CN110069809B - Uncertainty analysis method in GO method based on Monte Carlo simulation method - Google Patents
Uncertainty analysis method in GO method based on Monte Carlo simulation method Download PDFInfo
- Publication number
- CN110069809B CN110069809B CN201910180687.4A CN201910180687A CN110069809B CN 110069809 B CN110069809 B CN 110069809B CN 201910180687 A CN201910180687 A CN 201910180687A CN 110069809 B CN110069809 B CN 110069809B
- Authority
- CN
- China
- Prior art keywords
- reliability
- uncertainty
- sampling
- importance
- component
- 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.)
- Active
Links
Images
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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Complex Calculations (AREA)
Abstract
The invention provides an uncertainty analysis method in a GO method based on a Monte Carlo simulation method, which comprises the following steps: (1) taking point estimation values of reliability parameters of the components to perform importance analysis; (2) obtaining sampling probability P of each partsample(ii) a (3) Sampling reliability parameters of the component; (4) and (4) credible evaluation of uncertainty analysis results. The method provided by the invention improves the uncertainty analysis based on the Go method in the probability safety evaluation, and can provide a reference basis for the subsequent research.
Description
Technical Field
The invention belongs to the technical field of probability safety evaluation of spent fuel post-processing facilities, and particularly relates to an uncertainty analysis method in a GO method based on a Monte Carlo simulation method.
Background
The GO method is an effective system reliability analysis method. The basic principle of the GO method is to represent units by operators, represent connections among the units by signal streams and convert a system diagram into a GO diagram. The quantitative calculation of the GO method is to gradually calculate the state probability of the signal flow from the input operator along the signal flow sequence according to the operation rule of the operator until the final output signal of the system is represented. The GO method is continuously developed at the beginning of the century, and a new algorithm for quantitatively calculating the GO method is gradually provided; the method and the transmission rule of the common signals in the system are provided, and the state probabilities of all signal streams can be directly calculated under most conditions; further, a GO method accurate quantitative algorithm with a common signal and a GO method algorithm in a repairable system are developed, so that the system quantitative calculation of the GO method is more direct and simple.
The GO method is also an important deductive system analysis method in Probabilistic Safety Assessment (PSA). In general, parameters cannot be accurately obtained in various models (reliability models) used in probabilistic security rating, and therefore, all possible input and output results need to be described, that is, uncertainty analysis is performed. Targets of uncertainty analysis are divided into two types, and in general engineering research, the variance of distribution is obtained; the results expected in PSA are obviously not limited to variance, but rather a complete distribution or a sufficient number of representative discrete points to give various confidence intervals.
Methods for uncertainty analysis typically include a moment method and an integral method, the latter including analytical integral method, numerical integral method (discrete function distribution method), Monte Carlo simulation method (MC for short). The analytical integration method is only practical and feasible in operation under the conditions of simple function form, small number of variables and mutual independence; the method of moments and numerical integration also have similar disadvantages. The MC simulation method is also called as statistical simulation method, utilizes random numbers to carry out numerical simulation, essentially belongs to an integral method, accumulates by means of probability of a large number of random events, and generally comprises three parts of a test model (function relation), sampling and statistical analysis on sampling results.
The uncertainty analysis in the present invention refers to the uncertainty analysis caused by the reliability parameters of the component, and does not include the uncertainty analysis of the degree of accuracy of the mode formation. At present, foreign research work on uncertainty analysis of the GO method is less, and domestic work on the GO method is basically blank and has not been reported in any literature.
Therefore, it is necessary to provide a method for analyzing uncertainty in the GO method based on the monte carlo simulation method to solve the above problems.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the uncertainty analysis method in the GO method based on the Monte Carlo simulation method, so that the uncertainty analysis based on the GO method in probability safety evaluation is improved, and a reference basis can be provided for subsequent research.
In order to achieve the above purposes, the invention adopts the technical scheme that:
a method of uncertainty analysis in a GO method based on a monte carlo simulation method, the method comprising: (1) taking point estimation values of reliability parameters of the components to perform importance analysis;
(2) obtaining sampling probability P of each partsample；
(3) Sampling reliability parameters of the component;
(4) and (4) credible evaluation of uncertainty analysis results.
Further, the point estimation value obtaining method in step (1) includes:
directly adopting, namely, an original point estimation value;
the mathematical expectation is obtained by a probability density function of the component reliability parameters.
Further, the importance in the step (1) is FV importance and RAW importance.
Further, the sampling probability P of the step (2)sampleThe calculation formula is as follows:
Psample,i＝Itype,i/Itype,max
wherein, Psample,iIs the sampling probability of component i; i istype,iIs the importance of the type of component i, type FV or RAW; i istype,maxIs a whole partMaximum value of piece type importance.
