CN113011638B - Prediction method for leakage rate of locomotive radiator - Google Patents
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Abstract
The invention discloses a prediction method of leakage rate of a locomotive radiator, which can be used for observing and detecting corrosion states of the radiator disassembled according to different overhaul periods of the locomotive and grasping macroscopic and microscopic corrosion states of the radiator; obtaining the data of the maximum corrosion depth of the local corrosion of the radiator; predicting the maximum corrosion depth and the median of the radiator by using a probability statistical analysis method; and summarizing the dynamics rules of the maximum corrosion depth and the median of the local corrosion of the radiator by taking the maximum corrosion depth and the median of the local corrosion of the radiator as characteristic parameters, and predicting the leakage rate of the radiator by using a probability statistical analysis method. Provides a basis for scientifically and reasonably formulating a radiator maintenance strategy and improving the application reliability.
Description
Technical Field
The invention relates to the technical field of heat exchange, in particular to a method for predicting leakage rate of a locomotive radiator.
Background
The cooling system is used for effectively transferring heat generated when important equipment (such as a traction system, a braking system and the like) of the rolling stock is operated, so that the equipment is ensured to be operated at a proper temperature. Therefore, the cooling system is an important guarantee for safe, reliable and efficient operation of rolling stock equipment. The radiator is a core component of the cooling system, once the radiator fails, the cooling system cannot continue to operate, and the rolling stock traction system or the braking system cannot operate normally.
With the large-scale application of domestic electric locomotives and electric motor car groups, the extension of the advanced repair period of key parts of locomotives and vehicles and the improvement of the service life thereof have become key technical problems concerned by all parties.
The research on the service life and the leakage rate of the radiator for the locomotive cooling system at home and abroad is still in an exploration stage, and no mature service life and leakage rate evaluation method exists. Few studies on radiator failure have been conducted, and leakage failure rules, leakage failure causes, and protective measures have been almost focused.
Disclosure of Invention
The invention provides a prediction method for leakage rate of a locomotive radiator, which can be used for observing and detecting corrosion states of the radiator disassembled according to different overhaul periods of the locomotive and grasping macroscopic and microscopic corrosion states of the radiator; obtaining the data of the maximum corrosion depth of the local corrosion of the radiator; predicting the maximum corrosion depth and the median of the radiator by using a statistical analysis method; and summarizing the dynamics rules of the maximum corrosion depth and the median of the local corrosion of the radiator by taking the maximum corrosion depth and the median of the local corrosion of the radiator as characteristic parameters, and predicting the leakage rate of the radiator by using a probability statistical analysis method.
In order to achieve the above object, the technical scheme of the present invention is as follows:
a method of predicting a leakage rate of a locomotive radiator, the method comprising the steps of:
s1, acquiring sample data corresponding to analysis of leakage rate of a locomotive radiator, wherein the sample data is used for representing surface corrosion state data corresponding to the condition that the locomotive radiator is marked as a radiator surface corrosion sampling point under different maintenance period conditions, and the surface corrosion state data at least comprises local corrosion maximum corrosion depth data;
s2, obtaining prediction data of the local corrosion maximum corrosion depth data and creating a prediction function, wherein the prediction function is used for representing the change trend of the local corrosion maximum corrosion depth data corresponding to the time change;
s3, obtaining the total service time when the predicted data of the local corrosion maximum corrosion depth data reach the corrosion damage tolerance;
s4, acquiring median data of the local corrosion maximum corrosion depth data and creating a median computing function, wherein the median computing function is used for representing the change trend of the median data of the local corrosion maximum corrosion depth data corresponding to time change in different overhaul periods;
s5, determining the leakage rate of the radiator corresponding to the time point of a certain maintenance period by searching a probability fitting function, wherein the probability fitting function is used for representing the change trend of the local corrosion maximum corrosion depth data overrun probability and the local corrosion maximum corrosion depth data.
