Numerical control machine tool reliability evaluation method and system

Abstract

The invention relates to the technical field of reliability evaluation of precision industrial equipment, in particular to a reliability evaluation method and system for a numerical control machine tool. The method mainly comprises the steps that a weight distribution model and an original matrix are established, an index matrix is obtained according to the original matrix, then the conflict intensity and the comparison intensity are obtained, and the conflict intensity and the comparison intensity are used for improving an entropy weight method to conduct weight distribution on numerical control machine tool reliability comprehensive evaluation. On the basis of solving the index change problem, the weights can be allocated by comprehensively considering the difference between the indexes, so that the weight allocation between the indexes is more accurate. And finally, obtaining the comprehensive correlation degree and drawing a machine tool correlation degree table, carrying out comprehensive evaluation on the generalized reliability of the numerical control machine tool, and solving the problem that the subjectivity of an evaluation model is too large.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability evaluation of precision industrial equipment, and in particular to a reliability evaluation method and system for numerically controlled machine tools. Background Art

[0002] CNC machine tools are the basic equipment of the manufacturing industry and are also known as "industrial mother machines" due to their precision and powerful manufacturing capabilities. In recent years, with the rapid development of various downstream application industries such as new energy vehicles, automation equipment, and electronic equipment, there has also been a huge market demand for high-end CNC machine tools with high performance and high reliability.

[0003] Reliability reflects the law of change of product quality on a time scale. Depending on the purpose, demand and occasion of use, the characteristic quantity of reliability is also different. Narrow reliability refers to the difficulty of failure of the target (component, product or system) within a stable time, while general reliability is based on the failure in narrow reliability and takes into account indicators such as fault hazard and maintainability. Narrow reliability indicators are applicable to non-repairable products, and general reliability indicators are applicable to repairable products. CNC machine tools are typical repairable products. Reliability indicators should be combined with maintainability and availability indicators to fully reflect the operating reliability level of machine tools. The general reliability indicators of CNC machine tools have the characteristics of multiple angles and multiple categories. For example, from the perspective of maintainability, there are maximum maintenance time, maintenance man-hour rate, etc.; from the perspective of failure, there are failure rate, average failure interval time, etc. There are many types of CNC machine tools and many manufacturers, and the indicators that each machine tool and each manufacturer pays attention to are not the same. With the development of science and technology, the reliability level of CNC machine tools will inevitably be improved. At this time, the indicators that are emphasized will undoubtedly change. At the same time, with the deepening of research, more specific and complete generalized reliability indicators will gradually emerge; in addition, the data source of the target comparison machine tool also has limitations on the comprehensive evaluation of the generalized reliability of CNC machine tools. For example, the maximum maintenance time and maintenance man-hour rate also represent maintainability. If the target comparison machine tool only publishes the maximum maintenance time data, and we use the maintenance man-hour rate for comprehensive evaluation modeling, then we cannot obtain the data and can only re-model.

[0004] The reliability evaluation of CNC machine tools runs through the entire production process. By evaluating them, reliability can be quantified intuitively, scientifically and efficiently, helping R&D personnel and users to better understand their production status and expected economic value. In the prior art, for example, the publication number CN 109143972 A discloses a CNC machine tool reliability evaluation method based on Bayesian and fault tree, which applies Bayesian theory and fault tree model to evaluate the reliability of CNC machine tools; it mainly uses system fault data as prior information to obtain reliability distribution, mainly considering the impact of faults on reliability, focusing on narrow reliability indicators, and ignoring the broad reliability indicators of CNC machine tools as repairable equipment. The evaluation surface is narrow and cannot be applied to modern data machine tool evaluation systems, which has great limitations. Summary of the invention

[0005] The purpose of the present invention is to provide a reliability evaluation method and system for CNC machine tools. The method starts from the generalized reliability index, performs reasonable weight distribution in multiple angles and categories, and has more scientific application value and a wide range of application scenarios.

