CN113011638A - Locomotive radiator leakage rate prediction method - Google Patents
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Abstract
The invention discloses a method for predicting the leakage rate of a locomotive radiator, which can observe and detect the corrosion state of the radiator disassembled in different overhaul periods of a locomotive vehicle and master the macroscopic corrosion state and the microscopic corrosion state of the radiator; obtaining the maximum corrosion depth data of the local corrosion of the radiator; predicting the maximum corrosion depth and median of the radiator by using a probability statistical analysis method; and (3) 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. The method provides a basis for scientifically and reasonably formulating the maintenance strategy of the radiator 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 the leakage rate of a locomotive vehicle radiator.
Background
The function of the cooling system is to effectively transfer heat generated by the operation of important equipment (such as a traction system, a brake system and the like) of the rolling stock, and ensure that the equipment operates at a proper temperature. Therefore, the cooling system is an important guarantee for the safe, reliable and efficient operation of the locomotive vehicle equipment. The radiator is a core component of the cooling system, and once the radiator fails, the cooling system cannot continue to work, and the traction system or the brake system of the rolling stock cannot normally operate.
With the large-scale application of domestic electric locomotives and electric motor train units, the prolonging of the advanced repair cycle of key parts of the locomotives and the prolonging of the service life of the key parts of the locomotives have become key technical problems which are commonly concerned by all parties.
The research on the service life and the leakage rate of the radiator for the cooling system of the rolling stock at home and abroad is still in an exploration stage, and no mature evaluation method for the service life and the leakage rate exists. Research literature on radiator failure is few, and almost all focuses on leakage failure laws, leakage failure causes, and safeguards.
Disclosure of Invention
The invention provides a method for predicting the leakage rate of a locomotive radiator, which can observe and detect the corrosion state of the radiator disassembled in different overhaul periods of a locomotive vehicle and master the macroscopic corrosion state and the microscopic corrosion state of the radiator; obtaining the maximum corrosion depth data of the local corrosion of the radiator; predicting the maximum corrosion depth and median of the radiator by using a statistical analysis method; and (3) 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 purpose, the technical scheme of the invention is as follows:
a method of predicting a locomotive vehicle radiator leak rate, the method comprising the steps of:
s1, obtaining sample data corresponding to the analysis of the leakage rate of the locomotive vehicle radiator, wherein the sample data is used for representing surface corrosion state data corresponding to the locomotive vehicle radiator marked as a radiator surface corrosion sampling point under different overhaul 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 variation trend of the local corrosion maximum corrosion depth data corresponding to the time variation;
s3, acquiring the total service time when the predicted data of the local corrosion maximum corrosion depth data reaches the corrosion damage tolerance;
s4, obtaining median data of the maximum local corrosion depth data and creating a median calculation function, wherein the median calculation function is used for representing the variation trend of the median data of the maximum local corrosion depth data corresponding to the time variation in different overhaul periods;
s5, determining the leakage rate of the radiator corresponding to the time point of a certain overhaul period by searching a probability fitting function, wherein the probability fitting function is used for representing the variation trend of the overrun probability of the local corrosion maximum corrosion depth data and the change trend of the local corrosion maximum corrosion depth data.