Further, the sample data acquisition method comprises the following steps:
s11, selecting a sample
Firstly, selecting a radiator which is disassembled in a maintenance period t of at least 3 different time periods of a rolling stock as a leakage rate analysis sample;
secondly, dividing a radiator serving as an analysis sample into N areas along the length direction, determining the total number of radiator liquid channels as N, taking the radiator liquid channels as units, combining the N areas, and determining and manufacturing a radiator leakage rate analysis sample;
checking the corrosion state of the macroscopic surface of each leakage rate analysis template, primarily judging the corrosion maximum area of the analysis template, then detecting the corrosion state of the microscopic surface of each leakage rate analysis template, and determining and marking the local area with the most serious corrosion of each leakage rate analysis template;
intercepting the marked area according to the same size as a radiator microscopic corrosion analysis sample block blank, marking each intercepted sample block blank, trimming the blank, removing redundant fins and impurities, placing the blank into an injection mold, pouring substances which are favorable for grinding observation and measurement, and standing until the substances are completely solidified to form a radiator leakage rate analysis sample block;
s12, acquiring sample data
The maximum corrosion depth of the local corrosion of each analysis sample block is detected by the following method:
(1) Grinding sections of the radiator leakage rate analysis sample blocks perpendicular to the air flow direction for a plurality of times by using a polishing machine;
(2) After each grinding to a specified size, observing the sample block by a microscope to grind the surface;
(3) Measuring the maximum corrosion depth of the local corrosion of the surface and marking d max i 。
Further, the prediction function establishment method comprises the following steps:
s21, predicting the maximum corrosion depth of the radiator local corrosion:
(1) Maximum corrosion depth d of radiator leakage rate analysis sample blocks of detected rolling stock in same overhaul period t max Sequencing from small to large, sequentially marking the data sequence numbers as 1, 2, … …, i, … … and M, and marking the corresponding maximum corrosion depth data sequence d max 1 、d max 2 、……、d max i 、……、d max M ；
(2) According to the extremum I type distribution formula F (y) i ) =i/(m+1) and y i ＝-ln[-ln(F(y i ))]Respectively calculating the cumulative probability F (y i ) Hidden function y i ；
(3) To detect a set of maximum local corrosion depths d max In abscissa, with a corresponding set of hidden functions y calculated i Plotted as ordinate, y i -d max A data map;
(4) For y i -d max Fitting the data curve to obtain a regression equation：
y＝kx+b (1)
(5) Calculating a statistical variable alpha, alpha=1/k according to the extremum type I distribution; calculating a statistical parameter lambda, lambda= -alpha b;
(6) Calculating regression period according to reliability data collection and analysis principle and specific template collection rule, wherein T is M The ratio of the sum of the upper surface area and the lower surface area of the corrosion analysis sample plate to the surface area involved in the maximum corrosion depth sampling of the sampling area of the corrosion analysis sample block;
(7) Calculating the hidden function y, y= -ln [ -ln (1-1/T) M )]；
(8) Calculating a maximum corrosion depth predicted value x, x=αy+λ;
(9) Repeating the steps (1) - (8), and calculating a predicted value x of the maximum corrosion depth of the radiator in each overhaul period t;
s22, summarizing the dynamics rule of the maximum corrosion depth of the local corrosion of the radiator to obtain the change trend of the maximum corrosion depth data of the local corrosion corresponding to the time change, wherein the method comprises the following steps:
(1) Taking the overhaul period t as an abscissa and taking the maximum corrosion depth predicted value x as an ordinate, and making an x-t relation curve;
(2) Data fitting is carried out on the x-t relation curve, and a kinetic equation of the maximum corrosion depth x of the radiator along with the change of the overhauling period t is obtained:
x＝f(t) (2)。
further, the total service time t when the maximum corrosion depth reaches the corrosion damage tolerance delta is predicted by the formula (2) δ The corrosion damage tolerance delta is the maximum allowable reduced thickness of the radiator diaphragm as determined by the strength simulation analysis method.
