CN108536926A - Based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions - Google Patents
Based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions Download PDFInfo
- Publication number
- CN108536926A CN108536926A CN201810237169.7A CN201810237169A CN108536926A CN 108536926 A CN108536926 A CN 108536926A CN 201810237169 A CN201810237169 A CN 201810237169A CN 108536926 A CN108536926 A CN 108536926A
- Authority
- CN
- China
- Prior art keywords
- gas pipeline
- oil
- corrosion
- pipeline
- maximum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
Abstract
The invention discloses a kind of based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions, includes the following steps：1) the maximum corrosion depth data sequence X of corrosion oil-gas pipeline is obtained(i)=(x1,x2,Λ,xG)；2) the maximum corrosion depth data sequence for corroding oil-gas pipeline is input to and is improved in GEV distributed models, it recycles MCMC methodology to carry out simulation and forecast to the parameter for improving GEV distributed models, obtains the statistical parameter value of threshold parameter η, location parameter μ and scale parameter σ；3) the affiliated extreme value type of maximum corrosion depth of corrosion oil-gas pipeline is judged according to the size of threshold parameter η, then according to the remaining life of the affiliated extreme value type analysis corrosion oil-gas pipeline of the maximum corrosion depth of corrosion oil-gas pipeline, this method solves the confinement problems of the single distribution of predicting oil/gas pipeline maximum corrosion depth, realizes the high-precision forecast of corrosion oil-gas pipeline remaining life.
Description
Technical field
The invention belongs to oil-gas pipeline safety technical fields, are related to a kind of based on the corrosion oil for improving adaptive GEV distributions
Feed channel Residual Life method.
Background technology
Oil, natural gas have extremely important status in China's energy strategy, with national security, economic development and society
It can stablize closely bound up.Pipe-line is the guarantee that these resource securities use, and integrality and reliability are resource fortune
The basis of defeated normal operation.Corrosive pipeline is brought to world economy per annual meeting billions of one of the reason of causing pipeline damage
The loss of dollar.Therefore, research corrosion predicts its remaining longevity to the integrality of pipe-line and the influence degree of reliability
Life ensures that safe operation is of great significance.
Currently, domestic and foreign scholars conduct in-depth research pipeline predicting residual useful life, if foreign scholar Ossai is with one
The pure Markov model prediction corrosion depth of pipeline of kind non-linear growth and negative binomial distribution predict that its corrosion rate sets up the service life
Prediction model；The inadequate disadvantage of the newborn equal data precision for GM (1,1) model is opened, secondary model is carried out to endpiece residual error
Change, so as to improve the accuracy of prediction.Result is obtained using these methods to approach with actual value, but corrosion rate is not solid
It is fixed but change over time, therefore still have larger deficiency.
Its maximum corrosion depth is predicted using the extreme value distribution, is ratio to set up oil-gas pipeline predicting residual useful life model
It is relatively rational.It opens new life etc. and pipeline maximum corrosion depth prediction model is established using Gumbel distributions, to establish oil-gas pipeline
Predicting residual useful life model；Chloroazotic acid bravely utilizes Gumbel distributions and returns phase prediction stainless steel except silt pipeline maximum is corroded deeply
Degree；Then Han Kejiang finds out it in Different Reliability using the maximum corrosion depth of extreme value Ⅰ distribution prediction larger hydrocarbon storage tank
Under remaining life；In addition, also Song Yisi, thanking to girl etc. and being predicted based on extreme value Ⅰ distribution Matlab maximum corrosion depths
Algorithm is realized, the results showed that Gumbel distributions are suitable only for short term corrosion depth prediction.Chaves I A, Wang and Luo Zhengshan
Deng discovery relative to conventional method Gumbel distribution description offshore oilfield pipeline maximum corrosion depths, Frechet distributions are more suitable
Close prolonged spot corrosion situation；Melchers has found that point group can be divided into (the corrosion rate distribution of stable point group in corrosive pipeline
Average value and variance it is constant) and meta-stable group (average value of corrosion rate distribution and variance variation), and it is traditional
Gumbel distributions may be only available for the maximum corrosion depth distribution of the stabilization point group of short-term in-service pipeline.