Further, step (3) comprises:
when the reliability parameter of the component is the original point estimated value, sampling is not needed;
comparing P by adopting a Russian roulette method when the reliability parameter of the part is a probability density functionsample,iThe random number a generated at the beginning of the random simulation, if Psample,iAnd if not, sampling, and directly adopting a point estimation value without sampling.
Further, after the step (3), before the step (4), the method further comprises:
calculating a relative error R of the reliability result;
where R ═ the variance of the reliability results/reliability results.
Further, the step (4) specifically comprises:
evaluating the credibility of the uncertainty analysis result according to the relative error R of the reliability result, and determining that the reliability result is garbage when R is more than 0.5 and less than 1; when R is more than 0.2 and less than or equal to 0.5, the product is not credible; when 0.1< R.ltoreq.0.2, it is suspect; when R is more than 0.05 and less than or equal to 0.1, the product is not credible; when R is less than or equal to 0.05, the comparison is reliable.
The step (4) is followed by a step (5): the analytical results of uncertainty were verified according to the central limit law:
when the results of the uncertainty analysis are expressed as: the basic corroboration is that the uncertainty analysis in the GO method based on the monte carlo simulation method is reliable in the case of a lognormal distribution curve (data graph form) or a corresponding lognormal distribution parameter (probability density function form).
The method has the advantages that the method improves the uncertainty analysis based on the Go method in the probability safety evaluation, and can provide reference basis for subsequent research.
Drawings
FIG. 1 is a flow chart of uncertainty analysis in the GO process based on the MC simulation;
fig. 2 is a GO diagram of a certain power supply system.
Detailed Description
In order to make the technical problems solved, the technical solutions adopted, and the technical effects achieved by the present invention clearer, the technical solutions of the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, fig. 1 is a flow chart illustrating uncertainty analysis in the GO method based on the MC simulation method according to the present invention.
An uncertainty analysis method in a GO method based on a Monte Carlo simulation method comprises the following steps:
step 101: and (4) taking point estimation values for reliability parameters of the components to perform importance analysis.
A typical reliability parametric model of the components in the GO process is shown in table 1. Common reliability parameters are: frequency events, request failure events, non-serviceable components, non-periodically tested serviceable components, and the like.
TABLE 1 exemplary reliability parameter model of components
Since reliability tests are always lossy and the data collected is limited, the reliability parameters of the components are generally lacking. There are two ways to obtain the point estimation value of the reliability parameter of the component in the present invention.
First, the raw point estimate is used directly. For the sake of easy calculation in reliability analysis, only the case where the reliability parameter of the component is the raw point estimation value is considered.
Second, the reliability parameters of the component (the required parameters shown in the third column of table 1) may be described by probability density functions. For example, in general, the occurrence frequency of the frequency event follows poisson distribution, the required failure frequency of the request failure type event follows binomial distribution, and the failure rate λ of the (non-) repairable component follows Γ distribution.
After the reliability parameters of the components are collected, a GO graph is drawn according to a system diagram. This uncertainty in the parameters will propagate through the GO graph structure to the final reliability analysis result, for example, in the GO graph of a certain power supply system shown in fig. 2, the randomness of the reliability parameters of all the components will affect the reliability result of the power supply output.
Finally, the importance of each component is also required to be obtained, mainly the cut-set importance (Fussell-Vesely, FV for short) and the Risk-causing value importance (RAW for short), because the FV importance and the RAW importance are the calculation importance required by the PSA guideline of the nuclear canal council (NRC) in the united states.
Step 102: obtaining sampling probability P of each partsample。
The sampling probability calculation formula is as follows:
Psample,i＝Itype,i/Itype,max
wherein, Psample,iIs the sampling probability of component i; i istype,iIs the importance of the type of component i, type FV or RAW; i istype,maxIs the maximum value of all component type importance.
That is, when determining whether the random simulation needs to be sampled or not according to the importance of each component, the random simulation can be based on either of the two importance. By doing so, the overall computational effort can be reduced.
Step 103: the reliability parameters of the component are sampled.
Sampling here is only for components whose reliability parameters are derived from the probability density function description, and if the reliability parameters of the components are represented only in the original point estimate values, the values are taken directly without sampling. In particular, according to Psample,iDetermining whether the random simulation needs sampling, namely comparing P by adopting a Russian roulette methodsample,iThe random number a generated at the beginning of the random simulation, if Psample,iAnd if not, sampling, and directly adopting a point estimation value without sampling.