Further, the method for obtaining the median computing function of the local corrosion maximum corrosion depth data comprises the following steps:
s31, determining the median M of the maximum corrosion depth data of each overhaul period t by the partial corrosion maximum corrosion depth data sequences of different overhaul periods t e ；
The median value M e The acquisition method comprises the following steps: when the maximum corrosion is deepWhen the number M of the degree data is odd, the median value M of the maximum corrosion depth e Maximum corrosion depth data value for the intermediate position; when the data number M is even, the median value M of the maximum corrosion depth e Average of 2 maximum corrosion depth data values at intermediate positions;
s32: median value M of maximum corrosion depth of local corrosion according to different overhaul periods t e Calculating the median value M of the maximum corrosion depth of the local corrosion of the radiator e Kinetic equation as a function of service period t:
M e ＝f(t) (3)。
further, the leak rate determination method is as follows:
(1) Calculating the maximum corrosion depth median value M of a certain overhaul period t in the future according to the formula (3) et ；
(2) Calculating the median M of the maximum corrosion depth of the radiator with the longest overhaul period in the leak rate analysis sample ej And M is as follows et Is not equal to the difference Δd:
Δd＝M et -M ej
(3) Analyzing each maximum corrosion depth data array d of the radiator with the longest overhaul period in the sample piece according to the leakage rate max 1 、d max 2 、……、d max i 、……、d max M According to the statistical analysis method, calculating to obtain corresponding each accumulated probability F (y i ) Calculating the maximum corrosion depth exceeding d max i Probability lambda of (2) ti ：
λ ti ＝1-F(y i ) (4)
From equation (4) a series lambda is obtained t1 、λ t2 、……、λ ti 、……、λ tM ；
(4) Analyzing radiator maximum corrosion depth data d with longest overhaul period in sample piece at leakage rate max On the abscissa, with corresponding probability lambda t Drawing lambda-d for ordinate max Data graph A, and for λ -d max Fitting the data to obtain a regression curve Q; other leak rate analysis samples may also be selected for steps (2), (3) and (4).
(5) On the regression curve Q of the data graph A, searching the ordinate value corresponding to the maximum corrosion depth delta-delta d of the abscissa, wherein the value is the radiator leakage probability lambda corresponding to a future overhaul period t t 。
The beneficial effects are that: according to the method for predicting the leakage rate of the locomotive radiator, disclosed by the invention, the leakage rate of a certain service time of the radiator in the future is predicted by utilizing the dynamics law of the maximum corrosion depth and the median of the local corrosion of the radiator, so that a basis is provided for scientifically and reasonably formulating a radiator maintenance strategy and improving the application reliability.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting a radiator leakage rate of a rolling stock according to the present invention;
FIG. 2 is a schematic diagram of a radiator core structure for a rolling stock according to the present invention;
FIG. 3 is a schematic illustration of a microscopic corrosion analysis block of a locomotive radiator according to the present invention;
FIG. 4 is a schematic view of a radiator y for rolling stock according to the present invention i -d max Fitting a graph with the data;
FIG. 5 is a fitted graph of the relationship between the radiator x-t of a rolling stock according to the present invention;
FIG. 6 is a schematic illustration of corrosion damage tolerance of a locomotive radiator according to the present invention;
FIG. 7 shows a radiator leakage rate lambda of a certain service time t of a locomotive radiator according to the invention t Is a data diagram a of (a).
Wherein: 1. a liquid passage unit; 2. a liquid passage unit lower surface; 3. the upper surface of the liquid channel unit; 4. an air side fin; n (N) 1 、N 2 、……、N i … …, N are radiator liquid channel sequence numbers; n is n 1 、n 2 、……、n i … …, n are regions divided along the length of the liquid passage; A. the direction of air flow; B. the direction of the flow.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment provides a method for predicting the leakage rate of a radiator of a rolling stock, as shown in fig. 1, comprising the following steps:
s1, acquiring sample data corresponding to analysis of leakage rate of a locomotive radiator, wherein the sample data is used for representing surface corrosion state data corresponding to the condition that the locomotive radiator is marked as a radiator surface corrosion sampling point under different maintenance period conditions, and the surface corrosion state data at least comprises local corrosion maximum corrosion depth data;
preferably, the sample data acquisition method is as follows:
firstly, selecting a sample;
selecting a radiator which is disassembled in a maintenance period t of at least 3 different time periods of the rolling stock as a leakage rate analysis sample;
for a motor train unit radiator: selecting a radiator detached by the motor train unit A3 repair (three-stage repair, 120 ten thousand kilometers or 3 years), A4 repair (four-stage repair, 240 ten thousand kilometers or 6 years) and A5 repair (five-stage repair, 480 ten thousand kilometers or 12 years) as a leakage rate analysis sample piece;
for locomotive radiator: and selecting a radiator disassembled by C4 repair (four-stage repair, 50 km or 3 years), C5 repair (five-stage repair, 100 km or 6 years) and C6 repair (six-stage repair, 200 km or 12 years) as a leakage rate analysis sample.