[0006] In order to achieve the above object, the specific technical solution adopted by the present invention is as follows:

[0007] In one aspect, the present invention provides a method for evaluating the reliability of a CNC machine tool, the method comprising the following steps:

[0008] S01, establishing a weight distribution model, wherein the weight distribution model includes multiple samples to be evaluated and multiple evaluation indicators, and establishing an original matrix according to the multiple samples to be evaluated and the multiple evaluation indicators;

[0009] S02, performing dimensionless processing on the original matrix G to obtain an indicator matrix; obtaining a correlation coefficient according to the original matrix G; obtaining a standard deviation according to the indicator matrix; the correlation coefficient represents the conflict intensity, and the standard deviation represents the contrast intensity;

[0010] S03, obtaining a standard strength matrix according to the conflict strength and the contrast strength, performing weight distribution on the standard conflict strength and the standard contrast strength in the standard strength matrix and calculating information entropy, and calculating weights of the standard conflict strength and the standard contrast strength according to the information entropy;

[0011] S04, determining a machine tool to be evaluated, obtaining characteristics representing various evaluation indicators and corresponding characteristic values, dividing the characteristic values ​​into intervals and obtaining a correlation function;

[0012] S05. Obtain a comprehensive correlation degree according to the weight calculation of the standard conflict strength and the standard comparison strength, and draw a machine tool correlation degree table.

[0013] As a preferred embodiment of the present invention, in step S01, the weight distribution model includes n samples to be evaluated and m evaluation indicators, and the original matrix G formed is specifically:

[0014]

[0015] Among them G ij Represents the value of the jth evaluation index of the ith sample.

[0016] As a preferred embodiment of the present invention, in step S02, after the original matrix G is dimensionlessly processed, if the data indicator is a forward indicator, forward processing is performed; if the data indicator is a reverse indicator, reverse processing is performed.

[0017] As a preferred embodiment of the present invention, in step S02, the chatterjee nonlinear correlation coefficient is used to represent the conflict intensity.

[0018] As a preferred embodiment of the present invention, in step S02, when expressing the conflict intensity, if the number of samples to be evaluated is small and the number fluctuates greatly, the difference method is used to increase the accuracy.

[0019] As a preferred embodiment of the present invention, in step S03, before the standard intensity matrix is ​​obtained, a step of obtaining an intensity matrix and standardizing the intensity matrix is ​​also included.

[0020] As a preferred embodiment of the present invention, in step S03, the entropy weight method is used to weight the standard conflict strength and the standard comparison strength, specifically:

[0021]

[0022] Among them, E i is information entropy, w i is the weight, n is the number of samples to be evaluated, c' ij is the standard strength matrix element.

[0023] As a preferred embodiment of the present invention, in step S04, an adaptive method is used to divide the characteristic value into intervals.

[0024] As a preferred embodiment of the present invention, the CNC machine tool reliability evaluation method further comprises the step of obtaining a weight distribution table according to the weights of the standard conflict intensity and the standard comparison intensity; the weight distribution table is used to reflect the differences between indicators.

[0025] In another aspect, the present invention provides a numerical control machine tool reliability evaluation system, the system comprising:

[0026] A weight distribution model, wherein the weight distribution model includes a plurality of samples to be evaluated and a plurality of evaluation indicators, and an original matrix G is established according to the plurality of samples to be evaluated and the plurality of evaluation indicators;

[0027] A factor acquisition module, the factor acquisition module is used to perform dimensionless processing on the original matrix G to obtain an indicator matrix; obtain a correlation coefficient according to the original matrix G; obtain a standard deviation according to the indicator matrix; the correlation coefficient represents the conflict intensity, and the standard deviation represents the contrast intensity;

[0028] A weight acquisition module, the weight acquisition module is used to acquire a standard strength matrix according to the conflict strength and the contrast strength, perform weight allocation on the standard conflict strength and the standard contrast strength in the standard strength matrix and calculate information entropy, and calculate the weights of the standard conflict strength and the standard contrast strength according to the information entropy;

[0029] An evaluation module is used to determine the machine tool to be evaluated, obtain the characteristics representing various evaluation indicators and the corresponding characteristic values, divide the characteristic values ​​into intervals and obtain the correlation function; and obtain the comprehensive correlation according to the weight calculation of the standard conflict intensity and the standard comparison intensity and draw a machine tool correlation table.

