variable x confidential intervals [XL, XU]=[233.329Nm, 251.897Nm]:
Floor value XLObtained with formula (4):
Upper dividing value XUObtained with formula (5):
In formula (4), formula (5), x is the performance stochastic variable of description rolling bearing performance;S1It is feasible for performance stochastic variable x The floor value in domain;S2For the upper dividing value of performance stochastic variable x feasible zones;[XL, XU] be performance stochastic variable x confidential interval;p (x) it is performance sample rate function;P is confidence level;
Wherein, the confidence level P is using Little Probability Event Princiole as foundation, and prior result of performance test determines, specific method For:
I) select same rolling bearing to make a service test in advance, enter in rolling bearing runnability in best period Row detection, obtains K >=1000 performance data, K is performance data number;Performance sample rate letter is built with K performance data Number p (x);Select reliability estimating value PqBe followed successively by 1,0.999,0.99,0.95,0.9,0.85,0.8 etc. 7 value respectively, with point Digit method is obtained corresponding to PqQ-th of performance stochastic variable confidential interval [XLq, XUq](XLqWith XUqComputational methods difference Same XLAnd XU), it is recorded in that how many data in K performance data fall in performance stochastic variable confidential interval [XLq, XUq] outside, And thus obtain performance data and fall in performance stochastic variable confidential interval [XLq, XUq] outside q-th of frequency values λq, X hereLq With XUqIt is floor value and upper dividing value respectively, sequence number q=1,2,3 ..., 7;
Ii) after rolling bearing runnability is in best period K performance data of acquisition, pause experiment, taking out should Rolling bearing, the failure when the rolling surface of its raceway constructs performance failure, i.e., with acidic materials raceway rolling table Along the circumferential direction corroded altogether with the angle at 120 degree of interval on face and 3 small and macroscopic spot, simulate performance failure When failure, then the rolling bearing of failure detects during to there is simulated performance failure, obtains W performance mistake during performance failure Imitate data;
Iii) it is recorded in that how many data in W performance fail data fall in performance stochastic variable confidential interval [XLq, XUq] outside, and thus obtain performance fail data and fall in performance stochastic variable confidential interval [XLq, XUq] outside q-th of frequency Value βq, X hereLqWith XUqIt is floor value and upper dividing value respectively, sequence number q=1,2,3 ..., 7;
Iv formula d) is pressedq={ [exp (- βq)-exp(-λq)]/exp(-λq) × 100% calculate performance failure when rolling Bearing performance keeps Relative Reliability dq, obtain q-th of dqIt is worth, here λqFall for performance data in performance stochastic variable confidence area Between [XLq, XUq] outside q-th of frequency values, βqFall for performance failure data in performance stochastic variable confidential interval [XLq, XUq] it Q-th outer of frequency values, sequence number q=1,2,3 ..., 7;From 7 dqChoose in value be less than and near -10% that, mark Its subscript q is q*, corresponding reliability estimating value Pq*It is exactly the prior result of performance test using Little Probability Event Princiole as foundation The confidence level P of determination;Confidence level P=0.99 obtained by the present embodiment;
4) frequency outside performance stochastic variable confidential interval is fallen according to Poisson counting process, acquisition performance data;
It is recorded in that how many data in 20000 data fall in moment of friction stochastic variable x confidential intervals [XL, XU] it Outside, moment of friction data are obtained to fall in moment of friction stochastic variable x confidential intervals [XL, XU] outside frequency lambda:
In formula (6), λ is that performance data falls in performance stochastic variable x confidential intervals [XL, XU] outside frequency, n is performance Data fall in performance stochastic variable x confidential intervals [XL, XU] outside number, K is performance data number;
