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CN108920806A - A kind of heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method - Google Patents

A kind of heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method Download PDF

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CN108920806A
CN108920806A CN201810665649.3A CN201810665649A CN108920806A CN 108920806 A CN108920806 A CN 108920806A CN 201810665649 A CN201810665649 A CN 201810665649A CN 108920806 A CN108920806 A CN 108920806A
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程强
孙东洋
赵永胜
王昊
杨聪彬
刘志峰
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Beijing University of Technology
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Abstract

The heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method that the invention discloses a kind of, belong to lathe reliability design field, heavy machine tool Reliability Distribution problem is specifically considered as Multiple Attribute Decision Problems, realizes lathe Reliability Distribution using the correlation technique of Multiple Attribute Decision Problems.It is expressed using decision information of the intuition Trapezoid Fuzzy Number to each expert, the decision matrix for merging each expert later obtains integrated decision-making matrix model, finally the Reliability Distribution coefficient of subsystems is obtained using similarity to ideal solution ranking method, the Reliability Distribution for completing heavy machine tool, improves the reliability of domestic heavy digital control machine tool.

Description

A kind of heavy machine tool reliability allocation methods based on Trapezoid Fuzzy Number and ranking method
Technical field
The present invention relates to the reliability allocation methods of heavy digital control machine tool, belong to lathe reliability design field.
Background technique
Numerically-controlled machine tool is the modern electromechanical equipment of a kind of high-precision, high efficiency, high-tech, the base as advanced manufacturing technology Plinth and core equipment, are more and more widely used among machinery production, and restrict the hair of manufacturing field and each high and new technology Exhibition.Current domestic heavy digital control machine tool speed, precision and in terms of make remarkable progress, but reliability index There are obvious gaps with world level, seriously affect the product reputation of domestic weight equipment and the competitiveness of domestic and international market, Technical bottleneck as industry.Heavy machine tool is expensive, and possess usually as national economy priority industry field enterprise Key equipment, processing object are often the kernel component of consumer products, and event is frequently resulted in since the reliability of lathe is relatively low Barrier is shut down can cause huge economic loss to user.The reliability of product designs first, carries out heavy type numerical control machine The research of bed Reliability Distribution technology, improves the reliability level of heavy digital control machine tool from source, and forms a set of maturation Heavy digital control machine tool Reliability Distribution technical solution is the urgent need of industry, has great strategic importance.
The reliability of numerically-controlled machine tool is to measure the important indicator of machine mass quality.The reliability of lathe directly affects processing Quality, productivity and efficiency, and the confidence of the market competitiveness and user is further influenced, so the numerically-controlled machine tool of tool high reliability Manufacturing industry there is an urgent need to.The Reliability Distribution of numerically-controlled machine tool is the committed step in lathe reliability design.Utilize lathe Reliability Distribution technology, we can be designed that the numerically-controlled machine tool of high reliability, can also be improved the reliability of existing lathe.Weight Type structure of numerically controlled machine-tool is complicated, and workload is very big when being allocated using traditional Cnc ReliabilityintelligeNetwork Network distribution method, calculates Process is complicated, so propose that one kind is easy to calculate, process is simple and to be easily programmed the reliability allocation methods of realization be current weight Type Cnc ReliabilityintelligeNetwork Network design work there is an urgent need to.It solves Cnc ReliabilityintelligeNetwork Network assignment problem and needs to complete two weights Want step：
The first, integrated decision-making matrix model is established using intuition Trapezoid Fuzzy Number；
According to the principle of work and power and structure feature of lathe, lathe is divided into several subsystems, then determining influences reliability A number of factors of distribution.Then lathe Reliability Distribution is considered as Multiple Attribute Decision Problems, by these subsystems and influence factor It is considered as the scheme collection and property set of Multiple Attribute Decision Problems, combines intuition Trapezoid Fuzzy Number by industry specialists and Machine Tool design personnel Theory carries out decision to these subsystems and influence factor, obtains several decision matrixs, then believes the decision of all policymaker Breath is assembled, and an integrated decision-making matrix is obtained.