[0030] In summary, the present invention has the following beneficial effects:

[0031] 1. The present invention uses the contrast intensity and conflict intensity improved entropy weight method to perform weight allocation for comprehensive evaluation of CNC machine tool reliability. It can allocate weights by comprehensively considering the differences between indicators on the basis of solving the problem of indicator changes, making the weight allocation between indicators more accurate.

[0032] 2. The present invention also uses an improved extension matter-element model to model the generalized reliability comprehensive evaluation of CNC machine tools, so as to solve the problem of excessive subjectivity of the evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is the flow chart of the reliability evaluation method of CNC machine tools;

[0034] Figure 2 This is the system block diagram of the CNC machine tool reliability evaluation system. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Anyone can implement the present disclosure in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0036] Embodiment 1

[0037] In this embodiment, the CNC machine tool can also be referred to as a CNC machining center. Figure 1As shown, this embodiment provides a method for evaluating the reliability of a CNC machine tool, the method comprising the following steps:

[0038] S01, establishing a weight distribution model, wherein the weight distribution model includes multiple samples to be evaluated and multiple evaluation indicators, and establishing an original matrix according to the multiple samples to be evaluated and the multiple evaluation indicators;

[0039] S02, performing dimensionless processing on the original matrix G to obtain an indicator matrix; obtaining a correlation coefficient according to the original matrix G; obtaining a standard deviation according to the indicator matrix; the correlation coefficient represents the conflict intensity, and the standard deviation represents the contrast intensity;

[0040] S03, obtaining a standard strength matrix according to the conflict strength and the contrast strength, performing weight distribution on the standard conflict strength and the standard contrast strength in the standard strength matrix and calculating information entropy, and calculating weights of the standard conflict strength and the standard contrast strength according to the information entropy;

[0041] S04, determining a machine tool to be evaluated, obtaining characteristics representing various evaluation indicators and corresponding characteristic values, dividing the characteristic values ​​into intervals and obtaining a correlation function;

[0042] S05. Obtain a comprehensive correlation degree according to the weight calculation of the standard conflict strength and the standard comparison strength, and draw a machine tool correlation degree table.

[0043] More specifically, according to the above-mentioned CNC machine tool reliability evaluation method, the specific steps of this embodiment are as follows.

[0044] Firstly, a weight distribution model is established. The weight model is an improved model of the entropy weight method weight distribution model, which includes multiple samples to be evaluated and multiple evaluation indicators. Different samples to be evaluated refer to different CNC machine tools. In this embodiment, five dimensions are selected as evaluation indicators, namely, mean time between failures (MTBF), mean time to repair (MTTR), availability (A), mean time to first failure (MTTFF) and equivalent failure rate (D). The data are shown in Table 1.

[0045] Table 1 Generalized reliability index data of five-axis CNC machining center

[0046] index MTBF(h) MTTR(h) A MTTFF(h) D(times / 1000h) data 1765 2.09 0.9988 1324.11 2.02

[0047] The target CNC machine tool, i.e. the five-axis CNC machining center, is defined as (M1). In addition, a machining center from three years ago (M2), a competitive machining center (M3, M4), and a five-axis CNC machining center with a good reliability level (M5) are selected as comparison machine tools. Their reliability data are shown in Table 2.

[0048] Table 2 Reliability data of each machining center

[0049]

[0050] Next, an original matrix G is established based on the above-mentioned multiple samples to be evaluated and multiple evaluation indicators.