Above method may due to the complexity of oil-gas pipeline local environment both for single extreme value type
Fitting can be caused to have error, prediction result inaccurate.
Invention content
It is an object of the invention to overcome the above-mentioned prior art, provide a kind of GEV points adaptive based on improving
The corrosion oil-gas pipeline Residual Life method of cloth, this method solve the single distribution of predicting oil/gas pipeline maximum corrosion depth
Confinement problems, realize corrosion oil-gas pipeline remaining life high-precision forecast.
In order to achieve the above objectives, of the present invention based on the corrosion oil-gas pipeline remaining longevity for improving adaptive GEV distributions
Life analysis method includes the following steps：
1) the maximum corrosion depth data sequence X of corrosion oil-gas pipeline is obtained(i)=(x1,x2,Λ,xG), further according to corrosion
Deep size is cheated on oil-gas pipeline to be ranked up the maximum corrosion depth data sequence for corroding oil-gas pipeline；
2) the maximum corrosion depth data sequence for corroding oil-gas pipeline is input to and is improved in GEV distributed models, recycled
MCMC methodology carries out simulation and forecast to the parameter for improving GEV distributed models, obtains threshold parameter η, location parameter μ and scale parameter
The statistical parameter value of σ；
3) the affiliated extreme value type of maximum corrosion depth of corrosion oil-gas pipeline is judged according to the size of threshold parameter η,
Then according to the remaining life of the affiliated extreme value type analysis corrosion oil-gas pipeline of the maximum corrosion depth of corrosion oil-gas pipeline.
As threshold parameter η>When 0, then it represents that the maximum corrosion depth for corroding oil-gas pipeline meets Frechet distributions；Work as threshold
When value parameter η 0, then it represents that the maximum corrosion depth for corroding oil-gas pipeline meets Weibull distributions；As η → 0, then it represents that rotten
The maximum corrosion depth of erosion oil-gas pipeline meets Gumbel distributions.
As threshold parameter η>When 0, the detailed process of the remaining life of analysis corrosion oil-gas pipeline is：
11) Frechet distributions are met by the maximum corrosion depth of corrosion oil-gas pipeline, the corrosion of oil-gas pipeline maximum must be corroded
The distribution function of depth isWherein, y=σ-1(x- μ), x is pole
It is worth variable；
12) distribution function of the corrosion oil-gas pipeline maximum corrosion depth obtained to step 11) takes logarithm, obtainsG sections of samples are extracted, and the length of every section of sample is unit 1, obtains cumulative distribution function GEV
(Xi)=i/G+1, i=1,2. ..., G corrodes the corresponding GEV (X of dell by each maximumi) seekValue, then willWith xiCarry out data fitting, i.e., withFor ordinate, with xiFor abscissa, obtain after Graphics testing
Simulation curve, when after Graphics testing simulation curve present negative exponent relationship when, show corrode oil-gas pipeline maximum
Corrosion depth meets Frechet distributions, then observes data to predict the maximum corrosion depth of whole section of pipeline section, so using local pipeline section
Afterwards according to the remaining life of the maximum corrosion depth of whole section of pipeline section analysis corrosion oil-gas pipeline.
Detailed process according to the remaining life of the maximum corrosion depth of whole section of pipeline section analysis corrosion oil-gas pipeline is：
21) recurrence phase T (y) is calculated, wherein return the sample that the length L that phase T (y) is whole section of pipeline section extracts pipeline section with unit
The ratio between this quantity G；
22) according to the maximum corrosion depth GEV (y) for returning phase T (y) whole section of pipeline section of prediction, whereinThen whole pipeline section maximum corrosion depth xmFor：
xm=σ y+ μ=(- lnGEV (y))-1/ησ+μ；
23) byThe pre- of corrosion allowance Δ is obtained using the target reliability of pipeline location
Survey model；
24) the projected life t of run of designingd, whereinThe remaining life t of oil-gas pipeline must be corrodedsFor ts
=td-t。
The invention has the advantages that：
Corrosion oil-gas pipeline Residual Life method of the present invention based on the adaptive GEV distributions of improvement is specific
When operation, does not utilize instead of traditional Gumbel distributions or Frechet to be distributed, utilizes generalized extreme value distribution on its basis,
Suitable extreme value type is chosen automatically for observation data to predict its maximum corrosion depth, is then corroded according to its maximum
Its remaining life of depth prediction solves predicting oil/gas pipeline most to overcome error problem caused by single distribution limitation
The confinement problems of the single distribution of big corrosion depth, realize the high-precision forecast of corrosion oil-gas pipeline remaining life.