The second, each scheme is ranked up using similarity to ideal solution ranking method, obtains the Reliability Distribution power of each subsystem Weight completes lathe Reliability Distribution task.
Using the approach degree of each scheme in similarity to ideal solution ranking method calculating integrated decision-making matrix to ideal scheme, then These approach degrees are converted to the Reliability Distribution weight of each subsystem, to complete lathe Reliability Distribution.
The present invention is merged using decision information of the intuition Trapezoid Fuzzy Number to expert and designer, obtains integrated decision-making Then matrix model obtains the Reliability Distribution weight of each subsystem using similarity to ideal solution ranking method.
Summary of the invention
The object of the present invention is to provide a kind of heavy digital control machine tool based on Intuitionistic Fuzzy Numbers and similarity to ideal solution ranking method Reliability allocation methods, hereinafter referred to as ITrFNs method.It is asked first lathe Reliability Distribution problem is considered as multiple attribute decision making (MADM) Topic, resolves into several subsystems for lathe, then scheme collection of these subsystems as Multiple Attribute Decision Problems determines several again These factors, are considered as the property set of Multiple Attribute Decision Problems by the factor for influencing Reliability Distribution.According to Multiple Attribute Decision Problems Resolving ideas, indicate decision information using intuition Trapezoid Fuzzy Number by multidigit expert and designer, finally obtain integrated decision-making Matrix model.Each scheme is ranked up using similarity to ideal solution ranking method, is assigned to lathe overall goal reasonably respectively On a subsystem, to improve the reliability of domestic heavy digital control machine tool.
The technical solution adopted by the present invention is a kind of heavy machine tool Reliability Distribution based on Trapezoid Fuzzy Number and ranking method Heavy digital control machine tool Reliability Distribution problem is considered as Multiple Attribute Decision Problems by method, this method, by heavy digital control machine tool subsystem System and the factor for influencing heavy digital control machine tool Reliability Distribution are considered as the scheme collection and property set of Multiple Attribute Decision Problems, by straight Feel that Trapezoid Fuzzy Number expresses the decision information of expert and designer, and all decision informations are merged and are integrated Then decision matrix is ranked up each scheme using similarity to ideal solution ranking method, by the approach degree of each scheme and ideal solution Coefficient is converted into the weight vectors of Reliability Distribution, is finally completed the Reliability Distribution of heavy digital control machine tool and improves domestic heavy The reliability level of type numerically-controlled machine tool.
Specifically comprise the following steps：
Step 1：According to the structure feature and the principle of work and power of heavy digital control machine tool, system subdivision is carried out to it and is enumerated Come.These subsystems constitute the scheme collection O={ o of heavy digital control machine tool Reliability Distribution problem1,o2,…,om}.Wherein O is The set for the scheme that all subsystems are constituted.M indicates the number of divided subsystem and the number of scheme.o1, o2,…,omExpression scheme 1, scheme 2 and scheme m, wherein each scheme is a subsystem.
Heavy digital control machine tool is divided into eight subsystems, as shown in table 1 below：
The system subdivision result of the common lathe of table 1
Step 2：The reliability index that heavy digital control machine tool entirety is determined by heavy digital control machine tool designer, then will affect The factor of heavy digital control machine tool Reliability Distribution itemizes out.These influence factors constitute heavy digital control machine tool reliability Property set C={ the c of assignment problem1,c2,…,cn, wherein C indicates the set of all properties, and considered influence heavy type The set of the factor of Cnc ReliabilityintelligeNetwork Network distribution.N indicates the number of influence factor, and the number of attribute.c1,c2,…,cn Indicate attribute 1, attribute 2 and attribute n, wherein each attribute is an influence factor.
