Risk assessment method for cruise ship glass curtain wall based on ordered comprehensive weight FMEA

Abstract

The present invention relates to a risk assessment method for cruise ship glass curtain walls based on ordered comprehensive weighted FMEA, comprising the following steps: identifying failure modes of cruise ship glass curtain walls; obtaining risk factor and failure mode evaluations from an expert team, and converting the experts' subjective evaluations of risk factors and failure modes using intuitionistic fuzzy numbers; aggregating subjective scores of failure modes and subjective weights of risk factors using an intuitionistic fuzzy weighted average operator; using the maximum value of the subjective score of the failure mode as a positive reference sequence; using the minimum value of the subjective score as a negative reference sequence; calculating ordered comprehensive weights; calculating the grey correlation degree of the failure mode; and ranking the risk priorities of the failure modes.

Description

Technical Field

[0001] The present invention relates to a cruise ship glass curtain wall risk assessment method based on ordered comprehensive weight FMEA, belonging to a cruise ship glass curtain wall risk rating method. Background Art

[0002] With my country's rapid economic development, the cruise tourism consumer market continues to grow, providing a crucial market foundation for the development of my country's cruise industry and the entire cruise supply chain. Achieving independently designed and built luxury cruise ships not only creates economic benefits but also enhances my country's industrialization level, a significant achievement. Consequently, China prioritizes the development of the luxury cruise ship manufacturing industry. Cruise ship glass curtain walls, as one of the specialized structures on a cruise ship, perform load-bearing and decorative functions, providing crucial support for the ship's navigation and entertainment functions.

[0003] However, due to inherent flaws in design, construction, and materials, or loads exceeding design requirements from collisions, earthquakes, typhoons, and other factors, or the combined effects of environmental erosion, material aging, and the long-term and sudden effects of loads, cruise ship glass curtain walls are prone to various types of failure or breakdown. These can lead to glass dusting, water seepage, and aging and failure of structural adhesives. In severe cases, these can cause the glass to explode or even damage the load-bearing structure, resulting in not only significant economic losses and casualties, but also adverse social impacts. Therefore, evaluating and analyzing cruise ship glass curtain walls is crucial for safe operation and accident prevention.

[0004] Most methods used for glass curtain wall failure risk assessment both domestically and internationally rely on expert experience and fuzzy comprehensive evaluation, which are highly subjective. However, compared to typical land-based glass curtain walls, cruise ship glass curtain walls face more complex environmental erosion and loading conditions, and lack relevant empirical data. Therefore, traditional glass curtain wall evaluation methods cannot meet the risk assessment needs. The improved Failure Mode and Effects Analysis (FMEA) method combines fuzzy methods with multi-attribute decision-making methods, comprehensively considering both subjective scoring and objective laws. It has been widely used in various fields such as equipment maintenance and medical services, but its application in the risk assessment of cruise ship glass curtain walls remains underdeveloped.

[0005] Related Literature

[0006] [1]Liu HC, Liu L, LiP. Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator[J]. International Journal of Systems Science, 2014, 45(10): 2012-2030.

[0007] [2]Liu HC, You JX, Shan MM, et al. Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach [J]. Soft Computing, 2015, 19(4): 1085-1098. Summary of the Invention

[0008] The purpose of the present invention is to provide a risk assessment method for cruise ship glass curtain walls. The present invention provides a risk assessment method for cruise ship glass curtain walls based on ordered comprehensive weight FMEA. First, an expert team is formed to identify the failure modes of cruise ship glass curtain walls, and each risk factor and failure mode is evaluated using terms represented by intuitive fuzzy numbers. Then, the subjective weights given by experts and the objective weights based on the ordered weighted average operator are comprehensively considered to calculate the ordered comprehensive weights of the risk factors. Finally, the grey correlation analysis method is used to achieve risk ranking. The proposed method combines intuitive fuzzy numbers, ordered comprehensive weights and grey correlation analysis methods, which can provide guidance for accident prevention and safety decision-making for cruise ship glass curtain walls. The technical solution is as follows:

[0009] A risk assessment method for cruise ship glass curtain walls based on ordered comprehensive weighted FMEA includes the following steps:

[0010] Step 1: Assemble an expert team and assign weights to each expert based on their importance in the team; identify failure modes of cruise ship glass curtain walls; obtain risk factor and failure mode evaluations from the expert team. Suppose the expert team identifies m types of cruise ship glass curtain wall failure modes and performs a subjective evaluation of each failure mode based on three risk factors: occurrence, severity, and detectability; and use intuitionistic fuzzy numbers to convert the experts' subjective evaluations of risk factors and failure modes.

[0011] Step 2: Aggregate the subjective scores of failure modes and subjective weights of risk factors through the intuitionistic fuzzy weighted average operator; take the maximum value of the subjective score of the failure mode as the positive reference sequence; and take the minimum value of the subjective score as the negative reference sequence. The method is as follows:

[0012] (1) The subjective scores of failure modes and subjective weights of risk factors are aggregated by the intuitionistic fuzzy weighted average operator, and the aggregated failure mode score is recorded as α ij =(μ ij ,ν ij ), where μ ij represents the membership degree after the failure mode score is aggregated, ν ijIt represents the non-membership degree of the failure mode score after aggregation; the subjective weight of the risk factor after aggregation is recorded as w j =(μ j ,ν j ), where μ j represents the membership degree after the risk factor score is aggregated, ν j It represents the non-membership degree after the risk factor score is aggregated;

[0013] (2) According to the aggregation results, the maximum value of the subjective score of the failure mode is used as the positive reference sequence, and the minimum value of the subjective score of the failure mode is used as the negative reference sequence to establish the positive reference sequence. and negative reference sequences in, They represent the highest scores of the three risk factors of occurrence, severity and detection, namely where maxμ ij represents the maximum membership in the score aggregation of the j-th risk factor, minν ij represents the minimum non-membership degree in the score aggregation of the j-th risk factor; They represent the lowest scores of the three risk factors of occurrence, severity and detection, namely where minμ ij represents the minimum membership in the score aggregation of the j-th risk factor, maxν ij represents the maximum non-membership degree in the score aggregation of the j-th risk factor;

[0014] Step 3: Calculate the ordered comprehensive weights: Use a standardized formula to process the subjective weights of risk factors; calculate the distance between the subjective scores and the positive and negative reference sequences, and determine the ordered objective weights of risk factors relative to the positive and negative reference sequences based on the ordered weighted average operator; comprehensively consider the subjective and objective weight values ​​and calculate the ordered comprehensive weights of each risk factor relative to the positive and negative reference sequences as follows:

[0015] (1) Standardize the subjective weights of the aggregated risk factors. for:

[0016]

[0017] (2) Calculate the distance between the subjective score and the positive reference sequence The distance between the subjective score and the negative reference sequence The calculation formula is:

[0018]

[0019]

[0020] (3) According to the ordered weighted average operator based on the normal distribution law, when there are three risk factors, the ordered objective weight is (0.243, 0.514, 0.243), that is, after arranging from large to small, the risk factor ranked first is weighted 0.243, the risk factor ranked second is weighted 0.514, and the risk factor ranked third is weighted 0.243; the i-th failure mode is weighted according to The ordered objective weight after sorting is recorded as according to The ordered objective weight after sorting is recorded as

[0021] (4) Calculate the positive ordered comprehensive weight of the i-th failure mode as follows: The negative ordered comprehensive weight is in, is a comprehensive weight parameter, which represents different subjective and objective weight combinations and is adjusted in the range of [0,1] according to actual conditions; in Indicates the positive ordered comprehensive weight of the i-th failure mode corresponding to the j-th risk factor; in Indicates the negative ordered comprehensive weight of the i-th failure mode corresponding to the j-th risk factor;

[0022] Step 4: Based on the grey correlation analysis method, calculate the grey correlation coefficient of each failure mode and the positive and negative reference sequences, and then calculate the grey correlation degree of the failure mode. The method is as follows:

[0023] First, calculate the grey correlation coefficient between the i-th failure mode and the positive reference sequence and the grey correlation coefficient between the i-th failure mode and the negative reference sequence for:

[0024]

[0025]

[0026] Then, calculate the grey relational degree ξ of the i-th failure mode i for:

[0027]

[0028] Step 5, risk priority ranking of failure modes: sort the failure modes in descending order of grey correlation degree. The larger the grey correlation degree, the higher the correlation degree between the failure mode and the positive reference sequence; the greater the risk, the higher the risk priority.