Further, predict that its corrosion is abundant using corresponding target reliability for the corrosion oil-gas pipeline of different regions
Its corrosion allowance, is finally combined with its maximum corrosion depth to predict the remaining life of pipeline, prediction result is compared with subject to by amount
Really.
Description of the drawings
The iteration trajectory diagram of location parameter μ when Fig. 1 a are the statistical parameter value for obtaining location parameter μ；
The iteration trajectory diagram of scale parameter σ when Fig. 1 b are the statistical parameter value for obtaining scale parameter σ；
The iteration trajectory diagram of threshold parameter η when Fig. 1 c are the statistical parameter value for obtaining threshold parameter η；
The iteration history figure of location parameter μ when Fig. 2 a are the statistical parameter value for obtaining location parameter μ；
The iteration history figure of scale parameter σ when Fig. 2 b are the statistical parameter value for obtaining scale parameter σ；
The iteration history figure of threshold parameter η when Fig. 2 c are the statistical parameter value for obtaining threshold parameter η；
Fig. 3 a are the autocorrelation function graph of location parameter μ；
Fig. 3 b are the autocorrelation function graph of scale parameter σ；
Fig. 3 c are the autocorrelation function graph of threshold parameter η；
Fig. 4 is maximum corrosion depth cumulative probability distribution map；
Fig. 5 is pipe design life prediction figure.
Specific implementation mode
The present invention is described in further detail below in conjunction with the accompanying drawings：
It is of the present invention based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distribution include with
Lower step：
1) the maximum corrosion depth data sequence X of corrosion oil-gas pipeline is obtained(i)=(x1,x2,Λ,xG), further according to corrosion
Deep size is cheated on oil-gas pipeline to be ranked up the maximum corrosion depth data sequence for corroding oil-gas pipeline；
2) the maximum corrosion depth data sequence for corroding oil-gas pipeline is input to and is improved in GEV distributed models, recycled
MCMC methodology carries out simulation and forecast to the parameter for improving GEV distributed models, obtains threshold parameter η, location parameter μ and scale parameter
The statistical parameter value of σ；
3) the affiliated extreme value type of maximum corrosion depth of corrosion oil-gas pipeline is judged according to the size of threshold parameter η,
Then according to the remaining life of the affiliated extreme value type analysis corrosion oil-gas pipeline of the maximum corrosion depth of corrosion oil-gas pipeline.
As threshold parameter η>When 0, then it represents that the maximum corrosion depth for corroding oil-gas pipeline meets Frechet distributions；Work as threshold
When value parameter η 0, then it represents that the maximum corrosion depth for corroding oil-gas pipeline meets Weibull distributions；As η → 0, then it represents that rotten
The maximum corrosion depth of erosion oil-gas pipeline meets Gumbel distributions.
As threshold parameter η>When 0, according to case verification, the detailed process of the remaining life of analysis corrosion oil-gas pipeline is：
11) Frechet distributions are met by the maximum corrosion depth of corrosion oil-gas pipeline, the corrosion of oil-gas pipeline maximum must be corroded
The distribution function of depth isWherein, y=σ-1(x- μ), x is pole
It is worth variable；
12) distribution function of the corrosion oil-gas pipeline maximum corrosion depth obtained to step 11) takes logarithm, obtainsG sections of samples are extracted, and the length of every section of sample is unit 1, obtains cumulative distribution function GEV
(Xi)=i/G+1, i=1,2. ..., G corrodes the corresponding GEV (X of dell by each maximumi) seekValue, then willWith xiCarry out data fitting, i.e., withFor ordinate, with xiFor abscissa, obtain after Graphics testing
Simulation curve, when after Graphics testing simulation curve present negative exponent relationship when, show corrode oil-gas pipeline maximum
Corrosion depth meets Frechet distributions, then observes data to predict the maximum corrosion depth of whole section of pipeline section, so using local pipeline section
Afterwards according to the remaining life of the maximum corrosion depth of whole section of pipeline section analysis corrosion oil-gas pipeline.