Considered influence heavy digital control machine tool Reliability Distribution because being known as：Complexity, reliability, maintainability, safety and human factors, technical level, Working environment, cost and working time.
Step 3：Decision information is provided for scheme collection and property set by related fields expert, lists the decision of each expert Matrix Rk, wherein RkIndicate that the decision matrix of kth position expert, k=1,2 ..., h indicate to share h experts.
Expert carries out using language when decision, for that need to convert language to intuition Trapezoid Fuzzy Number convenient for calculating Form, the following table 2 gives the transformational relation between language and intuition Trapezoid Fuzzy Number.Language is converted into intuition ladder After shape fuzzy number, expert decision-making matrix RkIn element intuition Trapezoid Fuzzy Number is all converted to by language, at this time For Intuition Trapezoid Fuzzy Number form.
Transfer standard between 2 language of table and intuition Trapezoid Fuzzy Number
For expert when carrying out decision, since everyone experience is different, the decision made is different, so expert in order to prevent Opinion there is disagreement, so brainstrust needs standard when making decision, this decision criteria is as shown in table 3 below：
3 decision criteria of table
Step 4：Assemble the decision matrix of all experts, constructs integrated decision-making matrix R.
The decision matrix of all policymaker is collected using intuition Trapezoid Fuzzy Number weighted average operator (ITrFNWA) Knot.
Intuition Trapezoid Fuzzy Number weighted average operator (ITrFNWA) is defined as follows：
If Aβ(β=1,2 ..., p) is one group of intuition Trapezoid Fuzzy Number, w=(w1,w2,…,wp)TIt is AβWeight vectors, Then have：
Work as wβWhen=1/p, formula (1) becomes：
In addition, setting Aβ=<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β=1,2) are that two intuition are trapezoidal fuzzy Number, then have：
A1+A2=<(a11+a21,a12+a22,a13+a23,a14+a24),(b11+b21,b12+b22,b13+b23,b14+b24)>(3)
The decision matrix of all experts is assembled using formula (2) and (3), obtains integrated decision-making matrix R= (rij)m×n, i=1,2 ..., m, j=1,2 ..., n, wherein
Step 5：Use the desired value of intuition Trapezoid Fuzzy Number as the weight of attribute.
The desired value of intuition Trapezoid Fuzzy Number is defined as follows：
If A=<(a1,a2,a3,a4),(b1,b2,b3,b4)>It is an intuition Trapezoid Fuzzy Number, under its desired value passes through Formula obtains：
Using these desired values, the weight matrix U=(u of all properties is obtainedij)m×n, wherein
uij=EV (rij), (i=1,2 ... m, j=1,2 ..., n) (6)
Step 6：The positive ideal dematrix of definition and minus ideal result matrix.
The positive ideal solution of decision matrixAnd minus ideal resultWherein
Step 7：It calculates and weights positive distance measure and negative distance measure.
It is calculated separately using formula (9) and (10) and assembles the Intuitionistic Fuzzy Decision matrix R and positive ideal solution R of decision matrix+With it is negative Ideal solution R-Between Weighted distanceWith
Wherein uijFor the element in attribute weight matrix U,Indicate intuition Trapezoid Fuzzy Number rijWithBetween Distance,Indicate intuition Trapezoid Fuzzy Number rijWithThe distance between.The distance between two intuition Trapezoid Fuzzy Numbers are fixed Justice is as follows：
If Aβ=<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β=1,2) is two intuition Trapezoid Fuzzy Numbers, then A1With A2The distance between be：
Step 8：Calculate relative similarity degree coefficient lambda.
Scheme oiRelative similarity degree coefficient lambdaiCalculation formula is as follows：
Step 9：Calculate Reliability Distribution coefficient k.
Scheme oiReliability Distribution coefficient kiCalculation formula is as follows：
Step 10：The Reliability Distribution of heavy digital control machine tool is completed according to Reliability Distribution coefficient.
