CN113626970B - Method and system for evaluating corrosion residual life of public pipe gallery pipeline - Google Patents
Method and system for evaluating corrosion residual life of public pipe gallery pipeline Download PDFInfo
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Abstract
The invention discloses a method for evaluating the corrosion residual life of a public pipe gallery pipeline, which comprises the following steps: dividing a detected pipeline into a plurality of pipeline sections, sequencing the maximum value of corrosion depth values of all the pipeline sections, obtaining a sample sequence of the corrosion depth values of the detected pipeline, and obtaining the cumulative probability of any corrosion depth value; adopting a generalized extremum distribution model, carrying out parameter estimation, and selecting a corrosion data distribution model of the detected pipeline according to the determined parameters; predicting the limit corrosion depth of the detected pipeline under different reliability by using the determined corrosion data distribution model; and determining the corrosion residual life of the detected pipeline under different reliability according to the ultimate corrosion depth of the detected pipeline under different reliability. The invention also discloses a system for evaluating the corrosion residual life of the public pipe gallery pipeline, which comprises the following steps: the system comprises a data acquisition module, a state analysis module and a state prediction module. The method can effectively predict the corrosion depth of the pipe gallery pipeline and evaluate the residual life of the pipe gallery pipeline.
Description
Technical Field
The invention relates to the technical field of pipeline safety evaluation, in particular to a method and a system for evaluating corrosion residual life of a public pipe gallery pipeline.
Background
The piping lane pipeline distribution is intensive, and the condition is complicated, and most pipeline is responsible for transporting high-risk or medium-risk medium, in case take place the pipeline inefficacy, not only can influence the garden normal operating, still easily lead to conflagration, explosion or poisoning accident, causes the domino effect even, constitutes serious threat to the life and property safety of masses around. One of the important causes of failure of the common piping lane is corrosion, which reduces the pressure bearing capacity of the piping lane after corrosion, possibly causing leakage of the piping, and finally causing serious production accidents. Therefore, how to accurately evaluate the corrosion rate of a pipe rack pipe, predict the corrosion depth of the pipe, and evaluate the corrosion residual life of the pipe has become a major problem to be solved in the current pipe rack pipe safety evaluation.
The prediction of the corrosion depth of the pipeline is mainly performed by carrying out statistical analysis on the detection data, and the corrosion depth of the pipeline material is predicted by adopting a reasonable model. The existing researches are mostly conducted on corrosion conditions of materials under different conditions by adopting a certain data distribution model. The unified data distribution model cannot simultaneously fit various conditions of different detection data corresponding to different pipelines, and the pipeline corrosion depth prediction method in the prior art is obviously unsuitable in consideration of the specificity and complexity of the pipeline gallery. Moreover, the current research on corrosion conditions of the pipe gallery pipeline is insufficient, and a complete and reliable corrosion depth prediction method and an evaluation method for the residual life of the pipe gallery pipeline are lacked.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the corrosion residual life of a public pipe gallery pipeline, which can effectively predict the corrosion depth of the pipe gallery pipeline and evaluate the residual life of the pipe gallery pipeline.
The technical scheme for achieving the purpose is as follows:
a method of evaluating the remaining life of a utility tunnel pipe corrosion comprising:
and (3) corrosion data processing and selection: dividing a detected pipeline into a plurality of pipeline sections, sequencing the maximum value of corrosion depth values of all the pipeline sections, obtaining a sample sequence of the corrosion depth values of the detected pipeline, and obtaining the cumulative probability of any corrosion depth value;
selecting a corrosion data distribution model: adopting a generalized extremum distribution model and carrying out parameter estimation, and selecting a corrosion data distribution model of the detected pipeline as a gummy distribution model, a Frechet distribution model or a Weibull distribution model according to the determined parameters;
predicting corrosion depth: predicting the limit corrosion depth of the detected pipeline under different reliability by using the determined corrosion data distribution model;
predicting corrosion residual life: and determining the corrosion residual life of the detected pipeline under different reliability according to the ultimate corrosion depth of the detected pipeline under different reliability.