Further, the sample data obtaining method includes:
s11, selecting a sample
Firstly, selecting a radiator disassembled in at least 3 overhaul periods t of different time periods of a locomotive vehicle as a leakage rate analysis sample piece;
secondly, dividing a radiator serving as an analysis sample piece into N regions along the length direction, determining the total number of liquid channels of the radiator as N, and determining and manufacturing a radiator leakage rate analysis sample plate by taking the liquid channels of the radiator as a unit and combining the N regions;
checking the corrosion state of the macroscopic surface of each leakage rate analysis sample plate, preliminarily judging the corrosion maximum area of the analysis sample plate, then detecting the corrosion state of the microscopic surface of each leakage rate analysis sample plate, and determining and marking the most serious local area of each leakage rate analysis sample plate;
cutting the marked area as a radiator microcosmic corrosion analysis sample block blank according to the same size, marking each cut sample block blank, trimming the blank, removing redundant fins and impurities, putting the blank into an injection mold, pouring a substance beneficial to grinding observation and measurement, and standing until the substance is completely solidified to form a radiator leakage rate analysis sample block;
s12, obtaining sample data
Detecting the maximum local corrosion depth of each analysis sample block by the following method:
(1) grinding the section of the radiator leakage rate analysis sample block perpendicular to the air flow direction for multiple times by using a polishing machine;
(2) after grinding to a specified size each time, observing the sample block by using a microscope to grind the surface;
(3) measuring the local maximum corrosion depth of the surface and marking dmax i。
Further, the method for establishing the prediction function is as follows:
s21, predicting the maximum corrosion depth of the local corrosion of the heat radiator:
(1) analyzing the maximum corrosion depth d of the detected radiator leakage rate of the rolling stock in the same overhaul period tmaxSorting the data sequence numbers from small to large, recording the data sequence numbers as 1, 2, … …, i, … … and M in sequence, and recording the corresponding maximum corrosion depth data sequence dmax 1、dmax 2、……、dmax i、……、dmax M；
(2) According to distribution formula F (y) of extreme value I type of statistical analysis methodi) i/(M +1) and yi＝ -ln[-ln(F(yi))]Respectively calculating the cumulative probability F (y) corresponding to each datai) And implicit function yi；
(3) To detect a set of maximum localized corrosion depths dmaxAs abscissa, with a corresponding set of implicit functions y calculatediFor ordinate, y is plottedi-dmaxA data graph;
(4) for yi-dmaxFitting a data curve to obtain a regression equation:
y＝kx+b (1)
(5) calculating a statistical variable alpha according to the extreme value I type distribution, wherein the alpha is 1/k; calculating a statistical parameter lambda, wherein lambda is-alpha b;
(6) calculating a regression period according to the reliability data collection and analysis principle and the concrete sample collection rule, wherein T isMThe ratio of the sum of the upper and lower surface areas of the corrosion analysis sample plate to the surface area related to the maximum corrosion depth sampling of the sampling area of the corrosion analysis sample block;
(7) computing an implicit function y, y ═ -ln [ -ln (1-1/T)M)]；
(8) Calculating a predicted value x of the maximum corrosion depth, wherein x is alpha y + lambda;
(9) repeating the steps (1) to (8), and calculating the predicted value x of the maximum corrosion depth of the radiator in each overhaul period t;
s22, summarizing the dynamics law of the maximum corrosion depth of the local corrosion of the radiator to obtain the variation trend of the maximum corrosion depth data of the local corrosion corresponding to the time variation, wherein the method comprises the following steps:
(1) taking the overhaul period t as an abscissa and the predicted value x of the maximum corrosion depth as an ordinate to make an x-t relation curve;
(2) and (3) performing data fitting on the x-t relation curve to obtain a kinetic equation of the maximum corrosion depth x of the radiator along with the change of the overhaul period t:
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)δThe corrosion damage tolerance delta is heat dissipation determined by a strength simulation analysis methodThe maximum allowable reduced thickness of the separator plate.