Specifically, according to the detailed process of the remaining life of the maximum corrosion depth of whole section of pipeline section analysis corrosion oil-gas pipeline
For：
21) recurrence phase T (y) is calculated, wherein return the sample that the length L that phase T (y) is whole section of pipeline section extracts pipeline section with unit
The ratio between this quantity G；
22) according to the maximum corrosion depth GEV (y) for returning phase T (y) whole section of pipeline section of prediction, whereinThen whole pipeline section maximum corrosion depth xmFor：
xm=σ y+ μ=(- lnGEV (y))-1/ησ+μ；
23) byThe pre- of corrosion allowance Δ is obtained using the target reliability of pipeline location
Survey model；
24) the projected life t of run of designingd, whereinThe remaining life t of oil-gas pipeline must be corrodedsFor ts
=td-t。
Emulation experiment
The pipeline of test is corrosion of the two level risk region to buried pipeline, and the specification that pipeline section uses is D260 × 7, material
For X52 steel, the region for corroding more serious is excavated, segmentation obtains data, takes 30 sections to be sampled per km, Mei Yiduan
100 monitoring points are randomly selected, most corrosion depth is found out from these points, is shown in Table 1.
Table 1
It is as follows to improve GEV distribution function formulas：
Y=x- μ/σ
The parameter effect gone out using MCMC steady-state simulations as shown in Fig. 1 a to Fig. 3 c, analog parameter result be respectively μ=
1.564, σ=0.6646 and η=0.01864, standard deviation are respectively 0.004892,0.00197 and 0.002287；According to η values
Known to extreme value type be II type Extremal Type Frechet distribution, then the fitting effect of its prediction result and observation data is such as
Shown in Fig. 4；Linear fit standard deviation is respectively 0.05431 and 0.08280, and its level of signifiance is three star (0.1% notable water
It is flat)；Pipe design life prediction is：
Obtain pipe design service life Tf=31.0408mm, then its remaining life is T=21.59a, projected life prediction such as Fig. 5
It is shown.
The content that description in the present invention is not described in detail belongs to the known existing disclosure of professional and technical personnel in the field
Technology.
Embodiment of above is merely to illustrate the present invention, and not limitation of the present invention.Although disclosing for the purpose of illustration
The related embodiment and attached drawing of the present invention, but it will be appreciated by those skilled in the art that；It is of the invention and appended not departing from
Spirit and scope by the claims in, it is various replace, variation, modification be all possible.Therefore, all equivalent technical solutions
Scope of the invention is also belonged to, scope of patent protection of the invention should be defined by the claims, and should not be limited to most preferably implement
Example and attached drawing disclosure of that.
Claims (4)
1. a kind of based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions, which is characterized in that including
Following steps：
1) the maximum corrosion depth data sequence X of corrosion oil-gas pipeline is obtained(i)=(x1,x2,Λ,xG), further according to corrosion oil gas
Deep size is cheated on pipeline to be ranked up the maximum corrosion depth data sequence for corroding oil-gas pipeline；
2) the maximum corrosion depth data sequence for corroding oil-gas pipeline is input to and is improved in GEV distributed models, recycle MCMC
Method carries out simulation and forecast to the parameter for improving GEV distributed models, obtains threshold parameter η, location parameter μ and scale parameter σ
Statistical parameter value；
3) the affiliated extreme value type of maximum corrosion depth of corrosion oil-gas pipeline is judged according to the size of threshold parameter η, then
According to the remaining life of the affiliated extreme value type analysis corrosion oil-gas pipeline of the maximum corrosion depth of corrosion oil-gas pipeline.
