Method for evaluating failure probability of pressure vessel beyond design service life

Abstract

The invention belongs to the technical field of pressure vessel risk assessment, and discloses a failure probability assessment method for a pressure vessel beyond design service life, which comprises the following steps: establishing a risk assessment index system; determining the weight of each risk factor based on an analytic hierarchy process, an entropy weight method, a CRITIC weight method and a minimum identification information principle through the subjective experience of the expert group and the actual collection values of the risk factors; and determining the failure probability value and grade of the pressure vessel exceeding the design service life based on a multi-level grey comprehensive evaluation method through the evaluation score of the expert group on the risk factor, and applying the failure probability value and grade to subsequent risk evaluation. According to the method, a pressure vessel failure probability evaluation index system exceeding the design service life is established, the uncertainty in the evaluation process is fully considered, the contradiction that the importance of risk factors is the same and does not accord with the actual situation in an existing method is solved, weight distribution is optimized, and a more scientific and reasonable evaluation result is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk assessment of pressure vessels, and in particular relates to a failure probability assessment method for pressure vessels exceeding their design service life. Background Art

[0002] As a special equipment, the operation safety of pressure vessels is very important. With the increase of service time, pressure vessels that exceed the design service life have problems such as aging equipment, low automation, insufficient monitoring and early warning facilities, and rising system failure rate, which increase the safety risks. Therefore, it is of great practical significance to conduct safety risk assessment and control.

[0003] At present, the risk assessment methods for old equipment mainly include quantitative analysis and qualitative analysis, among which qualitative analysis is the most common. In the actual application process, since the current supervision of pressure vessels mainly adopts the principle of leakage before explosion, the failure consequence assessment is mainly carried out for leakage, so its method is basically in a relatively perfect stage; but in terms of the assessment of failure probability, qualitative analysis methods such as safety checklists are mainly adopted. Although such methods are simple and fast, they need to be prepared in advance and cannot be adjusted in time according to actual conditions.

[0004] At present, there are existing standards for the assessment of pressure vessels exceeding their design service life, among which the assessment framework of failure probability is as follows: Figure 1 As shown. During the evaluation process, the equipment correction factor F E It is an important factor that affects the final failure probability assessment result, among which the technical module factor has the greatest impact on the failure probability. However, in the process of determining the technical module factor, this standard determines it by summing up the risk factors, which makes the importance of each risk factor the same. Obviously, this is inconsistent with the actual assessment and there is a large deviation. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating the failure probability of a pressure vessel that has exceeded its design service life, so as to solve the above-mentioned technical problems.

[0006] In order to solve the above technical problems, a specific technical solution of the present invention for a method for evaluating the failure probability of a pressure vessel exceeding its design service life is as follows:

[0007] A method for evaluating the failure probability of a pressure vessel exceeding its design service life comprises the following steps:

[0008] Step 1: Identify the risk factors of pressure vessel operation failure possibility and build a basic framework for safety risk failure possibility assessment;

[0009] Step 2: Determine the scale of the AHP weight method, and the expert group compares the importance of risk factors and determines the judgment matrix;

[0010] Step 3: Determine the AHP weights based on the judgment matrix determined by the expert group;

[0011] Step 4: Collect data and determine the actual matrix;

[0012] Step 5: According to the evaluation matrix determined by the expert group, the entropy weight and CRITIC weight in the objective weight are determined respectively;

[0013] Step 6: Determine the comprehensive weight according to the minimum discriminant information principle and sort the importance;

[0014] Step 7: Determine the gray class and gray number, and build the whitening weight function based on this;

[0015] Step 8: Determine the final failure probability and failure level based on the multi-level grey comprehensive evaluation method, and focus on the risk factors with the highest importance ranking, establish a safety risk plan, and eliminate them when necessary.

[0016] Furthermore, in step 1, a hierarchical risk assessment indicator system including primary, secondary and tertiary indicators is established for the technical module factor. The primary indicator represents the status of the technical module factor, the secondary indicator represents the five major factors leading to the increase of the technical module factor, and the tertiary indicator represents the root cause of the increase of the technical module factor.