[0029] Compared with the existing technology, the beneficial effects of the present invention are: (1) the evaluation information is converted by intuitive fuzzy numbers, and the uncertainty description of the expert evaluation of the cruise glass curtain wall is more comprehensive and accurate; (2) the subjective score and objective rules are comprehensively considered by the ordered comprehensive weight algorithm, which reduces the evaluation error caused by the decision maker's lack of experience, and is more suitable for the cruise glass curtain wall that currently lacks empirical data; (3) the risk rating of the cruise glass curtain wall is realized by the gray correlation analysis method, and the sorting mechanism is reasonable and accurate, which overcomes the defects of the traditional risk rating method, and the process is clear, simple and easy to operate; (4) during the use of the cruise glass curtain wall, different maintenance and repair strategies can be arranged according to the risk rating results obtained by the present invention, thereby improving the maintenance and repair efficiency and reducing the maintenance and repair costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the risk assessment method of cruise ship glass curtain wall based on ordered comprehensive weighted FMEA;

[0031] Figure 2 It is the failure mode of the cruise ship glass curtain wall. DETAILED DESCRIPTION

[0032] The specific structure and implementation process of the present invention are described in detail below through specific embodiments and drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0033] like Figure 1 As shown, in one embodiment of the present invention, a risk assessment method for cruise ship glass curtain wall based on ordered comprehensive weighted FMEA is disclosed, comprising the following steps:

[0034] Step 1: Identify failure modes and convert expert evaluations. First, assemble an expert team and assign weights to experts based on their importance within the team. Next, identify the failure modes of the cruise ship's glass curtain wall. Finally, obtain the expert team's evaluations of risk factors and failure modes. Use intuitionistic fuzzy numbers to convert the experts' subjective evaluations of risk factors and failure modes.

[0035] First, a cruise glass curtain wall risk assessment team consisting of l experts is formed, and the kth team member is assigned a weight λ k , note that the sum of the weights of l members must be equal to 1.

[0036] The expert team then identified m failure modes of cruise ship glass curtain walls based on their experience and historical data, and evaluated each failure mode based on three risk factors: occurrence, severity, and detectability.

[0037] Finally, the expert evaluation is transformed using intuitionistic fuzzy numbers. The transformation rules are shown in Tables 1 and 2. [1] shown.

[0038] Table 1 Conversion rules for failure mode evaluation using intuitionistic fuzzy numbers

[0039]

[0040] Table 2 Conversion rules for risk factor evaluation using intuitionistic fuzzy numbers

[0041]

[0042] The score provided by the kth team member when evaluating the i-th failure mode corresponding to the j-th risk factor is recorded as in, represents the membership degree of the failure mode score, represents the non-membership degree of the failure mode score; at the same time, the score provided when evaluating the j-th risk factor is recorded as in, represents the membership degree of the risk factor score, Indicates the non-membership degree of the risk factor score.

[0043] Step 2: Aggregate subjective scores to establish a reference sequence. First, aggregate the subjective scores of failure modes and the subjective weights of risk factors using the intuitionistic fuzzy weighted average operator. Then, the maximum subjective score is used as the positive reference sequence, and the minimum subjective score is used as the negative reference sequence.

