Improved FMEA method based on interval-valued intuitionistic fuzzy sets and DEMATEL-VIKOR hybrid multiple criteria decision making

Abstract

The application provides an improved FMEA method based on interval two-type fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision, and on the basis of traditional FMEA, a more perfect IMS key hardware risk assessment model is constructed by improving related methods. The failure modes involved are obtained by the fault tree analysis method. The application combines the interval two-type fuzzy sets and the FMEA to solve the problem of inaccurate RPN value in the traditional FMEA; the similarity aggregation method is used to calculate the comprehensive weight of the experts in combination with the personal background of the experts; the combination weight method is used in the risk factor aspect to improve the analysis precision, the weight of multiple indexes is comprehensively considered, and the contribution of each factor to the overall risk can be more comprehensively evaluated; and the DEMATEL-VIKOR hybrid multi-criteria decision method of preference relation analysis fully considers the correlation between factors and the comprehensive influence of multi-criteria, and provides more comprehensive and intuitive decision support.

Description

TECHNICAL FIELD

[0001] The present invention belongs to the field of risk assessment and management, particularly in the application of industrial systems or complex mechanical systems. The invention mainly emphasizes improving the accuracy and reliability of risk assessment by combining different techniques and methods to address some limitations in traditional FMEA, such as the inaccuracy of RPN (Risk Priority Number) and the subjectivity of expert evaluation. In addition, it also considers the interaction and comprehensive influence between risk factors, thus providing a more comprehensive and systematic risk assessment. BACKGROUND

[0002] In the management of modern industrial systems and complex mechanical equipment, risk assessment and failure prevention are crucial. The traditional Failure Mode and Effects Analysis (FMEA) method is widely used to identify potential failure modes and their potential impact on system performance. However, traditional FMEA faces a series of challenges, including the inaccuracy of risk priority number (RPN) calculation and the lack of consideration of the interaction between failure modes. In addition, the FMEA process often relies on the subjective judgment of experts, which may lead to inconsistency and unreliability of risk assessment results.

[0003] In order to overcome these limitations, in recent years, various methods and technologies have been integrated to improve the accuracy and systematicness of risk assessment. For example, the introduction of fuzzy logic theory provides an effective means to deal with uncertainty and fuzziness. Relative interval type-2 fuzzy sets provide a more flexible and detailed method to express and handle uncertainty in expert evaluation. At the same time, multi-criteria decision-making methods such as DEMATEL-VIKOR hybrid method, by considering the correlation between factors and the comprehensive influence of multi-criteria, enhance the comprehensiveness and intuitiveness of the decision-making process. In addition, the application of similarity aggregation method and risk factor combination weight method, etc. advanced technology, further improves the accuracy and reliability of the evaluation process.

[0004] With the increasing complexity of industrial systems, there is a growing demand for more efficient and accurate risk assessment methods. This requires the development of new risk assessment models that not only solve the limitations of traditional FMEA methods, but also adapt to the complexity and dynamics of modern industrial environments. The present invention is produced in this background, aiming to provide a comprehensive and advanced risk assessment solution to meet the challenges of modern industrial systems. SUMMARY

[0005] 1. Objectives: The purpose of the present invention is to integrate and improve various risk assessment tools and methods to build a more efficient and accurate risk assessment model, thereby better serving the risk management needs of modern industrial systems.

[0006] 2. Technical solutions:

[0007] S1, form a FMEA team consisting of K experts, identify potential failure modes FM of IMS critical hardware i (i = 1, 2, …, m) to build a set of failure modes of IMS critical hardware.

[0008] S2, based on the set of failure modes of IMS critical hardware, expand the traditional three risk factors (severity S, occurrence O, detection D) to four (severity S, occurrence O, detection D, economic degree E).