Further, the method for obtaining the median calculation function of the local maximum erosion depth data comprises the following steps:
s31, determining the median M of the maximum corrosion depth data of each overhaul period t from the local corrosion maximum corrosion depth data sequences of different overhaul periods te；
The median value MeThe acquisition method comprises the following steps: when the number M of the maximum corrosion depth data is odd, the median M of the maximum corrosion deptheThe maximum corrosion depth data value at the middle position; when the number M of data is even number, the median M of maximum corrosion deptheIs the average of the 2 maximum erosion depth data values at the intermediate position;
s32: median M of maximum depth of local corrosion according to different overhaul periods teCalculating the median M of the maximum corrosion depth of the local corrosion of the radiatoreKinetic equation varying with the overhaul period t:
Me＝f(t) (3)。
further, the leakage rate determination method is as follows:
(1) calculating the median M of the maximum corrosion depth of a future overhaul period t by the formula (3)et；
(2) Calculating the maximum corrosion depth median M of the radiator with the longest maintenance period in the leakage rate analysis sample pieceejAnd MetDifference Δ d of (d):
Δd＝Met-Mej
(3) analyzing each maximum corrosion depth data array d of the radiator with the longest maintenance period in the sample piece according to the leakage ratemax 1、dmax 2、……、dmax i、……、dmax MCalculating each corresponding cumulative probability F (y) according to a statistical analysis methodi) Calculating the maximum etch depth exceeding dmax iProbability of (a)ti：
λti＝1-F(yi) (4)
From equation (4), a sequence of numbers λ is obtainedt1、λt2、……、λti、……、λtM；
(4) Analyzing the maximum corrosion depth data d of the radiator with the longest maintenance period in the sample piece by using the leakage ratemaxAs abscissa, with corresponding probability λtPlotting lambda-d for the ordinatemaxData plot A, and for λ -dmaxFitting the data to obtain a regression curve Q; other leakage rate analysis samples can be selected to be used in the steps (2), (3) and (4).
(5) On a regression curve Q of a data graph A, searching a longitudinal coordinate value corresponding to the maximum corrosion depth delta-delta d of the abscissa, wherein the value is the corresponding leakage probability lambda of the radiator at a certain future overhaul period tt。
Has the advantages that: according to the method for predicting the leakage rate of the locomotive radiator, the leakage rate of the radiator in a certain future service time is predicted by utilizing the dynamics rules of the maximum corrosion depth and the median of the local corrosion of the radiator, and 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 in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting a leakage rate of a radiator of a rolling stock according to the present invention;
FIG. 2 is a schematic structural view of a radiator core of a rolling stock according to the present invention;
FIG. 3 is a schematic view of a sample block for microscopic corrosion analysis of a radiator of a rolling stock according to the present invention;
FIG. 4 is a view of a radiator y for a rolling stock according to the inventioni-dmaxFitting a curve graph to the data;
FIG. 5 is a graph of a fit of the x-t relationship of a locomotive vehicle radiator according to the present invention;
FIG. 6 is a schematic illustration of the corrosion damage tolerance of the locomotive vehicle radiator according to the present invention;
FIG. 7 shows the leakage rate λ of the radiator of the rolling stock of the present invention during a certain service time ttData of (1) graph a.
Wherein: 1. a liquid passage unit; 2. a liquid passage unit lower surface; 3. a liquid passage unit upper surface; 4. an air-side fin; n is a radical of1、N2、……、Ni… … and N are serial numbers of radiator liquid channels; n is1、n2、……、ni… …, n are the areas divided along the length direction of the liquid channel; A. the direction of air flow; B. the direction of the flow.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment provides a method for predicting the leakage rate of a locomotive radiator, as shown in the attached figure 1, and the method comprises the following steps:
s1, obtaining sample data corresponding to the analysis of the leakage rate of the locomotive vehicle radiator, wherein the sample data is used for representing surface corrosion state data corresponding to the locomotive vehicle radiator marked as a radiator surface corrosion sampling point under different overhaul period conditions, and the surface corrosion state data at least comprises local corrosion maximum corrosion depth data;
preferably, the sample data obtaining method includes:
firstly, selecting a sample;
selecting a radiator disassembled in at least 3 overhaul periods t of different time periods of the rolling stock as a leakage rate analysis sample piece;
for the radiator of the motor train unit: selecting a radiator disassembled from the motor train unit A3 repair (third-level repair, 120 kilometers or 3 years), A4 repair (fourth-level repair, 240 kilometers or 6 years) and A5 repair (fifth-level repair, 480 kilometers or 12 years) as a leakage rate analysis sample piece;
for locomotive radiators: the radiator disassembled from the locomotive C4 repair (fourth-class repair, 50 kilometers or 3 years), C5 repair (fifth-class repair, 100 kilometers or 6 years) and C6 repair (sixth-class repair, 200 kilometers or 12 years) is selected as a leakage rate analysis sample.