[0017] Furthermore, in step 2, multiple experts are invited to rank the importance of adjacent indicators of the second and third level indicators and construct a judgment matrix; the expert group should use the risk factors of two adjacent items to compare each other to determine the judgment matrix:

[0018]

[0019] Furthermore, the implementation steps of determining the weight of the hierarchical analysis method in step 3 are as follows:

[0020] Step 3.1: Perform geometric averaging and normalization on the judgment matrix to obtain the weights of the second and third level indicators:

[0021]

[0022] Step 3.2: Multiply the corresponding second- and third-level indicators to obtain the overall weight of the third-level indicator.

[0023] Furthermore, in step 4, for the risk factors of the third-level indicators, if the data is easy to collect, the actual values ​​are used; if the data is not easy to collect, the expert scoring method is used to construct an actual matrix:

[0024]

[0025] Among them, a ij It represents the evaluation value, m and n represent the number of risk factors of the third-level indicators and the number of experts in the group, respectively.

[0026] Furthermore, the implementation steps of determining the entropy weight and the CRITIC weight in step 5 are as follows:

[0027] Step 5.1: Normalize the actual matrix:

[0028] Positive indicators:

[0029]

[0030] Reverse indicators:

[0031]

[0032] Step 5.2: Calculate the proportion of the three-level indicators:

[0033]

[0034] Step 5.3: Calculate the entropy value of the third-level indicator:

[0035]

[0036] Step 5.4: Calculate the entropy weight of the three-level indicators:

[0037]

[0038] Step 5.5: Calculate the standard deviation of the third-level indicators, which reflects the volatility between indicators:

[0039]

[0040] Step 5.6: Calculate the correlation coefficient of the indicators, which reflects the degree of conflict between the indicators:

[0041]

[0042] Among them, r ij represents the correlation coefficient between the i-th indicator and the j-th indicator;

[0043] Step 5.7: Calculate the CRITIC weight of the third-level indicator:

[0044]

[0045] Furthermore, the objective function is established in step 6 as follows. When the sum of the identification information represented by the weights determined by different weight calculation methods is the smallest, the comprehensive weight determined by the weights should be the weight closest to reality:

[0046]

[0047] By constructing the Lagrangian function to solve, the comprehensive weight is obtained:

[0048]

[0049] Furthermore, the step seven determines 9 gray categories, namely, excellent, excellent, relatively excellent, good, relatively good, relatively poor, bad and extremely poor, and determines the corresponding gray numbers, namely, 95, 90, 85, 80, 75, 70, 60, 50 and 40, and constructs a whitening weight function based on this.

[0050] Furthermore, the specific steps of step eight are as follows:

[0051] Step 8.1: Invite the expert group to score each level 3 indicator and construct an evaluation matrix:

[0052]

[0053] Step 8.2: Calculate the evaluation coefficient of the i-th risk factor and the e-th evaluation gray class, and normalize them to obtain the gray evaluation matrix:

[0054] R=(r ie ) 41×9 ,

[0055]

[0056] Among them, m and n are the total number of risk factors and the number of expert groups, respectively;

[0057] Step 8.2: Calculate the comprehensive evaluation vector of the technical module factor:

[0058] M=(w×R) 1×9 ,

[0059] Step 8.3: Calculate the comprehensive evaluation value of the technical module factor based on the gray number vector O:

[0060]

[0061] Step 8.4: Determine the equipment correction factor F E and failure probability F are 23.78 and

[0062] 1.71×10 -4, the failure probability level is determined to be level 3, which provides an important basis for subsequent risk assessment.

[0063] F=F G ×F M ×F E ×F L .