As shown in fig. 2 and 3, wherein B in the figure indicates the direction of the liquid flow, the method for determining the radiator leakage rate analysis template is as follows:
(1) Dividing the radiator of the rolling stock in different overhaul periods into n areas along the length direction according to the same area division rule, wherein n=1-20 generally;
(2) Determining the total number of liquid channels of the radiator, and marking each liquid channel together with the air side fins on the surface of the liquid channel as liquid channels 1, 2, … … and N respectively in the sequence of up, middle and down (when the air flow direction A in the radiator is horizontal) or left, middle and right (when the air flow direction A in the radiator is vertical);
(3) The radiator liquid channel is taken as a unit, N areas are combined, and a radiator leakage rate analysis sample plate is determined and manufactured according to the principle that the upper, middle and lower (or left, middle and right) areas are basically uniformly distributed and marked as N-N;
preferably, liquid channel 1, ≡N/4, ≡N/2, ≡3/4N, N is selected; combining n areas of the radiator sample piece divided along the length direction to determine a radiator leakage rate analysis sample plate:
1-1、1-2、……、1-n i 、……、1-n；
N/4-1、N/4-2、……、N/4-n i 、……、N/4-n；
N/2-1、N/2-2、……、N/2-n i 、……、N/2-n；
3/4N-1、3/4N-2、……、3/4N-n i 、……、3/4N-n；
N-1、N-2、……、N-n i 、……、N-n。
this selection rule is only a preferred embodiment of the present invention, and may be selected according to other sampling rules determined in advance.
Preferably, the macroscopic surface (liquid passage plate or pipe wall surface) of each leak rate analysis template is visually inspected for corrosion status; then observing and detecting the microscopic surface corrosion state of each leakage rate analysis template by using a microscope and other instruments, and determining and marking the local area of each leakage rate analysis template with the most serious corrosion.
Preferably, according to the same size (the length a multiplied by the width b is not less than 10 multiplied by 10), intercepting a local area with the most serious corrosion of each leakage rate analysis template in the radiator as a radiator microscopic corrosion analysis sample block blank, and marking the intercepted blank of each sample block; trimming the microscopic corrosion analysis sample block blank, removing redundant fins, placing the blank into an injection mold, pouring substances which are favorable for grinding observation and measurement, and standing until the substances are completely solidified to form a radiator leakage rate analysis sample block, wherein the transparent organic resin is used for pouring in the embodiment.
Secondly, acquiring sample data;
(1) Grinding sections of the radiator leakage rate analysis sample blocks perpendicular to the air flow direction for multiple times by using a polishing machine, wherein the ground sections are perpendicular to the air flow direction; controlling the grinding process to be 20 mu m-3mm each time;
(2) After each grinding to a specified size, observing the sample block by a microscope to grind the surface;
(3) Measuring the maximum corrosion depth of the local corrosion of the surface and marking d max i 。
S2, obtaining prediction data of the local corrosion maximum corrosion depth data and creating a prediction function, wherein the prediction function is used for representing the change trend of the local corrosion maximum corrosion depth data corresponding to the time change;
preferably, the maximum corrosion depth of the radiator local corrosion of each overhaul period t is predicted by the following method:
(1) And (5) sorting the maximum corrosion depth data: maximum corrosion depth d of radiator leakage rate analysis sample blocks of detected rolling stock in same overhaul period t max Sequencing from small to large, sequentially marking the data sequence numbers as 1, 2, … …, i, … … and M, and marking the corresponding maximum corrosion depth data sequence d max 1 、d max 2 、……、d max i 、……、d max M ；
(2) According to the extremum I type distribution formula F (y) i ) =i/(m+1) and y i ＝-ln[-ln(F(y i ))]Respectively calculating the cumulative probability F (y i ) Hidden function y i ；
(3) To detect a set of maximum local corrosion depths d max In abscissa, with a corresponding set of hidden functions y calculated i Plotted as ordinate, y i -d max A data map;
(4) For y i -d max Fitting the data curves to obtain a regression equation, as shown in fig. 4:
y＝kx+b (1)
(5) Calculating a statistical variable alpha, alpha=1/k according to the extremum type I distribution; calculating a statistical parameter lambda, lambda= -alpha b;
(6) Calculating regression period (also called reproduction time) T according to reliability data collection and analysis principle and specific template collection rule M Wherein T is M The ratio of the sum of the upper surface area and the lower surface area of the corrosion analysis sample plate to the surface area involved in the maximum corrosion depth sampling of the sampling area of the corrosion analysis sample plate.