5) according to the no-failure probability of Poisson counting process, fallen by performance data outside performance stochastic variable confidential interval Frequency obtain rolling bearing performance keep reliability, obtain Frictional Moment for Rolling Bearings property retention reliability R (t) time Course;
It is R (t) that rolling bearing performance, which keeps reliability,:
R (t)=exp (- λ t);T >=1 (7),
In formula (7), t is the time, t >=1;Rolling bearing performance keeps reliability when R (t) is time t, for characterizing the time Rolling bearing operation can keep the possibility of optimum performance situation during t;λ is that performance data falls in performance stochastic variable x confidences Section [XL, XU] outside frequency;
6) according to the relative error concept of measure theory, it is relatively reliable in the property retention of future time to obtain rolling bearing Degree, obtain the time history that Frictional Moment for Rolling Bearings keeps Relative Reliability d (t);
Rolling bearing is d (t) in the property retention Relative Reliability of future time:
In formula (8), rolling bearing performance keeps reliability when R (1) is current time t=1;T is future time, t>1;R (t) rolling bearing performance keeps reliability when being future time t;D (t) is that rolling bearing performance keeps Relative Reliability, is used for Rolling bearing operation keeps the failure degree of optimum performance situation when characterizing future time t;
Frictional Moment for Rolling Bearings keeps Relative Reliability d (t) time history as shown in Figure 3;
7) according to significance tests principle and measure theory, rolling bearing runnability is divided into S1, S2, S3, S4 Totally 4 ranks:
S1:Rolling bearing performance keeps Relative Reliability d (t) >=0%, i.e. operation of the rolling bearing in future time t Performance reaches optimal, and optimum performance situation is almost without the possibility of failure;
S2:Rolling bearing performance holding Relative Reliability d (t) ∈ [- 5%, 0%), i.e., rolling bearing is in future time t Runnability it is normal, optimum performance situation failure possibility it is small;
S3:Rolling bearing performance holding Relative Reliability d (t) ∈ [- 10%, -5%), i.e., rolling bearing is in future time t When runnability be deteriorated, optimum performance situation failure possibility increase;
S4:Rolling bearing performance keeps Relative Reliability d (t) < -10%, i.e. fortune of the rolling bearing in future time t Row degradation, the possibility of optimum performance situation failure become big;
8) 4 grades being classified according to above-mentioned rolling bearing runnability, predicted roll bearing optimum performance situation are lost The time history of effect degree is as follows:
In figure 3, as t=6, and the ∈ of d (t)=- 4.43% [- 5%, 0%), d (t) values are close to -5%;
As t=7, and the ∈ of d (t)=- 5.29% [- 10%, -5%), d (t) is already less than -5%;
As t=12, and the ∈ of d (t)=- 9.47% [- 10%, -5%), d (t) values are close to -10%;
As t=13, the < -10% of d (t)=- 10.29%, d (t) values are already less than -10%.
The failure degree of optimum performance situation is kept in future time according to the above predicted roll bearing:
Accordingly it is expected that to before the 6th year, the runnability of the rolling bearing is normal, moment of friction optimum performance gesture The possibility of state failure is small;To before the 12nd year after the 7th year, the runnability of the rolling bearing is deteriorated, frictional force The possibility of square optimum performance situation failure increases;Until the 13rd year, the runnability of the rolling bearing was deteriorated, frictional force The possibility of square optimum performance situation failure is big.
According to above-mentioned time history, between the 12nd year and the 13rd year, intervening measure should be taken, the rolling bearing is carried out Safeguard or change, avoid that the severe safety accident brought by the failure of bearing frictional torque optimum performance situation occurs.