Step 104: and (4) credible evaluation of uncertainty analysis results.
And simultaneously carrying out uncertainty analysis, calculating the relative error R of the reliability result, wherein R is the variance of the reliability result/the reliability result. The credibility of the uncertainty analysis result can be known according to the range of R. Specifically, when 0.5< R <1, the product is garbage; when R is more than 0.2 and less than or equal to 0.5, the product is not credible; when 0.1< R.ltoreq.0.2, it is suspect; when R is more than 0.05 and less than or equal to 0.1, the test result is not credible; when R is less than or equal to 0.05, the comparison is reliable.
Step 104 is followed by step 105: the analytical results of uncertainty were verified according to the central limit law:
from the central limit theorem, it can be known that a new variable, which is a product of a large number of independent, identically distributed variables, follows a lognormal distribution. Therefore, the distribution of the final output event occurrence probability in the GO graph is approximated to a log-normal distribution. This can be used to verify the results of the analysis of the GO graph uncertainty by MC simulation. When the results of the uncertainty analysis are expressed as: the basic corroboration is that the uncertainty analysis in the GO method based on the monte carlo simulation method is reliable in the case of a lognormal distribution curve (data graph form) or a corresponding lognormal distribution parameter (probability density function form).
Different from the prior art, the method for analyzing the uncertainty in the GO method based on the Monte Carlo simulation method improves the uncertainty analysis based on the GO method in probability safety evaluation, and can provide a reference basis for subsequent research.
It will be appreciated by persons skilled in the art that the method of the present invention is not limited to the examples described in the specific embodiments, and that the above detailed description is for the purpose of illustrating the invention only and is not intended to limit the invention. Other embodiments will be apparent to those skilled in the art from the following detailed description, which is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A method for analyzing uncertainty in a GO method based on a Monte Carlo simulation method, the method comprising:
(1) taking point estimation values of reliability parameters of the components to perform importance analysis;
(2) obtaining sampling probability P of each partsample；
(3) Sampling reliability parameters of the component;
(4) credible evaluation of uncertainty analysis results;
the point estimation value acquisition mode in the step (1) includes:
directly adopting, namely, an original point estimation value;
mathematical expectations, i.e., obtained by the probability density function of the component reliability parameters;
the importance in the step (1) is FV importance and RAW importance;
the sampling probability P of the step (2)sampleThe calculation formula is as follows:
Psample,i＝Itype,i/Itype,max
wherein, Psample,iIs the sampling probability of component i; i istype,iIs the importance of the type of component i, type FV or RAW; i istype,maxThe maximum value of all component type importance;
the step (3) comprises the following steps:
when the reliability parameter of the component is the original point estimated value, sampling is not needed;
comparing P by adopting a Russian roulette method when the reliability parameter of the part is a probability density functionsample,iThe random number a generated at the beginning of the random simulation, if Psample,iIf the sampling rate is more than or equal to a, sampling, otherwise, not sampling, and directly adopting a point estimation value;
the step (4) specifically comprises:
evaluating the credibility of the uncertainty analysis result according to the relative error R of the reliability result, and determining that the reliability result is garbage when R is more than 0.5 and less than 1; when R is more than 0.2 and less than or equal to 0.5, the product is not credible; when 0.1< R.ltoreq.0.2, it is suspect; when R is more than 0.05 and less than or equal to 0.1, the product is not credible; when R is less than or equal to 0.05, the comparison is credible.
2. The method for analyzing uncertainty in the GO method based on the monte carlo simulation method as claimed in claim 1, wherein after step (3) and before step (4), further comprising:
calculating a relative error R of the reliability result;
where R ═ the variance of the reliability results/reliability results.