(7) Calculating the hidden function y, y= -ln [ -ln (1-1/T) M )]；
(8) Calculating a maximum corrosion depth predicted value x, x=αy+λ;
(9) And (5) repeating the steps (1) - (8), and calculating the predicted value x of the maximum corrosion depth of the radiator in each overhaul period t.
Preferably, the dynamics rule of the maximum corrosion depth of the local corrosion of the radiator is summarized to obtain the change trend of the maximum corrosion depth data of the local corrosion corresponding to the time change, and the method comprises the following steps:
(1) Taking the overhaul period t as an abscissa and taking the maximum corrosion depth predicted value x as an ordinate, and making an x-t relation curve;
(2) Data fitting is carried out on the x-t relation curve, so that a kinetic equation of the maximum corrosion depth x of the radiator along with the overhauling period t is obtained, and the kinetic equation is shown in the following figure 5:
x＝f(t) (2)。
s3, obtaining the total service time when the predicted data of the local corrosion maximum corrosion depth data reach the corrosion damage tolerance;
preferably, the total service time when the maximum corrosion depth reaches the corrosion damage tolerance delta is predicted by the formula (2)Interval t δ The method comprises the steps of carrying out a first treatment on the surface of the The corrosion damage tolerance delta is the maximum allowable reduced thickness of the radiator diaphragm as determined by the strength simulation analysis method, as shown in fig. 6.
S4, acquiring median data of the local corrosion maximum corrosion depth data and creating a median computing function, wherein the median computing function is used for representing a change trend of the median data of the local corrosion maximum corrosion depth data corresponding to time change;
preferably, the median M of the maximum corrosion depth data of each overhaul period t is determined according to a probability statistical analysis method by the local maximum corrosion depth data sequences of different overhaul periods t e The method comprises the steps of carrying out a first treatment on the surface of the When the number M of the maximum corrosion depth data is odd, M e Maximum corrosion depth data value for the intermediate position; when the data M is even, M e Average of 2 maximum corrosion depth data values at intermediate positions;
median value M of maximum corrosion depth of local corrosion according to different overhaul periods t e Calculating the median value M of the maximum corrosion depth of the local corrosion of the radiator e Kinetic equation as a function of service period t:
M e ＝f(t) (3)。
s5, determining the leakage rate of the radiator corresponding to the time point of a certain maintenance period by searching a probability fitting function, wherein the probability fitting function is used for representing the change trend of the local corrosion maximum corrosion depth data overrun probability and the local corrosion maximum corrosion depth data.
(1) Calculating the maximum corrosion depth median value M of a certain overhaul period t in the future according to the formula (3) et ；
(2) Calculating the median M of the maximum corrosion depth of the radiator with the longest overhaul period in the leak rate analysis sample ej And M is as follows et Is not equal to the difference Δd:
Δd＝M et -M ej
(3) Analyzing each maximum corrosion depth data array d of the radiator with the longest overhaul period in the sample piece according to the leakage rate max 1 、d max 2 、……、d max i 、……、d max M According to the statistical analysis method, calculating to obtain corresponding each accumulated probability F (y i ) Calculating the maximum corrosion depth exceeding d max i Probability lambda of (2) ti ：
λ ti ＝1-F(y i ) (4)
From equation (4) a series lambda is obtained t1 、λ t2 、……、λ ti 、……、λ tM ；
(4) Analyzing radiator maximum corrosion depth data d with longest overhaul period in sample piece at leakage rate max On the abscissa, with corresponding probability lambda t Drawing lambda-d for ordinate max Data graph A, and for λ -d max Fitting the data to obtain a regression curve Q;
other samples may also be selected for steps (2), (3) and (4).