2. the corrosion oil-gas pipeline Residual Life method according to claim 1 based on the adaptive GEV distributions of improvement,
It is characterized in that, working as threshold parameter η>When 0, then it represents that the maximum corrosion depth for corroding oil-gas pipeline meets Frechet distributions；When
When threshold parameter η 0, then it represents that the maximum corrosion depth for corroding oil-gas pipeline meets Weibull distributions；As η → 0, then it represents that
The maximum corrosion depth of corrosion oil-gas pipeline meets Gumbel distributions.
3. the corrosion oil-gas pipeline Residual Life method according to claim 1 based on the adaptive GEV distributions of improvement,
It is characterized in that, working as threshold parameter η>When 0, the detailed process of the remaining life of analysis corrosion oil-gas pipeline is：
11) Frechet distributions are met by the maximum corrosion depth of corrosion oil-gas pipeline, oil-gas pipeline maximum corrosion depth must be corroded
Distribution function beWherein, y=σ-1(x- μ), x become for extreme value
Amount；
12) distribution function of the corrosion oil-gas pipeline maximum corrosion depth obtained to step 11) takes logarithm, obtainsG sections of samples are extracted, and the length of every section of sample is unit 1, obtains cumulative distribution function GEV
(Xi)=i/G+1, i=1,2. ..., G corrodes the corresponding GEV (X of dell by each maximumi) seekValue, then willWith xiCarry out data fitting, i.e., withFor ordinate, with xiFor abscissa, obtain after Graphics testing
Simulation curve, when after Graphics testing simulation curve present negative exponent relationship when, show corrode oil-gas pipeline maximum
Corrosion depth meets Frechet distributions, then observes data to predict the maximum corrosion depth of whole section of pipeline section, so using local pipeline section
Afterwards according to the remaining life of the maximum corrosion depth of whole section of pipeline section analysis corrosion oil-gas pipeline.
4. the corrosion oil-gas pipeline Residual Life method according to claim 3 based on the adaptive GEV distributions of improvement,
It is characterized in that, being according to the detailed process of the remaining life of the maximum corrosion depth of whole section of pipeline section analysis corrosion oil-gas pipeline：
21) recurrence phase T (y) is calculated, wherein return the sample number that the length L that phase T (y) is whole section of pipeline section extracts pipeline section with unit
Measure the ratio between G；
22) according to the maximum corrosion depth GEV (y) for returning phase T (y) whole section of pipeline section of prediction, whereinThen
Whole pipeline section maximum corrosion depth xmFor：
xm=σ y+ μ=(- lnGEV (y))-1/ησ+μ；
23) byThe prediction mould of corrosion allowance Δ is obtained using the target reliability of pipeline location
Type；
24) the projected life t of run of designingd, whereinThe remaining life t of oil-gas pipeline must be corrodedsFor ts=td-
t。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810237169.7A CN108536926A (en) | 2018-03-21 | 2018-03-21 | Based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810237169.7A CN108536926A (en) | 2018-03-21 | 2018-03-21 | Based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108536926A true CN108536926A (en) | 2018-09-14 |
Family
ID=63484052
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810237169.7A Pending CN108536926A (en) | 2018-03-21 | 2018-03-21 | Based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108536926A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109710888A (en) * | 2018-11-16 | 2019-05-03 | 佛山科学技术学院 | A kind of natural gas line damage prediction method and device based on penalized regression |
| CN109784590A (en) * | 2019-03-20 | 2019-05-21 | 西安建筑科技大学 | One kind being based on the in-service oil-gas pipeline corrosion prediction technique of CAGM (1,1)-BPNN |
| CN110400608A (en) * | 2019-06-13 | 2019-11-01 | 浙江大学建筑设计研究院有限公司 | The assessment method that cement-based material intensity changes with age |
