CN103617364A - Method for predicting remaining service life of large rotating support on basis of small sample - Google Patents

Abstract

The invention discloses a method for predicting the remaining service life of a large rotating support. The method comprises the steps of deriving a remaining service life prediction model of the rotating support on the basis of the Weibull service life distribution theory, utilizing a test bed to exert a specific load on the rotating support, continuously running until the rotating support fails, dividing a roller path into 4n sections according to the rotating support roller path load distribution characteristics, measuring the volume abrasion loss of every section of rotating support roller path, and then utilizing the Archard abrasion theory and an inverse power law service life model to build a load-fatigue life distribution-remaining service life prediction model. According to the method for predicting the remaining service life of the large rotating support, remaining service life prediction of similar products can be achieved only by performing whole service life fatigue life testing on one rotating support, plenty of resources can be saved for enterprises, the product utilization rate is improved, and accident risks are reduced.

Description

Large-size pivoting support remaining life Forecasting Methodology based on small sample

Technical field:

The present invention relates to a kind of bearing life Forecasting Methodology, be specifically related to a kind of large-size pivoting support method for predicting residual useful life based on small sample test.

Background technology:

Large-size pivoting support, as the huge revolving web member in blower fan, engineering machinery, can bear great axial force, radial force and upsetting moment conventionally.The stuck shutdown that conventionally can cause equipment of inefficacy of large-size pivoting support; when serious, may produce catastrophic consequence; if can predict exactly its remaining life; just can instruct operating personnel to safeguard in time or change pivoting support; both avoid unnecessary maintenance work, and can reduce again the generation of major accident.Therefore, the Accurate Prediction of pivoting support residual life is necessary.

The life-span prediction method widely using has two classes conventionally: the method based on data-driven and the method based on reliability.Life-span prediction method based on data-driven extracts feature conventionally from the vibration signal previously having obtained, then according to the variation tendency of signal characteristic, set up the life-span degenerated mode of equipment, finally the life-span degenerated mode of setting up before the signal substitution of on-line monitoring is carried out to life prediction.Yet, pivoting support is used in harbour machinery, blower fan, engineering machinery conventionally, and operating mode is severe, and rotating speed is low to moderate 0.1-10rpm, the signal to noise ratio (S/N ratio) of therefore monitoring gained vibration signal is very low, and conventional vibration signal characteristics extracting method almost can not therefrom get effective information.

For this reason, a lot of scholars study the bearing fatigue life computing method in international standard ISO281 (formula 1), have proposed a lot of life test method and Life Prediction Models for middle-size and small-size bearing.Yet large-size pivoting support is more much bigger than common middle-size and small-size bearing, the load distribution form of its raceway and failure mode are not identical, and conventional life-span prediction method obviously can not directly be used on pivoting support.

${\mathrm{<figure-callout\; id="10"\; label="L"\; filenames="HDA0000429411440000022.png,HDA0000429411440000051.png"\; state="\{\{state\}\}">L</figure-callout>}}_{10}=a_1{a}_2{a}_3{\left(\frac{\mathrm{Ca}}{\mathrm{Pa}}\right)}^3---\left(1\right)$

In above formula, a1 is reliability coefficient, and a2 is material coefficient, and a3 is application factor, and Ca is dynamic load rating, and Pa is equivalent radial load, and L10 is the fatigue lifetime of fiduciary level bearing while being 90%, and the number of turns of conventionally crossing with bearing rotary represents.

In addition, existing fatigue life test method is, under different operating modes, bearing is in batches carried out to torture test mostly, to obtain the FATIGUE LIFE DISTRIBUTION of bearing.No matter large-size pivoting support diameter, conventionally at 800-5000mm, is to utilize testing table, or utilizes the on-the-spot equipment that pivoting support is installed, fatigue life test in batches from the time with in expense, be all unacceptable.

The present invention is directed to large-size pivoting support and proposed a predicting residual useful life model based on small sample test, can, according to actual condition, to the pivoting support of Arbitrary Loads, carry out predicting residual useful life.According to Weibull distribution, derive predicting residual useful life model; Based on Hertz theory, pivoting support raceway is carried out to force analysis, the load that obtains raceway distributes; Theoretical according to Archard, small sample acceleration service life test method is proposed, set up the Weibull distribution of fatigue lifetime of raceway under different loads, and obtain the load-life curve of pivoting support by contrary power rate accelerated life model, thereby completed the foundation of pivoting support predicting residual useful life model.The current domestic similar pivoting support method for predicting residual useful life of not yet finding.

Summary of the invention:

The object of the present invention is to provide a kind of large-size pivoting support remaining life Forecasting Methodology based on small sample, it is basis that this method be take the Weibull distribution that bearing fatigue life follows, and derives the forecast model of pivoting support residual life; In conjunction with Archard theory of wear and contrary power rate accelerated life model, provided the fatigue life test method based on small sample; And proposed to obtain by Processing Test data the method for pivoting support residual life model parameter.