Preferably, in the corrosion data processing and selection,
the corrosion depth values of each pipe section are sequenced from small to large, and the maximum value of the corrosion depth values of the pipe section is obtained according to the sequencing result;
sequencing the maximum values of the corrosion depth values of all the pipe sections in order from small to large to form a sample sequence of the corrosion depth values of the detected pipeline:
D＝{x 1 ,x 2 ,x 3 ,…x i ,…,x m }；
wherein x is i Representing the ith corrosion depth value of the detected pipeline; m represents m pipe sections;
for the ith corrosion depth value, the cumulative probability is:
all (x) of the obtained (x i ,F(x i ) Data points are mapped, and curves are drawn to obtain an empirical distribution diagram of the corrosion depth of the detected pipeline.
Preferably, in the selective corrosion data distribution model,
the generalized extremum distribution model has the expression:
wherein alpha is a position parameter, beta is a scale parameter, k is a generalized extremum model distribution parameter, and x represents a random variable;
when the value of the k parameter is within a range value near the preset 0, the corrosion data distribution of the detected pipeline accords with the gumme distribution, and the cumulative probability function is as follows:
wherein alpha is a position parameter, and beta is a scale parameter;
when the value of the k parameter is larger than 0 and is out of the range value near the preset 0, the corrosion data distribution of the detected pipeline accords with the Frechet distribution, and the cumulative probability function is as follows:
wherein alpha is a position parameter, beta is a scale parameter, and gamma is a shape parameter; x represents the maximum value of the random variable;
when the value of the k parameter is smaller than 0 and is out of the preset range value near 0, the corrosion data distribution of the detected pipeline accords with Weibull distribution, and the cumulative probability function is as follows:
wherein alpha is a position parameter, beta is a scale parameter, and gamma is a shape parameter; x represents the maximum value of the random variable;
the parameter k is obtained by fitting a curve described by an expression of the generalized extremum distribution model.
Preferably, after the corrosion data distribution model is selected, suitability of the selected corrosion data distribution model is determined by a goodness-of-fit test.
Preferably, the method for checking the goodness of fit comprises a K-S checking method, a mapping method, a regression method, an X2 checking method and an A-D checking method.
Preferably, in the selective corrosion data distribution model,
the parameter estimation adopts a probability weight moment method:
wherein omega r Representing probability weight moments; f represents the cumulative probability; r represents a real number; dF represents a variable at integration;
the method for determining the position parameter alpha and the scale parameter beta is obtained, and the expressions of the two parameters are as follows:
wherein Γ represents a Gamma function; b 0 ,b 1 Indicating an unbiased estimate.
Preferably, in said predicted corrosion residual life,
the minimum allowable remaining thickness of the pipe is calculated as follows:
ΔA＝A-A min
wherein A is min Is the minimum allowable residual thickness; p is the pipeline operating pressure; d is the outer diameter of the pipeline; SYMS is the pipeline steel yield strength; a is the wall thickness of the pipeline; Δa is the critical corrosion depth of the pipeline;
and determining corrosion progress curves under different reliabilities by combining corrosion depth values under different reliabilities, wherein the corrosion progress curves are as follows:
x＝c·t n
wherein x represents a pipeline corrosion depth value; c represents a coefficient; t represents the service time of the pipeline structure to start to break; n is a time constant, and 0.3-0.5 is taken;
and calculating to obtain the residual service life of the pipeline under different reliability.
The utility tunnel pipe corrosion remaining life assessment system of the present invention comprises: a data acquisition module, a state analysis module and a state prediction module,
the data acquisition module acquires pipe wall corrosion thinning data of different positions of the target pipe section, counts and sorts the corrosion data, and transmits the corrosion data to the state analysis module;
the state analysis module adopts a generalized extremum distribution model to classify and sort analysis results of the pipeline corrosion condition according to different pipe sections of the detected target pipeline;
and the state prediction module predicts the state of the pipe gallery pipeline according to the result obtained by the state analysis module, predicts the development condition of pipeline corrosion and obtains the residual service life of the pipeline under different reliability.
The beneficial effects of the invention are as follows: according to the invention, aiming at the specificity and complexity of the pipe gallery pipeline, the corrosion data distribution model is processed and selected, so that the limit corrosion depth of the detected pipeline under different reliability is predicted by utilizing the coincidence model of different detection data of different pipelines, and the corrosion residual life of the detected pipeline under different reliability is completely and reliably determined. And selecting the maximum value of the corrosion depth values, and sequencing to obtain a sample sequence, so that the accuracy of the subsequent results is guaranteed. And determining the most suitable corrosion data distribution model of the detected pipeline by using the generalized extremum distribution model, and improving the accuracy and efficiency of the final result. And determining the fitting applicability of the selected corrosion data distribution model to the detected pipeline corrosion data through fitting goodness test, and ensuring the selection of the optimal corrosion data distribution model. Therefore, the method for accurately evaluating the running state of the pipeline and predicting the residual life of the pipeline is provided for the pipeline in the complex condition, the safe running capacity of the pipeline in the pipeline is improved, and technical support is provided for the safe operation of a chemical industry park.