[0044] First, the subjective scores of failure modes and subjective weights of risk factors are aggregated by the intuitionistic fuzzy weighted average operator. The aggregated failure mode score is denoted as α ij =(μ ij ,ν ij ), where μ ij represents the membership degree after the failure mode score is aggregated, ν ij It represents the non-membership degree of the failure mode score after aggregation; the subjective weight of the risk factor after aggregation is recorded as w j =(μ j ,ν j ), where μ j represents the membership degree after the risk factor score is aggregated, ν j It represents the non-membership degree after the risk factor score is aggregated. The aggregation calculation formula is:

[0045]

[0046]

[0047] Then, based on the aggregation results, the maximum value of the subjective score of the failure mode is used as the positive reference sequence, and the minimum value of the subjective score of the failure mode is used as the negative reference sequence. and negative reference sequences in, They represent the highest scores of the three risk factors of occurrence, severity and detection, namely where maxμ ij represents the maximum membership in the score aggregation of the j-th risk factor, minν ij represents the minimum non-membership degree in the score aggregation of the j-th risk factor; They represent the lowest scores of the three risk factors of occurrence, severity and detection, namely where minμ ij represents the minimum membership in the score aggregation of the j-th risk factor, maxν ij represents the maximum non-membership degree in the score aggregation of the j-th risk factor.

[0048] Step 3: Calculate the ordered composite weights. First, use a standardized formula to process the subjective weights of risk factors. Then, calculate the distance between the subjective scores and the positive and negative reference sequences, and determine the ordered objective weights of risk factors relative to the positive and negative reference sequences using the ordered weighted average operator. Finally, comprehensively considering the subjective and objective weights, calculate the ordered composite weights of each risk factor relative to the positive and negative reference sequences.

[0049] First, the subjective weights of the aggregated risk factors are standardized. for:

[0050]

[0051] Then, the distance between the subjective score and the positive reference sequence is calculated The distance between the subjective score and the negative reference sequence The calculation formula is:

[0052]

[0053]

[0054] According to the ordered weighted average operator based on the normal distribution law, when there are three risk factors, the ordered objective weights are (0.243, 0.514, 0.243) [2] , that is, after arranging from large to small, the first risk factor is given a weight of 0.243, the second risk factor is given a weight of 0.514, and the third risk factor is given a weight of 0.243. The ordered objective weight after sorting is recorded as If or but like or but otherwise according to The ordered objective weight after sorting is recorded as If or but like or but otherwise

[0055] Finally, the positive ordered comprehensive weight of the i-th failure mode is calculated as The negative ordered comprehensive weight is in, is a comprehensive weight parameter, which represents different subjective and objective weight combinations and can be adjusted in the range of [0,1] according to actual conditions. in It represents the positive ordered comprehensive weight of the i-th failure mode corresponding to the j-th risk factor; in It represents the negative ordered comprehensive weight of the i-th failure mode corresponding to the j-th risk factor.

[0056] Step 4: Calculate the grey relational degree of the failure mode. Based on the grey relational analysis method, first calculate the grey relational coefficient of each failure mode and the positive and negative reference sequences, and then calculate the grey relational degree of the failure mode.

[0057] First, calculate the grey correlation coefficient between the i-th failure mode and the positive reference sequence and the grey correlation coefficient between the i-th failure mode and the negative reference sequence for:

[0058]

[0059]

[0060] Then, calculate the grey relational degree ξ of the i-th failure mode i for:

[0061]

[0062] Step 5: Sort the failure modes by risk priority. Sort the failure modes in descending order of grey correlation. The larger the grey correlation, the higher the correlation between the failure mode and the positive reference sequence, the greater the risk, and the higher the risk priority.

[0063] This implementation provides a risk assessment method for cruise ship glass curtain walls based on an ordered comprehensive weighted FMEA. First, an expert team is assembled to identify failure modes for cruise ship glass curtain walls. Each risk factor and failure mode is evaluated using terms expressed as intuitionistic fuzzy numbers. The subjective weights assigned by the experts are then combined with objective weights based on an ordered weighted average operator to calculate the ordered comprehensive weights of the risk factors. Finally, risk ranking is achieved using grey relational analysis. This proposed method, combining intuitionistic fuzzy numbers, ordered comprehensive weights, and grey relational analysis, can provide guidance for accident prevention and safety decision-making for cruise ship glass curtain walls.