[0064] The method for evaluating the failure probability of pressure vessels exceeding their design service life of the present invention has the following advantages: the present invention fully considers the uncertainty in the evaluation process by establishing an evaluation index system for the failure probability of pressure vessels exceeding their design service life, solves the contradiction between the same importance of risk factors in the existing methods and the actual situation, optimizes the weight distribution, and obtains a more scientific and reasonable evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A framework diagram for determining the failure probability of the method in an embodiment of the present invention;

[0066] Figure 2 A flowchart of determining technical module factors of the method in an embodiment of the present invention;

[0067] Figure 3 A risk assessment framework diagram constructed by the method in an embodiment of the present invention;

[0068] Figure 4 The evaluation scores and weight diagrams of the three-level indicators in the embodiment of the present invention;

[0069] Figure 5 It is a whitening weight function diagram constructed by the method in the embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to better understand the purpose, structure and function of the present invention, a failure probability assessment method for a pressure vessel exceeding its design service life is further described in detail below in conjunction with the accompanying drawings.

[0071] like Figure 2 As shown, the present invention provides a method for calculating the technical module factor in the failure probability. This embodiment obtains the failure probability level of each risk factor by utilizing expert judgment and objective data, and uses hierarchical analysis method, entropy weight method, CRITIC weight method, and multi-level grey comprehensive evaluation method to process expert opinions or objective data, so as to evaluate the possibility of operation failure of pressure vessels exceeding the design service life, identify key risk factors, and provide an important basis and basis for subsequent operation failure risks, and also provide guarantee for the construction and safe operation of pressure vessels exceeding the design service life.

[0072] The specific steps include:

[0073] Step 1: Identify the risk factors for the possibility of pressure vessel operation failure and build a basic framework for safety risk failure possibility assessment, such as Figure 3 As shown;

[0074] Based on possible risk factors, through accident causal analysis, we classified the relevant factors of risk problems, and investigated each branch step by step, and analyzed and sorted them out. For the technical module factor, we established a hierarchical risk assessment indicator system including primary, secondary and tertiary indicators. The primary indicator represents the status of the technical module factor, the secondary indicator represents the five major factors that lead to the increase of the technical module factor, and the tertiary indicator represents the root cause of the increase of the technical module factor.

[0075] Step 2: Determine the scale t of the AHP weight method, as shown in Table 1. Invite three experts to rank the importance of adjacent indicators of the second and third level indicators and construct a judgment matrix;

[0076] In the traditional analytic hierarchy process, each expert should determine the judgment matrix by comparing risk factors and perform consistency test. However, in the actual operation evaluation process, consistency test is often difficult. Therefore, in step 2, unlike the traditional analytic hierarchy process, the expert group should use two adjacent risk factors to compare and determine the judgment matrix:

[0077]

[0078] Among them, a ij It represents the evaluation value, m and n represent the number of risk factors of the third-level indicators and the number of experts in the group, respectively.

[0079] Table 1 Analytical hierarchy process scale

[0080]

[0081] Step 3: Determine the weights of the AHP method based on the judgment matrix constructed by the three experts’ evaluations;

[0082] The implementation steps for determining the weights of the analytic hierarchy process are:

[0083] Step 3.1: Perform geometric averaging and normalization on the judgment matrix to obtain the weights of the second and third level indicators:

[0084]

[0085] Among them, a ij It represents the evaluation value, m and n represent the number of risk factors of the third-level indicators and the number of experts in the group, respectively.

[0086] Step 3.2: Multiply the corresponding second-level and third-level indicators to obtain the overall weight of the third-level indicators. The results are shown in Table 3:

[0087] Step 4: For the risk factors of the third-level indicators, if the data is easy to collect, the actual values ​​are used. If the data is not easy to collect, the expert scoring method is used to construct the actual matrix:

[0088]

[0089] Step 5: According to the evaluation matrix determined by the three experts, the entropy weight and CRITIC weight in the objective weight are determined respectively;

[0090] The implementation steps for determining the entropy weight and the CRITIC weight are:

[0091] Step 5.1: Normalize the actual matrix:

[0092] Positive indicators:

[0093]

[0094] Reverse indicators:

[0095]

[0096] Step 5.2: Calculate the proportion of the three-level indicators:

[0097]

[0098] Step 5.3: Calculate the entropy value of the third-level indicator:

[0099]

[0100] Step 5.4: Calculate the entropy weight of the three-level indicators:

[0101]

[0102] Step 5.5: Calculate the standard deviation of the third-level indicators, which reflects the volatility between indicators:

[0103] Step 5.6: Calculate the correlation coefficient of the indicators, which reflects the degree of conflict between the indicators:

[0104]

[0105] Among them, r ij Represents the correlation coefficient between the i-th indicator and the j-th indicator.