Drawings
FIG. 1 is a flow chart of a method of evaluating the remaining life of a common piping lane pipe corrosion of the present invention;
FIG. 2 is a system for evaluating the corrosion residual life of a common piping lane pipeline of the present invention;
FIG. 3 is a schematic diagram of a piping lane 1 of the present invention;
FIG. 4 is a schematic diagram of a piping lane 2 of the present invention;
FIG. 5 is a schematic diagram of a piping lane 3 of the present invention;
FIG. 6 is a graph of a fit of corrosion data for piping lane 1 in accordance with the present invention;
FIG. 7 is a graph of a fit of corrosion data for piping lane 2 in accordance with the present invention;
FIG. 8 is a graph of a fit of corrosion data for piping lane 3 in accordance with the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, the method for evaluating the corrosion residual life of the common pipe rack pipeline of the invention comprises the following steps:
and S01, processing and selecting corrosion data.
Firstly, determining the whole length of a detected pipeline, and carrying out sectional treatment on the detected pipeline according to the specific condition of a detected pipeline line. The segmentation process needs to be combined with actual conditions, the segmentation number of the pipeline is reasonably determined, and the segmentation number is not too high or too low.
And then, after the detected pipeline is subjected to average segmentation, carrying out statistical treatment on the corrosion depth detected by each pipe segment of the detected pipeline. Specifically, the corrosion depth values of each pipe section are ranked in order from small to large, and the maximum corrosion depth value of the pipe section is obtained according to the ranking result. And obtaining the maximum value of the corrosion depth value of each pipe section of the detected pipeline after the statistics of the data.
Then, the maximum value of the corrosion depth values of all the pipe sections of the detected pipeline is ranked. Specifically, the sorting method adopted is also to sort the data in order from small to large. By sequencing, a sample sequence for obtaining the corrosion depth value of the detected pipeline is formed:
D＝{x 1 ,x 2 ,x 3 ,…x i ,…,x m } (1)
in the sample sequence:
x 1 ≤x 2 ≤x 3 ≤…≤x i ≤…≤x m (2)
wherein x is i Representing the ith corrosion depth value of the detected pipeline, namely the maximum value of the corrosion depth value of the ith large pipeline section; m represents m pipe sections.
And then, according to the sample sequence of the detected pipeline corrosion depth values obtained by sequencing, the cumulative probability of any measured value can be obtained. Specifically, for the i-th corrosion depth value, the cumulative probability thereof is:
applying formula (3) to the maximum value of corrosion depth values of each pipe section of the detected pipeline to calculate and obtain any corrosion depth value x i Corresponding cumulative probability F (x i )。
Finally, for all (x i ,F(x i ) Data points are mapped, and curves are drawn to obtain an empirical distribution diagram of the corrosion depth of the detected pipeline. The curve obtained here was used for fitting in the following steps.
And S02, selecting a corrosion data distribution model.
The generalized extremum distribution model is adopted for analysis of corrosion data distribution, and specifically, the expression of the model is as follows:
in the model, three parameters are involved, alpha is a position parameter, beta is a scale parameter, and k is a generalized extremum model distribution parameter. x represents a random variable. The generalized extremum distribution model integrates a gummy distribution model, a Frechet distribution model and a Weibull distribution model, and a specific selected model is determined through a parameter k. The following are provided:
1) When the value of the k parameter in the generalized extremum distribution model is close to 0 (within a range of values around 0 preset), the corrosion data distribution of the detected pipeline accords with the gumme distribution. The gummy distribution, i.e. the maximum distribution of the first asymptotic distribution or the bi-exponential distribution, analyzes the maximum of localized corrosion. The cumulative probability function is:
in the model, α is a position parameter, and β is a scale parameter.