[0009] S3, establish interval type-2 fuzzy set, interval type-2 fuzzy set is the set of interval type-2 trapezoidal fuzzy numbers , and calculate the relative consistency of each expert based on the expert's score results. Describe the evaluation criteria of the four risk factors under the corresponding language variables (Table 1), and make the type-2 fuzzy numbers corresponding to the language variables (Table 2);

[0010] Further, K experts score based on the evaluation table (Table 1), let the evaluation results of each expert under the severity, occurrence, detection, and economic degree of each failure mode be

[0011] Convert each value in the expert's score results (Table 11) to an interval type-2 trapezoidal fuzzy number in the corresponding language variable:

[0012] Let be an interval type-2 fuzzy number, if the upper membership function and the lower membership function of an interval type-2 fuzzy number are two trapezoidal type-1 fuzzy numbers, then is called a trapezoidal interval type-2 fuzzy number; the reference point in the language domain and the height of the upper and lower membership functions of the trapezoidal interval type-2 fuzzy number are used to depict the trapezoidal interval type-2 fuzzy number

[0013] denotes the membership value of element in the upper trapezoidal membership function , denotes the membership value of element in the lower trapezoidal membership function ,

[0014] Combine the expert background weight with the relative consistency of each expert The combined weight of the kth expert in the ith failure mode and the jth risk factor can be calculated by the formula:

[0015]

[0016] S4, collecting the personal background scores of the experts The consistency of the expert evaluation results is calculated by the similar aggregation method The personal weight of the expert in each risk factor under each failure mode is obtained That is, the weight of each expert in each risk factor.

[0017] S5, then according to the personal weight of each expert in each risk factor The evaluation results The corresponding two-type trapezoidal fuzzy numbers are weighted and averaged to obtain the comprehensive fuzzy evaluation matrix F = (f ij ) m×4 of severity, occurrence, detection, and economy under each failure mode, where m represents the number of failure modes, and 4 represents the number of risk factors.

[0018] S6, the influence degree and the affected degree of each failure mode are calculated using DEMETAL.

[0019] Further, the comprehensive evaluation matrix

[0020] S7, the subjective weight of the risk factor is calculated by using the analytic hierarchy process to calculate the scoring results of the expert risk factor influence degree, and the final combined weight of the risk factor is obtained by combining the objective weight of the risk factor.

[0021] S8, the distance between fuzzy numbers is calculated based on the relative preference relationship analysis, and the failure modes are sorted by the VIKOR method, and the relevant personnel develop corresponding prevention and response measures according to the sorting.

[0022] Further, in S3, the language evaluation terms include five levels: extremely low, low, general, high, and extremely high, and the five levels of language evaluation terms correspond to 1 point, 2 points, 3 points, 4 points, and 5 points, respectively, as shown in Table 1, and the corresponding two-type fuzzy numbers are shown in Table 2.

[0023] Table 1 language evaluation terms

[0024]

[0025] Table 2 two-type fuzzy numbers corresponding to language variables

[0026]

[0027] Furthermore, in S4, the expert weight The calculation includes the following steps:

[0028] S41. In this patent, the expert background score standard table is established based on the survey indicators of educational background, professional title, years of work experience, and research direction / position, where the total score of the kth expert background is It is obtained by adding up the scores of each indicator. The formula for calculating the expert background weight is:

[0029]

[0030] S42. Use the distance formula between interval trapezoidal fuzzy numbers to calculate the distance d between the evaluation results of the kth expert and the evaluation results of other experts. ij (k, u), the calculation formula is as follows:

[0031]

[0032] Then according to Calculate the average distance between the evaluation results of the kth expert and other experts u represents other experts except the k-th expert,

[0033] represents the interval trapezoidal fuzzy number corresponding to the evaluation result of the k-th expert,

[0034] represents the interval trapezoidal fuzzy number corresponding to the evaluation results of other experts among the K experts;

[0035] S43, according to Calculate the average agreement of the k-th expert;

[0036] S44, according to The relative consistency of each expert is calculated using the normalization method

[0037] S45. Combine expert background weights Relative agreement with experts The combined weight of the j-th risk factor of the k-th expert under the i-th failure mode can be calculated as follows:

[0038]

[0039] Where α and β are relaxation factors of background weight and relative consistency, respectively, reflecting the relative importance between weights, satisfying 0≤α≤1, 0≤β≤1 and α+β=1. Generally, α=β=0.5.