Referring to fig. 2 and 3, wherein B indicates the direction of the fluid flow, the method for determining the sample plate for analyzing the leakage rate of the heat sink is as follows:
(1) dividing radiators of the rolling stock in different overhaul periods into n regions along the length direction according to the same region division rule, wherein n is 1-20 generally;
(2) determining the total number of liquid channels of the radiator, and respectively recording each liquid channel and air side fins on the surface of the liquid channel as liquid channels 1, 2, … … and N 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) determining and manufacturing a radiator leakage rate analysis sample plate by taking a radiator liquid channel as a unit and combining N areas according to the principle that the upper part, the middle part and the lower part (or the left part, the middle part and the right part) are basically and uniformly distributed, and marking the sample plate as N-N;
preferably, liquid channel 1, ≈ N/4, ≈ N/2, ≈ 3/4N, N is selected; determining a radiator leakage rate analysis sample plate by combining n areas divided along the length direction of a radiator sample piece:
1-1、1-2、……、1-ni、……、1-n；
N/4-1、N/4-2、……、N/4-ni、……、N/4-n；
N/2-1、N/2-2、……、N/2-ni、……、N/2-n；
3/4N-1、3/4N-2、……、3/4N-ni、……、3/4N-n；
N-1、N-2、……、N-ni、……、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 corrosion state of the macroscopic surface (liquid channel plate or pipe wall surface) of each leak rate analysis template is visually inspected; then, the microscopic surface corrosion state of each sample is observed and detected by a microscope or other instruments, and the local area of each sample with the most serious corrosion is determined and marked.
Preferably, according to the same size (the length a is multiplied by the width b is not less than 10 multiplied by 10), a local area with the most serious corrosion of each leakage rate analysis sample plate in the radiator is intercepted and used as a radiator micro corrosion analysis sample block blank, and the intercepted blank of each sample block is marked; trimming the microscopic corrosion analysis sample block blank, removing redundant fins, putting the blank into an injection mold, pouring a substance beneficial to grinding, observation and measurement, standing until the substance is completely solidified to form a radiator leakage rate analysis sample block, wherein the transparent organic resin is used for pouring.
Secondly, sample data is obtained;
(1) grinding the section of the radiator leakage rate analysis sample block perpendicular to the air flow direction for multiple times by using a polishing machine, wherein the grinding section is perpendicular to the air flow direction; each grinding process is controlled to be 20 mu m-3 mm;
(2) after grinding to a specified size each time, observing the sample block by using a microscope to grind the surface;
(3) measuring the local maximum corrosion depth of the surface and marking dmax 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 variation trend of the local corrosion maximum corrosion depth data corresponding to the time variation;
preferably, the maximum corrosion depth of the local corrosion of the radiator in each overhaul period t is predicted by the following method:
(1) arranging the maximum corrosion depth data: analyzing the maximum corrosion depth d of the detected radiator leakage rate of the rolling stock in the same overhaul period tmaxPress from small to largeSorting, sequentially marking the data sequence numbers as 1, 2, … …, i, … … and M, and marking the corresponding maximum corrosion depth data sequence dmax 1、 dmax 2、……、dmax i、……、dmax M；
(2) According to distribution formula F (y) of extreme value I type of statistical analysis methodi) i/(M +1) and yi＝ -ln[-ln(F(yi))]Respectively calculating the cumulative probability F (y) corresponding to each datai) And implicit function yi；
(3) To detect a set of maximum localized corrosion depths dmaxAs abscissa, with a corresponding set of implicit functions y calculatediFor ordinate, y is plottedi-dmaxA data graph;
(4) for yi-dmaxFitting the data curves to obtain a regression equation, as shown in FIG. 4:
y＝kx+b (1)
(5) calculating a statistical variable alpha according to the extreme value I type distribution, wherein the alpha is 1/k; calculating a statistical parameter lambda, wherein lambda is-alpha b;
(6) calculating regression period (also called reproduction time) T according to reliability data collection and analysis principle and concrete sample collection ruleMWherein, TMThe ratio of the sum of the upper and lower surface areas of the corrosion analysis template to the surface area involved in the sampling of the maximum corrosion depth of the sample area of the corrosion analysis template.