Technical scheme of the present invention is as follows:

A kind of large-size pivoting support remaining life Forecasting Methodology based on small sample of the present invention, only need to do the torture test of life-cycle to a large-size pivoting support, can simulate the predicting residual useful life model of similar pivoting support, for the remaining life of predicting in real time pivoting support in actual.

A kind of large-size pivoting support remaining life Forecasting Methodology based on small sample of the present invention, according to the symmetry of the size of load and distribution, pivoting support is divided into n group, every group 4 sections, amount to 4n section pivoting support raceway, by the wear extent producing after test life cycle test---raceway wear extent---the pivoting support life model of setting up pivoting support raceway load.

A large-size pivoting support remaining life Forecasting Methodology based on small sample, comprises the following steps:

1) according to Weibull life-span distribution theory R=exp[-(t/ η) ^β], derive the predicting residual useful life model of the pivoting support based on reliability

r is fiduciary level, and t is the number of turns that pivoting support has turned round, and β, η need Forecasting Methodology of the present invention to determine the characteristics life of corresponding Weibull distribution slope and pivoting support, and x is residual life;

2) load that solves pivoting support raceway according to hertz contact theory and ISO281 method distributes.If the external applied load that pivoting support is subject to respectively: axial force F _a, radial force F _rwith upsetting moment M, for a certain ball, establish 4 contact forces that contact of itself and raceway and be respectively Q _1x, Q _1y, Q _2xand Q _2y, due to pivoting support in actual condition except rotation vertically other degree of freedom all restrained, so each ball to raceway should reach balance with external applied load with joint efforts, can obtain thus:

Wherein, Z is the quantity of ball,

with

respectively that all balls are in axial force F _a, radial force F _rwith the expression formula of making a concerted effort in upsetting moment M direction.Thus, just can solve whole raceway along the loading conditions of ball.Then according to large young pathbreaker's pivoting support of load, be divided into n group, according to the symmetry distributing, every group has 4 sections of raceways, amounts to 4n section pivoting support raceway;

3) choose certain pivoting support, to it, impose 100% ultimate design load, utilize bench run to pivoting support to cause testing machine stuck because raceway is of serious failure;

4) record the number of turns t that the stuck luck of pivoting support turns over _f, tested pivoting support is taken off to testing table and disassembles.According to step 2) in method pivoting support is surely enclosed and cuts into 4n section, measure the volume wear W of every section of pivoting support raceway _i, wherein maximum volume wear is Wmax;

5) according to formula

the puppet that calculates respectively each section of pivoting support raceway lost efficacy fatigue lifetime, t _ithe pseudo-fatigue failure life that represents each section of pivoting support raceway.

6) puppet of the 4n pivoting support of trying to achieve in step 5) was lost efficacy to fatigue lifetime according to 2) in method be divided into n group, every group of 4 samples, solve n the pivoting support life-span Weibull Function under stress level

in n β and the value of η.

7) according to formula ln η=a+blnQ, utilize step 2) in the corresponding load value Q that solves _mthe η solving in (m=1,2,3......n) He 6) _m(m=1,2,3......n) and simulate constant a, and the value of b, thus set up the relational model of the maximum load Q bearing in pivoting support characteristics life η and pivoting support raceway;

8) for the pivoting support of choosing, by step 2) can try to achieve the maximum raceway load Q under specific operation, by known its Weibull distribution fatigue lifetime slope of step 6)

by known its characteristics life of step 7) η _rso, just set up the reliability prediction model of its remanent fatigue life:

β is the slope of Weibull distribution, and R is fiduciary level, and η is characteristics life, and x is residual life.

In described step 1), the value of n is n >=4.Being more than or equal to 4 object is the precision that guarantees data fitting, and n is larger, and the precision of data fitting is higher, and the model of acquisition is more accurate, but does not advise being greater than 10 because n too conference cause high cost.

The present invention combines the theoretical and contrary power rate accelerated life model of Archard bearing wear, utilizes size that large-size pivoting support is huge and the symmetry of raceway load---raceway wear extent---relational model in pivoting support life-span of setting up large-size pivoting support load.

The present invention only need to carry out the fatigue life test of life-cycle to a pivoting support, can obtain the predicting residual useful life model of homologous series pivoting support.

Carry out needing to prepare before the fatigue life test of life-cycle a set ofly can to large-size pivoting support, applying the testing table of axial force, radial force and upsetting moment, it imposed to 100% ultimate load after assembling pivoting support, move to pivoting support because of inefficacy stuck.