[0040] Furthermore, in S6, the steps of determining the influence and the affected degree of the failure mode are as follows:

[0041] S61, analyze the influence relationship between failure modes by experts to obtain a direct correlation matrix between failure modes

[0042]

[0043] Among them represents the evaluation of the kth expert on the failure mode FM i The influence degree of failure mode FM j , if there is no influence, the score is 0;

[0044] S62, calculate the direct correlation aggregation matrix

[0045]

[0046] The element in the direct correlation aggregation matrix is aggregated by the weight of each expert

[0047]

[0048] After aggregation, the clear direct correlation aggregation matrix is obtained by deblurring

[0049]

[0050] S63, calculate the normalized direct correlation aggregation matrix D. The commonly used method of direct correlation aggregation matrix normalization is based on the maximum value of the sum of the elements of each row vector of the matrix

[0051]

[0052] is the element in , and represents the maximum value of the sum of each row element;

[0053] S64, calculate the total correlation matrix T, where I is the unit matrix

[0054]

[0055] where D n represents the multiplication of n D matrices;

[0056] S65, determine the influence degree and the influenced degree of failure mode

[0057] Define the sum of the i-th row of matrix T as R i , and the sum of the j-th column as C j ,

[0058] R​​i represents the failure mode FM i direct and indirect influence on other failure modes, C j represents the failure mode FM i direct and indirect influence on other failure modes

[0059]

[0060] Let i = j, the influence degree R i +C i expresses the failure mode FM i total degree of influence on and influence on other failure modes; the affected degree R i -C i expresses the failure mode FM i the difference between the degree of influence on and influence on other failure modes;

[0061] S66, R i and C i are normalized respectively to obtain and Let the initial comprehensive severity S evaluation value of the expert on the failure mode FM i be

[0062] After DEMATEL analysis, the corrected failure severity is

[0063] Further, in S7, the risk factor relative importance degree scoring standard and the scoring table are as shown in Tables 3 and 4.

[0064] Table 3 Risk factor relative importance degree scoring standard

[0065]

[0066] Table 4 Risk factor relative importance degree scoring table

[0067]

[0068]

[0069] When determining the weight of the risk factor, first, the analytic hierarchy process is used to calculate the scoring results of the expert risk factor influence degree to obtain the subjective weight of the risk factor;

[0070] Even if the score standard mentioned in table 3 is used to score the risk factors in table 4 two by two, the weighted average of each group of score results is summarized. Similarly, each expert has the same weight, and the subjective weight ω1 of the risk factor is calculated by the analytic hierarchy process, combined with the objective weight ω2 of the risk factor, and the combined weight ω of the risk factor is finally obtained by using the following formula, where α is usually taken as 0.5, wherein ω2 = 1 / K

[0071] ω = α × ω1 + (1 - α) × ω2

[0072] Further, in S8, the priority ranking of the failure modes is calculated by the VIKOR method through the relative preference relationship analysis:

[0073] S81, the comprehensive evaluation matrix calculated according to the expert evaluation results (table 11) Find the positive ideal solution in the failure mode Negative ideal solution

[0074] Wherein, The severity of the ith failure mode after aggregation by experts and modified by DEMATEL method, The occurrence degree, detection degree and economic degree of the ith failure mode after aggregation by experts and modified by DEMATEL method, respectively.

[0075] S82, assume interval two type fuzzy number And The upper and lower membership functions of And B L The upper and lower membership functions of We want to calculate The preference degree of Wherein, And The preference score from To And from To Is calculated as follows:

[0076]

[0077] Then the group benefit B of each failure mode is calculated i And the maximum individual regret T i As follows:

[0078] ​​

[0079] wherein ω O , ω S , ω D and ω E all refer to the combined weight of the risk factor.