[0051] Specifically, from Table 2, the original matrix G can be obtained as:

[0052]

[0053] The original matrix G is dimensionless processed to obtain the indicator matrix X; if the data indicator is a positive indicator, that is, the larger the value, the better the performance, then positive processing is performed:

[0054]

[0055] If the data indicator is a reverse indicator, that is, the larger the value, the worse the performance, then reverse processing is performed:

[0056]

[0057] After processing the original matrix G through equations (5) and (6), the indicator matrix X is obtained:

[0058]

[0059] Next, the correlation coefficient R is obtained according to the original matrix G; the standard deviation S is obtained according to the indicator matrix X; the correlation coefficient R represents the conflict intensity, and the standard deviation S represents the contrast intensity;

[0060] Substituting the indicator matrix X into formula (8), the comparative strength of each evaluation indicator can be calculated as shown in Table 3, where the comparative strength refers to the fluctuation of the data between different machine tools for the same indicator, expressed in the form of standard deviation.

[0061] The larger the standard deviation, the greater the fluctuation, that is, the greater the difference in values ​​between the various plans, and the higher the weight will be.

[0062]

[0063] Table 3 Comparison of the strength of each index

[0064] index MTBF MTTR A MTTFF D Numeric 0.4117 0.3892 0.3684 0.3975 0.3965

[0065] Substitute the original matrix G into formula (9), and use the chatterjee nonlinear correlation coefficient to represent the conflict intensity. Here, the correlation coefficient R represents the correlation between a certain evaluation index and other evaluation indexes. If there is a strong positive correlation between two evaluation indexes, the smaller the conflict, the lower the weight. When representing the conflict intensity, if the number of samples to be evaluated is small and the number fluctuates greatly, the difference method is used to increase the accuracy.

[0066]

[0067] Where R(X,Y) represents the correlation coefficient between the evaluation index X and the evaluation index Y. X and Y can be any value of the above five indicators. n represents the number of data of the evaluation index, r i represents the level of Y(i).

[0068] Substitute the original matrix into formula (9) and use the interpolation function provided by MATLAB to interpolate. The correlation coefficients between the evaluation indicators are shown in Table 4.

[0069] Table 4 Correlation coefficients among indicators

[0070]

[0071] Next, take (1-the maximum value of the dependent variable correlation) as the conflict intensity to establish the conflict intensity matrix, as shown in Table 5.

[0072] Table 5 Conflict intensity of each indicator

[0073] index MTBF MTTR A MTTFF D Numeric 0.2941 0.1801 0.1285 0.3661 0.2353

[0074] From this table, we can see that MTTFF, which has no obvious reference and functional relationship with other indicators, has a relatively large conflict intensity value, while MTTR and A, which have a strong correlation, have relatively small conflict intensity values.

[0075] Next, the steps of acquiring an intensity matrix and normalizing the intensity matrix are performed.

[0076] The intensity matrix of the indicator can be obtained by comparing the contrast intensity and the conflict intensity:

[0077]

[0078] Normalize the above intensity matrix and take the standard intensity matrix:

[0079]

[0080] The above contrast intensity and standard intensity are standardized by formula (11), and the standard intensity matrix is ​​obtained:

[0081]

[0082] Then, the entropy weight method is used to assign weights to the above standard conflict strength and standard comparison strength and calculate the information entropy. The weights wi of the standard conflict strength and standard comparison strength are calculated according to the information entropy, specifically:

[0083]

[0084] Among them, E i is information entropy, w i is the weight, n is the number of samples to be evaluated, c' ij is the standard strength matrix element. Specifically, from equations (2) and (3), the weights of contrast strength and conflict strength are 0.5171 and 0.4829 respectively; thus, the weights of each indicator are [0.2264 0.1747 0.1485 0.2515 0.1988].

[0085] Next, determine the machine tool N to be evaluated, which refers to the CNC machine tool to be evaluated in the CNC machine tool reliability, and obtain the characteristics C representing each evaluation index and the corresponding characteristic value V, which is the specific value of each index. The machine tool N to be evaluated has multiple indicators c1, c2, …, c n , the corresponding n values ​​are v1,v2,…,v n , it can be expressed as

[0086]

[0087] R is an n-dimensional numerical control machine tool, which is abbreviated as R = (N, C, V). C represents the n indicators of the CNC machine tool to be evaluated. C = (c1, c2, ..., c n ), V represents the specific value of n indicators V = (v1, v2, ..., v n ).

[0088] Then, the characteristic value V is divided into intervals and the correlation function k is obtained. Specifically, the present invention adopts the method of numerically adaptive numerical control machine tool index value to be evaluated to divide the intervals. Specifically, assuming that there are m CNC machine tools with a positive reliability index participating in the evaluation, there are values ​​g1, g2, ..., g m , sorting these values ​​from high to low as g′1, g′2, ..., g′ m , then this indicator is composed of a total of m evaluation levels, namely [g′1,g′2), [g′2,g′3), …, [g′ m ,0), if there are repeated values ​​in the indicator, the low-level interval is an empty set, and if the indicator is a reverse indicator, the sorting is from low to high. Therefore, n j represents the jth level, c i represents the i-th evaluation index, v ij represents the value range of the jth evaluation level of the i-th evaluation indicator, that is, (a ij ,b ij ), then n, c, v are combined in the form of ordered triplets to form the classical matter-element domain r j :

[0089]

[0090] The section domain is the union of the classical domains, and r p represents the section domain, v ip is the domain matter element with respect to index c i The value range: v ip =(a ip ,b ip )(i=1...m), where (a ip ,b ip ) is (a ij ,b ij ) is the union of all level ranges of the i-th indicator, the domain element r p It can be expressed as:

[0091]

[0092] In determining the correlation function of the extension object-element model, the modulus of the bounded interval X = [a, b] is:

[0093] |X|=|ba| (16)

[0094] The distance from a point x to interval X is:

[0095]

[0096] Then the correlation function k is defined as:

[0097]

[0098] Among them, k xi (v j ) represents the jth level of the ith indicator of the xth machine tool, v ix is the value of the i-th index of the x-th machine tool, v ij For the jth level of the i-th indicator of this machine tool, |v ij | is the modulus of the interval defined in formula (18), v ip is the value range of the ith indicator of the node domain matter-element.

[0099] According to the weight w of the standard conflict strength and the standard contrast strength i Calculate the comprehensive correlation and draw the machine tool correlation table.

[0100] In this embodiment, the extension matter-element classical domain established according to the original matrix G is specifically:

[0101]

[0102] According to the obtained weight w i And the correlation k xi (v j ) can get the comprehensive correlation:

[0103]

[0104] Where N x represents the xth machine tool, k j represents the association degree of the jth level, w i represents the weight of the i-th indicator. The final correlation is shown in Table 6.

[0105] Table 6 Machining center correlation table

[0106]

[0107]

[0108] It can be seen from Table 6 that the target five-axis CNC machining center (M1), its machining center three years ago (M2), competitor machining centers (M3, M4) and the five-axis CNC machining center with better reliability level (M5) belong to level 2, level 5, level 2, level 5 and level 1 respectively. M1 and M3 belong to level 2, and according to the correlation value, M1 has a better reliability level. In summary, the reliability levels of the five machining centers are M5, M1, M3, M2 and M4 from high to low. By comparing M1 and M3, it can be seen that MTBF and MTTFF have the highest weights among the five indicators, and the MTBF of the two is almost the same, so MTTFF is the key indicator affecting the reliability level of the two.

[0109] The level corresponding to the maximum correlation value is the machine tool reliability level. If the target machine tool and the comparison machine tool have the same level, it proves that the generalized reliability of the two is equivalent. Otherwise, the generalized reliability level of the higher level is higher.

[0110] Furthermore, an indicator weight distribution table can be drawn. According to the indicator weight distribution table, it can be clearly seen which indicators between the target machine tool and the comparison machine tool are similar, and which indicators have large differences. That is, the lower the weight, the closer they are, and the higher the weight, the greater the difference. In this way, it can be clearly understood which indicators should be focused on improving.

[0111] According to the above CNC machine tool reliability evaluation method, Figure 2 As shown, this embodiment also provides a CNC machine tool reliability evaluation system, which includes:

[0112] A weight distribution model, wherein the weight distribution model includes a plurality of samples to be evaluated and a plurality of evaluation indicators, and an original matrix G is established according to the plurality of samples to be evaluated and the plurality of evaluation indicators;

[0113] A factor acquisition module, the factor acquisition module is used to perform dimensionless processing on the original matrix G to obtain an indicator matrix; obtain a correlation coefficient according to the original matrix G; obtain a standard deviation according to the indicator matrix; the correlation coefficient represents the conflict intensity, and the standard deviation represents the contrast intensity;

[0114] A weight acquisition module, the weight acquisition module is used to acquire a standard strength matrix according to the conflict strength and the contrast strength, perform weight allocation on the standard conflict strength and the standard contrast strength in the standard strength matrix and calculate information entropy, and calculate the weights of the standard conflict strength and the standard contrast strength according to the information entropy;

[0115] An evaluation module is used to determine the machine tool to be evaluated, obtain the characteristics representing various evaluation indicators and the corresponding characteristic values, divide the characteristic values ​​into intervals and obtain the correlation function; and obtain the comprehensive correlation according to the weight calculation of the standard conflict intensity and the standard comparison intensity and draw a machine tool correlation table.

[0116] Specifically, the weight allocation model includes multiple samples to be evaluated and multiple evaluation indicators. In this embodiment, the evaluation indicators are selected from five dimensions, namely, mean time between failures (MTBF), mean time to repair (MTTR), availability (A), mean time to first failure (MTTFF) and equivalent failure rate (D). The data are shown in Table 1. The weight allocation model establishes the original matrix G based on multiple samples to be evaluated and multiple evaluation indicators.

[0117] The factor acquisition module is used to perform dimensionless processing on the original matrix G to obtain the indicator matrix X; if the data indicator is a positive indicator, that is, the larger the value, the better the performance, then forward processing is performed; if the data indicator is a reverse indicator, that is, the larger the value, the worse the performance, then reverse processing is performed. The factor acquisition module obtains the correlation coefficient R according to the original matrix G; obtains the standard deviation S according to the indicator matrix X; the correlation coefficient R represents the conflict intensity, and the standard deviation S represents the contrast intensity. The contrast intensity refers to the fluctuation of the data between each machine tool of the same indicator, which is expressed in the form of standard deviation. The larger the standard deviation, the greater the fluctuation, that is, the greater the value difference between each scheme, and the higher the weight. The correlation coefficient R represents the correlation between a certain evaluation indicator and other evaluation indicators. If there is a strong positive correlation between two evaluation indicators, it means that the conflict is smaller and the weight will be lower.

[0118] The weight acquisition module is used to obtain the standard intensity matrix according to the conflict intensity and contrast intensity. That is, after obtaining the standard deviation S and the correlation coefficient R, the intensity matrix of the indicator can be obtained by equations (10), (11) and (12), and the intensity matrix is ​​standardized and the standard intensity matrix is ​​obtained. Then, the standard conflict intensity and the standard contrast intensity in the standard intensity matrix are weighted and the information entropy is calculated according to equation (2). The weights of the standard conflict intensity and the standard contrast intensity are calculated according to equation (3).