The reliability R of subsystems is obtained according to Reliability Distribution coefficientiCalculation formula：
Wherein RsIt is the reliability of lathe entirety, RiIt is allocated to the reliability of i-th of subsystem.
Detailed description of the invention
Fig. 1 is the flow chart that the method for the present invention is implemented.
Specific embodiment
The present invention verifies above-mentioned heavy machine tool reliability allocation methods by taking certain heavy type numerical control planer-type milling machine as an example. Specifically comprise the following steps：
Step 1：System subdivision is carried out to heavy CNC planer type milling machine, 8 sons are divided into according to the mechanism of lathe System, as shown in table 4 below.This 8 subsystems constitute the scheme collection of Multiple Attribute Decision Problems, i.e. O={ o1,o2,…,o8, often A subsystem is all a kind of scheme.
The system subdivision result of 4 heavy type numerical control planer-type milling machine of table
Step 2：It is required according to user, the reliability R of lathe entirety is determined by designers, then in conjunction with actual conditions Determine the factor and Reliability Distribution principle for influencing lathe Reliability Distribution.
The reliability R of this heavy type numerical control planer-type milling machine entirety determines according to actual conditionssIt is 0.85.Influence lathe reliability Distribution because being known as 6, shown in these influence factors table 5 specific as follows.
The factor and Reliability Distribution principle of the influence Reliability Distribution of table 5
The factors composition property set C={ c of Multiple Attribute Decision Problems of this 6 influence Reliability Distributions1,c2,…,c6, Each influence factor is an attribute.Wherein, in order to consistent with the distribution principle of other influences factor, for technical level With two influence factors of working environment, we provide expert when judging the two influence factors, consider the non-maturity of technology and Bad environments degree carries out decision, and in this way when expert carries out decision to the two influence factors, the decision value that the two obtains is got over The reliability of height, distribution is lower, this is consistent with the distribution principle of other factors, is convenient for next calculating.
Step 3：List all expert decision-making matrix Rk。
It invites three experts to carry out decision under 6 influence factors to this 8 subsystems, obtains three decision matrixs.
Step 4：Assemble the decision matrix of each expert, constructs the comprehensive trapezoidal fuzzy decision matrix R of intuition.
Using formula (2), (3), (4) and table 2, the decision matrix of three experts is assembled, available one comprehensive The trapezoidal fuzzy decision matrix R of intuition is closed, it is represented with the form of table.
Step 5：The desired value for calculating the intuition Trapezoid Fuzzy Number in the comprehensive trapezoidal fuzzy matrix R of intuition, these it is expected It is worth the weight as attribute.
The desired value that intuition Trapezoid Fuzzy Number is calculated using formula (5) and (6), finally obtains the weight matrix U of attribute, such as Shown in lower：
Step 6：Positive ideal solution and minus ideal result are determined using formula (7) and (8), then according to the weight matrix U of attribute, The positive distance measure D of weighting is calculated in conjunction with formula (9), (10) and (11)i +With negative distance measure Di+.Its result such as the following table 6 institute Show.
Table 6 weights positive distance measure and negative distance measure
Step 7：Relative similarity degree λ and Reliability Distribution coefficient k are calculated using formula (12) and (13), as a result such as the following table 7 It is shown.
The Reliability Distribution coefficient of table 7 relative similarity degree coefficient and each subsystem
Step 8：It is as a result as follows as a result, calculating the reliability that each subsystem distributes in conjunction with formula (14) according to table 7 Shown in table 8.In addition the method used in AHP method and this patent carries out Comparative result, to illustrate the method in this patent Validity and accuracy.
8 Reliability Distribution result of table and compare
Reliability Distribution result：
The method proposed according to this patent, the reliability for obtaining subsystems are as follows：It is hydraulic reliable with pneumatic system Degree is 0.9803；Feed system reliability is 0.98；Axis system reliability is 0.9818；Servo-system reliability is 0.9761；Lubricating system reliability is 0.9785；Cooling system reliability is 0.9771；Automatic tool changer reliability is 0.9848；Digital control system reliability is 0.9805.

Claims (1)

1.一种基于梯形模糊数和排序法的重型机床可靠性分配方法，其特征在于：该方法将重型数控机床可靠性分配问题视为多属性决策问题，将重型数控机床子系统和影响重型数控机床可靠性分配的因素视为多属性决策问题的方案集和属性集，通过直觉梯形模糊数对专家和设计者的决策信息进行表达，并将所有决策信息进行融合得到综合决策矩阵，然后利用逼近理想解排序法对各个方案进行排序，将各方案与理想解的贴近度系数转化为可靠性分配的权重向量，最终完成重型数控机床的可靠性分配；1. A heavy-duty machine tool reliability distribution method based on trapezoidal fuzzy numbers and sorting method, characterized in that: the method regards the heavy-duty CNC machine tool reliability distribution problem as a multi-attribute decision-making problem, and regards the heavy-duty CNC machine tool subsystem and the influence of heavy-duty The factors of machine tool reliability allocation are regarded as the scheme set and attribute set of the multi-attribute decision-making problem, and the decision information of experts and designers is expressed by the intuitionistic trapezoidal fuzzy number, and all decision information is fused to obtain a comprehensive decision matrix, and then the approximation The ideal solution sorting method sorts each scheme, and converts the closeness coefficient of each scheme to the ideal solution into a weight vector of reliability distribution, and finally completes the reliability distribution of heavy-duty CNC machine tools; 具体包括如下步骤：Specifically include the following steps: 步骤1：根据重型数控机床的结构特征和功能原理，对其进行子系统划分并列举出来；这些子系统组成了重型数控机床可靠性分配问题的方案集O＝{o1,o2,…,om}；其中O是所有子系统所构成的方案的集合；m表示所划分的子系统的个数，也是方案的个数；o1,o2,…,om表示方案1、方案2和方案m，其中每一个方案都是一个子系统；Step 1: According to the structural characteristics and functional principles of heavy-duty CNC machine tools, divide them into subsystems and list them out; these subsystems constitute the solution set O={o 1 ,o 2 ,…, o m }; where O is the set of schemes composed of all subsystems; m represents the number of partitioned subsystems, which is also the number of schemes; o 1 , o 2 ,...,o m represent scheme 1 and scheme 2 and scheme m, each of which is a subsystem; 重型数控机床被划分成八个子系统，如下表1所示：Heavy-duty CNC machine tools are divided into eight subsystems, as shown in Table 1 below: 表1 常见机床的子系统划分结果Table 1 Subsystem division results of common machine tools 步骤2：由重型数控机床设计者确定重型数控机床整体的可靠性指标，然后将影响重型数控机床可靠性分配的因素详细列举出来；这些影响因素组成了重型数控机床可靠性分配问题的属性集C＝{c1,c2,…,cn}，其中C表示所有属性的集合，也是被考虑的影响重型数控机床可靠性分配的因素的集合；n表示影响因素的个数,也是属性的个数；c1,c2,…,cn表示属性1、属性2和属性n，其中每一个属性都是一个影响因素；Step 2: The designer of the heavy-duty CNC machine tool determines the overall reliability index of the heavy-duty CNC machine tool, and then lists the factors that affect the reliability allocation of the heavy-duty CNC machine tool in detail; these influencing factors constitute the attribute set C of the reliability allocation problem of the heavy-duty CNC machine tool ＝{c 1 ,c 2 ,…,c n }, where C represents the set of all attributes, and is also the set of factors considered to affect the reliability distribution of heavy-duty CNC machine tools; n represents the number of influencing factors, which is also the number of attributes number; c 1 , c 2 ,..., c n represent attribute 1, attribute 2 and attribute n, each of which is an influencing factor; 被考虑的影响重型数控机床可靠性分配的因素有：复杂度、易维修性、技术水平、工作环境、成本和工作时间；The factors considered to affect the reliability allocation of heavy-duty CNC machine tools are: complexity, ease of maintenance, technical level, working environment, cost and working time; 步骤3：由相关领域专家针对方案集和属性集给出决策信息，列出各个专家的决策矩阵Rk，其中Rk表示第k位专家的决策矩阵，k＝1,2,…,h，表示共有h位专家；Step 3: Experts in related fields give decision-making information for the scheme set and attribute set, and list the decision matrix R k of each expert, where R k represents the decision matrix of the k-th expert, k=1,2,...,h, Indicates that there are h experts in total; 专家进行决策时使用语言术语，为便于计算需将语言术语转化为直觉梯形模糊数的形式，下表2给出了语言术语和直觉梯形模糊数之间的转换关系；语言术语转换成直觉梯形模糊数之后，专家决策矩阵Rk中的元素都由语言术语转换为直觉梯形模糊数，此时i＝1,2,…,m,j＝1,2,…,n,k＝1,2,…,h，为直觉梯形模糊数形式；Experts use language terms when making decisions. For the convenience of calculation, language terms need to be transformed into the form of intuitionistic trapezoidal fuzzy numbers. Table 2 below shows the conversion relationship between language terms and intuitionistic trapezoidal fuzzy numbers; After counting, the elements in the expert decision matrix R k are converted from linguistic terms to intuitionistic trapezoidal fuzzy numbers, at this time i=1,2,...,m,j=1,2,...,n,k=1,2,...,h, is the intuitionistic trapezoidal fuzzy number form; 表2 语言术语和直觉梯形模糊数之间的转换标准Table 2 Conversion criteria between linguistic terms and intuitionistic trapezoidal fuzzy numbers 专家在进行决策时，由于每个人的经验不同，做出的决策不同，所以为了防止专家的意见出现分歧，所以专家们在作决策时需要有个标准，这个决策标准如下表3所示：When experts make decisions, because everyone has different experiences, they make different decisions. Therefore, in order to prevent the opinions of experts from diverging, experts need to have a standard when making decisions. This decision-making standard is shown in Table 3 below: 表3 决策标准Table 3 Decision criteria 步骤4：集结所有专家的决策矩阵，构建综合决策矩阵R；Step 4: Gather the decision matrices of all experts to construct a comprehensive decision matrix R; 利用直觉梯形模糊数加权平均算子ITrFNWA对所有决策者的决策矩阵进行集结；Use the intuitionistic trapezoidal fuzzy number weighted average operator ITrFNWA to assemble the decision matrices of all decision makers; 直觉梯形模糊数加权平均算子ITrFNWA的定义如下：The intuitionistic trapezoidal fuzzy number weighted average operator ITrFNWA is defined as follows: 设Aβ(β＝1,2,…,p)是一组直觉梯形模糊数，w＝(w1,w2,…,wp)T是Aβ的权重向量，则有：Suppose A β (β=1,2,…,p) is a group of intuitionistic trapezoidal fuzzy numbers, w=(w 1 ,w 2 ,…,w p ) T is the weight vector of A β , then: 当wβ＝1/p时，公式(1)变为：When w β =1/p, formula (1) becomes: 另外，设Aβ＝<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β＝1,2)是两个直觉梯形模糊数，则有：In addition, let A β =<(a β1 ,a β2 ,a β3 ,a β4 ),(b β1 ,b β2 ,b β3 ,b β4 )>(β=1,2) be two intuitionistic trapezoidal fuzzy numbers, Then there are: A1+A2＝<(a11+a21,a12+a22,a13+a23,a14+a24),(b11+b21,b12+b22,b13+b23,b14+b24)> (3)A 1 +A 2 =<(a 11 +a 21 ,a 12 +a 22 ,a 13 +a 23 ,a 14 +a 24 ),(b 11 +b 21 ,b 12 +b 22 ,b 13 +b 23 ,b 14 +b 24 )> (3) 利用公式(2)和(3)对所有专家的决策矩阵进行集结，得到综合决策矩阵Use the formulas (2) and (3) to gather the decision matrices of all experts to get the comprehensive decision matrix R＝(rij)m×n，i＝1,2,…,m,j＝1,2,…,n，其中，R=(r ij ) m×n , i=1,2,…,m,j=1,2,…,n, where, 步骤5：用直觉梯形模糊数的期望值作为属性的权重；Step 5: Use the expected value of the intuitionistic trapezoidal fuzzy number as the weight of the attribute; 直觉梯形模糊数的期望值的定义如下：The definition of the expected value of an intuitionistic trapezoidal fuzzy number is as follows: 设A＝<(a1,a2,a3,a4),(b1,b2,b3,b4)>是一个直觉梯形模糊数，它的期望值通过下式得到：Let A=<(a 1 ,a 2 ,a 3 ,a 4 ),(b 1 ,b 2 ,b 3 ,b 4 )> be an intuitionistic trapezoidal fuzzy number, and its expected value can be obtained by the following formula: 利用这些期望值，得到所有属性的权重矩阵U＝(uij)m×n,其中，Using these expected values, the weight matrix U=(u ij ) m×n of all attributes is obtained, where, uij＝EV(rij)，(i＝1,2,…m,j＝1,2,…,n) (6)u ij =EV(r ij ), (i=1,2,...m,j=1,2,...,n) (6) 步骤6：定义正理想解矩阵和负理想解矩阵；Step 6: Define positive ideal solution matrix and negative ideal solution matrix; 决策矩阵的正理想解和负理想解其中Positive Ideal Solution of Decision Matrix and negative ideal solution in 步骤7：计算加权正距离测度和负距离测度；Step 7: Calculate the weighted positive distance measure and negative distance measure; 利用式(9)和(10)分别计算集结直觉模糊决策矩阵R与决策矩阵正理想解R+和负理想解R-之间的加权距离和 Use formulas (9) and (10) to calculate the weighted distance between the assembly intuitionistic fuzzy decision matrix R and the positive ideal solution R + and negative ideal solution R - of the decision matrix, respectively and 其中uij为属性权重矩阵U中的元素，表示直觉梯形模糊数rij和之间的距离，表示直觉梯形模糊数rij和之间的距离；两直觉梯形模糊数之间的距离定义如下：Where u ij is the element in the attribute weight matrix U, Represents the intuitionistic trapezoidal fuzzy number r ij and the distance between, Represents the intuitionistic trapezoidal fuzzy number r ij and The distance between; the distance between two intuitionistic trapezoidal fuzzy numbers is defined as follows: 设Aβ＝<(aβ1,aβ2,aβ3,aβ4),(bβ1,bβ2,bβ3,bβ4)>(β＝1,2)是两个直觉梯形模糊数，则A1与A2之间的距离为：Suppose A β =<(a β1 ,a β2 ,a β3 ,a β4 ),(b β1 ,b β2 ,b β3 ,b β4 )>(β=1,2) are two intuitionistic trapezoidal fuzzy numbers, then A The distance between 1 and A 2 is: 步骤8：计算相对贴近度系数λ；Step 8: Calculate the relative closeness coefficient λ; 方案oi的相对贴近度系数λi计算公式如下所示：The calculation formula of the relative closeness coefficient λ i of the scheme o i is as follows: 步骤9：计算可靠性分配系数k；Step 9: Calculate the reliability distribution coefficient k; 方案oi的可靠性分配系数ki计算公式如下所示：The calculation formula of reliability distribution coefficient k i of scheme o i is as follows: 步骤10：根据可靠性分配系数完成重型数控机床的可靠性分配；Step 10: Complete the reliability distribution of heavy-duty CNC machine tools according to the reliability distribution coefficient; 根据可靠性分配系数得到各个子系统的可靠度Ri的计算公式：According to the reliability distribution coefficient, the calculation formula of the reliability R i of each subsystem is obtained: 其中Rs是机床整体的可靠度，Ri是分配给第i个子系统的可靠度。Among them, R s is the overall reliability of the machine tool, and R i is the reliability assigned to the i-th subsystem.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109901515A (en) * 2019-03-28 2019-06-18 北京工业大学 A Reliability Allocation Method of Heavy Machine Tool Based on OWA Operator