Pivoting support raceway is divided into 4n region, and n is the bigger the better, and requires n to be not less than 4, and this is in order to ensure the precision when fitting n pivoting support Weibull distribution fatigue lifetime.To the pivoting support after test failure, by formula (2), formula (3) and formula (4), can obtain the parameter beta that its residual life distributes _meanand η _r, finally by formula (5), drawn the predictive equation of pivoting support residual life.

$t_i=\frac{t_f{W}_{\max }}{W_i}---\left(2\right)$

lnη＝a+blnQ （3）

${\mathrm{\&beta;}}_{\mathrm{mean}}=\frac{\underset{m=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&beta;}}_m}{n}---\left(4\right)$

$x=\mathrm{\&eta;}{\{{\left(\frac{t}{\mathrm{\&eta;}}\right)}^{{\mathrm{\&beta;}}_{\mathrm{mean}}}-\ln [R\left(x\right)\left]\right\}}^{\frac{1}{{\mathrm{\&beta;}}_{\mathrm{mean}}}}-t---\left(5\right)$

The invention has the beneficial effects as follows:

A kind of large-size pivoting support method for predicting residual useful life superiority based on small sample test of the present invention is as follows:

1, a kind of method for predicting residual useful life (at present without pertinent literature and patent) of the large-size pivoting support based on Weibull distribution reliability is provided

2, in conjunction with Archard theory of wear and contrary power rate accelerated life model, set up the model of " pivoting support load---raceway wear extent---pivoting support fatigue lifetime "

3, pivoting support raceway is divided into 4n section by load distribution characteristic, by an accelerated life test, obtain the test figure of many groups burn-out life, comparing traditional Weibull modeling process need to test many groups test specimen in bulk, guaranteeing that model greatly reduces experimentation cost ，Wei enterprise under prerequisite accurately and economizes on resources.

4, in test, to pivoting support, load 100% ultimate load, under the prerequisite that does not change failure mechanism, accelerated test process, improved test efficiency.

5, pivoting support test platform structure is simple, easy to operate, makes pivoting support Life Prediction Model modeling process succinctly efficient, easy to implement.

Accompanying drawing explanation:

Fig. 1 is the implementing procedure figure of this method.

Fig. 2 is ball and the stressed schematic diagram that contacts of raceway.

Fig. 3 is the loading diagram that pivoting support encloses raceway surely.

Fig. 4 a is for surely enclosing the Facad structure schematic diagram of the testing table that carries out fatigue life test to pivoting support.

Fig. 4 b is for surely enclosing the side structure schematic diagram of the testing table that carries out fatigue life test to pivoting support.

Fig. 5 is the wear extent testing result figure of each section of raceway cutting apart after pivoting support lost efficacy.

Fig. 6 is pivoting support predicting residual useful life curve map.

Embodiment:

Below in conjunction with accompanying drawing, the invention will be further described:

As shown in Figure 1, the implementation step of this method is as follows:

1) according to Weibull life-span distribution theory R=exp[-(t/ η) ^β], derive the predicting residual useful life model of the pivoting support based on reliability

r is fiduciary level, and t is the number of turns that pivoting support has turned round, and β, η are respectively the slope of Weibull distribution and the characteristics life of corresponding bearing, needs to determine by test.

2) load that solves pivoting support raceway according to hertz contact theory and ISO281 method distributes.If the external applied load that pivoting support is subject to respectively: axial force F _a, radial force F _rwith upsetting moment M, for a certain ball, as shown in Figure 2, establish 4 contact forces that contact of itself and raceway and be respectively Q _1x, Q _1y, Q _2xand Q _2y, due to pivoting support in actual condition except rotation vertically other degree of freedom all restrained, so each ball to raceway should reach balance with external applied load with joint efforts, can obtain thus:

Wherein, Z is the quantity of ball,

with represented that respectively all balls are in axial force F _a, radial force F _rwith making a concerted effort in upsetting moment M direction.Thus, just can solve whole raceway along the loading conditions of ball as shown in Figure 3.Then according to large young pathbreaker's pivoting support of load, be divided into n group, according to the symmetry distributing, every group has 4 sections of raceways, amounts to 4n section pivoting support raceway;

3) choose certain pivoting support, to it, impose 100% ultimate design load, comprise axial force, radial force and upsetting moment, utilize bench run to pivoting support to cause testing machine stuck because raceway is of serious failure, as shown in Figure 4, G1, G2 are respectively the first axial force load cylinder and youngster's axial force load cylinder, and G3 is radial force load cylinder, and G4 is hydraulic drive motor.

4) record the number of turns t that the stuck luck of pivoting support turns over _f, tested pivoting support is taken off to testing table and disassembles.According to step 2) in method pivoting support is surely enclosed and cuts into 4n section, measure the volume wear W of every section of pivoting support raceway _i, wherein maximum volume wear is Wmax, as shown in Figure 5.

