CN114021347A - Method for predicting remaining life of hook body of heavy-duty truck based on hypothesis distribution - Google Patents
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Abstract
The invention relates to the technical field of rail transit, in particular to a method for predicting the residual service life of a hook body of a heavy-duty truck based on assumed distribution, which aims at the root of a traction flange below the hook body to predict the residual service life of the hook body with visible cracks and obtains crack data of the hook body through nondestructive testing; then calculating the surface length of the crack of the root of the traction flange under the hook body under different service life fractions, adopting 3PWD, 2PWD, ND, LND, EMVD1 and EMVD2 as the assumed distribution of the crack length under a certain service life fraction, carrying out goodness-of-fit inspection on crack data, determining good assumed distribution types, and obtaining the cumulative failure probability curve of the surface length of the crack under different service life fractions; and predicting the residual life of the target hook body with the visible cracks based on the cumulative failure probability curve. The method for predicting the residual life based on the hypothesis distribution effectively improves the prediction precision of the residual life of the hook body, and has important significance for making hook body maintenance strategies.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a method for predicting the residual service life of a hook body of a heavy-duty truck based on assumed distribution.
Background
Most of the existing 16/17-type cast hooks used on heavy haul railways have structures as shown in fig. 4-5, and according to research results, the damaged position of the hook often occurs at the root of the contact part between the hook and the knuckle, and the hook is also an important component of the heavy haul hook. And carrying out crack position statistics on the damaged hook body of the Shenhuang line ten-thousand-ton and two ten-thousand-ton mixed running marshalling train after the train is driven for one-section maintenance, wherein the result shows that the crack of the damaged hook body mainly appears at the root of the lower traction table.
Accurate prediction of remaining life not only ensures that a structure or system operates at a higher reliability level, but also avoids resource waste caused by too frequent preventive maintenance. The method for predicting the residual life widely applied at present can be divided into the following steps according to the adopted basic theory: 1. constructing a residual life prediction method of the model according to system mechanisms such as physics, chemistry or experience of the detected object; 2. a data driving method for constructing a model completely according to historical characteristic data, such as a statistical model, a neural network, a Gaussian regression process, support vector regression, fuzzy reasoning and the like; 3. a method in which a mechanism model is combined with a data-driven model; 4. a method for combining multiple data-driven models.
The residual life prediction method based on the mechanism model can have higher precision on the premise of mechanism definition, but for some complex structures or systems, the mechanism model is difficult to establish. The residual life prediction method based on data driving does not need to express a formula by a clear and real mechanism, is easier to apply to a complex structure or a system, but generally has a larger demand on data volume, and has a certain risk although a large amount of processing methods aiming at small data volume exist.
The mixed model is selectively established based on different theories, the defects of the theories can be overcome, the prediction precision is improved, and the mixed model becomes a research hotspot of residual life prediction, so that the invention provides a method for predicting the residual life of the coupler body of the heavy-duty railway freight car based on hypothesis distribution.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for predicting the residual life of a hook body of a heavy-duty truck based on assumed distribution.
The purpose of the invention is realized by the following technical scheme:
the utility model provides a heavy goods vehicle coupler body residual life prediction method based on hypothesis distribution, draws flange root under the coupler body, carries out residual life prediction to having visible crack coupler body, specifically includes: obtaining crack data of the hook body through nondestructive testing, wherein the crack data comprise the total service life of the hook body, and the service mileage, the crack size and the reliability corresponding to the condition that visible cracks exist at the root of a traction flange below the hook body; then calculating the surface length of the crack of the root of the traction flange under the hook body under different service life fractions, wherein the service life fractions refer to the ratio of the current service life to the total life, adopting 3PWD, 2PWD, ND, LND, EMVD1 or EMVD2 as the assumed distribution of the crack length under certain service life fractions, carrying out goodness-of-fit inspection on crack data, determining good assumed distribution types, and obtaining the cumulative failure probability curve of the surface length of the crack under different service life fractions; and predicting the residual life of the target hook body with the visible cracks based on the cumulative failure probability curve.
Further, when the residual life of the target hook with visible cracks is predicted based on the cumulative failure probability curve, an evaluation point of the target hook in the cumulative failure probability curve can be obtained by selecting a certain reliability according to the service mileage of the target hook and the surface length of the cracks at the root of the traction flange under the hook, and the residual life of the target hook under the reliability is obtained by calculating according to the life score corresponding to the evaluation point.
Further, calculating the surface length of the crack at the root of the traction flange under the hook body under different service life fractions through linear interpolation.
Further, after calculating the surface length of the crack at the root of the traction flange under the hook body under different service life fractions through linear interpolation, judging and eliminating abnormal data by utilizing a Grubbs criterion method.