Embodiment 2
The rolling bearing performance of the present embodiment 2 keeps the Forecasting Methodology of reliability, comprises the following steps:
1) in rolling bearing runnability best period, the rolling in time interval is assessed by measuring system periodic measurement The vibration acceleration of bearing, in current time t=1, obtain K performance data of vibration acceleration, K=20000, k-th of property Energy data are xk, k=1,2 ..., 20000, data unit for μm/s2;The bearing vibration acceleration information distribution map of acquisition As shown in Figure 4;
In the present embodiment 2, it is 1 year to assess time interval, and each predicted time section is 1 year, and time t unit is Year;The bearing vibration acceleration is predicted using the Forecasting Methodology of the rolling bearing performance holding reliability of the present embodiment Time history of the energy failure degree in future time;
2) according to principle of maximum entropy, performance data builds performance sample rate function p (x) obtained by step 1):
In formula (1), x is the performance stochastic variable of description rolling bearing performance;M is highest moment of the orign order;I is moment of the orign Order;λ0, λ1..., λmFor Lagrange multiplier, and there is first Lagrange multiplier λ0For:
In formula (2), x is the performance stochastic variable of description rolling bearing performance;S1For under performance stochastic variable x feasible zones Dividing value;S2For the upper dividing value of performance stochastic variable x feasible zones;I is moment of the orign order;λiFor i-th of Lagrange multiplier, glug Bright day multiplier λ1, λ2..., λmObtained by m equation group of formula (3):
In formula (3), x is the performance stochastic variable of description rolling bearing performance;xkFor k-th of performance data, k represents performance The sequence number of data, K are performance data number;S1For the floor value of performance stochastic variable x feasible zones, S2Can for performance stochastic variable x The upper dividing value in row domain;I and j is moment of the orign order, and m is highest moment of the orign order, λiFor i-th of Lagrange multiplier;
With 20000 vibration acceleration performance datas structure vibration acceleration sample rate function p obtained by step 1) (x), as a result as shown in Figure 5;
3) confidence level is obtained according to Little Probability Event Princiole, the confidence area of performance stochastic variable is obtained with quantile method Between;
According to Little Probability Event Princiole, confidence level P=0.99 is obtained by testing in advance, vibration acceleration can be calculated Stochastic variable x confidential intervals [XL, XU- 0.0549 μm of]=[/s2, 0.0692 μm/s2]:
Floor value XLObtained with formula (4):
Upper dividing value XUObtained with formula (5):
In formula (4), formula (5), x is the performance stochastic variable of description rolling bearing performance;S1It is feasible for performance stochastic variable x The floor value in domain;S2For the upper dividing value of performance stochastic variable x feasible zones;[XL, XU] be performance stochastic variable x confidential interval;p (x) it is performance sample rate function;P is confidence level;
Wherein, the confidence level P is using Little Probability Event Princiole as foundation, and prior result of performance test determines, specific method With embodiment 1;Confidence level P=0.99 obtained by the present embodiment;
4) frequency outside performance stochastic variable confidential interval is fallen according to Poisson counting process, acquisition performance data;
It is recorded in that how many data in 20000 data fall in vibration acceleration stochastic variable x confidential intervals [XL, XU] Outside, obtain vibration acceleration data and fall in vibration acceleration stochastic variable x confidential intervals [XL, XU] outside frequency lambda:
In formula (6), λ is that performance data falls in performance stochastic variable x confidential intervals [XL, XU] outside frequency, n is performance Data fall in performance stochastic variable x confidential intervals [XL, XU] outside number, K is performance data number;
5) according to the no-failure probability of Poisson counting process, fallen by performance data outside performance stochastic variable confidential interval Frequency obtain rolling bearing performance keep reliability, obtain bearing vibration acceleration property retention reliability R (t) when Between course;
It is R (t) that rolling bearing performance, which keeps reliability,:
R (t)=exp (- λ t);T >=1 (7),