2) When the value of the k parameter in the generalized extremum distribution model is larger than 0 and larger (outside the range of values around the preset 0), then the corrosion data distribution of the detected pipeline conforms to the Frechet distribution. Frechet distribution with cumulative probability function:
in the model, α is a position parameter, β is a scale parameter, and γ is a shape parameter. X represents the maximum value of the random variable.
3) When the value of the k parameter in the generalized extremum distribution model is smaller than 0 and smaller (outside the range value around the preset 0), the corrosion data distribution of the detected pipeline accords with Weibull distribution. Weibull distribution, whose cumulative probability function is:
in the model, specifically, α is a position parameter, β is a scale parameter, and γ is a shape parameter.
In this embodiment, the parameter k is obtained by fitting a generalized extremum model to corrosion data obtained from the pipe being inspected. Namely: fitting the curve obtained in step S01 by using equation (4) can obtain the parameter k in equation (4), so as to determine which distribution model is most suitable to select. After the k parameter is determined, the specific model distribution which is consistent with the detected pipeline corrosion data can be determined.
And S03, estimating parameters of the distribution model.
When estimating parameters involved in the corrosion data distribution model, a probability weight moment method is adopted, and specifically, the method comprises the following steps:
wherein omega r Representing probability weight moments; f represents the cumulative probability; r represents a real number; dF represents a variable at integration;
the method for determining the position parameter alpha and the scale parameter beta based on the probability weight moment method comprises the following expression of the two parameters:
wherein Γ represents a Gamma function; b 0 ,b 1 Indicating an unbiased estimate.
And S04, checking the goodness of fit. And processing and selecting corrosion data of the detected pipeline, and determining fitting applicability of the selected distribution model to the corrosion data of the detected pipeline after the corrosion data distribution model is selected. In particular, the method comprises the steps of,
the suitability of the selected corrosion data distribution model may be determined by a goodness-of-fit test. And determining whether the selected distribution model meets the requirements or not according to the result of the goodness-of-fit test, and if not, selecting other distribution models. For the fitting goodness test, a proper method is required to be selected, and the fitting goodness test method in the application comprises a K-S test method, a mapping method, a regression method, an X2 test, an A-D test and the like. For the corrosion data of the specific detected pipeline, one or more fitting goodness test methods are adopted for testing. Wherein:
the K-S assay refers to: and comparing the data needing to be subjected to statistical analysis with another group of standard data with certain distribution, and obtaining the deviation between the data and the standard data. Typically in the K-S test, the cumulative distribution function of two sets of observed data is calculated first, and then the maximum of the absolute values of the differences between the corresponding values of the two cumulative distribution functions is calculated. Finally, whether the maximum value falls within the confidence interval corresponding to the requirement is determined through table lookup. If the value is within the corresponding confidence interval, it is stated that the detected data satisfies the condition, and vice versa.
The mapping method refers to: the fitting degree of the empirical distribution and the generalized extremum distribution, gumbel distribution and the Weibull distribution of the two parameters can be intuitively compared in the graph, the distribution form with the best fitting degree with the empirical distribution can be compared through the cumulative probability distribution graph, the best fitting degree of the distribution and the empirical distribution can be further verified through the P-P scatter diagram, and the judgment standard of the P-P scatter diagram is that the fitting degree of the distribution and the empirical distribution can be considered to be better when the data point distribution is near the straight line y=x.
And S05, predicting the corrosion depth.
After steps S01 and S02 are performed on the detected pipeline corrosion data, a corrosion data distribution model is determined. After step S03 is performed on the detected pipeline corrosion data, model parameters related to the corrosion data distribution model are determined. After S04 is performed on the detected pipe corrosion data, the suitability of the selected distribution model is determined. Based on the results of steps S01-S04, the determined corrosion data distribution model is applied to predict the extreme corrosion depth of the pipeline at different reliabilities.
And S06, predicting the corrosion residual life. After steps S01 to S05 are performed on the detected pipeline corrosion data, the corrosion residual life of the pipeline under different reliability can be determined according to the determined ultimate corrosion depth of the pipeline under different reliability.
Specifically, the minimum allowable remaining thickness of the pipe is calculated first as follows:
ΔA＝A-A min (12)
wherein A is min Is the minimum allowable residual thickness, mm; p is pipeline operating pressure, MPa; d is the outer diameter of the pipeline, and mm; SYMS is the yield strength of pipeline steel and MPa; a is the wall thickness of the pipeline, and mm; ΔA is the critical corrosion depth of the pipe, mm.