5) according to formula

the puppet that calculates respectively each section of pivoting support raceway lost efficacy fatigue lifetime, t _ithe pseudo-fatigue failure life that represents each section of pivoting support raceway.

6) by 5) in the puppet of the 4n pivoting support of trying to achieve lost efficacy fatigue lifetime according to 2) in method be divided into n group, every group of 4 samples, solve n the pivoting support life-span Weibull Function under stress level in n β and the value of η.

7) according to formula ln η=a+blnQ, utilize 2) in the corresponding load value Q that solves _mthe η solving in (m=1,2,3......n) He 6) _m(m=1,2,3......n) and simulate constant a, and the value of b, thus set up the relational model of the maximum load Q bearing in pivoting support characteristics life η and pivoting support raceway.

8) for the pivoting support of choosing, by step 2) can try to achieve the maximum raceway load Q under specific operation, by known its Weibull distribution fatigue lifetime slope of step 6) by known its characteristics life of step 7) η _rso, just set up the reliability prediction model of its remanent fatigue life: $x=\mathrm{\&eta;}{\{{\left(\frac{t}{\mathrm{\&eta;}}\right)}^{{\mathrm{\&beta;}}_{\mathrm{mean}}}-\ln [R\left(x\right)\left]\right\}}^{\frac{1}{{\mathrm{\&beta;}}_{\mathrm{mean}}}}-t,$ As shown in Figure 6.

Embodiment recited above is described the preferred embodiment of the present invention; not the spirit and scope of the present invention are limited; do not departing under design concept prerequisite of the present invention; various modification and improvement that in this area, common engineering technical personnel make technical scheme of the present invention; all should fall into protection scope of the present invention, the technology contents that the present invention asks for protection is all documented in claims.

Claims (2)

1. the large-size pivoting support remaining life Forecasting Methodology based on small sample, is characterized in that comprising the following steps:

1) according to Weibull life-span distribution theory R=exp[-(t/ η) ^β], derive the predicting residual useful life model of the pivoting support based on reliability

r is fiduciary level, and t is the number of turns that pivoting support has turned round, and β, η need Forecasting Methodology of the present invention to determine the characteristics life of corresponding Weibull distribution slope and pivoting support, and x is residual life;

2) load that solves pivoting support raceway according to hertz contact theory and ISO281 method distributes.If the external applied load that pivoting support is subject to respectively: axial force F _a, radial force F _rwith upsetting moment M, for a certain ball, establish 4 contact forces that contact of itself and raceway and be respectively Q _1x, Q _1y, Q _2xand Q _2y, due to pivoting support in actual condition except rotation vertically other degree of freedom all restrained, so each ball to raceway should reach balance with external applied load with joint efforts, can obtain thus:

Wherein, Z is the quantity of ball,

with

respectively that all balls are in axial force F _a, radial force F _rwith the expression formula of making a concerted effort in upsetting moment M direction.Thus, just can solve whole raceway along the loading conditions of ball.Then according to large young pathbreaker's pivoting support of load, be divided into n group, according to the symmetry distributing, every group has 4 sections of raceways, amounts to 4n section pivoting support raceway;

3) choose certain pivoting support, to it, impose 100% ultimate design load, utilize bench run to pivoting support to cause testing machine stuck because raceway is of serious failure;

4) record the number of turns t that the stuck luck of pivoting support turns over _f, tested pivoting support is taken off to testing table and disassembles.According to step 2) in method pivoting support is surely enclosed and cuts into 4n section, measure the volume wear W of every section of pivoting support raceway _i, wherein maximum volume wear is Wmax;

5) according to formula

the puppet that calculates respectively each section of pivoting support raceway lost efficacy fatigue lifetime, t _ithe pseudo-fatigue failure life that represents each section of pivoting support raceway.

6) puppet of the 4n pivoting support of trying to achieve in step 5) was lost efficacy to fatigue lifetime according to 2) in method be divided into n group, every group of 4 samples, solve n the pivoting support life-span Weibull Function under stress level

in n β and the value of η.

7) according to formula ln η=a+blnQ, utilize step 2) in the corresponding load value Q that solves _mthe η solving in (m=1,2,3......n) He 6) _m(m=1,2,3......n) and simulate constant a, and the value of b, thus set up the relational model of the maximum load Q bearing in pivoting support characteristics life η and pivoting support raceway;

8) for the pivoting support of choosing, by step 2) can try to achieve the maximum raceway load Q under specific operation, by known its Weibull distribution fatigue lifetime slope of step 6)

by known its characteristics life of step 7) η _rso, just set up the reliability prediction model of its remanent fatigue life:

β is the slope of Weibull distribution, and R is fiduciary level, and η is characteristics life, and x is residual life.

2. the large-size pivoting support remaining life Forecasting Methodology based on small sample according to claim 1, the value that it is characterized in that n in described step 1) is n >=4.

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