[0080] S83, the comprehensive VIKOR index Q i of each failure mode is calculated.

[0081] S84, the results Q i after deblurring are sorted, and if the following two conditions are met, the failure mode FM (1) is the optimal compromise solution.

[0082] (1) Q(FM (2) )-Q(FM (1) )≥1 / (n-1), wherein FM (1) is the Q value of the optimal scheme in the sorting result, FM (2) is the Q value of the suboptimal scheme in the sorting result, and n is the total number of failure modes;

[0083] (2) the B i value and T i value of the optimal scheme are also optimal, so as to ensure that the scheme with the minimum Q value is the optimal scheme. If the above two conditions cannot be met simultaneously, a group of compromise solutions can be obtained, as follows:

[0084] (3) only condition (1) is met, a group of compromise solutions are obtained: FM (1) , FM (2) are compromise solutions;

[0085] (4) only condition (2) is met, a group of compromise solutions are obtained: FM (1) ,..., FM (n) , i.e., all are the most important failure modes;

[0086] Compared with the prior art, the present application has the following beneficial effects:

[0087] 1. The present application adds economic degree on the basis of the traditional severity, occurrence degree and detectability, which can more comprehensively reflect the economic loss that may be caused by the failure mode and improve the comprehensiveness of the evaluation.

[0088] 2. The present application effectively solves the problem of inconsistent expert subjective evaluation results by combining the personal background of experts and the similarity aggregation method to calculate the expert weight.

[0089] 3. The present application uses the DEMATEL method to calculate the influence relationship between failure modes and clearly defines the influence degree and the influenced degree of each failure mode, thereby better optimizing the risk decision.

[0090] 4、The application adopts the DEMATEL-VIKOR mixed decision method, considers the comprehensive influence of multiple risk factors, and provides more intuitive and comprehensive decision support through the sorting method.

[0091] 5、The application makes the risk assessment model have stronger adaptability in the dynamic environment through the combination weight method of risk factors and the strategy of modifying the severity evaluation value. BRIEF DESCRIPTION OF DRAWINGS

[0092] Figure 1 is the principle diagram of the method. DETAILED DESCRIPTION

[0093] The application will be further described below in combination with examples.

[0094] This embodiment analyzes and evaluates the turbine engine on the airplane. In this embodiment, the FMEA team is composed of expert one, senior manager of informatization project k1, expert two, intelligent manufacturing system implementation consultant k2, expert three, technical manager k3, and expert four, university scholar in the field of intelligent manufacturing k4.

[0095] Firstly, the turbine engine is analyzed from top to bottom, and the risks of the turbine engine are identified by combining brainstorming, expert interviews and the sorting and induction of related literature. The turbine engine is divided into six modules, i.e. turbocharging system, cooling system, electronic control system, fuel supply system, body structure and support system and lubricating system. Then, each system is analyzed in detail, the key failure modes of each system are determined, and the possible impact is evaluated. For convenience of expression, these failure modes are represented by numbers FM i (i = 1, 2,..., 19), and the related failure modes and their consequences and causes are listed in Tables 5, 6, 7, 8, 9 and 10.

[0096] Table 5 Failure modes and consequences of the turbine engine system

[0097]

[0098]

[0099] Table 6 Failure modes and consequences of the cooling system

[0100]

[0101] Table 7 Failure modes and consequences of the electronic control system

[0102]

[0103] Table 8 Failure modes of fuel supply system and their consequences

[0104]

[0105] Table 9 Failure modes of body structure and support system and their consequences

[0106]

[0107] Table 10 Failure modes of lubrication system and their consequences

[0108]

[0109] Each expert of FMEA team evaluates the risk factor of failure mode according to the corresponding evaluation standard of language variable in Table 1, and the score of five language variables is obtained, and the expert evaluation table is shown in Table 11. According to the two-type fuzzy number of fuzzy language variable, it is replaced.