And (3) after the minimum allowable residual thickness of the pipeline is calculated, combining corrosion depth values under different reliability, and determining a corrosion progress curve under different reliability, wherein the corrosion progress curve is as follows:
x＝c·t n (13)
wherein, the corrosion depth value of the x-pipeline is mm; c-coefficient; the service time of the t-pipeline structure to be damaged is the year. n is a time constant and is determined by factors such as corrosion system, and is generally 0.3-0.5.
After the parameters in the corrosion progress curves under different reliability are calculated, the corrosion residual life of the pipeline, namely the application time for starting to damage the pipeline structure, is further determined. After steps S01-S06 are carried out on the detected pipeline corrosion data, the residual service life of the pipeline under different reliability can be calculated.
Referring to fig. 2, the system for evaluating the corrosion residual life of a common piping lane pipeline of the present invention comprises: a data acquisition module 1, a state analysis module 2 and a state prediction module 3.
And detecting the target pipe section by adopting a public pipe gallery pipeline nondestructive detection technology, determining the corrosion condition of the pipeline, and obtaining various data. The data acquisition module 1 acquires pipe wall corrosion thinning data of different positions of the target pipe section, counts and sorts the corrosion data, and transmits the corrosion data to the state analysis module 2.
The state analysis module 2 adopts a generalized extremum distribution model to classify and sort analysis results of the pipeline corrosion condition according to different pipe sections of the detected target pipeline.
And the state prediction module 3 predicts the state of the pipeline in the pipeline corridor according to the result obtained by the state analysis module 2, predicts the development condition of pipeline corrosion and obtains the residual service life of the pipeline under different reliability.
In order to improve the application efficiency of the evaluation system, the data acquisition module 1, the state analysis module 2 and the state prediction module 3 are performed in a programmed mode.
The following describes specific steps and practical effects of the present application in detail with reference to a specific embodiment.
And (3) processing and selecting pipe gallery pipeline corrosion data: the pipe lane pipe numbers for the analysis were pipe 1,2,3.
The basic parameters of the piping lane 1 are: the design pressure is 2.1MPa, the design temperature is 65 ℃, the working pressure is 0.8MPa, the working temperature is 12 ℃, the conveying medium is styrene, the pipe material is 20# steel, the pipe length is 1447.5m, the DN150x7.1, the corrosion margin is 2.6mm, and the trend diagram of the pipe gallery pipeline 1 is shown in figure 3.
The basic parameters of the piping lane 2 are: length is 1100m, DN130x6.9, 20# steel, corrosion margin 2.5mm. The design pressure is 2.2MPa, the working pressure is 0.9-2.0MPa, the working temperature is 15 ℃, the design temperature is 70 ℃, the conveying medium is hydrogen, the actual service time is 4.5 years, and the trend chart of the pipe gallery pipeline 2 is shown in figure 4.
The basic parameters of the piping lane 3 are: and (3) conveying sewage in a chemical industry park, wherein the design pressure is 6.0MPa, the design temperature is 80 ℃, the pipe length is 5496.6m, the actual service life is 10.3 years, and the trend chart of the pipe gallery and the pipeline 3 is shown in figure 5.
The statistical summary of the corrosion depths of piping lane lines 1,2,3 are shown in tables 1,2,3.
| Local corrosion depth interval range | Frequency number |
| 1-2.5 | 16 |
| 2.6-2 | 8 |
| 4.1-4.5 | 3 |
| 4.6-5.0 | 1 |
| 5.1-5.5 | 4 |
| >5.5 | 1 |
Table 1 summary table of corrosion depth statistics for piping lane 1
| Local corrosion depth interval range | Frequency number |
| 0.5-1 | 6 |
| 2.1-2.5 | 17 |
| 2.6-2 | 5 |
| >2 | 5 |
Table 2 table of corrosion depth statistics summary table for piping lane piping 2
| Local corrosion depth interval range | Frequency number |
| 0.5-1 | 7 |
| 2.1-2.5 | 26 |
TABLE 3 statistical summary of corrosion depths for piping lane piping 3
And determining a pipe gallery pipeline corrosion data distribution model:
and (3) carrying out distribution model and parameter determination on the statistical results of the corrosion depths of the pipeline 1,2 and 3, and firstly judging whether the local corrosion depth data of the pipeline 1 obeys Gumbel distribution.