Further, using the EMVD1 as the hypothetical distribution of crack lengths at a certain life fraction, reliability curves of crack surface lengths at different life fractions were obtained.
Further, the cumulative failure probability is (1-reliability) × 100%.
Further, calculating reliability service life indexes through data obtained by crack propagation of the root of the traction flange under the hook body to obtain statistical distribution of the service life of the traction flange; if the probability density function of the service life t of the structure is f (t), the reliability R (t) is defined as the probability that the service life is greater than a specified value t, namely
The cumulative failure probability F (t), also known as the uncertainty, can be calculated using the following equation
F(t)＝1-R(t)
The key point of calculating the structure reliability lies in obtaining a structure failure probability density function f (t), for a large sample problem (the capacity exceeds 50 or 70), the probability density function can be directly determined according to data, for a small sample problem, the overall fitting effect, the fatigue failure mechanism and the safety of an algorithm can be comprehensively considered, and a good hypothesis distribution is selected from common statistical distribution types, namely three-parameter weibull distribution (3PWD), two-parameter weibull distribution (2PWD), Normal Distribution (ND), lognormal distribution (LND), minimum value distribution (EMVD1), maximum value distribution (EMVD2) and Exponential Distribution (ED), and the basic flow is as follows:
a) sorting the data from small to large, and calculating the empirical failure probability P based on the median rankem(xi)；
b) Based on linear regression, fit (x) with the above common statistical distribution types, respectivelyi，Pem(xi) Data) to obtain statistical distribution parameters and fitting correlation coefficient RXY；
c) For high reliability problems by calculating dF1And dF2To determine tail prediction security.
For the left tail problem, there are
dF1＝Pem(x1)-Pth(x1)
dF2＝Pem(x2)-Pth(x2)
For the right tail problem, there are
dF1＝Pem(xn)-Pth(xn)
dF2＝Pem(xn-1)-Pth(xn-1)
And comprehensively considering the fitting correlation coefficient, the fatigue failure mechanism and the tail prediction safety, and determining a good statistical distribution type.
The invention has the beneficial effects that: the invention relates to a heavy-duty truck hook body residual life prediction method based on hypothesis distribution, which is characterized in that hook body crack data are obtained through nondestructive testing, when a hook body has visible fatigue cracks, the hook body is in an expansion stage after the cracks are initiated, parameters such as service mileage, crack positions, crack sizes, reliability and the like of the hook body at the moment are counted, a residual life prediction model is constructed, a residual life prediction method based on the hypothesis distribution is provided, the common characteristics and the current state of a research object are considered, the prediction precision of the residual life of the hook body is effectively improved, and the method has important significance for establishing hook body maintenance strategies.
Drawings
FIG. 1 is a graph of mean change in crack length at the root of a lower traction flange of a hook body at different life fractions;
FIG. 2 is a graph of the variation coefficients for different life fractions;
FIG. 3 is a graph of the cumulative probability of failure of EMVD1 for crack sizes at different life fractions;
FIG. 4 is a schematic structural view of a cast hook body of 16 and 17 types;
FIG. 5 is a schematic structural view of a type 17 cast hook;
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
Examples
A method for predicting the residual life of a hook body of a heavy-duty truck based on assumed distribution aims at the root of a traction flange under the hook body to predict the residual life of the hook body with visible cracks, and specifically comprises the steps of obtaining crack data of the hook body through nondestructive testing based on a fatigue bench test, wherein the crack data comprises the total life of the hook body and the corresponding service mileage, crack size and reliability when the root of the traction flange under the hook body has the visible cracks; then calculating the surface length of the crack of the root of the traction flange under the hook body under different service life fractions, wherein the service life fractions refer to the ratio of the current service life to the total life, adopting 3PWD, 2PWD, ND, LND, EMVD1 or EMVD2 as the assumed distribution of the crack length under certain service life fractions, carrying out goodness-of-fit inspection on crack data, determining good assumed distribution types, and obtaining the cumulative failure probability curve of the surface length of the crack under different service life fractions; and predicting the residual life of the target hook body with the visible cracks based on the cumulative failure probability curve.
Specifically, when the residual life of the target hook with visible cracks is predicted based on the cumulative failure probability curve, an evaluation point of the target hook in the cumulative failure probability curve can be obtained by selecting a certain reliability according to the service mileage of the target hook and the surface length of the cracks at the root of the traction flange under the hook, and the residual life of the target hook under the reliability is calculated according to the life score corresponding to the evaluation point.
Specifically, the surface length of the crack at the root of the traction flange under the hook body under different service life fractions is calculated through linear interpolation.
Specifically, after calculating the surface length of the crack at the root of the traction flange under the hook body under different service life fractions through linear interpolation, judging and eliminating abnormal data by using a Grubbs criterion method.
