Risk assessment method for LNG storage tank area spray water system based on improved FMEA
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJIN UNIV
- Filing Date
- 2026-02-10
- Publication Date
- 2026-06-19
Smart Images
Figure CN122243181A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of risk assessment technology, and in particular to a risk assessment method for LNG tank farm spray water systems based on improved FMEA. Background Technology
[0002] As a critical facility in the entire LNG industry chain, liquefied natural gas (LNG) storage tank areas store large quantities of hazardous media. Leaks can easily trigger catastrophic accidents such as fires and explosions, posing serious threats to personnel, the environment, and the economy. Spray water systems, as an active safety barrier for LNG storage tank areas, prevent accident escalation and fire domino effects through cooling, fire suppression, and thermal insulation. Therefore, conducting a comprehensive and systematic risk assessment of the LNG storage tank area's spray water system using reliable risk analysis methods is of paramount importance to ensuring the safety of the LNG storage tank area.
[0003] Failure Mode and Effects Analysis (FMEA) is a systematic, preventative risk assessment tool designed to identify potential failure modes, analyze their possible causes and effects, and assess their risk levels. This allows for proactive measures to be taken during the product or system design phase to prevent or reduce the occurrence and consequences of failures. Traditional FMEA typically uses a 10-point scale to score severity (S), occurrence (O), and detectability (D) to calculate a Risk Priority Number (RPN) to prioritize potential failure modes. While traditional FMEA has significant advantages in identifying and mitigating potential risks, it has several limitations in practical applications, including ignoring ambiguity and uncertainty in the assessment information, the problem of identical RPNs, lack of relative weights between risk indicators, and neglect of the correlation between risk indicators. Summary of the Invention
[0004] This invention aims to address at least one of the technical problems existing in related technologies. To this end, this invention provides a risk assessment method for LNG tank farm spray water systems based on an improved FMEA (Functional Factor Analysis and Evaluation). It employs an improved Decision Experiment and Evaluation Laboratory Method (DEMATEL) and entropy weight method to calculate the subjective and objective weights of risk indicators, determining the comprehensive weight of each risk indicator; and utilizes an improved CoCoSo method to determine and rank the risk priorities of failure modes. This invention expands the scope of fuzzy information expression, improves the flexibility of the assessment process, and considers the interrelationships between evaluation information, thus obtaining more reliable risk assessment results.
[0005] This invention provides a risk assessment method for LNG tank farm spray water systems based on improved FMEA, comprising: S1: Form an expert group and assign an expert weight to each expert. Based on the LNG tank parameters and historical failure and accident data of the LNG tank area spray water system, determine the potential failure modes and risk indicators of the LNG tank area spray water system. S2: Divide the risk levels and construct a Fermat fuzzy language terminology set based on the risk levels. Each expert evaluates the risk level of each failure mode under each risk index based on the Fermat fuzzy language terminology set and converts the evaluation results into Fermat fuzzy numbers for each expert. S3: Calculate the comprehensive evaluation of failure modes by each risk index using the Fermat fuzzy weighted average operator, the Fermat fuzzy number of each expert, and the expert weights; S4: The subjective weights of each risk indicator are determined using the improved DEMATEL method, the objective weights of each risk indicator are determined using the entropy weight method, and the subjective and objective weights of each risk indicator are linearly weighted to obtain the comprehensive weight of each risk indicator. S5: The CoCoSo method is extended by Fermat fuzzy weighted average operator and Fermat fuzzy geometric weighted average operator. It combines the comprehensive evaluation of failure modes by each risk indicator and the comprehensive weight of each risk indicator. The evaluation score of each failure mode by each risk indicator is calculated by the extended CoCoSo method. The risk priority is ranked according to the evaluation score of each failure mode by each risk indicator.
[0006] Furthermore, the calculation expression for the comprehensive evaluation of failure modes by each risk indicator is as follows: in, For the expert panel based on the first The risk indicator for the first A comprehensive evaluation of each failure mode. For the first The risk indicator for the first Membership degree of each failure mode expert evaluation. For the first The risk indicator for the first Non-membership degree of failure mode expert evaluation For the Fermat fuzzy weighted average operator, For experts The importance of assessing the impact, For experts The importance of assessing the impact, For the first The expert based on the first The risk indicator for the first Fermat fuzzy number evaluation of each failure mode For one of the experts , For experts The weight, To remove experts Another expert in the expert group , For experts The weight, For experts Based on the The risk indicator for the first Membership degree of each failure mode evaluation, For experts Based on the The risk indicator for the first Non-membership degree of failure mode evaluation For experts Membership degree of evaluation For experts The degree of non-membership in the evaluation These are parameters that affect the sensitivity to extreme evaluations during the aggregation process.
[0007] Furthermore, the subjective weights for each risk indicator are determined using an improved version of DEMATEL, including: S411: Based on the Fermat fuzzy language terminology set, each expert evaluates the degree of mutual influence among various risk indicators and obtains the Fermat fuzzy direct influence matrix for each expert. S412: Calculate the comprehensive direct impact evaluation matrix of each risk indicator based on the Fermat fuzzy weighted average operator, expert weights, and the Fermat fuzzy direct impact matrix of each expert; S413: Defuzzify the comprehensive direct impact evaluation matrix of each risk indicator using the Fermat fuzzy scoring function to obtain a clear comprehensive direct impact matrix; S414: Normalize the clear integrated direct influence matrix, and determine the total influence matrix based on the normalized direct influence matrix; S415: Calculate the subjective weights of each risk indicator based on the total impact matrix.
[0008] Furthermore, the objective weights of each risk indicator are determined using the entropy weight method, including: S421: Normalize the comprehensive evaluation of each risk indicator on the failure mode to obtain the normalized evaluation matrix of each risk indicator. S422: Calculate the entropy value of each risk indicator based on the normalized evaluation matrix of each risk indicator; S423: Calculate the objective weight of each risk indicator based on its entropy value.
[0009] Furthermore, step S5 includes: S51: Extending the CoCoSo method by using the Fermat fuzzy weighted average operator and the Fermat fuzzy geometric weighted average operator; S52: Based on the extended CoCoSo method, the weighted sum measure of the normalized evaluation matrix of each risk indicator and the weighted product measure of the normalized evaluation matrix of each risk indicator are calculated by combining the comprehensive evaluation of each risk indicator on the failure mode and the comprehensive weight of each risk indicator. S53: Calculate the weighted sum of the normalized evaluation matrix and the weighted product of the normalized evaluation matrix of each risk indicator using the Fermat fuzzy scoring function. S54: Calculate the compromise score of each risk indicator based on the weighted sum of the normalized evaluation matrix and the weighted product of the normalized evaluation matrix. S55: Calculate the evaluation score of each risk indicator for each failure mode based on the compromise score of each risk indicator; S56: Prioritize the risk of each failure mode based on the evaluation scores of each risk indicator.
[0010] Furthermore, potential failure modes in the LNG tank area spray water system include water supply failure, power drive failure, pipeline and valve failure, nozzle function failure, control and monitoring failure, maintenance and management failure, and environmental adaptability failure. Risk indicators include severity, occurrence, and detectability.
[0011] Furthermore, the risk levels of LNG storage tank spray water system failure include extremely low, very low, low, slightly low, medium, slightly high, high, very high, and extremely high.
[0012] Furthermore, the risk levels correspond one-to-one with Fermat fuzzy numbers.
[0013] This invention also provides a risk assessment device for an LNG tank farm spray water system based on improved FMEA, used to perform the aforementioned risk assessment method for an LNG tank farm spray water system based on improved FMEA, comprising: The identification module forms an expert group and assigns an expert weight to each expert. Based on the LNG storage tank parameters and historical failure and accident data of the LNG storage tank area spray water system, it determines the potential failure modes and risk indicators of the LNG storage tank area spray water system. The risk assessment module divides risk levels and constructs a Fermat fuzzy language terminology set based on the risk levels. Each expert evaluates the risk level of each failure mode under each risk indicator based on the Fermat fuzzy language terminology set, and converts the evaluation results into Fermat fuzzy numbers for each expert. The comprehensive evaluation module calculates the comprehensive evaluation of each risk index on the failure mode using the Fermat fuzzy weighted average operator, the Fermat fuzzy number of each expert, and the expert weights. The weight calculation module uses an improved DEMATEL to determine the subjective weight of each risk indicator, uses the entropy weight method to determine the objective weight of each risk indicator, and linearly weights the subjective weight and objective weight of each risk indicator to obtain the comprehensive weight of each risk indicator. The risk ranking module extends the CoCoSo method using the Fermat fuzzy weighted average operator and the Fermat fuzzy geometric weighted average operator. It combines the comprehensive evaluation of failure modes by each risk indicator and the comprehensive weight of each risk indicator. The extended CoCoSo method is used to calculate the evaluation score of each failure mode by each risk indicator and to rank the risks based on the evaluation scores of each failure mode by each risk indicator.
[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described risk assessment method for an LNG tank area spray water system based on an improved FMEA.
[0015] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: This invention addresses the problem that existing research cannot accurately represent expert evaluation information. It utilizes Fermat fuzzy numbers to represent expert verbal evaluations, handling the randomness and uncertainty of expert assessments. The CoCoSo method is integrated into the FMEA method, prioritizing failure mode risks through multi-criteria decision-making, and further improved to make the risk assessment process more flexible and the results more reliable. To address the issue that traditional FMEA methods do not consider expert weights and risk indicator weights, expert weights are assigned based on expert information, and risk indicator weights are determined comprehensively using an improved Dematel and entropy weight method, resulting in more accurate risk assessment results. This invention expands the scope of fuzzy information representation, improves the flexibility of the assessment process, and considers the interrelationships between evaluation information, leading to more reliable risk assessment results.
[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0018] Figure 1 This is a flowchart illustrating a risk assessment method for LNG tank area spray water systems based on an improved FMEA, provided by the present invention.
[0019] Figure 2 This is a schematic diagram of the structure of a risk assessment device for an LNG tank area spray water system based on an improved FMEA, provided by the present invention.
[0020] Figure label: 101. Identification Module; 102. Risk Assessment Module; 103. Comprehensive Evaluation Module; 104. Weight Calculation Module; 105. Risk Ranking Module. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.
[0022] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0023] The following is combined Figures 1 to 2 This invention describes a risk assessment method for LNG tank farm spray water systems based on an improved FMEA.
[0024] Fermat fuzzy sets are an extension of fuzzy mathematics. They are defined as having a sum of cubes of membership and non-membership less than 1, which expands the range of fuzzy information that can better handle the uncertainty of human cognition. Therefore, they are widely used in risk assessment, decision analysis, control systems and other fields.
[0025] As a commonly used multi-attribute decision analysis method, the CoCoSo method systematically scores solutions from three dimensions by integrating aggregation strategies such as simple weighting and exponential weighted product to seek the optimal compromise solution. This multi-perspective fusion mechanism not only enhances the robustness of the decision results but also significantly improves the discriminative power and reliability of the solution ranking.
[0026] like Figure 1 As shown, a risk assessment method for LNG tank farm spray water systems based on improved FMEA includes: S1: Form an expert group and assign an expert weight to each expert. Based on the LNG tank parameters and historical failure and accident data of the LNG tank area spray water system, determine the potential failure modes and risk indicators of the LNG tank area spray water system. In some specific embodiments of the present invention, an expert panel consisting of five experts from various fields is formed, and each expert is assigned a weight based on their individual qualifications. As the first expert Expert weighting, , The second expert Expert weighting, , The third expert Expert weighting, , The fourth expert Expert weighting, , The fifth expert Expert weighting, .
[0027] Acquire LNG tank parameters and historical failure and accident data of the LNG tank area spray water system, and acquire LNG tank area spray water system parameters; LNG storage tank parameters include tank capacity, pressure, temperature, cryogenic characteristics of the stored medium (LNG), and layout of the tank area; Sprinkler system parameters: nozzle type, layout density, pipe material, pipe diameter, pressure rating, pump flow rate, head, power, valve type and control method, pipe network topology, etc.
[0028] Based on LNG storage tank parameters, spray water system parameters, and historical failure and accident data of the LNG storage tank area spray water system, the failures of the water supply source were identified. Power drive failure Pipeline and valve failure Spray head malfunction Control and monitoring failures Maintenance and management failure Environmental adaptability failure Potential failure modes in the spray system of LNG storage tank area.
[0029] Define risk indicators: Based on the data-quantified frequency of risk occurrence, risk indicators are divided into severity (S), occurrence (O), and detectability (D).
[0030] Therefore, potential failure modes in the LNG tank area spray system include water supply failure. Power drive failure Pipeline and valve failure Spray head malfunction Control and monitoring failures Maintenance and management failure Environmental adaptability failure ; Risk indicators include severity (S), occurrence (O), and detectability (D).
[0031] S2: Divide the risk levels and construct a Fermat fuzzy language terminology set based on the risk levels. Each expert evaluates the risk level of each failure mode under each risk index based on the Fermat fuzzy language terminology set and converts the evaluation results into Fermat fuzzy numbers for each expert. Risk levels were categorized, and a Fermat fuzzy language terminology set was constructed based on these risk levels, with each risk level corresponding to a Fermat fuzzy number. Experts were invited to sequentially... Risk indicators right Failure modes The evaluation was conducted using Fermat's fuzzy language terminology to obtain individual language evaluations from each expert; these individual language evaluations were then converted into corresponding Fermat fuzzy numbers.
[0032] In some specific embodiments of the present invention, the risk level of failure of the LNG storage tank spray water system is divided into 9 levels: extremely low (EL), very low (VL), low (L), moderately low (ML), medium (M), moderately high (MH), high (H), very high (VH), and extremely high (EH). The correspondence between each risk level and the Fermat fuzzy number is shown in Table 1. Experts were invited to evaluate the risk level of each failure mode under each risk index, and their individual evaluations were obtained, as shown in Table 2. Based on Table 1, the experts' individual evaluations were converted into Fermat fuzzy evaluations, and the calculation expression is: in, For the first The expert based on the first Risk indicators For the Failure modes Fermat fuzzy number evaluation, For the first The risk indicator for the first The failure mode is the first Membership degree of expert evaluation, For the first The risk indicator for the first The failure mode is the first The degree of non-membership as evaluated by experts.
[0033] Table 1. Correspondence between risk levels and Fermat fuzzy numbers
[0034] Table 2 Individual Evaluations from Each Expert
[0035] S3: Calculate the comprehensive evaluation of each risk index on the failure mode using Fermatean Fuzzy Dombi Weighted HeronianMean operators (FFDWHM), the Fermate number of each expert, and the expert weights. By improving FMEA using FFDWHM and considering expert weights, a comprehensive evaluation matrix of failure modes based on risk indicators by the expert panel is obtained. , , in, For the expert panel based on the first The risk indicator for the first A comprehensive evaluation of each failure mode. For the first The risk indicator for the first Membership degree of each failure mode expert evaluation. For the first The risk indicator for the first Non-membership degree of failure mode expert evaluation For the Fermat fuzzy weighted average operator, For experts The importance of assessing the impact, For experts The importance of assessing the impact, , , For the first The expert based on the first The risk indicator for the first Fermat fuzzy number evaluation of each failure mode For one of the experts , For experts The weight, To remove experts Another expert in the expert group , For experts The weight, For experts Based on the The risk indicator for the first Membership degree of each failure mode evaluation, For experts Based on the The risk indicator for the first Non-membership degree of failure mode evaluation For experts Membership degree of evaluation For experts The degree of non-membership in the evaluation The parameters that affect the sensitivity to extreme evaluations during the aggregation process, The number of risk indicators, The number of failure modes, For the number of experts, .
[0036] In some specific embodiments of the present invention , , , , .
[0037] The expert panel's overall evaluation is shown in Table 3.
[0038] Table 3. Overall Evaluation by the Expert Panel
[0039] S4: The subjective weights of each risk indicator are determined using the improved DEMATEL method, the objective weights of each risk indicator are determined using the entropy weight method, and the subjective and objective weights of each risk indicator are linearly weighted to obtain the comprehensive weight of each risk indicator. S411: Based on the Fermat fuzzy terminology set, experts evaluate the degree of mutual influence among various risk indicators, obtaining the Fermat fuzzy direct influence matrix for each expert; the calculation expression is: in, For experts Fermat blur directly affects the matrix. For the first The first expert gave the first Risk indicators For the Risk indicators Directly affects the evaluation of Fermat fuzzy numbers. For the first Each expert directly influences the membership degree of the evaluation. For the first Each expert directly influences the degree of non-membership in the evaluation. The evaluation results of the experts on the degree of mutual influence among the various risk indicators are shown in Table 4.
[0040] Table 4 Evaluation results of the degree of mutual influence of risk indicators among experts
[0041] S412: Calculate the comprehensive direct impact evaluation matrix for each risk indicator based on the Fermat fuzzy weighted average operator, expert weights, and the Fermat fuzzy direct impact matrix of each expert. The calculation expression is as follows: in, To comprehensively evaluate the direct impact matrix, The first one given by the expert panel Risk indicators For the Risk indicators The overall impact directly affects the evaluation. The degree of membership is the result of the comprehensive evaluation by the expert panel. The degree of non-membership as comprehensively evaluated by the expert panel. For experts The given first Risk indicators For the Risk indicators membership degree For experts The given first Risk indicators For the Risk indicators non-membership degree, For experts Membership degree of evaluation For experts The non-membership degree of the evaluation and the comprehensive direct impact assessment of the risk indicators by the expert panel are shown in Table 5.
[0042] Table 5. Expert Panel's Comprehensive Direct Impact Assessment of Risk Indicators
[0043] S413: Defuzzify the comprehensive direct impact evaluation matrix of each risk indicator using the Fermat fuzzy scoring function to obtain a clear comprehensive direct impact matrix. The calculation expression is: in, For the first Risk indicators For the Each risk indicator has a clear and comprehensive direct impact. A clear and comprehensive direct impact, This is the Fermat fuzzy scoring function.
[0044] S414: Normalize the clear integrated direct influence matrix, and determine the total influence matrix based on the normalized direct influence matrix; in, For the normalized direct influence matrix, The normalization coefficient is... To find the maximum value; The formula for calculating the total influence matrix is: in, The overall influence matrix, For the first Risk indicators For the Risk indicators The overall impact between them.
[0045] The clear comprehensive direct impact matrix is shown in Table 6, the normalized clear direct impact matrix is shown in Table 7, and the total impact matrix is shown in Table 8.
[0046] Table 6. Clear Comprehensive Direct Influence Matrix
[0047] Table 7. Clarity Direct Impact Matrix of Normalization
[0048] Table 8 Overall Impact Matrix
[0049] S415: Calculate the subjective weights of each risk indicator based on the overall impact matrix. The calculation expression is as follows: in, For the first The subjective weight of each risk indicator, The first in the total influence matrix The sum of the rows represents a risk indicator. Overall impact on other risk indicators; The first in the total influence matrix The sum of the columns represents the risk indicators. The risk is influenced by the total impact of other risk indicators. The subjective weight calculation results for each risk indicator are shown in Table 9. R represents the total impact of the risk indicators, and C represents the degree to which the risk indicators are affected.
[0050] Table 9 Subjective weights of each risk indicator
[0051] The objective weights for each risk indicator are determined using the entropy weight method, including: S421: Normalize the comprehensive evaluation of each risk indicator on the failure mode to obtain the normalized evaluation matrix of each risk indicator. ; in, For the expert panel based on the first Risk indicators For the Failure modes Comprehensive normalized evaluation, For the normalized evaluation of membership degree, For the non-membership degree of the normalized evaluation, For revenue attributes; In some specific embodiments of the present invention, the risk indicators S, O, and D all belong to the return attribute B, so the normalized matrix does not change.
[0052] S422: Calculate the entropy value of each risk indicator based on the normalized evaluation matrix of each risk indicator. The calculation expression is as follows: in, For the first Risk indicators The entropy value, For the expert panel based on the first Risk indicators For the Failure modes The entropy value of the comprehensive normalized evaluation, For Fermat fuzzy scoring function, For the expert panel based on the first Risk indicators For the Failure modes The degree of hesitation in the comprehensive normalized evaluation.
[0053] S423: Calculate the objective weight of each risk indicator based on its entropy value. The calculation expression is as follows: in, For the first The objective weights of each risk indicator are shown in Table 10.
[0054] Table 10 Objective weights of each risk indicator
[0055] The comprehensive weight of each risk indicator is obtained through linear weighting, and the calculation expression is as follows: in, For the first The combined weight of each risk indicator This is a proportionality coefficient, representing the proportion of subjective weight. The proportion representing objective weight. .
[0056] In some specific embodiments of the present invention This yields the comprehensive weight of each risk indicator, and the overall severity is determined. , Occurrence degree comprehensive weight , Comprehensive weight of detection degree , .
[0057] S5: The extended CoCoSo method is used to prioritize the risk of each failure mode, and the risk assessment of the LNG tank area spray water system is carried out based on the ranking results. S51: Extending the CoCoSo method by using the Fermatean Fuzzy Dombi Weighted Geometric Heronian Mean operator (FFDWGHM); S52: Based on the extended CoCoSo method, combining the comprehensive evaluation of failure modes by each risk indicator and the comprehensive weight of each risk indicator, the weighted sum measure of the normalized evaluation matrix of each risk indicator and the weighted product measure of the normalized evaluation matrix of each risk indicator are calculated. The calculation expression is as follows: in, For the first Failure modes The weighted sum and measure of WSM, For the first Failure modes The weighted product measure WPM For the membership degree of WSM, For the non-membership degree of WSM, For Fermat's fuzzy geometric weighted average operator, For WPM's membership degree, For WPM's non-membership degree, For the first The weight of each risk indicator For the first The weight of each risk indicator For the expert panel based on the first The risk indicator for the first Failure modes The membership degree of the comprehensive normalized evaluation. For the expert panel based on the first The risk indicator for the first Failure modes The non-membership degree of the comprehensive normalized evaluation, For the expert panel based on the first The risk indicator for the first Failure modes The membership degree of the comprehensive normalized evaluation. For the expert panel based on the first The risk indicator for the first Failure modes The non-membership degree of the comprehensive normalized evaluation, For the expert panel based on the first Risk indicators For the Failure modes A comprehensive normalized evaluation.
[0058] In some specific embodiments of the present invention, the calculation results of WSM and WPM are shown in Table 11.
[0059] Table 11 Calculation results of WSM and WPM
[0060] S53: Calculate the weighted sum of the normalized evaluation matrix and the weighted product of the normalized evaluation matrix of each risk indicator using the Fermat fuzzy scoring function. in, for The weighted sum measure score is calculated based on the Fermat fuzzy scoring function. for The weighted product measure score is calculated based on the Fermat fuzzy scoring function. S54: Calculate the compromise score of each risk indicator based on the weighted sum of the normalized evaluation matrix and the weighted product of the normalized evaluation matrix. in, For the first Failure modes of and The arithmetic mean of the scores For the first Failure modes of and Total score To achieve a balanced and fair compromise in scoring, For balance coefficient, , The safety and adaptability of the CoCoSo method are determined. To find the minimum value.
[0061] S55: Calculate the evaluation score of each risk indicator for each failure mode based on the compromise score of each risk indicator. The calculation expression is as follows: in, For the first Failure modes The final evaluation score.
[0062] S56: Prioritize the risk of each failure mode based on the evaluation scores of each risk indicator.
[0063] The higher the rating, the better the failure mode. The higher the risk, the better.
[0064] In some specific embodiments of the present invention, the evaluation scores and risk priority ranking results of each failure mode are shown in Table 12.
[0065] Table 12 Evaluation scores and risk priority ranking results for each failure mode
[0066] As shown in Table 12, the final sorting result is as follows: Monitoring failure, power drive failure, and water supply failure are the main failure modes of the spray water system in LNG storage tank areas. These should be given special attention during routine engineering operation and maintenance to prevent risks from occurring.
[0067] like Figure 2 As shown, a risk assessment device for an LNG tank farm spray system based on improved FMEA is used to perform the aforementioned risk assessment method for an LNG tank farm spray water system based on improved FMEA, comprising: The identification module 101 forms an expert group and assigns an expert weight to each expert. Based on the LNG storage tank parameters and the historical failure and accident data of the LNG storage tank area spray water system, it determines the potential failure modes and risk indicators of the LNG storage tank area spray water system. The risk assessment module 102 divides the risk levels and constructs a Fermat fuzzy language terminology set based on the risk levels. Each expert evaluates the risk level of each failure mode under each risk index based on the Fermat fuzzy language terminology set and converts the evaluation results into Fermat fuzzy numbers for each expert. The comprehensive evaluation module 103 calculates the comprehensive evaluation of each risk index on the failure mode using the Fermat fuzzy weighted average operator, the Fermat fuzzy number of each expert, and the expert weight; The weight calculation module 104 uses an improved DEMATEL to determine the subjective weight of each risk indicator, uses the entropy weight method to determine the objective weight of each risk indicator, and linearly weights the subjective weight and objective weight of each risk indicator to obtain the comprehensive weight of each risk indicator. The risk ranking module 105 extends the CoCoSo method through the Fermat fuzzy weighted average operator and the Fermat fuzzy geometric weighted average operator. It combines the comprehensive evaluation of failure modes by each risk indicator and the comprehensive weight of each risk indicator. It calculates the evaluation score of each failure mode by each risk indicator through the extended CoCoSo method and ranks the risks according to the evaluation scores of each failure mode by each risk indicator.
[0068] By leveraging the collaborative efforts of the aforementioned modules, this invention addresses the problem of existing research failing to accurately represent expert evaluation information. It employs Fermat fuzzy numbers to represent expert verbal evaluations, effectively handling the randomness and uncertainty of expert assessments. The CoCoSo method is integrated into the FMEA method, prioritizing failure mode risks through multi-criteria decision-making, and further improvements make the risk assessment process more flexible and the results more reliable. To address the issue that traditional FMEA methods do not consider expert weights and risk indicator weights, expert weights are assigned based on expert circumstances, and risk indicator weights are determined comprehensively using an improved Dematel and entropy weight method, resulting in more accurate risk assessment results. This invention expands the scope of fuzzy information representation, enhances the flexibility of the assessment process, and considers the interrelationships between evaluation information, leading to more reliable risk assessment results.
[0069] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer is able to execute a risk assessment method for an LNG tank area spray water system based on an improved FMEA provided by the above methods.
[0070] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the aforementioned risk assessment method for an LNG tank farm spray water system based on an improved FMEA.
[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the method of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0072] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical methods, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A LNG storage tank farm spray water system risk assessment method based on improved FMEA, characterized in that, include: S1: Form an expert group and assign an expert weight to each expert. Based on the LNG tank parameters and historical failure and accident data of the LNG tank area spray water system, determine the potential failure modes and risk indicators of the LNG tank area spray water system. S2: Divide the risk levels and construct a Fermat fuzzy language terminology set based on the risk levels. Each expert evaluates the risk level of each failure mode under each risk index based on the Fermat fuzzy language terminology set and converts the evaluation results into Fermat fuzzy numbers for each expert. S3: Calculate the comprehensive evaluation of failure modes by each risk index using the Fermat fuzzy weighted average operator, the Fermat fuzzy number of each expert, and the expert weights; S4: The subjective weights of each risk indicator are determined using the improved DEMATEL method, the objective weights of each risk indicator are determined using the entropy weight method, and the subjective and objective weights of each risk indicator are linearly weighted to obtain the comprehensive weight of each risk indicator. S5: The CoCoSo method is extended by Fermat fuzzy weighted average operator and Fermat fuzzy geometric weighted average operator. It combines the comprehensive evaluation of failure modes by each risk indicator and the comprehensive weight of each risk indicator. The evaluation score of each failure mode by each risk indicator is calculated by the extended CoCoSo method. The risk priority is ranked according to the evaluation score of each failure mode by each risk indicator.
2. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 1, characterized in that, The calculation formula for the comprehensive evaluation of failure modes by each risk indicator is as follows: in, For the expert panel based on the first The risk indicator for the first A comprehensive evaluation of each failure mode. For the first The risk indicator for the first Membership degree of each failure mode expert evaluation. For the first The risk indicator for the first Non-membership degree of failure mode expert evaluation For the Fermat fuzzy weighted average operator, For experts The importance of assessing the impact, For experts The importance of assessing the impact, For the first The expert based on the first The risk indicator for the first Fermat fuzzy number evaluation of each failure mode For one of the experts , For experts The weight, To remove experts Another expert in the expert group , For experts The weight, For experts Based on the The risk indicator for the first Membership degree of each failure mode evaluation, For experts Based on the The risk indicator for the first Non-membership degree of failure mode evaluation For experts Membership degree of evaluation For experts The degree of non-membership in the evaluation These are parameters that affect the sensitivity to extreme evaluations during the aggregation process.
3. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 1, characterized in that, The subjective weights for each risk indicator were determined using an improved version of DEMATEL, including: S411: Based on the Fermat fuzzy language terminology set, each expert evaluates the degree of mutual influence among various risk indicators and obtains the Fermat fuzzy direct influence matrix for each expert. S412: Calculate the comprehensive direct impact evaluation matrix of each risk indicator based on the Fermat fuzzy weighted average operator, expert weights, and the Fermat fuzzy direct impact matrix of each expert; S413: Defuzzify the comprehensive direct impact evaluation matrix of each risk indicator using the Fermat fuzzy scoring function to obtain a clear comprehensive direct impact matrix; S414: Normalize the clear integrated direct influence matrix, and determine the total influence matrix based on the normalized direct influence matrix; S415: Calculate the subjective weights of each risk indicator based on the total impact matrix.
4. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 1, characterized in that, The objective weights for each risk indicator are determined using the entropy weight method, including: S421: Normalize the comprehensive evaluation of each risk indicator on the failure mode to obtain the normalized evaluation matrix of each risk indicator. S422: Calculate the entropy value of each risk indicator based on the normalized evaluation matrix of each risk indicator; S423: Calculate the objective weight of each risk indicator based on its entropy value.
5. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 4, characterized in that, The S5 steps include: S51: Extending the CoCoSo method by using the Fermat fuzzy weighted average operator and the Fermat fuzzy geometric weighted average operator; S52: Based on the extended CoCoSo method, the weighted sum measure of the normalized evaluation matrix of each risk indicator and the weighted product measure of the normalized evaluation matrix of each risk indicator are calculated by combining the comprehensive evaluation of each risk indicator on the failure mode and the comprehensive weight of each risk indicator. S53: Calculate the weighted sum of the normalized evaluation matrix and the weighted product of the normalized evaluation matrix of each risk indicator using the Fermat fuzzy scoring function. S54: Calculate the compromise score of each risk indicator based on the weighted sum of the normalized evaluation matrix and the weighted product of the normalized evaluation matrix. S55: Calculate the evaluation score of each risk indicator for each failure mode based on the compromise score of each risk indicator; S56: Prioritize the risk of each failure mode based on the evaluation scores of each risk indicator.
6. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 1, characterized in that, Potential failure modes in the LNG tank area spray water system include water supply failure, power drive failure, pipeline and valve failure, nozzle function failure, control and monitoring failure, maintenance and management failure, and environmental adaptability failure. Risk indicators include severity, occurrence, and detectability.
7. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 1, characterized in that, The risk levels of LNG storage tank spray water system failure include extremely low, very low, low, slightly low, medium, slightly high, high, very high, and extremely high.
8. The risk assessment method for LNG tank farm spray water system based on improved FMEA according to claim 1, characterized in that, The risk level corresponds one-to-one with the Fermat fuzzy number.
9. A risk assessment device for LNG tank farm spray water system based on improved FMEA, characterized in that, To implement the risk assessment method for an LNG tank farm spray water system based on an improved FMEA as described in any one of claims 1 to 8, comprising: The identification module forms an expert group and assigns an expert weight to each expert. Based on the LNG storage tank parameters and historical failure and accident data of the LNG storage tank area spray water system, it determines the potential failure modes and risk indicators of the LNG storage tank area spray water system. The risk assessment module divides risk levels and constructs a Fermat fuzzy language terminology set based on the risk levels. Each expert evaluates the risk level of each failure mode under each risk indicator based on the Fermat fuzzy language terminology set, and converts the evaluation results into Fermat fuzzy numbers for each expert. The comprehensive evaluation module calculates the comprehensive evaluation of each risk index on the failure mode using the Fermat fuzzy weighted average operator, the Fermat fuzzy number of each expert, and the expert weights. The weight calculation module uses an improved DEMATEL to determine the subjective weight of each risk indicator, uses the entropy weight method to determine the objective weight of each risk indicator, and linearly weights the subjective weight and objective weight of each risk indicator to obtain the comprehensive weight of each risk indicator. The risk ranking module extends the CoCoSo method using the Fermat fuzzy weighted average operator and the Fermat fuzzy geometric weighted average operator. It combines the comprehensive evaluation of failure modes by each risk indicator and the comprehensive weight of each risk indicator. The extended CoCoSo method is used to calculate the evaluation score of each failure mode by each risk indicator and to rank the risks based on the evaluation scores of each failure mode by each risk indicator.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements a risk assessment method for an LNG tank farm spray water system based on an improved FMEA, as described in any one of claims 1 to 8.