In formula (7), t is the time, t >=1;Rolling bearing performance keeps reliability when R (t) is time t, for characterizing the time Rolling bearing operation can keep the possibility of optimum performance situation during t;λ is that performance data falls in performance stochastic variable x confidences Section [XL, XU] outside frequency;
6) according to the relative error concept of measure theory, it is relatively reliable in the property retention of future time to obtain rolling bearing Degree, obtain the time history that bearing vibration acceleration keeps Relative Reliability d (t);
Rolling bearing is d (t) in the property retention Relative Reliability of future time:
In formula (8), rolling bearing performance keeps reliability when R (1) is current time t=1;T is future time, t>1;R (t) rolling bearing performance keeps reliability when being future time t;D (t) is that rolling bearing performance keeps Relative Reliability, is used for Rolling bearing operation keeps the failure degree of optimum performance situation when characterizing future time t;
Bearing vibration acceleration keeps Relative Reliability d (t) time history as shown in Figure 6;
7) according to significance tests principle and measure theory, rolling bearing runnability is divided into S1, S2, S3, S4 Totally 4 ranks:
S1:Rolling bearing performance keeps Relative Reliability d (t) >=0%, i.e. operation of the rolling bearing in future time t Performance reaches optimal, and optimum performance situation is almost without the possibility of failure;
S2:Rolling bearing performance holding Relative Reliability d (t) ∈ [- 5%, 0%), i.e., rolling bearing is in future time t Runnability it is normal, optimum performance situation failure possibility it is small;
S3:Rolling bearing performance holding Relative Reliability d (t) ∈ [- 10%, -5%), i.e., rolling bearing is in future time t When runnability be deteriorated, optimum performance situation failure possibility increase;
S4:Rolling bearing performance keeps Relative Reliability d (t) < -10%, i.e. fortune of the rolling bearing in future time t Row degradation, the possibility of optimum performance situation failure become big;
8) 4 grades being classified according to above-mentioned rolling bearing runnability, predicted roll bearing optimum performance situation are lost The time history of effect degree is as follows:
In figure 6, as t=6, and the ∈ of d (t)=- 4.66% [- 5%, 0%), d (t) values are very close to -5%;
As t=7, and the ∈ of d (t)=- 5.57% [- 10%, -5%), d (t) is already less than -5%;
As t=12, and the ∈ of d (t)=- 9.97% [- 10%, -5%), d (t) values are very close to -10%;
As t=13, the < -10% of d (t)=- 10.83%, d (t) values are already less than -10%.
The failure degree of optimum performance situation is kept in future time according to the above predicted roll bearing:
Accordingly it is expected that to before the 6th year, the runnability of the rolling bearing is normal, vibration acceleration optimum performance The possibility of situation failure is small;To before the 12nd year after the 7th year, the runnability of the rolling bearing is deteriorated, vibration The possibility of acceleration optimum performance situation failure increases;Until the 13rd year, the runnability of the rolling bearing was deteriorated, and shakes The possibility of dynamic acceleration optimum performance situation failure is big.
According to above-mentioned time history, between the 12nd year and the 13rd year, intervening measure should be taken, the rolling bearing is carried out Safeguard or change, avoid that the severe safety accident brought by the failure of bear vibration acceleration optimum performance situation occurs.
The performance of rolling bearing has vibration, noise, moment of friction, temperature rise, running accuracy etc., and all performances can adopt Property retention reliability prediction is carried out with technical scheme.In other embodiments of the invention, using the present invention's Rolling bearing performance keeps the Forecasting Methodology of reliability, relatively reliable according to property retentions such as noise, temperature rise, running accuracies respectively Degree, predicted roll bearing keep the failure degree of optimum performance situation in future time;Concrete operation method is the same as embodiment 1.
When the prediction result according to technical solution of the present invention carries out intervening measure, if the dissimilarity to same rolling bearing The crash time of the prediction result of energy is different, then should take intervening measure before the most short crash time.