[0064] Compared with the prior art, this embodiment has the following beneficial effects:

[0065] (1) Using intuitionistic fuzzy numbers to transform evaluation information, the uncertainty description of experts' evaluation of cruise ship glass curtain walls is more comprehensive and accurate;

[0066] (2) The use of an ordered comprehensive weight algorithm comprehensively considers subjective scores and objective rules, reducing evaluation errors caused by decision makers' lack of experience, and is more applicable to cruise ship glass curtain walls, which currently lack empirical data;

[0067] (3) The grey correlation analysis method is used to implement risk rating of cruise ship glass curtain walls. The sorting mechanism is reasonable and accurate, overcoming the defects of traditional risk rating methods. The process is clear, simple and easy to operate.

[0068] (4) During the use of the cruise ship glass curtain wall, different maintenance strategies can be arranged according to the risk rating results obtained by the present invention, thereby improving maintenance efficiency and reducing maintenance costs.

[0069] Example

[0070] In this embodiment, the failure risk of the glass curtain wall of a medium-sized cruise ship is evaluated to obtain a risk assessment level of the glass curtain wall of the cruise ship.

[0071] Step 1: Form a team of 5 domain experts. Based on their different knowledge and experience, assign weights of (0.225, 0.150, 0.200, 0.165, 0.260) to each of the 5 experts (respectively TM1, TM2, TM3, TM4, and TM5). Based on the survey data and expert opinions, establish the failure mode of the cruise ship glass curtain wall, such as Figure 2 Experts were invited to evaluate the failure modes and risk factors based on the evaluation language provided in Tables 1 and 2, as shown in Table 3.

[0072] Table 3 Evaluation information of the expert team

[0073]

[0074] In step 2, the subjective scores of failure modes and subjective weights of risk factors are aggregated using the intuitionistic fuzzy weighted average operator. The results are shown in Table 4.

[0075] Table 4 Aggregation results

[0076]

[0077]

[0078] According to the aggregation results, the positive and negative reference sequences are established as follows:

[0079] R + =((0.753,0.233),(0.875,0.115),(0.860,0.129))

[0080] R - =((0.255,0.679),(0.203,0.756),(0.133,0.847))

[0081] Step 3: First, standardize the subjective weight of risk factors to Then the distance between the subjective score and the reference sequence is calculated to determine the ordered objective weight of each failure mode. The positive ordered comprehensive weight and negative ordered comprehensive weight of each failure mode are calculated as shown in Table 5.

[0082] Table 5 Ordered comprehensive weights

[0083]

[0084]

[0085] Step 4: Calculate the grey correlation coefficient between the i-th failure mode and the positive reference sequence and the grey correlation coefficient between the i-th failure mode and the negative reference sequence Calculate the grey relational degree ξ of the i-th failure mode i , as shown in Table 6.

[0086] Table 6 Grey correlation degree and ranking of each failure mode

[0087]

[0088]

[0089] Step 5: Sort the failure modes in descending order of their grey correlation degree. The results are also shown in Table 6. The risk priority order of the 16 failure modes is:

[0090] FM08 > FM09 > FM10 > FM15 > FM12 > FM03 > FM01 > FM11 > FM02 > FM04 > FM14 > FM16 > FM07 > FM06 > FM13 > FM05. Therefore, during routine inspection and maintenance of the glass curtain wall of this medium-sized cruise ship, attention should be paid to FM08 as the most serious failure mode, and FM08 should be given the highest priority for improvement measures.

[0091] At this point, those skilled in the art will recognize that, although the present invention has been shown and described in detail with respect to exemplary embodiments of the present invention, many other variations or modifications consistent with the principles of the present invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.