(7) Computing an implicit function y, y ═ -ln [ -ln (1-1/T)M)]；
(8) Calculating a predicted value x of the maximum corrosion depth, wherein x is alpha y + lambda;
(9) and (4) repeating the steps (1) to (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 local corrosion depth of the heat sink is summarized to obtain the variation trend of the maximum local corrosion depth data corresponding to the time variation, and the method comprises the following steps:
(1) taking the overhaul period t as an abscissa and the predicted value x of the maximum corrosion depth as an ordinate to make an x-t relation curve;
(2) and (3) performing data fitting on the x-t relation curve to obtain a kinetic equation of the maximum corrosion depth x of the radiator along with the change of the overhaul period t, wherein the kinetic equation is shown in the attached drawing 5:
x＝f(t) (2)。
s3, acquiring the total service time when the predicted data of the local corrosion maximum corrosion depth data reaches the corrosion damage tolerance;
preferably, the total service time t when the maximum corrosion depth reaches the corrosion damage tolerance delta is predicted by the formula (2)δ(ii) a The corrosion damage tolerance delta is the maximum allowable thinning thickness of the heat radiator spacer determined by a strength simulation analysis method, as shown in fig. 6.
S4, obtaining median data of the maximum local corrosion depth data and creating a median calculation function, wherein the median calculation function is used for representing the variation trend of the median data of the maximum local corrosion depth data corresponding to the time variation;
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 corrosion maximum corrosion depth data sequences of different overhaul periods te(ii) a When the number of the maximum corrosion depth data is M odd, MeThe maximum corrosion depth data value at the middle position; when the number of data M is even, MeIs the average of the 2 maximum erosion depth data values at the intermediate position;
median M of maximum depth of local corrosion according to different overhaul periods teCalculating the median M of the maximum corrosion depth of the local corrosion of the radiatoreKinetic equation varying with the overhaul period t:
Me＝f(t) (3)。
s5, determining the leakage rate of the radiator corresponding to the time point of a certain overhaul period by searching a probability fitting function, wherein the probability fitting function is used for representing the variation trend of the overrun probability of the local corrosion maximum corrosion depth data and the change trend of the local corrosion maximum corrosion depth data.
(1) Calculating the median M of the maximum corrosion depth of a future overhaul period t by the formula (3)et；
(2) Calculating the maximum corrosion depth median M of the radiator with the longest maintenance period in the leakage rate analysis sample pieceejAnd MetDifference Δ d of (d):
Δd＝Met-Mej
(3) analyzing each maximum corrosion depth data array d of the radiator with the longest maintenance period in the sample piece according to the leakage ratemax 1、dmax 2、……、dmax i、……、dmax MCalculating each corresponding cumulative probability F (y) according to a statistical analysis methodi) Calculating the maximum etch depth exceeding dmax iProbability of (a)ti：
λti＝1-F(yi) (4)
From equation (4), a sequence of numbers λ is obtainedt1、λt2、……、λti、……、λtM；
(4) Analyzing the maximum corrosion depth data d of the radiator with the longest maintenance period in the sample piece by using the leakage ratemaxAs abscissa, with corresponding probability λtPlotting lambda-d for the ordinatemaxData plot A, and for λ -dmaxFitting the data to obtain a regression curve Q;
other samples can be selected to be used in the steps (2), (3) and (4).