CN110704986A (en) * 2019-10-18 2020-01-17 重庆大学 Mechanical system reliability distribution method based on minimum variability OWGA and fuzzy DEMATEL
CN112632739A (en) * 2020-09-30 2021-04-09 北京工业大学 Machine tool reliability distribution method based on intuitive trapezoidal fuzzy and grey correlation
CN112904294A (en) * 2021-03-04 2021-06-04 西安电子科技大学 Radar interference effect evaluation method based on intuitive trapezoidal fuzzy multi-attribute decision
CN118246736A (en) * 2024-03-11 2024-06-25 成都信息工程大学 Typhoon countermeasure processing method integrating risk assessment and improving fuzzy comprehensive assessment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5262941A (en) * 1990-03-30 1993-11-16 Itt Corporation Expert credit recommendation method and system
US20140279801A1 (en) * 2013-03-15 2014-09-18 International Business Machines Corporation Interactive method to reduce the amount of tradeoff information required from decision makers in multi-attribute decision making under uncertainty
CN107220498A (en) * 2017-05-26 2017-09-29 中南大学 A kind of mechanical material evaluation method and its system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5262941A (en) * 1990-03-30 1993-11-16 Itt Corporation Expert credit recommendation method and system
US20140279801A1 (en) * 2013-03-15 2014-09-18 International Business Machines Corporation Interactive method to reduce the amount of tradeoff information required from decision makers in multi-attribute decision making under uncertainty
CN107220498A (en) * 2017-05-26 2017-09-29 中南大学 A kind of mechanical material evaluation method and its system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
米金华 等: "基于模糊理论的数控机床液压系统故障树分析", 《制造技术与机床》 *
谭壮 等: "基于模糊评判的数控机床零部件制造工艺FMECA研究", 《南京信息工程大学学报(自然科学版)》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109901515A (en) * 2019-03-28 2019-06-18 北京工业大学 A Reliability Allocation Method of Heavy Machine Tool Based on OWA Operator
CN110704986A (en) * 2019-10-18 2020-01-17 重庆大学 Mechanical system reliability distribution method based on minimum variability OWGA and fuzzy DEMATEL
CN112632739A (en) * 2020-09-30 2021-04-09 北京工业大学 Machine tool reliability distribution method based on intuitive trapezoidal fuzzy and grey correlation
CN112632739B (en) * 2020-09-30 2024-02-23 北京工业大学 Machine tool reliability distribution method based on intuitive trapezoidal fuzzy and gray correlation
CN112904294A (en) * 2021-03-04 2021-06-04 西安电子科技大学 Radar interference effect evaluation method based on intuitive trapezoidal fuzzy multi-attribute decision
CN112904294B (en) * 2021-03-04 2023-06-30 西安电子科技大学 Radar interference effect evaluation method based on intuitive trapezoidal fuzzy multi-attribute decision
CN118246736A (en) * 2024-03-11 2024-06-25 成都信息工程大学 Typhoon countermeasure processing method integrating risk assessment and improving fuzzy comprehensive assessment
CN118246736B (en) * 2024-03-11 2024-10-08 成都信息工程大学 Typhoon countermeasure processing method integrating risk assessment and improving fuzzy comprehensive assessment

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