(5) On the regression curve Q of the data graph A, searching the ordinate value corresponding to the maximum corrosion depth delta-delta d of the abscissa, wherein the value is the radiator leakage probability lambda corresponding to a future overhaul period t t As shown in fig. 7.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (5)
1. A method for predicting a leakage rate of a radiator of a rolling stock, the method comprising the steps of:
s1, acquiring sample data corresponding to analysis of leakage rate of a locomotive radiator, wherein the sample data is used for representing surface corrosion state data corresponding to the condition that the locomotive radiator is marked as a radiator surface corrosion sampling point under different maintenance period conditions, and the surface corrosion state data at least comprises local corrosion maximum corrosion depth data;
s2, obtaining prediction data of the local corrosion maximum corrosion depth data and creating a prediction function, wherein the prediction function is used for representing the change trend of the local corrosion maximum corrosion depth data corresponding to the time change;
the prediction function building method comprises the following steps:
s21, predicting the maximum corrosion depth of the radiator local corrosion:
(1) Maximum corrosion depth d of radiator leakage rate analysis sample blocks of detected rolling stock in same overhaul period t max In order from small to large, and the data sequence numbers are sequentially recorded as 1, 2, & gt, i & gt, M, recording a corresponding maximum corrosion depth data sequence d max1 、d max2 、......、d maxi 、...、d maxM ；
(2) Extreme value type I distribution formula F (y) according to statistical analysis method i ) =i/(m+1) and y i ＝-ln[-ln(F(y i ))]Respectively calculating the cumulative probability F (y i ) Hidden function y i ；
(3) To detect a set of maximum local corrosion depths d max In abscissa, with a corresponding set of hidden functions y calculated i Plotted as ordinate, y i -d max A data map;
(4) For y i -d max Fitting the data curves to obtain a regression equation:
y＝kx+b (1)
(5) Calculating a statistical variable alpha, alpha=1/k according to the extremum type I distribution; calculating a statistical parameter lambda, lambda= -alpha b;
(6) Calculating regression period T according to reliability data collection and analysis principle and specific template collection rule M The T is M The ratio of the sum of the upper surface area and the lower surface area of the corrosion analysis sample plate to the surface area involved in the maximum corrosion depth sampling of the sampling area of the corrosion analysis sample block;
(7) Calculating the hidden function y, y= -ln [ -ln (1-1/T) M )]；
(8) Calculating a maximum corrosion depth predicted value x, x=αy+λ;
(9) Repeating the steps (1) - (8), and calculating a predicted value x of the maximum corrosion depth of the radiator in each overhaul period t;
s22, summarizing the dynamics rule of the maximum corrosion depth of the local corrosion of the radiator to obtain the change trend of the maximum corrosion depth data of the local corrosion corresponding to the time change, wherein the method comprises the following steps:
(1) Taking the maintenance period t as an abscissa and taking the maximum corrosion depth predicted value x as an ordinate, and making an x-t relation curve;
(2) Data fitting is carried out on the x-t relation curve, and a kinetic equation of the maximum corrosion depth x of the radiator along with the change of the overhauling period t is obtained:
x＝f(t) (2)；
s3, obtaining the total service time when the predicted data of the local corrosion maximum corrosion depth data reach the corrosion damage tolerance;
s4, acquiring median data of the local corrosion maximum corrosion depth data and creating a median computing function, wherein the median computing function is used for representing a change trend of the median data of the local corrosion maximum corrosion depth data corresponding to time change;
s5, determining the leakage rate of the radiator corresponding to the time point of a certain maintenance period by searching a probability fitting function, wherein the probability fitting function is used for representing the change trend of the local corrosion maximum corrosion depth data overrun probability and the local corrosion maximum corrosion depth data.