[0106] Step 5.7: Calculate the CRITIC weight of the third-level indicator:

[0107]

[0108] Step 6: Calculate the comprehensive weight based on the calculated AHP weight, entropy weight, and CRITIC weight;

[0109] Since in the actual security assessment process, data may often be missing to a certain extent, the present invention is based on the principle of minimum identification information. Since the weights calculated by different weight calculation methods represent a certain amount of information to a certain extent, and the comprehensive weight represents the actual amount of information. Therefore, the objective function is established as follows. When the identification information represented by the weights determined by different weight calculation methods is the minimum, the comprehensive weight determined should be the weight closest to reality:

[0110]

[0111] By constructing the Lagrangian function to solve, the comprehensive weight is obtained:

[0112]

[0113] The calculation results of the above steps are shown in Table 2 and Figure 4 As shown:

[0114] Table 2 Calculation results of weights of three-level indicators

[0115]

[0116]

[0117]

[0118] It can be seen that according to the results of weight analysis, since some of the three-level indicators are the same, they need to be merged. From the results, it can be seen that operating pressure, operating temperature, inspection effectiveness, thickness, thinning type, service time, corrosion rate, maximum allowable working pressure, lining service life, and online monitoring of lining failure are the top 10 third-level indicators in importance, and the thinning factor among the second-level indicators is the most important indicator, which should be focused on in subsequent risk prevention and control.

[0119] Step 7: Determine 9 gray categories, namely: excellent, excellent, relatively excellent, good, relatively good, poor, bad and extremely poor, and determine the corresponding gray numbers, which are: 95, 90, 85, 80, 75, 70, 60, 50 and 40, and construct the whitening weight function based on this, such as Figure 5 As shown;

[0120] Step 8. Determine the final failure probability and failure level based on the multi-level grey comprehensive evaluation method, and focus on the risk factors ranked higher in importance, establish a safety risk plan, and eliminate them when necessary.

[0121] The specific steps are as follows:

[0122] Step 8.1: Invite the expert group to score each level 3 indicator and construct an evaluation matrix:

[0123]

[0124] Step 8.2: Calculate the evaluation coefficient of the i-th risk factor and the e-th evaluation gray class, and normalize them to obtain the gray evaluation matrix:

[0125] R=(r ie ) 41×9

[0126]

[0127] Among them, m and n are the total number of risk factors and the number of expert groups, respectively;

[0128] Step 8.2: Calculate the comprehensive evaluation vector of the technical module factor:

[0129] M=(w×R) 1×9

[0130] Step 8.3: Calculate the comprehensive evaluation value of the technical module factor based on the gray number vector O:

[0131]

[0132] Step 8.4: According to Figure 1 Determine its equipment correction factor F E and failure probability F are 23.78 and 1.71×10 -4 , and according to Table 3, the failure probability level is determined to be level 3, which provides an important basis for subsequent risk assessment.

[0133] F=F G ×F M ×F E ×F L .

[0134] Table 3 Failure probability classification table

[0135] Failure probability level F <![CDATA[F E ]]> 1 0.0000000＜F≤0.0000918 <![CDATA[F E ≤3]]> 2 0.0000918＜F≤0.0003060 <![CDATA[3<F E ≤10]]> 3 0.0003060＜F≤0.0030600 <![CDATA[10<F E ≤100]]> 4 0.0030600＜F≤0.0306000 <![CDATA[100<F E ≤1000]]> 5 0.0306000＜F≤1.0000000 <![CDATA[F E >1000]]>

[0136] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for evaluating the failure probability of pressure vessels exceeding their design service life, characterized in that: The steps include: Step 1: Identify the risk factors of pressure vessel operation failure possibility and build a basic framework for safety risk failure possibility assessment; Step 2: Determine the scale of the AHP weight method, and the expert group compares the importance of risk factors and determines the judgment matrix; Step 3: Determine the AHP weights based on the judgment matrix determined by the expert group; Step 4: Collect data and determine the actual matrix; Step 5: According to the evaluation matrix determined by the expert group, the entropy weight and CRITIC weight in the objective weight are determined respectively; Step 6: Determine the comprehensive weight according to the minimum discriminant information principle and sort the importance; Step 7: Determine the gray class and gray number, and build the whitening weight function based on this; Step 8: Determine the final failure probability and failure level based on the multi-level grey comprehensive evaluation method, and focus on the risk factors with the highest importance ranking, establish a safety risk plan, and eliminate them when necessary.

2. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1 is characterized in that: The step 1 establishes a hierarchical risk assessment indicator system for the technical module factor, which includes primary, secondary and tertiary indicators. The primary indicator represents the status of the technical module factor, the secondary indicator represents the five major factors that lead to the increase of the technical module factor, and the tertiary indicator represents the root cause of the increase of the technical module factor.

3. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1 is characterized in that: In step 2, multiple experts are invited to rank the importance of adjacent indicators of the second and third level indicators and construct a judgment matrix; the expert group should use the risk factors of two adjacent items to compare each other to determine the judgment matrix:

4. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1 is characterized in that: The implementation steps of the step 3 to determine the weight of the analytic hierarchy process are as follows: Step 3.1: Perform geometric averaging and normalization on the judgment matrix to obtain the weights of the second and third level indicators: Step 3.2: Multiply the corresponding second- and third-level indicators to obtain the overall weight of the third-level indicator.

5. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1, characterized in that: In step 4, for the risk factors of the third-level indicators, if the data is easy to collect, the actual values ​​are used. If the data is not easy to collect, the expert scoring method is used to construct the actual matrix: Among them, a ij It represents the evaluation value, m and n represent the number of risk factors of the third-level indicators and the number of experts in the group, respectively.

6. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1, characterized in that: The implementation steps of step 5 to determine the entropy weight and CRITIC weight are as follows: Step 5.1: Normalize the actual matrix: Positive indicators: Reverse indicators: Step 5.2: Calculate the proportion of the three-level indicators: Step 5.3: Calculate the entropy value of the third-level indicator: Step 5.4: Calculate the entropy weight of the three-level indicators: Step 5.5: Calculate the standard deviation of the third-level indicators, which reflects the volatility between indicators: Step 5.6: Calculate the correlation coefficient of the indicators, which reflects the degree of conflict between the indicators: Among them, r ij represents the correlation coefficient between the i-th indicator and the j-th indicator; Step 5.7: Calculate the CRITIC weight of the third-level indicator:

7. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1 is characterized in that: The objective function is established in step 6 as follows. When the sum of the identification information represented by the weights determined by different weight calculation methods is the smallest, the comprehensive weight determined by the weights should be the weight closest to reality: By constructing the Lagrangian function to solve, the comprehensive weight is obtained:

8. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1 is characterized in that: The step seven determines 9 gray categories, namely: excellent, excellent, relatively excellent, good, relatively good, relatively poor, poor and extremely poor, and determines the corresponding gray numbers, namely: 95, 90, 85, 80, 75, 70, 60, 50 and 40, and constructs a whitening weight function based on this.

9. The method for evaluating the failure probability of a pressure vessel exceeding its design service life according to claim 1, characterized in that: The specific steps of step eight are as follows: Step 8.1: Invite the expert group to score each level 3 indicator and construct an evaluation matrix: Step 8.2: Calculate the evaluation coefficient of the i-th risk factor and the e-th evaluation gray class, and normalize them to obtain the gray evaluation matrix: R＝(r ie ) 41×9 ， Among them, m and n are the total number of risk factors and the number of expert groups, respectively; Step 8.2: Calculate the comprehensive evaluation vector of the technical module factor: M=(w×R) 1×9 , Step 8.3: Calculate the comprehensive evaluation value of the technical module factor based on the gray number vector O: Step 8.4: Determine the equipment correction factor F E and failure probability F are 23.78 and 1.71×10 -4 , the failure probability level is determined to be level 3, which provides an important basis for subsequent risk assessment. F=F G ×F M ×F E ×F L 。

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