| CN110942156A (en) * | 2019-10-29 | 2020-03-31 | 中国石油化工股份有限公司 | Heat exchanger group preventive maintenance method based on operation risk |
| CN111122423A (en) * | 2018-10-31 | 2020-05-08 | 中国石油化工股份有限公司 | Reliability-based storage tank bottom plate corrosion residual life evaluation method and device |
| CN112883578A (en) * | 2021-03-01 | 2021-06-01 | 中车大连机车研究所有限公司 | Locomotive radiator residual life prediction method |
| CN113554183A (en) * | 2021-08-03 | 2021-10-26 | 同济大学 | Extreme value prediction method based on unsupervised machine learning algorithm |
| CN113626970A (en) * | 2020-05-08 | 2021-11-09 | 上海化学工业区公共管廊有限公司 | Method and system for evaluating corrosion residual life of common pipe gallery pipeline |
| CN115330094A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline service life prediction method, internet of things system, device and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000206298A (en) * | 1999-01-18 | 2000-07-28 | Hitachi Ltd | Solid target neutron generator |
| US20090301198A1 (en) * | 2006-06-30 | 2009-12-10 | Carnegie Mellon University | Methods, Apparatuses, and Systems for Damage Detection |
| CN102076862A (en) * | 2008-04-30 | 2011-05-25 | 希乐克公司 | Cellulosic and lignocellulosic material structural materials and methods and systems for making these materials by radiation |
| CN102156089A (en) * | 2011-01-18 | 2011-08-17 | 中国石油天然气股份有限公司 | Method for evaluating corrosion in buried pipeline |
-
2018
- 2018-03-21 CN CN201810237169.7A patent/CN108536926A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000206298A (en) * | 1999-01-18 | 2000-07-28 | Hitachi Ltd | Solid target neutron generator |
| US20090301198A1 (en) * | 2006-06-30 | 2009-12-10 | Carnegie Mellon University | Methods, Apparatuses, and Systems for Damage Detection |
| CN102076862A (en) * | 2008-04-30 | 2011-05-25 | 希乐克公司 | Cellulosic and lignocellulosic material structural materials and methods and systems for making these materials by radiation |
| CN102156089A (en) * | 2011-01-18 | 2011-08-17 | 中国石油天然气股份有限公司 | Method for evaluating corrosion in buried pipeline |
Non-Patent Citations (5)
| Title |
|---|
| GRAS, JM: "Life prediction for HLW containers - issues related to long-term extrapolation of corrosion resistance", 《COMPTES RENDUS PHYSIQUE》 * |
| 周国强 等: "基于改进广义极值分布的核管道最大腐蚀深度预测", 《中南大学学报(自然科学版)》 * |
| 张新生 等: "基于Gumbel极值I型分布埋地油气管道的剩余寿命预测", 《中国腐蚀与防护学报》 * |
| 张桃先 等: "预测压力管道腐蚀剩余寿命的可靠性方法", 《化工设计》 * |
| 骆正山 等: "Frechet分布的海底油气管道腐蚀预测", 《腐蚀与防护》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111122423A (en) * | 2018-10-31 | 2020-05-08 | 中国石油化工股份有限公司 | Reliability-based storage tank bottom plate corrosion residual life evaluation method and device |
| CN111122423B (en) * | 2018-10-31 | 2022-07-08 | 中国石油化工股份有限公司 | Reliability-based storage tank bottom plate corrosion residual life evaluation method and device |
| CN109710888B (en) * | 2018-11-16 | 2023-04-18 | 佛山科学技术学院 | Natural gas pipeline damage prediction method and device based on punishment regression |
| CN109710888A (en) * | 2018-11-16 | 2019-05-03 | 佛山科学技术学院 | A kind of natural gas line damage prediction method and device based on penalized regression |
| CN109784590B (en) * | 2019-03-20 | 2022-06-14 | 西安建筑科技大学 | In-service oil and gas pipeline corrosion prediction method based on CAGM (1,1) -BPNN |
| CN109784590A (en) * | 2019-03-20 | 2019-05-21 | 西安建筑科技大学 | One kind being based on the in-service oil-gas pipeline corrosion prediction technique of CAGM (1,1)-BPNN |
| CN110400608A (en) * | 2019-06-13 | 2019-11-01 | 浙江大学建筑设计研究院有限公司 | The assessment method that cement-based material intensity changes with age |
| CN110942156B (en) * | 2019-10-29 | 2022-12-02 | 中国石油化工股份有限公司 | Heat exchanger group preventive maintenance method based on operation risk |
| CN110942156A (en) * | 2019-10-29 | 2020-03-31 | 中国石油化工股份有限公司 | Heat exchanger group preventive maintenance method based on operation risk |