Carrying out 2 logarithms on two ends of the formula (5) simultaneously to obtain:
with the local corrosion depth as the abscissa,on the ordinate, each data point is plotted in a rectangular coordinate system, as shown in fig. 6,7 and 8.
For piping lane 1, as shown in FIG. 6, the linear fitting degree R 2 At 0.989, about 1, the pipe gallery pipe 1 corrosion depth probability follows the gummel extremum type I profile.
With respect to the piping lane 2, as shown in FIG. 7, the linear fitting degree R 2 At 0.987, about 1, the pipe gallery pipe 2 corrosion depth probability follows the gummel extremum type I profile.
For the piping lane 3, as shown in fig. 8, linesThe fitting degree is R 2 If the corrosion depth probability is 0.974 and about 1, the corrosion depth probability of the pipe gallery pipeline 3 is subjected to Gumbel extremum type I distribution.
Corrosion depth of pipe rack pipe under different reliability:
specifically, the extreme corrosion depths at different reliabilities for piping lane piping 1,2,3 are shown in table 4.
TABLE 4 pipeline corrosion depth at varying reliability
Corrosion remaining life assessment of piping lane tubing at different reliabilities:
and calculating according to the formula (11) and the formula (12) to obtain the minimum allowable residual thickness of the pipeline.
The corrosion remaining life evaluation flow of the piping lane pipe 3 will be described in detail below, and the corrosion remaining life evaluation flow of the piping lane pipes 1 and 2 is the same.
Specifically, for piping lane pipe 3, the service life of the pipe was evaluated at a reliability of 99%:
the value of the coefficient c in the corrosion progress curve formula (13) is calculated first from the time the pipeline is put into service and the predicted corrosion depth at 99% reliability.
6.231＝c·10.3 0.3 (14)
From this, c= 3.095 can be calculated, so the corrosion progress curve formula is:
x＝3.095·t 0.3 (15)
the thickness of the pipe is 7.1mm, the minimum allowable residual thickness is 0.54mm, so:
7.1-0.54＝3.095·t 0.3 (16)
t=12.23 is calculated, whereby the usable life of the pipe is calculated to be 12.23 years at a reliability of 99% according to the operating conditions of the pipe.
Specifically, for piping lane 3, the service life of the pipe was evaluated at a reliability of 99.9%:
the coefficient c value in the corrosion progress curve formula is calculated according to the time when the pipeline is put into use and the predicted corrosion depth under the reliability of 99.9%.
6.534＝c·10.3 0.3 (17)
From this, c= 3.246 can be calculated, so the corrosion progress curve formula is
x＝3.246·t 0.3 (18)
The thickness of the pipeline is 7.1mm, and the minimum allowable residual thickness is 0.54mm, so
7.1-0.54＝3.246·t 0.3 (19)
T=10.44 is calculated, whereby the usable life of the pipe is calculated to be 10.44 years at a reliability of 99.9% according to the operating conditions of the pipe.
Specifically, for piping lane 3, the service life of the pipe was evaluated at a reliability of 99.99%:
the coefficient c value in the corrosion progress curve formula is calculated according to the time when the pipeline is put into use and the predicted corrosion depth under 99.99% reliability.
6.837＝c·10.3 0.3 (20)
From this, c=3.396 can be calculated, so the corrosion progress curve is formulated as
x＝3.396·t 0.3 (21)
The thickness of the pipeline is 7.1mm, and the minimum allowable residual thickness is 0.54mm, so
7.1-0.54＝3.396·t 0.3 (22)
T= 8.972 is calculated, whereby the usable life of the pipeline is 8.972 years with a reliability of 99.99% according to the operating conditions of the pipeline.
The corrosion residual life evaluation results obtained by the corrosion residual life evaluation methods disclosed by the application of the pipe gallery pipes 1,2 and 3 are shown in table 5.
TABLE 5 evaluation of corrosion residual Life of piping lane 1,2,3 at different reliabilities
The above embodiments are provided for illustrating the present invention and not for limiting the present invention, and various changes and modifications may be made by one skilled in the relevant art without departing from the spirit and scope of the present invention, and thus all equivalent technical solutions should be defined by the claims.