[0110] Table 11 Expert scoring table

[0111]

[0112] The scores of each expert are collected, that is, Table 11, and the language variables (1-5) in the expert scoring table are converted into two-type interval fuzzy numbers according to the conversion relationship in Table 2. Because each expert has different professional background and personal judgment, the personal background score of each expert is collected The distance between the expert and other experts is calculated, such as the interval two-type fuzzy number corresponding to the evaluation result O of the first expert and the second expert in the occurrence degree of FM1 is [(1, 2, 4, 5; 1, 1), (1.2, 2.2, 3.8, 4.8; 0.8, 0.8)], [(1, 1, 1, 1; 1, 1), (1, 1, 1, 1; 1, 1)].

[0113] The formula is used

[0114]

[0115] The distance between the two people is calculated as d(1, 2) = 27.63, and the distances between the first expert and the third and fourth experts are d(1, 3) = 27.63 and d(1, 4) = 27.63 respectively; then the average distance Average agreement The average agreement of other experts is calculated as

[0116] Then the consistency of expert evaluation results is calculated by combining the similar aggregation method The personal weight of each expert in each risk factor under each failure mode is obtained To obtain more accurate evaluation results.

[0117] The expert weight matrix is ​​obtained (Table 12).

[0118] Table 12 Expert weight table

[0119]

[0120] The scoring results of each risk factor of each failure mode are summarized (Table 11), and the type II trapezoidal fuzzy numbers corresponding to the scoring results of the four experts are compared with the expert weights. The weighted average is used to obtain the comprehensive fuzzy evaluation matrix F = (f ij ) m×4 (Table 13) Table 13 is the comprehensive fuzzy evaluation matrix of an expert.

[0121]

[0122] Then use DEMETAL to calculate each failure mode FM i of and i Indicates the failure mode FM i Direct and indirect effects on other failure modes, C j Indicates the failure mode FM i Affected directly and indirectly by other failure modes, R i and C i Normalize them separately to get and use Modify the comprehensive evaluation value of severity to obtain the modified comprehensive fuzzy evaluation matrix (Table 15).

[0123] We need to calculate the direct correlation aggregation matrix. Since we divide failure modes into failure modes within different systems, and different systems have little impact on each other, we can simply analyze the impact between failure modes within different systems. For example, in an electronic control system, its direct correlation aggregation matrix is ​​shown in Table 14.

[0124] Table 14 Electronic control system direct association aggregation matrix

[0125]

[0126] The values ​​in Table 14 are obtained by aggregating the ratings of four experts. For example, the impact of FM8 on FM9 is calculated by the weight of each expert. And the evaluation results are aggregated, that is, wherein, is the two-type trapezoidal fuzzy number corresponding to the score results of the four experts on the influence degree of FM8 on FM9, represents the weight of the kth expert on the influence degree of FM8 on FM9, and the calculation method is consistent with the above method of calculating the expert weight of the comprehensive fuzzy evaluation matrix F, that is, the relative consistency of the score results of each expert can be obtained.

[0127] When determining the risk factor weight, first, the score results of the experts on the risk factor influence degree are calculated by the analytic hierarchy process, and the subjective weight of the risk factor is obtained. The risk factors in Table 4 are scored and compared with each other using the scoring criteria mentioned in Table 3, and then the weighted average of each group of score results is calculated.

[0128] Similarly, each expert has the same weight, and the total score of S is calculated according to the comprehensive score of the expert in the risk factor relative importance comprehensive score table (Table 16), which is 1+0.875+1=2.875, wherein the score is obtained by averaging the scores of the above experts, for example Similarly, the total scores of O, D, and E are 2, 1.768, and 1.6 respectively, and the normalized subjective weights are (0.386, 0.262, 0.203, 0.149) respectively.

[0129] The "-" in the expert score table of the risk factor relative importance indicates that the expert does not need to score. In the calculation of the comprehensive score matrix, the scores in the lower left corner correspond to the values in the upper right corner, wherein 1 and 0 correspond to each other, and the remaining values are inverses of each other, for example, the influence degree of O on S is 1 corresponding to 0 of the influence degree of S on O, and the influence degree of D on S is 1.413, which is the inverse of 0.875 of the influence degree of S on D.