Specifically, using the EMVD1 as the hypothetical distribution of crack lengths at a certain life fraction, reliability curves for crack surface lengths at different life fractions were obtained.
Specifically, the cumulative failure probability is (1-reliability) × 100%.
Specifically, for the reliability, the reliability service life index is calculated through data obtained by coupler knuckle crack propagation to obtain the statistical distribution of the service life of the coupler knuckle, and if the probability density function of the service life t of the structure is f (t), the reliability R (t) is defined as the probability that the service life is greater than a certain specified value t, namely
The cumulative failure probability F (t), also known as the uncertainty, can be calculated using the following equation
F(t)＝1-R(t)
The key point of calculating the structure reliability lies in obtaining a structure failure probability density function f (t), for a large sample problem (the capacity exceeds 50 or 70), the probability density function is generally directly determined according to data, for a small sample problem, the overall fitting effect, the fatigue failure mechanism and the safety of an algorithm can be comprehensively considered, and a good hypothesis distribution is selected from common statistical distribution types, namely three-parameter weibull distribution (3PWD), two-parameter weibull distribution (2PWD), Normal Distribution (ND), lognormal distribution (LND), minimum value distribution (EMVD1), maximum value distribution (EMVD2) and Exponential Distribution (ED), and the basic flow is as follows:
a) sorting the data from small to large, and calculating the empirical failure probability P based on the median rankem(xi)；
b) Based on linear regression, fit (x) with the above common statistical distribution types, respectivelyi，Pem(xi) Data) to obtain statistical distribution parameters and fitting correlation coefficient RXY；
c) For high reliability problems by calculating dF1And dF2To determine tail prediction security.
For the left tail problem, there are
dF1＝Pem(x1)-Pth(x1)
dF2＝Pem(x2)-Pth(x2)
For the right tail problem, there are
dF1＝Pem(xn)-Pth(xn)
dF2＝Pem(xn-1)-Pth(xn-1)
And comprehensively considering the fitting correlation coefficient, the fatigue failure mechanism and the tail prediction safety, and determining a good statistical distribution type.
Test examples
Obtaining crack data of the hook body through nondestructive testing, wherein the crack data comprise the total service life of the hook body, and the service mileage, the crack size and the reliability corresponding to the condition that visible cracks exist at the root of a traction flange below the hook body; then calculating the surface length of the root crack of the traction flange under the hook body under different service life fractions, wherein the service life fractions refer to the ratio of the current service life to the total service life, judging and eliminating abnormal data by utilizing a Grubbs criterion method, and the result is shown in a table 1,
TABLE 1 fraction evolution data of crack length with life for traction flange under hook
The change rule of the crack length mean value and the coefficient of variation is shown in fig. 1 and fig. 2, and it can be seen from the figure that the crack length mean value of the hook lower traction flange is constantly increased along with the increase of the life fraction, and the coefficient of variation is gradually reduced and tends to be stable; after the service life fraction is more than 0.7, the dispersity is small because multiple sections of small cracks are generated at the stress concentration part at the root part of the traction flange in the initial crack propagation stage, and the dispersity is large due to the randomness of the positions and the sizes of the small cracks; under the action of cyclic load, after the small cracks gradually fuse into a fatigue main crack, the crack position is relatively fixed, and the data dispersity is small.
Respectively adopting 3PWD, 2PWD, ND, LND, EMVD1 and EMVD2 as the assumed distribution of crack length under a certain service life fraction, and comparing the linear correlation coefficient R under each distribution functionXYThereby judging whether the assumed distribution is reasonable; the fitting correlation coefficient for each of the hypothetical distributions is shown in table 2, and since the crack length data for the life fractions 0.1, 0.2, and 0.3 are all 0, table 2 shows only the fitting correlation coefficient for the crack length data for the other life fractions.
TABLE 2 correlation coefficient of fit of crack length data under each hypothetical distribution
The labeled data in Table 2 are the absolute values of the fitted correlation coefficients | R for each life fractionXYMaximum, | from | RXYFrom the point of view of the degree of | approaching to 1, the fitting effect of the EMVD2 is best for the group 4, ND is best for the group 5 and 9, the fitting degree of the EMVD1 is highest for the group 6 and 8, and the fitting degree of the 3PWD is highest for the group 7 and 10. From the safety perspective of tail prediction, table 3 shows the difference between the empirical value and the predicted value (i.e., d) of the right tail failure probability of the 6 hypothetical distributionsF1And dF2)。
TABLE 3 Right Tail fitting error of six hypothetical distributions to crack length
dF1<dF2Indicating a tendency toward conservation with increasing reliability. dF1<0, then x is affirmatively>xnGiving a more conservative estimate. According to Table 3, d is satisfiedF1<dF2The data of (1) has 2 groups for 3PWD, 3 groups for 2PWD, 3 groups for ND, 1 group for LND, 6 groups for EMVD1, and 2 groups for EMVD 2. Full dF1<Data of 0, 1 group for 3PWD, 3 groups for 2PWD, 3 groups for ND, 1 group for LND, 6 groups for EMVD1, and 2 groups for EMVD 2. Comprehensively considering the fitting degree and the tail prediction safety, and selecting the EMVD1 which gives consideration to the fitting degree and the tail prediction safety as good assumed distribution of the crack length data of the root of the traction flange under the hook body; table 4 shows the cumulative failure probability function parameters of the EMVD1 of the surface crack length data under different life fractions, and the cumulative failure probability curves of the surface crack lengths under different life fractions are obtained, as shown in fig. 3.