Claims

1. A cruise ship glass curtain wall risk assessment method based on ordered comprehensive weighted FMEA, comprising the following steps: Step 1: Assemble an expert team and assign weights to each expert based on their importance in the team; identify failure modes of cruise ship glass curtain walls; obtain risk factor and failure mode evaluations from the expert team. Suppose the expert team identifies m types of cruise ship glass curtain wall failure modes and performs a subjective evaluation of each failure mode based on three risk factors: occurrence, severity, and detectability; and use intuitionistic fuzzy numbers to convert the experts' subjective evaluations of risk factors and failure modes. Step 2: Aggregate the subjective scores of failure modes and subjective weights of risk factors through the intuitionistic fuzzy weighted average operator; The maximum value of the subjective score of the failure mode is used as the positive reference sequence; the minimum value of the subjective score is used as the negative reference sequence, as follows: (1) The subjective scores of failure modes and subjective weights of risk factors are aggregated by the intuitionistic fuzzy weighted average operator, and the aggregated failure mode score is recorded as α ij =(μ ij ,ν ij ),in, μ ij represents the membership degree after the failure mode score is aggregated, ν ij It represents the non-membership degree of the failure mode score after aggregation; the subjective weight of the risk factor after aggregation is recorded as w j =(μ j ,ν j ), where μ j represents the membership degree after the risk factor score is aggregated, ν j It represents the non-membership degree after the risk factor score is aggregated; (2) According to the aggregation results, the maximum value of the subjective score of the failure mode is used as the positive reference sequence, and the minimum value of the subjective score of the failure mode is used as the negative reference sequence to establish the positive reference sequence. and negative reference sequences in, They represent the highest scores of the three risk factors of occurrence, severity and detection, namely where maxμ ij represents the maximum membership in the score aggregation of the j-th risk factor, minν ij represents the minimum non-membership degree in the score aggregation of the j-th risk factor; They represent the lowest scores of the three risk factors of occurrence, severity and detection, namely where minμ ij represents the minimum membership in the score aggregation of the j-th risk factor, maxν ij represents the maximum non-membership degree in the score aggregation of the j-th risk factor; Step 3: Calculate the ordered comprehensive weights: Use a standardized formula to process the subjective weights of risk factors; calculate the distance between the subjective scores and the positive and negative reference sequences, and determine the ordered objective weights of risk factors relative to the positive and negative reference sequences based on the ordered weighted average operator; comprehensively consider the subjective and objective weight values ​​and calculate the ordered comprehensive weights of each risk factor relative to the positive and negative reference sequences as follows: (1) Standardize the subjective weights of the aggregated risk factors. for: (2) Calculate the distance between the subjective score and the positive reference sequence The distance between the subjective score and the negative reference sequence The calculation formula is: (3) According to the ordered weighted average operator based on the normal distribution law, when there are three risk factors, the ordered objective weight is (0.243, 0.514, 0.243), that is, after arranging from large to small, the risk factor ranked first is weighted 0.243, the risk factor ranked second is weighted 0.514, and the risk factor ranked third is weighted 0.243; the i-th failure mode is weighted according to The ordered objective weight after sorting is recorded as according to The ordered objective weight after sorting is recorded as (4) Calculate the positive ordered comprehensive weight of the i-th failure mode as follows: The negative ordered comprehensive weight is in, is a comprehensive weight parameter, which represents different subjective and objective weight combinations and is adjusted in the range of [0,1] according to actual conditions; in It represents the positive ordered comprehensive weight of the i-th failure mode corresponding to the j-th risk factor; in Indicates the negative ordered comprehensive weight of the i-th failure mode corresponding to the j-th risk factor; Step 4: Based on the grey correlation analysis method, calculate the grey correlation coefficient of each failure mode and the positive and negative reference sequences, and then calculate the grey correlation degree of the failure mode. The method is as follows: First, calculate the grey correlation coefficient between the i-th failure mode and the positive reference sequence and the grey correlation coefficient between the i-th failure mode and the negative reference sequence for: Then, calculate the grey relational degree ξ of the i-th failure mode i for: Step 5, risk priority ranking of failure modes: sort the failure modes in descending order of grey correlation degree. The larger the grey correlation degree, the higher the correlation degree between the failure mode and the positive reference sequence; the greater the risk, the higher the risk priority.

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