Claims (10)

1. a kind of rolling bearing performance keeps the Forecasting Methodology of reliability, it is characterised in that:Comprise the following steps:
1) in rolling bearing runnability best period, rolling bearing performance is measured, obtains performance data;
2) according to principle of maximum entropy, performance data builds performance sample rate function obtained by step 1);
3) confidence level is obtained according to Little Probability Event Princiole, the confidential interval of performance stochastic variable is obtained with quantile method;
4) frequency outside performance stochastic variable confidential interval is fallen according to Poisson counting process, acquisition performance data;
5) according to the no-failure probability of Poisson counting process, the frequency outside performance stochastic variable confidential interval is fallen by performance data Rate obtains rolling bearing performance and keeps reliability;
6) according to the relative error concept of measure theory, property retention Relative Reliability of the rolling bearing in future time is obtained, The failure degree of optimum performance situation is kept in future time according to the property retention Relative Reliability predicted roll bearing;
The determination of confidence level comprises the following steps in step 3):
I) same rolling bearing is selected to make a service test in advance, being in best period in rolling bearing runnability is examined Survey, obtain K >=1000 performance data, K is performance data number;Performance sample rate function p is built with K performance data (x);Select reliability estimating value Pq1,0.999,0.99,0.95,0.9,0.85,0.8 totally 7 values are followed successively by respectively, with dividing position Counting method is obtained corresponding to PqQ-th of performance stochastic variable confidential interval [XLq, XUq], be recorded in K performance data have it is more A few data fall in performance stochastic variable confidential interval [XLq, XUq] outside, and thus obtain performance data and fall and become at random in performance Measure confidential interval [XLq, XUq] outside q-th of frequency values λq, X hereLqWith XUqIt is floor value and upper dividing value respectively, sequence number q= 1,2,3,…,7;
Ii) continue rolling bearing performance experiment and detection, until performance failure, obtain W >=1000 during performance failure Individual performance fail data, W are performance failure data amount check;Or it is in best period in rolling bearing runnability and obtains K After performance data, pause experiment, the rolling bearing is taken out, the event when the rolling surface of its raceway constructs performance failure Barrier, simulates failure during performance failure, then the rolling bearing of failure detects during to there is simulated performance failure, obtains performance W performance fail data during failure;
Iii) it is recorded in that how many data in W performance fail data fall in performance stochastic variable confidential interval [XLq, XUq] it Outside, performance fail data and is thus obtained to fall in performance stochastic variable confidential interval [XLq, XUq] outside q-th of frequency values βq, Here XLqWith XUqIt is floor value and upper dividing value respectively, sequence number q=1,2,3 ..., 7;
Iv formula d) is pressedq={ [exp (- βq)-exp(-λq)]/exp(-λq) × 100% calculate performance failure when rolling bearing Property retention Relative Reliability dq, obtain q-th of dqIt is worth, here λqFall for performance data in performance stochastic variable confidential interval [XLq, XUq] outside q-th of frequency values, βqFall for performance failure data in performance stochastic variable confidential interval [XLq, XUq] outside Q-th of frequency values, sequence number q=1,2,3 ..., 7;From 7 dqChoose in value be less than and near -10% that, mark it Subscript q is q*, corresponding reliability estimating value Pq*The confidence level being just to determine.
2. rolling bearing performance according to claim 1 keeps the Forecasting Methodology of reliability, it is characterised in that:Step 1) institute Obtain performance data and refer to the performance data that rolling bearing is run in assessment time interval, the assessment time interval refers to roll The bearing running phase terminate after a time interval, the end time for assessing time interval is current time;Assess time interval Time interval afterwards, referred to as predicted time section, each predicted time section have the identical time with assessing time interval Span, the end time in predicted time section are the future time described in step 6).
3. rolling bearing performance according to claim 1 or 2 keeps the Forecasting Methodology of reliability, it is characterised in that:Structure The performance data of performance sample rate function is K, and k-th of performance data is xk, k=1,2 ..., K;K≥1000;
The performance sample rate function is p (x):
<mrow> <mi>p</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>exp</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mn>0</mn> </msub> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>&amp;lambda;</mi> <mi>i</mi> </msub> <msup> <mi>x</mi> <mi>i</mi> </msup> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
In formula (1), x is the performance stochastic variable of description rolling bearing performance;M is highest moment of the orign order;I is moment of the orign rank It is secondary;λ0, λ1..., λmFor Lagrange multiplier, and there is first Lagrange multiplier λ0For:
<mrow> <msub> <mi>&amp;lambda;</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>-</mo> <mi>l</mi> <mi>n</mi> <mrow> <mo>(</mo> <msubsup> <mo>&amp;Integral;</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> <msub> <mi>S</mi> <mn>2</mn> </msub> </msubsup> <mrow> <mi>exp</mi> <mrow> <mo>(</mo> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>&amp;lambda;</mi> <mi>i</mi> </msub> <msup> <mi>x</mi> <mi>i</mi> </msup> </mrow> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> </mrow> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
In formula (2), x is the performance stochastic variable of description rolling bearing performance;S1For the floor value of performance stochastic variable x feasible zones; S2For the upper dividing value of performance stochastic variable x feasible zones;I is moment of the orign order;λiFor i-th of Lagrange multiplier, Lagrange multiplies Sub- λ1, λ2..., λmObtained by m equation group of formula (3):
<mrow> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msubsup> <mo>&amp;Integral;</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> <msub> <mi>S</mi> <mn>2</mn> </msub> </msubsup> <msup> <mi>x</mi> <mi>j</mi> </msup> <mi>exp</mi> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>&amp;lambda;</mi> <mi>i</mi> </msub> <msup> <mi>x</mi> <mi>i</mi> </msup> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> </mrow> <mrow> <mfrac> <mn>1</mn> <mi>K</mi> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <msubsup> <mi>x</mi> <mi>k</mi> <mi>j</mi> </msubsup> <msubsup> <mo>&amp;Integral;</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> <msub> <mi>S</mi> <mn>2</mn> </msub> </msubsup> <mi>exp</mi> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>&amp;lambda;</mi> <mi>i</mi> </msub> <msup> <mi>x</mi> <mi>i</mi> </msup> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> </mrow> </mfrac> <mo>=</mo> <mn>0</mn> <mo>;</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>m</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
In formula (3), x is the performance stochastic variable of description rolling bearing performance;xkFor k-th of performance data, k represents performance data Sequence number, K is performance data number;S1For the floor value of performance stochastic variable x feasible zones, S2For performance stochastic variable x feasible zones Upper dividing value;I and j is moment of the orign order, and m is highest moment of the orign order, λiFor i-th of Lagrange multiplier.
4. rolling bearing performance according to claim 1 keeps the Forecasting Methodology of reliability, it is characterised in that:Step 3) In, the confidential interval of performance stochastic variable is [XL, XU], floor value XLObtained with formula (4):
<mrow> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>P</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&amp;Integral;</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> <msub> <mi>X</mi> <mi>L</mi> </msub> </msubsup> <mi>p</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
Upper dividing value XUObtained with formula (5):
<mrow> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>P</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&amp;Integral;</mo> <msub> <mi>X</mi> <mi>U</mi> </msub> <msub> <mi>S</mi> <mn>2</mn> </msub> </msubsup> <mi>p</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
In formula (4), formula (5), x is the performance stochastic variable of description rolling bearing performance;S1For performance stochastic variable x feasible zones Floor value;S2For the upper dividing value of performance stochastic variable x feasible zones;[XL, XU] be performance stochastic variable x confidential interval;P (x) is Performance sample rate function;P is confidence level.
5. the rolling bearing performance according to claim 1 or 4 keeps the Forecasting Methodology of reliability, it is characterised in that:Step 4) in, the frequency that performance data falls outside performance stochastic variable confidential interval is λ:
<mrow> <mi>&amp;lambda;</mi> <mo>=</mo> <mfrac> <mi>n</mi> <mi>K</mi> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
In formula (6), λ is that performance data falls in performance stochastic variable x confidential intervals [XL, XU] outside frequency, n is performance data Fall in performance stochastic variable x confidential intervals [XL, XU] outside number, K is performance data number.
6. rolling bearing performance according to claim 5 keeps the Forecasting Methodology of reliability, it is characterised in that:Step 5) In, it is R (t) that rolling bearing performance, which keeps reliability,:
R (t)=exp (- λ t);T >=1 (7),
In formula (7), t is the time, t >=1;Rolling bearing performance keeps reliability when R (t) is time t, during for characterizing time t Rolling bearing runs the possibility that can keep optimum performance situation;λ is that performance data falls in performance stochastic variable x confidential intervals [XL, XU] outside frequency.
7. rolling bearing performance according to claim 6 keeps the Forecasting Methodology of reliability, it is characterised in that:Step 6) In, rolling bearing is d (t) in the property retention Relative Reliability of future time:
<mrow> <mi>d</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>R</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <mrow> <mi>R</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>&amp;times;</mo> <mn>100</mn> <mi>%</mi> <mo>;</mo> <mi>t</mi> <mo>&gt;</mo> <mn>1</mn> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
In formula (8), rolling bearing performance keeps reliability when R (1) is current time t=1;T is future time, t>1;R (t) is Rolling bearing performance keeps reliability during future time t;D (t) is that rolling bearing performance keeps Relative Reliability, for characterizing not Carry out the failure degree that rolling bearing operation during time t keeps optimum performance situation.