(5) On a regression curve Q of a data graph A, searching a longitudinal coordinate value corresponding to the maximum corrosion depth delta-delta d of the abscissa, wherein the value is the corresponding leakage probability lambda of the radiator at a certain future overhaul period ttAs shown in fig. 7.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A method for predicting a leakage rate of a locomotive radiator, the method comprising the steps of:
s1, obtaining sample data corresponding to the analysis of the leakage rate of the locomotive vehicle radiator, wherein the sample data is used for representing surface corrosion state data corresponding to the locomotive vehicle radiator marked as a radiator surface corrosion sampling point under different overhaul 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 variation trend of the local corrosion maximum corrosion depth data corresponding to the time variation;
s3, acquiring the total service time when the predicted data of the local corrosion maximum corrosion depth data reaches the corrosion damage tolerance;
s4, obtaining median data of the maximum local corrosion depth data and creating a median calculation function, wherein the median calculation function is used for representing the variation trend of the median data of the maximum local corrosion depth data corresponding to the time variation;
s5, determining the leakage rate of the radiator corresponding to the time point of a certain overhaul period by searching a probability fitting function, wherein the probability fitting function is used for representing the variation trend of the overrun probability of the local corrosion maximum corrosion depth data and the change trend of the local corrosion maximum corrosion depth data.
2. A method of predicting a leak rate of a locomotive radiator according to claim 1, wherein said sample data acquisition method comprises:
s11, selecting a sample
Firstly, selecting a radiator disassembled in at least 3 overhaul periods t of different time periods of a locomotive vehicle as a leakage rate analysis sample piece;
secondly, dividing a radiator serving as an analysis sample piece into N regions along the length direction, determining the total number of liquid channels of the radiator as N, and determining and manufacturing a radiator leakage rate analysis sample plate by taking the liquid channels of the radiator as a unit and combining the N regions;
checking the corrosion state of the macroscopic surface of each leakage rate analysis sample plate, preliminarily judging the corrosion maximum area of the analysis sample plate, then detecting the corrosion state of the microscopic surface of each leakage rate analysis sample plate, and determining and marking the most serious local area of each leakage rate analysis sample plate;
cutting the marked area as a radiator microcosmic corrosion analysis sample block blank according to the same size, marking each cut sample block blank, trimming the blank, removing redundant fins and impurities, putting the blank into an injection mold, pouring a substance beneficial to grinding observation and measurement, and standing until the substance is completely solidified to form a radiator leakage rate analysis sample block;
s12, obtaining sample data
Detecting the maximum local corrosion depth of each analysis sample block by the following method:
(1) grinding the section of the radiator leakage rate analysis sample block perpendicular to the air flow direction for multiple times by using a polishing machine;
(2) after grinding to a specified size each time, observing the sample block by using a microscope to grind the surface;
(3) measuring the local maximum corrosion depth of the surface and marking dmax i。
3. A method of predicting locomotive vehicle radiator leakage rate according to claim 2, wherein said prediction function is established by:
s21, predicting the maximum corrosion depth of the local corrosion of the heat radiator:
(1) analyzing the maximum corrosion depth d of the detected radiator leakage rate of the rolling stock in the same overhaul period tmaxSorting the data sequence numbers from small to large, recording the data sequence numbers as 1, 2, … …, i, … … and M in sequence, and recording the corresponding maximum corrosion depth data sequence dmax 1、dmax 2、……、dmax i、……、dmax M；
(2) Push buttonExtreme value I type distribution formula F (y) of statistical analysis methodi) i/(M +1) and yi＝-ln[-ln(F(yi))]Respectively calculating the cumulative probability F (y) corresponding to each datai) And implicit function yi；