[0130]

[0131] Table 16 Risk factor relative importance comprehensive score table

[0132]

[0133] In combination with the objective weight of the risk factor, the objective weight of each expert is usually given as (0.25, 0.25, 0.25, 0.25), and the combined weight of the final risk factor is calculated as 0.5×(0.386, 0.262, 0.203, 0.149)+0.5×(0.205, 0.25, 0.25, 0.25)=(0.318, 0.256, 0.226, 0.200).

[0134] The VIKOR method is used to determine the positive and negative ideal values in the failure mode based on the comprehensive evaluation matrix, and the comprehensive evaluation matrix is de-fuzzified to obtain the clear values of each failure mode corresponding to each risk factor S, O, D and E, as shown in Table 17. i and the minimum individual regret value T i , the comprehensive index Q value of each failure mode is calculated according to the B i , T i values. The B i , T i , Q values of each failure mode are obtained, as shown in Table 18.

[0135]

[0136]

[0137]

[0138] Table 18 B i , T i , Q values of each failure mode

[0139]

[0140] Q (FM (2) ) - Q (FM (1) ) = 0.203 >= 0.056, it is known that FM14 satisfies condition (1), and the T i value or B i value of FM14 is also optimal compared with other failure modes, it is known that FM14 also satisfies condition (2), and then FM14 is the compromise solution of this problem, that is, FM14 is the failure mode with the highest risk.

[0141] In addition, it can be known from Table 18 that the failure modes with high risk mainly include FM1, FM2, FM12, FM13, FM14 and FM17, and the most resources need to be invested for monitoring, prevention and correction; the failure modes with medium risk mainly include FM3, FM6, FM7, FM15, FM16, FM18 and FM19, and medium resources need to be invested for monitoring and prevention of related systems and equipment; and the failure modes with low risk mainly include FM4, FM5, FM8, FM9, FM10 and FM11, and a small amount of resources need to be invested for monitoring and prevention.

[0142] Through the example analysis, it can be seen that the application is applicable and effective in risk assessment and management, and can better serve the risk management needs of modern industrial systems.

[0143] ​​The above merely illustrates the specific embodiments of the present application, and does not limit the present application in any way. Any modification or equivalent replacement, etc. of the technical solutions and contents disclosed by the present application, which is made by any person skilled in the art without departing from the scope of the technical solutions of the present application, belongs to the content of the technical solutions of the present application, and still belongs to the protection scope of the present application.

Claims

1. An improved FMEA method based on interval type-2 fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision making, characterized by: The following steps are involved: S1. Establish a FMEA team consisting of K experts to analyze the potential failure modes of IMS key hardware. i Identify and build a set of IMS key hardware failure modes; S2. Expand the traditional three risk factors to four; S3. Establish interval type-2 fuzzy sets: Describe the evaluation criteria of the four risk factors under the corresponding language variables and create a language variable corresponding evaluation criteria table. K experts score based on the evaluation table. Let the evaluation results of severity, occurrence, detection and economy of each expert under each failure mode be The expert's rating results are converted into interval type-2 trapezoidal fuzzy numbers in the corresponding linguistic variables; S4. Collect experts’ personal background scores Combined with similarity aggregation method to calculate the consistency of expert evaluation results Derive the expert's personal weights for each risk factor under each failure mode S5, then follow the The evaluation results are The corresponding type-II trapezoidal fuzzy number weighted average is used to obtain the comprehensive fuzzy evaluation matrix of severity, occurrence, detection and economy under each failure mode; S6. Use DEMETAL to calculate the influence and affected degree of each failure mode, and modify the comprehensive evaluation value of severity in the comprehensive fuzzy evaluation matrix F; S7. Use the analytic hierarchy process to calculate the expert risk factor impact scores to obtain the subjective weights of the risk factors, and then combine them with the objective weights of the risk factors to obtain the final combined weights of the risk factors; S8. Calculate the distance between fuzzy numbers based on relative preference relationship analysis and rank the failure modes using the VIKOR method. Relevant personnel will formulate corresponding prevention and response measures based on the ranking.