Claims (3)
1. A method for evaluating the corrosion residual life of a common piping lane pipeline, comprising:
and (3) corrosion data processing and selection: dividing a detected pipeline into a plurality of pipeline sections, sequencing the maximum value of corrosion depth values of all the pipeline sections, obtaining a sample sequence of the corrosion depth values of the detected pipeline, and obtaining the cumulative probability of any corrosion depth value;
selecting a corrosion data distribution model: adopting a generalized extremum distribution model and carrying out parameter estimation, and selecting a corrosion data distribution model of the detected pipeline as a gummy distribution model, a Frechet distribution model or a Weibull distribution model according to the determined parameters;
predicting corrosion depth: predicting the limit corrosion depth of the detected pipeline under different reliability by using the determined corrosion data distribution model;
predicting corrosion residual life: determining the corrosion residual life of the detected pipeline under different reliability according to the ultimate corrosion depth of the detected pipeline under different reliability;
in the processing and selecting of the corrosion data,
the corrosion depth values of each pipe section are sequenced from small to large, and the maximum value of the corrosion depth values of the pipe section is obtained according to the sequencing result;
sequencing the maximum values of the corrosion depth values of all the pipe sections in order from small to large to form a sample sequence of the corrosion depth values of the detected pipeline:
D＝{x 1 ,x 2 ,x 3 ,…x i ,…,x m }；
wherein x is i Indicating the ith corrosion depth of the detected pipelineA degree value; m represents m pipe sections;
for the ith corrosion depth value, the cumulative probability is:
all (x) of the obtained (x i ,F(x i ) Drawing a graph by using the data points, and drawing a curve to obtain an empirical distribution diagram of the corrosion depth of the detected pipeline;
in the selective corrosion data distribution model,
the generalized extremum distribution model has the expression:
wherein alpha is a position parameter, beta is a scale parameter, k is a generalized extremum model distribution parameter, and x represents a random variable;
when the value of the k parameter is within a range value near the preset 0, the corrosion data distribution of the detected pipeline accords with the gumme distribution, and the cumulative probability function is as follows:
wherein alpha is a position parameter, and beta is a scale parameter;
when the value of the k parameter is larger than 0 and is out of the range value near the preset 0, the corrosion data distribution of the detected pipeline accords with the Frechet distribution, and the cumulative probability function is as follows:
wherein alpha is a position parameter, beta is a scale parameter, and gamma is a shape parameter; x represents the maximum value of the random variable;
when the value of the k parameter is smaller than 0 and is out of the preset range value near 0, the corrosion data distribution of the detected pipeline accords with Weibull distribution, and the cumulative probability function is as follows:
wherein alpha is a position parameter, beta is a scale parameter, and gamma is a shape parameter; x represents the maximum value of the random variable;
the parameter k is obtained by fitting a curve described by an expression of the generalized extremum distribution model;
in the selective corrosion data distribution model,
the parameter estimation adopts a probability weight moment method:
wherein omega r Representing probability weight moments; f represents the cumulative probability; r represents a real number; dF represents a variable at integration;
the method for determining the position parameter alpha and the scale parameter beta is obtained, and the expressions of the two parameters are as follows:
wherein Γ represents a Gamma function; b 0 ,b 1 Representing an unbiased estimate;
in the predicted corrosion residual life described above,
the minimum allowable remaining thickness of the pipe is calculated as follows:
△A＝A-A min
wherein A is min Is the minimum allowable residual thickness; p is the pipeline operating pressure; d is the outer diameter of the pipeline; SYMS is the pipeline steel yield strength; a is the wall thickness of the pipeline; delta A is the critical corrosion depth of the pipeline;
and determining corrosion progress curves under different reliabilities by combining corrosion depth values under different reliabilities, wherein the corrosion progress curves are as follows:
x＝c·t n
wherein x represents a pipeline corrosion depth value; c represents a coefficient; t represents the service time of the pipeline structure to start to break; n is a time constant, and 0.3-0.5 is taken;
and calculating to obtain the residual service life of the pipeline under different reliability.
2. The method of evaluating the corrosion residual life of a common piping lane pipe according to claim 1, wherein after selecting the corrosion data distribution model, suitability of the selected corrosion data distribution model is judged by a goodness-of-fit test.
3. The method for evaluating the residual life of the corrosion of the pipeline in the public pipe gallery according to claim 2, wherein the method for checking the goodness of fit comprises a K-S checking method, a mapping method, a regression method, an X2 checking method and an A-D checking method.
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