(3) To detect a set of maximum localized corrosion depths dmaxAs abscissa, with a corresponding set of implicit functions y calculatediFor ordinate, y is plottedi-dmaxA data graph;
(4) for yi-dmaxFitting a data curve to obtain a regression equation:
y＝kx+b (1)
(5) calculating a statistical variable alpha according to the extreme value I type distribution, wherein alpha is 1/k; calculating a statistical parameter lambda, wherein lambda is-alpha b;
(6) calculating regression period T according to reliability data collection and analysis principle and concrete sample collection ruleMSaid T isMThe ratio of the sum of the upper and lower surface areas of the corrosion analysis sample plate to the surface area related to the maximum corrosion depth sampling of the sampling area of the corrosion analysis sample block;
(7) computing an implicit function y, y ═ -ln [ -ln (1-1/T)M)]；
(8) Calculating a predicted value x of the maximum corrosion depth, wherein x is alpha y + lambda;
(9) repeating the steps (1) to (8), and calculating the predicted value x of the maximum corrosion depth of the radiator in each overhaul period t;
s22, summarizing the dynamics law of the maximum corrosion depth of the local corrosion of the radiator to obtain the variation trend of the maximum corrosion depth data of the local corrosion corresponding to the time variation, wherein the method comprises the following steps:
(1) taking the overhaul period t as an abscissa and the maximum corrosion depth predicted value x as an ordinate to make an x-t relation curve;
(2) and (3) performing data fitting on the x-t relation curve to obtain a kinetic equation of the maximum corrosion depth x of the radiator along with the change of the overhaul period t:
x＝f(t) (2)。
4. method for predicting leakage rate of locomotive radiator according to claim 3Characterized in that the total service time t when the maximum corrosion depth reaches the corrosion damage tolerance delta is predicted by the formula (2)δThe corrosion damage tolerance delta is the maximum allowable thinning thickness of the heat radiator spacer determined by a strength simulation analysis method.
5. A method of predicting a leak rate from a locomotive radiator as set forth in claim 3, wherein said median calculated function of said localized corrosion maximum erosion depth data is obtained by:
s31, determining the median M of the maximum corrosion depth data of each overhaul period t from the local corrosion maximum corrosion depth data sequences of different overhaul periods te；
The median value MeThe acquisition method comprises the following steps: when the number M of the maximum corrosion depth data is odd, the median M of the maximum corrosion deptheThe maximum corrosion depth data value at the middle position; when the number M of data is even number, the median M of maximum corrosion deptheIs the average of the 2 maximum erosion depth data values at the intermediate position;
s32: median M of maximum depth of local corrosion according to different overhaul periods teCalculating the median M of the maximum corrosion depth of the local corrosion of the radiatoreKinetic equation varying with the overhaul period t:
Me＝f(t) (3)。
6. a method of predicting a leak rate in a locomotive vehicle radiator as set forth in claim 5, wherein said leak rate determination method comprises:
(1) calculating the median M of the maximum corrosion depth of a future overhaul period t by the formula (3)et；
(2) Calculating the maximum corrosion depth median M of the radiator with the longest maintenance period in the leakage rate analysis sample pieceejAnd MetDifference Δ d of (d):
Δd＝Met-Mej
(3) analyzing each maximum of radiators with longest maintenance period in sample pieces according to leakage rateEtch depth data array dmax 1、dmax 2、……、dmax i、……、dmax MCalculating each corresponding cumulative probability F (y) according to a statistical analysis methodi) Calculating the maximum etch depth exceeding dmaxiProbability of (a)ti：
λti＝1-F(yi) (4)
From equation (4), a sequence of numbers λ is obtainedt1、λt2、……、λti、……、λtM；
(4) Analyzing the maximum corrosion depth data d of the radiator with the longest repair period in the sample piece by using the leakage ratemaxAs abscissa, with corresponding probability λtPlotting lambda-d for the ordinatemaxData plot A, and for λ -dmaxFitting the data to obtain a regression curve Q;
(5) on a regression curve Q of a data graph A, searching a longitudinal coordinate value corresponding to the maximum corrosion depth delta-delta d of the abscissa, wherein the value is the corresponding leakage probability lambda of the radiator at a certain future overhaul period tt。
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