3. The method for analyzing the uncertainty in the GO method based on the Monte Carlo simulation method as claimed in claim 1, wherein the step (4) is followed by the step (5): the analytical results of uncertainty were verified according to the central limit law:
when the results of the uncertainty analysis are expressed as: the logistic normal distribution curve or the corresponding logistic normal distribution parameter proves that the uncertainty analysis in the GO method based on the monte carlo simulation method is reliable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910180687.4A CN110069809B (en) | 2019-03-11 | 2019-03-11 | Uncertainty analysis method in GO method based on Monte Carlo simulation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910180687.4A CN110069809B (en) | 2019-03-11 | 2019-03-11 | Uncertainty analysis method in GO method based on Monte Carlo simulation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110069809A CN110069809A (en) | 2019-07-30 |
| CN110069809B true CN110069809B (en) | 2022-05-17 |
Family
ID=67366212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910180687.4A Active CN110069809B (en) | 2019-03-11 | 2019-03-11 | Uncertainty analysis method in GO method based on Monte Carlo simulation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110069809B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106295956A (en) * | 2016-07-27 | 2017-01-04 | 武汉大学 | A kind of reliability estimation method considering that nuclear power plant's electric power system can repair multimode complex characteristics |
| US9691046B1 (en) * | 2014-01-13 | 2017-06-27 | Joel Adler | System and method for risk analyzed program planning (RAPP) |
| CN107292516A (en) * | 2017-06-21 | 2017-10-24 | 合肥工业大学 | It is a kind of to count and load rating and the load reliability estimation method of energy scheduling |
| CN107527127A (en) * | 2016-06-21 | 2017-12-29 | 中国辐射防护研究院 | The computational methods of RAW importances in a kind of probabilistic safety assessment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120190000A1 (en) * | 2010-05-28 | 2012-07-26 | Nada Dabbagh | Learning Asset Technology Integration Support Tool |
-
2019
- 2019-03-11 CN CN201910180687.4A patent/CN110069809B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9691046B1 (en) * | 2014-01-13 | 2017-06-27 | Joel Adler | System and method for risk analyzed program planning (RAPP) |
| CN107527127A (en) * | 2016-06-21 | 2017-12-29 | 中国辐射防护研究院 | The computational methods of RAW importances in a kind of probabilistic safety assessment |
| CN106295956A (en) * | 2016-07-27 | 2017-01-04 | 武汉大学 | A kind of reliability estimation method considering that nuclear power plant's electric power system can repair multimode complex characteristics |
| CN107292516A (en) * | 2017-06-21 | 2017-10-24 | 合肥工业大学 | It is a kind of to count and load rating and the load reliability estimation method of energy scheduling |
Non-Patent Citations (2)
| Title |
|---|
| 基于GO-FLOW方法反应堆冷却剂泵组系统失效概率的不确定性分析;尚彦龙 等;《核动力工程》;20101031;第31卷(第5期);第118-123页 * |
| 基于GO法的后处理设施供电系统的可靠性分析;王任泽;《中国核科学技术进展报告（第三卷）——中国核学会2013年学术年会论文集第3册（核能动力分卷（下））》;20130930;第591-599页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110069809A (en) | 2019-07-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wen et al. | Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity | |
| CN111046564B (en) | Residual life prediction method for two-stage degraded product | |
| CN111123188A (en) | Electric energy meter comprehensive verification method and system based on improved least square method | |
| CN106897833B (en) | New energy power distribution network reliability assessment method and device | |
| CN110912737A (en) | Dynamic perception performance early warning method based on hybrid model | |
| CN112651119B (en) | Multi-performance parameter acceleration degradation test evaluation method for space harmonic reducer | |
| Hong et al. | An iterative model of the generalized Cauchy process for predicting the remaining useful life of lithium-ion batteries | |
| CN108388751B (en) | Virtual-real comparison analysis method based on virtual test system and actual system | |
| CN110442911B (en) | High-dimensional complex system uncertainty analysis method based on statistical machine learning | |
| CN114290960A (en) | Method and device for acquiring battery health degree of power battery and vehicle | |
| CN112100574A (en) | Resampling-based AAKR model uncertainty calculation method and system | |
| CN111025041A (en) | Electric vehicle charging pile monitoring method and system, computer equipment and medium | |
| CN109978259A (en) | Method and system for predicting residual life of product based on q-type Weibull distribution | |
| CN113158589B (en) | Simulation model calibration method and device of battery management system | |
| CN106056305A (en) | Power generation system reliability rapid assessment method based on state clustering | |
| CN117035563B (en) | Product quality safety risk monitoring method, device, monitoring system and medium | |
| CN116128690B (en) | Carbon emission cost value calculation method, device, equipment and medium | |
| CN110069809B (en) | Uncertainty analysis method in GO method based on Monte Carlo simulation method | |
| CN117391463A (en) | River pollution tracing method | |
| CN116823067A (en) | Method and device for determining water quality cleaning state of pipe network and electronic equipment | |
| CN103078850B (en) | Distributed Open Environments is supported the transmission Trust Values Asses method of cooperation safety | |
| CN115951292A (en) | Electronic voltage transformer error state online evaluation method and device | |
| CN115640542A (en) | Intelligent electric energy meter state evaluation method and evaluation device based on Bayesian theory | |
| Peng et al. | Developing a reliability model of CNC system under limited sample data based on multisource information fusion | |
| CN115453447A (en) | Online detection method for out-of-tolerance electric meter based on suspected electric meter stepwise compensation rejection |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |