FPSO single point mooring electric slip ring risk assessment method based on heterogeneous information

Abstract

The present application relates to the technical field of data processing, and more particularly to a FPSO single-point mooring electric slip ring risk evaluation method based on heterogeneous information, comprising the following steps: identifying failure modes, and constructing a risk model; initially scoring the occurrence degree of the failure modes, the severity of the failure modes and the detection degree of the failure modes; standardizing the initial scores and calculating comprehensive weights; and determining risk ranking. The method provided by the present application can comprehensively and quantitatively analyze the FPSO single-point mooring electric slip ring, and perform risk identification and evaluation, thereby providing protection and reference for the installation, operation and maintenance and repair of the FPSO single-point mooring electric slip ring.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for risk assessment of single-point mooring electric slip rings for FPSOs based on heterogeneous information. Background Technology

[0002] In the single-point mooring system of a Floating Production Storage and Offloading System (FPSO), electric slip rings play a crucial role in transmitting electrical energy and connecting rotating and non-rotating oil and gas pipelines within the system. They influence not only the structural arrangement and operation of other slip rings but also the overall structure and operation of the slip ring stack, and even the operation of the entire single-point mooring system and the FPSO itself. Current research on single-point mooring electric slip rings primarily focuses on their structural design and arrangement within the slip ring stack. However, research on potential failures and risk assessments of electric slip rings during production and use remains insufficient. Existing studies mostly list known failures and analyze their causes, lacking quantitative analysis and risk assessment of potential failure modes. Risk studies for single-point mooring systems mainly focus on long-term corrosion, design, and installation aspects of the entire system, with little or no mention of research on individual components of the electric slip ring. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a risk assessment method for FPSO single-point mooring electric slip rings based on heterogeneous information. This method can achieve comprehensive quantitative analysis of FPSO single-point mooring electric slip rings, and perform risk identification and assessment, providing assurance and reference for the installation, operation and maintenance of FPSO single-point mooring electric slip rings.

[0004] This invention is achieved through the following technical solution:

[0005] The FPSO single-point mooring electric slip ring risk assessment method based on heterogeneous information includes the following steps:

[0006] E1: Identify the failure modes of the slip rings in a single-point mooring FPSO, and determine the causal relationship between each failure mode based on the structure and arrangement of the slip rings. Use the monitorable failure modes among the identified failure modes as the symptom layer, and the failure modes that cause the monitorable failure modes to become abnormal as the cause layer. Establish a Bayesian network failure diagnosis model based on the causal relationship between the symptom layer and the cause layer, and classify the failure types of each failure mode according to the cause of the failure mode. Establish a risk assessment model based on heterogeneous information based on the failure types of each failure mode.

[0007] E2: Determine the prior probability and conditional probability of the Bayesian network fault diagnosis model based on the probability of occurrence of each fault mode based on experience or historical records, and obtain the probability of occurrence of each fault mode corresponding to each node of the Bayesian network fault diagnosis model through forward reasoning. Then, use the reasoned probability of occurrence as the initial score value of the occurrence degree O of the fault mode in the heterogeneous information risk assessment model.

[0008] E3: Determine the initial score of the severity S of each failure mode based on the severity of the failure mode occurrence, and determine the initial score of the detectability D of each failure mode based on the detection difficulty of the failure mode.

[0009] E4: Standardize the initial scores of the occurrence O, severity S, and detectability D of each failure mode. Calculate the combined weights of the occurrence O, severity S, and detectability D of each failure mode.

[0010] E5: Calculate the total advantage of each failure mode, determine the risk ranking of each failure mode according to the total advantage, and provide maintenance recommendations based on the risk ranking.

[0011] The optimized, monitorable fault modes identified in step E1 include bearing vibration, abnormal torque arm load, excessive slip ring speed, excessive electric slip ring temperature, substandard nitrogen sealing pressure, and substandard nitrogen dew point or cleanliness. Fault modes that cause abnormalities in the monitorable fault modes include uneven slip ring surface during operation, poor slip ring sealing performance or even leakage, non-smooth brush movement, sparking, insufficient brush pressure, poor contact between brush and slip ring, and impurities blocking the inner hole of the brush holder. The failure types of the fault modes include design and selection problems, installation, manufacturing or quality problems, long-term wear or corrosion problems, use or operation problems, and occasional or environmental problems.

[0012] Furthermore, the following method was used to standardize the scoring of each failure mode:

[0013] The initial score of the occurrence degree O of the failure mode is standardized according to equation (1):

[0014]

[0015] in: x represents the standardized score of the occurrence degree O of the failure mode, and x represents the initial score of the occurrence degree O of the failure mode. min x represents the minimum initial score value of the occurrence degree O of all failure modes. maxThis represents the maximum value among the initial scores for the occurrence degree O of all failure modes;

[0016] The initial score of the severity S of the failure mode is standardized according to equation (2):

[0017] Δ -1 (s i ,α)=i / 5+α (2);

[0018] Where: s i This represents the score assigned to each level of the severity S of the failure mode, where i = 0, 1, 2, 3, 4, 5 represents the level corresponding to the severity S of the failure mode, and α represents the correction value for the severity S of the failure mode between two levels. i ,α) represents the initial score of the severity S of each failure mode, Δ -1 For the inverse operation function; the initial score of the detectability D of the fault mode is standardized according to equation (3):

[0019] Δ -1 (s j )=j / 5 (3);

[0020] Where: s j The score assigned to each level of the detectability D of the fault mode is represented by j, where j represents the level corresponding to the detectability D of the fault mode, and j = 0, 1, 2, 3, 4, 5.

[0021] Furthermore, the comprehensive weights of the occurrence degree O, severity S, and detectability D of each failure mode are calculated according to the following steps:

[0022] F1: Calculate the optimal weights w1 for O, w2 for S, and w3 for D according to the optimization models (4) and (5):

[0023]

[0024]

[0025] Where: D(w) represents the deviation value between different failure modes, and maxD(w) indicates that the objective of the optimization model is to solve for the maximum deviation value; h = 1, 2, 3 represent the three risk factors O, S, and D, respectively, where h = 1 represents the occurrence degree O of the failure mode, h = 2 represents the severity S of the failure mode, and h = 3 represents the detectability D of the failure mode; w h This represents the optimal weight values ​​for the three risk factors O, S, and D; m = 1, 2, ..., 21 represents the failure mode number, and k = 1, 2, ..., 21 represents the failure mode number compared with the m-th failure mode. This represents the standardized score of the h-th risk factor corresponding to the m-th failure mode; This represents the standardized score of the h-th risk factor corresponding to the k-th failure mode; express and The distance between two standardized scores;

[0026] F2: The optimal weight values ​​w of the three risk factors O, S, and D calculated. h The objective weight values ​​of O are obtained by standardizing the calculation according to equation (6). The objective weight value of S and the objective weight value of D

[0027]

[0028] in: The objective weight values ​​for the three risk factors: O, S, and D;

[0029] F3: Determine the subjective weight values ​​of the three risk factors, O, S, and D, based on their relative importance. O's subjective weight value S's subjective weight value is The subjective weight values ​​of D are

[0030] F4: According to formula (7), combine the objective weight values ​​of O, S and D with the subjective weight values ​​to calculate the comprehensive weight value of O. S's overall weight value The combined weight value of D

[0031]

[0032] in: This is the combined weighting value of the three risk factors: O, S, and D.

[0033] Furthermore, the overall dominance of each failure mode is calculated according to the following steps:

[0034] L1: Based on the calculated combined weight values ​​of O, S, and D, find... and The maximum value w in r ;

[0035] L2: Calculate the relative weight w of O according to equation (8). 1r The relative weights w of S 2r The relative weights w of D 3r ;

[0036]

[0037] Where: w hr The relative weights of each failure mode;

[0038] L3: Calculate the relative dominance of each failure mode O, S, and D according to equation (9):

[0039]

[0040] Where: Φ h (FM m FM k ) represents the relative advantage of the m-th failure mode relative to the k-th failure mode for the h-th risk factor;

[0041] L4: Calculate the global dominance of each failure mode O, S and D according to equation (10), and calculate the sum of the global dominance δ of the m-th failure mode relative to all failure modes according to equation (11). m :

[0042]

[0043]

[0044] Where: δ(FM) m FM k ) represents the global dominance of the m-th failure mode relative to the k-th failure mode;

[0045] L5: Calculate the total dominance ξ of the m-th failure mode according to equation (12). m :

[0046]

[0047] Where: δ + δ is the sum of the global dominance of all failure modes relative to other failure modes. m The maximum value in, δ - δ is the sum of the global dominance of all failure modes relative to other failure modes. m The minimum value in the range. Beneficial effects of the invention:

[0048] The present invention provides a risk assessment method for FPSO single-point mooring electric slip rings based on heterogeneous information. This method establishes a risk assessment model based on heterogeneous information by associating it with a Bayesian network fault diagnosis model, thus more clearly expressing the logical relationships between nodes in a complex system. It allows for the input of heterogeneous information from multiple fault modes, including both objective probabilities based on historical data and subjective evaluations based on experts or experience, achieving mutual supplementation and interaction of information. By employing different methods to calculate the objective weights of O, S, and D, as well as the comprehensive weights of O, S, and D, it mitigates the negative impact of extreme evaluations on decision-making and avoids overly subjective decision results. This facilitates comprehensive and accurate risk identification and assessment of FPSO single-point mooring electric slip rings, providing assurance and reference for their installation, operation, and maintenance. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating the specific process of the present invention.

[0050] Figure 2 A schematic diagram of the Bayesian network fault diagnosis model of this invention. Detailed Implementation

[0051] The FPSO single-point mooring electric slip ring risk assessment method based on heterogeneous information can be summarized as follows: identifying failure modes and constructing a risk model; initially scoring the occurrence of failure modes; initially scoring the severity and detectability of failure modes, standardizing the initial scores, and calculating the comprehensive weights; calculating the risk ranking and providing risk prevention and control recommendations. The specific process is as follows: Figure 1 As shown, the specific steps include the following:

[0052] E1: Identify the failure modes of the slip rings in a single-point mooring FPSO, generally common or potential failure modes, and determine the causal relationship between each failure mode based on the structure and arrangement of the slip rings. Use the monitorable failure modes among the identified failure modes as the symptom layer, and the failure modes that cause the monitorable failure modes to become abnormal as the cause layer. Establish a Bayesian network failure diagnosis model based on the causal relationship between the symptom layer and the cause layer, and classify the failure types of each failure mode according to the cause of the failure mode. Establish a risk assessment model based on heterogeneous information based on the failure types of each failure mode.

[0053] Specific Bayesian network fault diagnosis models, such as Figure 2 As shown;

[0054] Specifically, the numbering and node descriptions of common or potential fault modes of the FPSO single-point mooring slip ring in the Bayesian network fault diagnosis model are shown in Table 1:

[0055] Table 1

[0056]

[0057] Among the fault modes identified in step E1, the monitorable fault modes include bearing vibration, abnormal torque arm load, excessive slip ring speed, excessive electric slip ring temperature, substandard nitrogen sealing pressure, and substandard nitrogen dew point or cleanliness. Fault modes that cause abnormalities in the monitorable fault modes include uneven slip ring surface during operation, poor slip ring sealing performance or even leakage, uneven brush movement, sparking, insufficient brush pressure, poor contact between brush and slip ring, and impurities blocking the inner hole of the brush holder. The failure types of the fault modes include design and selection problems, installation, manufacturing or quality problems, long-term wear or corrosion problems, use or operation problems, and occasional or environmental problems. The specific risk assessment model based on heterogeneous information and the corresponding Bayesian network fault diagnosis model node descriptions are shown in Table 2.

[0058] Table 2

[0059]

[0060] E2: Determine the prior probability and conditional probability of the Bayesian network fault diagnosis model based on the probability of occurrence of each fault mode recorded in experience or history, and obtain the probability of occurrence of each fault mode corresponding to each node of the Bayesian network fault diagnosis model through forward reasoning. Then, use the reasoned probability of occurrence as the initial score value of the occurrence degree O of the fault mode in the heterogeneous information risk assessment model. If a certain failure mode corresponds to multiple nodes, the initial score value of the occurrence degree O of the failure mode is the sum of the occurrence probabilities of all corresponding nodes.

[0061] E3: Determine the initial score of severity S for each failure mode based on the severity of the failure mode occurrence, and determine the initial score of detectability D for each failure mode based on the detection difficulty of the failure mode; the initial scores of severity S and detectability D for each failure mode can be given in the form of expert scoring.

[0062] E4: Standardize the initial scores of the occurrence O, severity S, and detectability D of each failure mode. Calculate the combined weights of the occurrence O, severity S, and detectability D of each failure mode.

[0063] E5: Calculate the total advantage of each failure mode, determine the risk ranking of each failure mode according to the total advantage, and provide maintenance recommendations based on the risk ranking.

[0064] In step E3, the severity S and detectability D of the fault mode can be classified as extremely low, very low, somewhat low, somewhat high, very high, and extremely high.

[0065] Furthermore, the following method was used to standardize the scoring of each failure mode:

[0066] The initial score of the occurrence degree O of the failure mode is standardized according to equation (1):

[0067]

[0068] in: x represents the standardized score of the occurrence degree O of the failure mode, and x represents the initial score of the occurrence degree O of the failure mode. min x represents the minimum initial score value of the occurrence degree O of all failure modes. max This represents the maximum value among the initial scores for the occurrence degree O of all failure modes;

[0069] The initial score of the severity S of the failure mode is standardized according to equation (2):

[0070] Δ -1 (s i ,α)=i / 5+α (2);

[0071] Where: s i This represents the score assigned to each level of the severity S of the failure mode, where i = 0, 1, 2, 3, 4, 5 represents the level corresponding to the severity S of the failure mode, and α represents the correction value for the severity S of the failure mode between two levels. i ,α) represents the initial score of the severity S of each failure mode, Δ -1 For the inverse operation function; the initial score of the detectability D of the fault mode is standardized according to equation (3):

[0072] Δ -1 (s j )=j / 5 (3);

[0073] Where: s j The score assigned to each level of the detectability D of the fault mode is represented by j, where j represents the level corresponding to the detectability D of the fault mode, and j = 0, 1, 2, 3, 4, 5.

[0074] The specific terminology corresponding to the severity (S) and detectability (D) of each failure mode is shown in Table 3.

[0075] Table 3

[0076]

[0077] The specific initial scores and standardized scores for each failure mode are shown in Table 4: Table 4

[0078]

[0079] Further, calculate the combined weights of O, S, and D using the following steps:

[0080] F1: Calculate the optimal weights w1 for O, w2 for S, and w3 for D according to the optimization models (4) and (5):

[0081]

[0082]

[0083] Where: D(w) represents the deviation value between different failure modes, and maxD(w) indicates that the objective of the optimization model is to solve for the maximum deviation value; h = 1, 2, 3 represent the three risk factors O, S, and D, respectively, where h = 1 represents the occurrence degree O of the failure mode, h = 2 represents the severity S of the failure mode, and h = 3 represents the detectability D of the failure mode; w h This represents the optimal weight values ​​for the three risk factors O, S, and D; m = 1, 2, ..., 21 represents the failure mode number, and k = 1, 2, ..., 21 represents the failure mode number compared with the m-th failure mode. This represents the standardized score of the h-th risk factor corresponding to the m-th failure mode; This represents the standardized score of the h-th risk factor corresponding to the k-th failure mode; express and The distance between two standardized scores, if both standardized scores are real numbers, then If there exists an interval number between the two, that is express The lower bound, express The upper bound; express The lower bound, express (the upper boundary),

[0084] F2: The optimal weight values ​​w of the three risk factors O, S, and D calculated. h The objective weight values ​​of O are obtained by standardizing the calculation according to equation (6). The objective weight value of S and the objective weight value of D

[0085]

[0086] in: The objective weight values ​​for the three risk factors: O, S, and D;

[0087] After calculating based on the data in Table 4, we can obtain...

[0088]

[0089] F3: Determine the subjective weight values ​​of the three risk factors, O, S, and D, based on their relative importance. O's subjective weight value S's subjective weight value is The subjective weight values ​​of D are

[0090] F4: According to formula (7), combine the objective weight values ​​of O, S and D with the subjective weight values ​​to calculate the comprehensive weight value of O. S's overall weight value The combined weight value of D

[0091]

[0092] in: This is the combined weighting value of the three risk factors: O, S, and D.

[0093] If the subjective weights of O, S, and D given by experts are [0.2, 0.5, 0.3], then the calculated comprehensive weight of the three risk factors O, S, and D is... Furthermore, the overall dominance of each failure mode is calculated according to the following steps:

[0094] L1: Based on the calculated combined weight values ​​of O, S, and D, find... and The maximum value w in r ;

[0095] L2: Calculate the relative weight w of O according to equation (8). 1r The relative weights w of S 2r The relative weights w of D 3r ;

[0096]

[0097] Where: w hr The relative weights of each failure mode;

[0098] Based on the data given in Table 4, the relative weights w of each failure mode are calculated. hrfor

[0099] w hr =[0.5018,1,0.8599], h=1,2,3.

[0100] L3: Calculate the relative dominance of each failure mode O, S, and D according to equation (9):

[0101]

[0102] Where: Φ h (FM m FM k ) represents the relative advantage of the m-th failure mode relative to the k-th failure mode for the h-th risk factor;

[0103] L4: Calculate the global dominance of each failure mode O, S and D according to equation (10), and calculate the sum of the global dominance δ of the m-th failure mode relative to all failure modes according to equation (11). m :

[0104]

[0105]

[0106] Where: δ(FM) m FM k ) represents the global dominance of the m-th failure mode relative to the k-th failure mode;

[0107] In this embodiment, twenty-one fault modes are identified, so twenty-one δ can be calculated. m .

[0108] L5: Calculate the total dominance ξ of the m-th failure mode according to equation (12). m :

[0109]

[0110] Where: δ + δ is the sum of the global dominance of all failure modes relative to other failure modes. m The maximum value in, δ - δ is the sum of the global dominance of all failure modes relative to other failure modes. m The minimum value in ξ. Calculated ξ m A larger value indicates a higher overall dominance for the m-th failure mode, a higher risk level, and a higher risk ranking. In this embodiment, the calculation results of the overall dominance of the failure modes and the risk ranking are shown in Table 5: Table 5

[0111]

[0112] Table 5 shows that the top five issues are FM13 (toner not cleaned in time), FM05 (excessive or insufficient clearance between brush and brush holder), FM17 (long-term brush wear), FM04 (improper installation leading to excessive or insufficient spring pressure), and FM06 (rough inner hole of brush holder due to quality problems). Classified by type, these are mainly "installation, manufacturing, or quality issues" and "long-term wear or corrosion issues." Therefore, to improve the maintenance efficiency of FPSO single-point mooring slip rings, it is essential to start with installation and manufacturing, selecting materials that meet quality requirements, and carefully adjusting the system after installation to avoid improper installation. Furthermore, according to this ranking, when slip rings malfunction, the first step should be to check for toner blockage, and the wear condition of the brushes should be checked regularly, with timely replacement and maintenance.

[0113] As can be seen from the above specific implementation methods, the FPSO single-point mooring electric slip ring risk assessment method provided by the present invention, by establishing a risk assessment model based on heterogeneous information in association with a Bayesian network fault diagnosis model, can more clearly express the logical relationships between nodes in a complex system. It can allow heterogeneous information input from multiple fault modes, including both objective probabilities based on historical data and subjective evaluations based on experts or experience, enabling mutual supplementation and interaction of information. Furthermore, by employing different methods to calculate the objective weights of O, S, D and the comprehensive weights of O, S, D, it can reduce the negative impact of extreme evaluations on decision-making and avoid overly subjective decision results. This is conducive to achieving comprehensive and accurate risk identification and assessment of FPSO single-point mooring electric slip rings, providing assurance and reference for their installation, operation, maintenance and repair.

[0114] In summary, the FPSO single-point mooring electric slip ring risk assessment method based on heterogeneous information proposed in this invention can achieve comprehensive quantitative analysis of FPSO single-point mooring electric slip rings, and perform risk identification and assessment, providing assurance and reference for the installation, operation and maintenance of FPSO single-point mooring electric slip rings.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for risk assessment of single-point mooring electric slip rings for FPSOs based on heterogeneous information, characterized in that: Includes the following steps: E1: Identify the failure modes of the slip rings in a single-point mooring FPSO, and determine the causal relationship between each failure mode based on the structure and arrangement of the slip rings. Use the monitorable failure modes among the identified failure modes as the symptom layer, and the failure modes that cause the monitorable failure modes to become abnormal as the cause layer. Establish a Bayesian network failure diagnosis model based on the causal relationship between the symptom layer and the cause layer, and classify the failure types of each failure mode according to the cause of the failure mode. Establish a risk assessment model based on heterogeneous information based on the failure types of each failure mode. E2: Determine the prior probability and conditional probability of the Bayesian network fault diagnosis model based on the probability of occurrence of each fault mode based on experience or historical records, and obtain the probability of occurrence of each fault mode corresponding to each node of the Bayesian network fault diagnosis model through forward reasoning. Then, use the reasoned probability of occurrence as the initial score value of the occurrence degree O of the fault mode in the heterogeneous information risk assessment model. E3: Determine the initial score of the severity S of each failure mode based on the severity of the failure mode occurrence, and determine the initial score of the detectability D of each failure mode based on the detection difficulty of the failure mode. E4: Standardize the initial scores of the occurrence O of each failure mode, the initial scores of the severity S of each failure mode, and the initial scores of the detectability D of each failure mode, and calculate the comprehensive weights of the occurrence O, severity S, and detectability D of each failure mode respectively. The following method was used to standardize the scoring of each failure mode: The initial score of the occurrence degree O of the failure mode is standardized according to equation (1): （1）； in: The standardized score representing the occurrence degree (O) of the failure mode. The initial score representing the occurrence degree O of the failure mode. This represents the minimum initial score value among all failure modes' occurrence rates (O). This represents the maximum value among the initial scores for the occurrence degree O of all failure modes; The initial score of the severity S of the failure mode is standardized according to equation (2): （2）； in: The score assigned to each level of severity (S) of the failure mode. This indicates the severity level (S) of the failure mode. The severity S of the failure mode is a correction value that falls between two levels. The initial score representing the severity S of each failure mode. It is the inverse operation function; The initial score of the detectability D of the failure mode is standardized according to equation (3): （3）； in: This represents the score assigned to each level of the detectability D of the failure mode. This indicates the level corresponding to the detectability D of the failure mode. ; E5: Calculate the total advantage of each failure mode, determine the risk ranking of each failure mode according to the total advantage, and provide maintenance recommendations based on the risk ranking.

2. The FPSO single-point mooring electric slip ring risk assessment method based on heterogeneous information according to claim 1, characterized in that: The monitorable fault modes identified in step E1 include bearing vibration, abnormal torque arm load, excessive slip ring speed, excessive electric slip ring temperature, substandard nitrogen sealing pressure, and substandard nitrogen dew point or cleanliness. Fault modes that cause abnormalities in the monitorable fault modes include uneven slip ring surface during operation, poor slip ring sealing performance or even leakage, non-smooth brush movement, sparking, insufficient brush pressure, poor contact between brush and slip ring, and impurities blocking the inner hole of the brush holder. The failure types of the fault modes include design and selection problems, installation, manufacturing or quality problems, long-term wear or corrosion problems, use or operation problems, and occasional or environmental problems.

3. The FPSO single-point mooring electric slip ring risk assessment method based on heterogeneous information according to claim 1, characterized in that: The following steps are used to calculate the combined weights of the occurrence (O), severity (S), and detectability (D) of each failure mode: F1: Calculate the optimal weight of O according to the optimization model (4) and (5). Optimal weights of S and the optimal weight of D : （4）； （5）； in: This represents the deviation value between different failure modes. The objective of the optimization model is to find the maximum deviation value; These represent the three risk factors O, S, and D, respectively. The risk factor is represented by the occurrence degree (O) of the failure mode. The severity S of the failure mode represents the risk factor. The detectability D represents the risk factor as the failure mode. This represents the optimal weight values ​​for the three risk factors, O, S, and D. The number representing the fault mode. Indicates the relationship with the first The fault mode number for each fault mode comparison. Indicates the first The fault mode corresponding to the first Standardized scores for each risk factor; Indicates the first The fault mode corresponds to the first Standardized scores for each risk factor; express and The distance between two standardized scores; F2: The optimal weight values ​​for the three risk factors O, S, and D calculated. The objective weight values ​​of O are obtained by performing standardized calculations according to equation (6). Objective weight value of S and the objective weight value of D : （6）； in: The objective weight values ​​for the three risk factors: O, S, and D; F3: Determine the subjective weight values ​​of the three risk factors, O, S, and D, based on their relative importance. The subjective weight value of O is The subjective weight value of S is The subjective weight values ​​of D are ; F4: According to formula (7), combine the objective weight values ​​of O, S and D with the subjective weight values ​​to calculate the comprehensive weight value of O. The comprehensive weight value of S The combined weight value of D : （7）； in: This is the combined weighting value of the three risk factors: O, S, and D.

4. The FPSO single-point mooring electric slip ring risk assessment method based on heterogeneous information according to claim 3, characterized in that: Calculate the overall dominance of each failure mode using the following steps: L1: Based on the calculated combined weight values ​​of O, S, and D, find... , and The maximum value in ; L2: Calculate the relative weights of O according to equation (8). The relative weights of S The relative weights of D ; （8）； in: The relative weights of each failure mode; L3: Calculate the relative dominance of each failure mode O, S, and D according to equation (9): （9）； in: Indicates the first The first risk factor The failure mode is relative to the first The relative dominance of each failure mode; L4: Calculate the global dominance of each failure mode O, S, and D according to equation (10), and calculate the first... The sum of the global dominance of each failure mode relative to all failure modes : （10）； （11）； in: Indicates the first The failure mode is relative to the first Global dominance of each failure mode; L5: Calculate the first according to equation (12) Overall dominance of each failure mode : （12）； in: The sum of the global dominance of all failure modes relative to other failure modes. The maximum value in, The sum of the global dominance of all failure modes relative to other failure modes. The minimum value in.

Images