Table 4 EMVD1 cumulative failure probability function parameters for crack length data
Based on FIG. 3, given the surface length and reliability (or cumulative probability of failure) of the crack at the root of the pulling flange under the hook, a prediction of the remaining life of the hook may be made; the specific method comprises the following steps: assuming that a certain hook body is already in service for k kilometers, the crack length of the root of the lower traction flange is detected to be 30mm (corresponding to the abscissa in the figure) through flaw detection, when the cumulative failure probability is 0.05 (corresponding to the ordinate in the figure), an evaluation point can be made in the figure, the point falls in the middle of a curve with the service life fraction of 0.6-0.7, the service life is about 67% of the full service life through linear interpolation, therefore, the full service life is 1.49k, namely, the residual service life of the hook body is 0.49k kilometer, and the result has the reliability of 95%.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The method for predicting the residual life of the hook body of the heavy-duty truck based on the assumed distribution is characterized in that the residual life of the hook body with visible cracks is predicted aiming at the root of a traction flange under the hook body, and specifically comprises the steps of obtaining crack data of the hook body through nondestructive testing, wherein the crack data comprise the total life of the hook body, and the service mileage, the crack size and the reliability corresponding to the condition that the root of the traction flange under the hook body has the visible cracks; then calculating the surface length of the crack of the root of the traction flange under the hook body under different service life fractions, wherein the service life fractions refer to the ratio of the current service life to the total life, adopting 3PWD, 2PWD, ND, LND, EMVD1 or EMVD2 as the assumed distribution of the crack length under certain service life fractions, carrying out goodness-of-fit inspection on crack data, determining good assumed distribution types, and obtaining the cumulative failure probability curve of the surface length of the crack under different service life fractions; and predicting the residual life of the target hook body with the visible cracks based on the cumulative failure probability curve.
2. The method for predicting the residual life of the hook body of the heavy-duty truck based on the hypothesis distribution as claimed in claim 1, wherein when the residual life of the target hook body with visible cracks is predicted based on the cumulative failure probability curve, an evaluation point of the target hook body in the cumulative failure probability curve can be obtained by selecting a certain reliability according to the service mileage of the target hook body and the surface length of the cracks at the root of the traction flange under the hook body, and the residual life of the target hook body under the reliability is calculated according to the life score corresponding to the evaluation point.
3. The method for predicting the residual life of the hook body of the heavy-duty truck based on the hypothesis distribution as claimed in claim 1, wherein the surface length of the crack at the root of the traction flange under the hook body at different life fractions is calculated through linear interpolation.
4. The method for predicting the residual life of the hook body of the heavy-duty truck based on the hypothesis distribution as claimed in claim 3, wherein after the surface length of the crack at the root of the traction flange under the hook body is calculated by linear interpolation under different life fractions, the Grubbs criterion method is used for judging and eliminating abnormal data.
5. The method for predicting the residual life of the hook body of the heavy-duty truck based on the hypothesis distribution is characterized in that the EMVD1 is selected as a good hypothesis distribution of the crack length data of the root of the traction flange under the hook body based on the fitting degree and the tail prediction safety, and a reliability curve of the surface length of the crack under different life fractions is obtained.
6. The method for predicting the remaining life of a hook of a heavy-duty truck according to claim 5, wherein the cumulative failure probability is (1-reliability) x 100%.
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| CN116227999A (en) * | 2023-02-09 | 2023-06-06 | 江苏省工商行政管理局信息中心 | Quantitative measuring and calculating system and method for operation and maintenance service quality evaluation indexes of market supervision software |
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| CN116227999A (en) * | 2023-02-09 | 2023-06-06 | 江苏省工商行政管理局信息中心 | Quantitative measuring and calculating system and method for operation and maintenance service quality evaluation indexes of market supervision software |
| CN116227999B (en) * | 2023-02-09 | 2024-04-05 | 江苏省工商行政管理局信息中心 | Quantitative measuring and calculating system and method for operation and maintenance service quality evaluation indexes of market supervision software |
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