2. The method for predicting a leakage rate of a radiator of a rolling stock according to claim 1, wherein the sample data acquisition method is as follows:
s11, selecting a sample
Firstly, selecting a radiator which is disassembled in a maintenance period t of at least 3 different time periods of a rolling stock as a leakage rate analysis sample;
secondly, dividing a radiator serving as an analysis sample into N areas along the length direction, determining the total number of radiator liquid channels as N, taking the radiator liquid channels as units, combining the N areas, and determining and manufacturing a radiator leakage rate analysis sample;
checking the corrosion state of the macroscopic surface of each leakage rate analysis template, primarily judging the corrosion maximum area of the analysis template, then detecting the corrosion state of the microscopic surface of each leakage rate analysis template, and determining and marking the local area with the most serious corrosion of each leakage rate analysis template;
intercepting the marked area according to the same size as a radiator microscopic corrosion analysis sample block blank, marking each intercepted sample block blank, trimming the blank, removing redundant fins and impurities, placing the blank into an injection mold, pouring substances which are favorable for grinding observation and measurement, and standing until the substances are completely solidified to form a radiator leakage rate analysis sample block;
s12, acquiring sample data
The maximum corrosion depth of the local corrosion of each analysis sample block is detected by the following method:
(1) Grinding sections of the radiator leakage rate analysis sample blocks perpendicular to the air flow direction for a plurality of times by using a polishing machine;
(2) After each grinding to a specified size, observing the sample block by a microscope to grind the surface;
(3) Measuring the maximum corrosion depth of the local corrosion of the surface and marking d maxi 。
3. The method for predicting leakage rate of radiator of rolling stock according to claim 1, wherein the total service time t when the maximum corrosion depth reaches the corrosion damage tolerance δ is predicted by the formula (2) δ The corrosion damage tolerance delta is the maximum allowable reduced thickness of the radiator diaphragm as determined by the strength simulation analysis method.
4. The method for predicting the leakage rate of a radiator of a rolling stock according to claim 1, wherein the method for obtaining the median function of the local corrosion maximum corrosion depth data is as follows:
s31, determining each maintenance period by the data sequence of the maximum corrosion depth of the local corrosion of the different maintenance periods tMedian M of maximum etch depth data for t e ；
The median value M e The acquisition method comprises the following steps: when the number M of the maximum corrosion depth data is odd, the median value M of the maximum corrosion depth e Maximum corrosion depth data value for the intermediate position; when the data number M is even, the median value M of the maximum corrosion depth e Average of 2 maximum corrosion depth data values at intermediate positions;
s32: median value M of maximum corrosion depth of local corrosion according to different overhaul periods t e Calculating the median value M of the maximum corrosion depth of the local corrosion of the radiator e Kinetic equation as a function of service period t:
M e ＝f(t) (3)。
5. the method for predicting the leakage rate of a radiator of a rolling stock according to claim 4, wherein the leakage rate determining method is as follows:
(1) Calculating the maximum corrosion depth median value M of a certain overhaul period t in the future according to the formula (3) et ；
(2) Calculating the median M of the maximum corrosion depth of the radiator with the longest overhaul period in the leak rate analysis sample ej And M is as follows et Is not equal to the difference Δd:
Δd＝M et -M ej
(3) Analyzing each maximum corrosion depth data array d of the radiator with the longest overhaul period in the sample piece according to the leakage rate max1 、d max2 、......、d maxi 、......、d maxM According to the statistical analysis method, calculating to obtain corresponding each accumulated probability F (y i ) Calculating the maximum corrosion depth exceeding d maxi Probability lambda of (2) ti ：
λ ti ＝1-F(y i ) (4)
From equation (4) a series lambda is obtained t1 、λ t2 、......、λ ti 、......、λ tM ；
(4) Analyzing maximum corrosion depth data d of radiator with longest overhaul period in sample piece at leakage rate max Is a horizontal sitting positionMarking, and drawing lambda-d by taking corresponding probability lambda t as ordinate max Data graph A, and for λ -d max Fitting the data to obtain a regression curve Q;
(5) On the regression curve Q of the data graph A, searching the ordinate value corresponding to the maximum corrosion depth delta-delta d of the abscissa, wherein the value is the radiator leakage probability lambda corresponding to a future overhaul period t t 。
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