| CN113626970A (en) * | 2020-05-08 | 2021-11-09 | 上海化学工业区公共管廊有限公司 | Method and system for evaluating corrosion residual life of common pipe gallery pipeline |
| CN113626970B (en) * | 2020-05-08 | 2024-01-09 | 上海化学工业区公共管廊有限公司 | Method and system for evaluating corrosion residual life of public pipe gallery pipeline |
| CN112883578A (en) * | 2021-03-01 | 2021-06-01 | 中车大连机车研究所有限公司 | Locomotive radiator residual life prediction method |
| CN112883578B (en) * | 2021-03-01 | 2024-01-30 | 中车大连机车研究所有限公司 | Method for predicting residual life of locomotive radiator |
| CN113554183B (en) * | 2021-08-03 | 2022-05-13 | 同济大学 | Extreme value prediction method based on unsupervised machine learning algorithm |
| CN113554183A (en) * | 2021-08-03 | 2021-10-26 | 同济大学 | Extreme value prediction method based on unsupervised machine learning algorithm |
| CN115330094A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline service life prediction method, internet of things system, device and medium |
| US11898704B2 (en) | 2022-10-14 | 2024-02-13 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and Internet of Things systems for smart gas pipeline life prediction based on safety |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108536926A (en) | Based on the corrosion oil-gas pipeline Residual Life method for improving adaptive GEV distributions | |
| CN103136539B (en) | Ground net corrosion speed grade Forecasting Methodology | |
| CN103870670B (en) | A kind of tube corrosion degree Forecasting Methodology and device | |
| CN103455682A (en) | Method for predicting residual life of corroded casing of high-temperature and high-pressure well | |
| CN107133400A (en) | A kind of aircraft structure fatigue reliability Bayes's combination forecasting method | |
| CN108388724A (en) | A kind of GM-Markov submarine pipeline corrosion prediction methods based on parameter optimization | |
| CN106372299B (en) | A kind of pipeline method for predicting residual useful life based on inverse Gaussian process and EM-PF | |
| CN108460230A (en) | Corrosion oil-gas pipeline remaining life based on data fusion and Reliability Prediction Method | |
| CN101915728A (en) | Testing method for predicting residual service life of buried metal water supply pipeline | |
| CN105930670A (en) | Model parameter uncertainty-based dynamic prediction method for river emergency pollution accident | |
| Ahmadi et al. | Fuzzy modeling and experimental investigation of minimum miscible pressure in gas injection process | |
| CN108615098B (en) | Bayesian survival analysis-based water supply network pipeline pipe burst risk prediction method | |
| CN102142063B (en) | A kind of method that long-term corrosion rates are calculated by short-term test result | |
| CN108537376B (en) | RS-PSO-GRNN-based buried oil and gas pipeline soil corrosion prediction method | |
| CN109596503A (en) | A kind of in-service high sulfur-containing natural gas pipe material Adaptability Evaluation Method | |
| CN101769147A (en) | Method for evaluating oilfield fireflood scheme | |
| CN115186590A (en) | Method for predicting residual life of pipeline corrosion | |
| Zhao et al. | Evaluating influences of the Manwan Dam and climate variability on the hydrology of the Lancang-Mekong River, Yunnan Province, southwest China | |
| CN105224707B (en) | A kind of Forecasting Methodology of the molten cavity volume shrinkage factor in salt cave underground natural gas storage tank | |
| Zhang et al. | SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence | |
| CN103699932A (en) | Real-time quantitative prediction method of three-dimensional cellular automaton of reinforced concrete steel bar pitting corrosion | |
| CN111476406B (en) | Oil-water well casing damage early warning method and device and storage medium | |
| Viswanathan et al. | Life-assessment technology for power-plant components | |
| CN104680010A (en) | Method for screening steady-state operation data of turbine unit | |
| CN109027700B (en) | Method for evaluating leakage detection effect of leakage point |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180914 |