2. The improved FMEA method based on interval type-2 fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision making according to claim 1, characterized in that: In step S2, economic degree is added to the traditional risk factors.

3. The improved FMEA method based on interval type-2 fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision making according to claim 1, characterized in that: The interval type-2 fuzzy set in step S3 includes five linguistic variables, including five levels: very low VL, low L, general M, high H, and very high VH, and the corresponding evaluation language evaluation numbers are 1, 2, 3, 4, and 5.

4. The improved FMEA method based on interval type-2 fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision making according to claim 1, characterized in that: Expert weight in step S4 The calculation includes the following steps: S41. Establish an expert background score standard table, where the total score of the kth expert background is The expert background weight is calculated by adding up the scores of each indicator. The formula is: S42. Use the distance formula between interval trapezoidal fuzzy numbers to calculate the distance d between the evaluation result of the kth expert and the evaluation results of other experts among the K experts. ij (k, u), the calculation formula is as follows: Then according to Calculate the average distance between the evaluation result of the kth expert and the other experts in the K experts u represents other experts except the k-th expert; S43, according to Calculate the average agreement; S44, according to The relative consistency of each expert is calculated using the normalization method S45. Combine expert background weights Relative agreement with experts The combined weight of the j-th risk factor of the k-th expert under the i-th failure mode can be calculated as follows: Where α and β are relaxation factors of background weight and relative consistency, respectively, 0≤α≤1, 0≤β≤1 and α+β=1.

5. The improved FMEA method based on interval type-2 fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision making according to claim 1, characterized in that: The steps for determining the influence and the affected degree of the failure mode in step S6 are as follows: S61. Experts analyze the impact relationship between failure modes and obtain the direct correlation matrix between failure modes: Among them Indicates that the kth expert evaluates the failure mode FM i Failure Mode FM j If there is no impact, the score is 0; S62. Calculate the direct association aggregation matrix: Directly associated aggregation matrix Elements in By each expert weight Aggregate After aggregation, defuzzification is performed to obtain a clear and directly related aggregation matrix S63. Calculate the normalized direct association aggregation matrix D. The common method for normalizing the direct association aggregation matrix is ​​based on the maximum value of the sum of the row vector elements of the matrix. for The elements in Represents the maximum value of the sum of elements in each row; S64. Calculate the total incidence matrix T, where I is the identity matrix: Among them D n Represents the multiplication of n D matrices; S65. Determine the influence and affected degree of the failure mode, and define the sum of the i-th row of the matrix T as R i , the sum of the columns is defined as C j , R i Indicates the failure mode FM i Direct and indirect effects on other failure modes, C j Indicates the failure mode FM i Directly and indirectly affected by other failure modes: Let i = j, influence R i +C i Expressed failure mode FM i The total degree of being affected by other failure modes and affecting other failure modes, the degree of influence R i -C i Expressed failure mode FM i The difference between the degree of being affected by other failure modes and the degree of affecting other failure modes; S66, R i and C i Normalize them separately and get and Assume that the expert has a good understanding of the failure mode FM i The initial severity S assessment value is After DEMATEL analysis, the corrected failure severity is 6. The improved FMEA method based on interval type-2 fuzzy sets and DEMATEL-VIKOR hybrid multi-criteria decision making according to claim 1, characterized in that: In step S8, relative preference relationship analysis is used to calculate the priority ranking of failure modes using the VIKOR method. The steps are as follows: S81. Comprehensive evaluation matrix calculated based on expert evaluation results Find the positive ideal solution in the failure mode Negative ideal solution S82. Calculate the group benefit B for each failure mode i and the maximum individual regret T i ; S83. Calculate the comprehensive VIKOR index Q for each failure mode i ; S84. Defuzzified result Q i Sort.

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