8. rolling bearing performance according to claim 7 keeps the Forecasting Methodology of reliability, it is characterised in that:Step 6) In, according to property retention Relative Reliability predicted roll bearing in the side of the failure degree of future time holding optimum performance situation Method includes:According to significance tests principle and measure theory, rolling bearing runnability is classified;Carried according to the axis of rolling Row grading performance, the time history of predicted roll bearing optimum performance situation failure degree.
9. rolling bearing performance according to claim 8 keeps the Forecasting Methodology of reliability, it is characterised in that:The rolling The classification of bearing runnability refers to rolling bearing runnability being divided into S1, S2, S3, S4 totally 4 ranks:
S1:Rolling bearing performance keeps Relative Reliability d (t) >=0%, i.e. runnability of the rolling bearing in future time t Reach optimal, optimum performance situation is almost without the possibility of failure;
S2:Rolling bearing performance keep Relative Reliability d (t) ∈ [- 5%, 0%), i.e. fortune of the rolling bearing in future time t Row performance is normal, and the possibility of optimum performance situation failure is small;
S3:Rolling bearing performance keep Relative Reliability d (t) ∈ [- 10%, -5%), i.e. rolling bearing is in future time t Runnability is deteriorated, and the possibility of optimum performance situation failure increases;
S4:Rolling bearing performance keeps Relative Reliability d (t) < -10%, i.e. maneuverability of the rolling bearing in future time t It can be deteriorated, the possibility of optimum performance situation failure becomes big.
10. rolling bearing performance according to claim 9 keeps the Forecasting Methodology of reliability, it is characterised in that:Correspond to Rolling bearing performance keep Relative Reliability d (t)=- 10% future time t, be rolling bearing performance be deteriorated it is critical when Between, before the crash time arrives, intervening measure is taken, for avoiding occurring because of rolling bearing optimum performance situation failure band The severe safety accident come.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102331348A (en) * 2011-06-09 2012-01-25 河南科技大学 Evaluation method for characteristic variation of starting friction torque of slewing bearing
CN102831325A (en) * 2012-09-04 2012-12-19 北京航空航天大学 Method for predicting bearing fault based on Gaussian process regression
CN103246803A (en) * 2013-04-07 2013-08-14 河南科技大学 Significance testing method for rolling bearing performance variation process
CN104318043A (en) * 2014-02-20 2015-01-28 河南科技大学 Rolling bearing vibration performance reliability variation process detection method and rolling bearing vibration performance reliability variation process detection device
CN104598734A (en) * 2015-01-22 2015-05-06 西安交通大学 Life prediction model of rolling bearing integrated expectation maximization and particle filter

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102331348A (en) * 2011-06-09 2012-01-25 河南科技大学 Evaluation method for characteristic variation of starting friction torque of slewing bearing
CN102831325A (en) * 2012-09-04 2012-12-19 北京航空航天大学 Method for predicting bearing fault based on Gaussian process regression
CN103246803A (en) * 2013-04-07 2013-08-14 河南科技大学 Significance testing method for rolling bearing performance variation process
CN104318043A (en) * 2014-02-20 2015-01-28 河南科技大学 Rolling bearing vibration performance reliability variation process detection method and rolling bearing vibration performance reliability variation process detection device
CN104598734A (en) * 2015-01-22 2015-05-06 西安交通大学 Life prediction model of rolling bearing integrated expectation maximization and particle filter

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Evaluatlon for optimum technical plan of rolling bearing vibration using grey system theory;Xin-Tao Xia 等;《Journal of Grey System》;20061231;第9卷(第1期);9-14 *
滚动轴承振动速度的乏信息真值估计;栗永非,时保吉;《轴承》;20150331(第3期);39-42, 53 *
航天轴承摩擦力矩的最大熵概率分布与bootstrap推断;夏新涛 等;《宇航学报》;20070930;第28卷(第5期);1395-1400 *

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