[0119] The evaluation module is used to determine the machine tool to be evaluated, obtain the characteristics representing various evaluation indicators and the corresponding characteristic values, divide the characteristic values ​​into intervals and obtain the correlation function; and obtain the comprehensive correlation according to the weight calculation of the standard conflict intensity and the standard comparison intensity and draw a machine tool correlation table.

[0120] The present invention uses the contrast intensity and conflict intensity improved entropy weight method to perform weight allocation for comprehensive evaluation of CNC machine tool reliability. It can allocate weights by comprehensively considering the differences between indicators on the basis of solving the problem of indicator changes, making the weight allocation between indicators more accurate.

[0121] A number of embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A reliability evaluation method for a CNC machine tool, characterized in that: The method comprises the following steps: S01, establishing a weight distribution model, wherein the weight distribution model includes multiple samples to be evaluated and multiple data evaluation indicators, and establishing an original matrix according to the multiple samples to be evaluated and the multiple data evaluation indicators; S02, performing dimensionless processing on the original matrix G to obtain an indicator matrix; obtaining a correlation coefficient according to the original matrix G; Obtaining a standard deviation according to the indicator matrix; the correlation coefficient represents the conflict intensity, and the standard deviation represents the contrast intensity; S03, obtaining a standard strength matrix according to the conflict strength and the contrast strength, performing weight allocation on the standard conflict strength and the standard contrast strength in the standard strength matrix and calculating information entropy, and calculating weights of the standard conflict strength and the standard contrast strength according to the information entropy; S04, determining a machine tool to be evaluated, obtaining characteristics representing various evaluation indicators and corresponding characteristic values, dividing the characteristic values ​​into intervals and obtaining a correlation function; S05. Obtain a comprehensive correlation degree according to the weight calculation of the standard conflict strength and the standard comparison strength, and draw a machine tool correlation degree table.

2. A method for evaluating the reliability of a CNC machine tool according to claim 1, characterized in that: In step S01, the weight distribution model includes n samples to be evaluated and m data evaluation indicators, and the original matrix G formed is specifically: Among them G ij Represents the value of the jth evaluation index of the ith sample.

3. A CNC machine tool reliability evaluation method according to claim 2, characterized in that: In step S02, after the original matrix G is dimensionlessly processed, if the data evaluation index is a forward index, forward processing is performed; if the data evaluation index is a reverse index, reverse processing is performed.

4. A method for evaluating the reliability of a numerically controlled machine tool according to claim 3, characterized in that: In step S02, the chatterjee nonlinear correlation coefficient is used to represent the conflict intensity.

5. A method for evaluating the reliability of a numerically controlled machine tool according to claim 4, characterized in that: In step S02, when expressing the conflict intensity, if the number of samples to be evaluated is small and the number fluctuates greatly, the difference method is used to increase the accuracy.

6. A method for evaluating the reliability of a numerically controlled machine tool according to claim 5, characterized in that: In step S03, before the standard intensity matrix is ​​obtained, the step of obtaining the intensity matrix and standardizing the intensity matrix is ​​also included.

7. A method for evaluating the reliability of a numerically controlled machine tool according to claim 6, characterized in that: In step S03, the entropy weight method is used to allocate weights to the standard conflict strength and the standard comparison strength, specifically: Among them, E i is information entropy, w i is the weight, n is the number of samples to be evaluated, c' ij is the standard strength matrix element.

8. A method for evaluating the reliability of a numerically controlled machine tool according to claim 7, characterized in that: In step S04, an adaptive method is used to divide the characteristic value into intervals.

9. A method for evaluating the reliability of a numerically controlled machine tool according to claim 8, characterized in that: The reliability evaluation method for CNC machine tools also includes the step of obtaining a weight distribution table according to the weights of the standard conflict strength and the standard comparison strength; the weight distribution table is used to reflect the differences between indicators.

10. A reliability evaluation system for a numerically controlled machine tool, characterized in that: The system includes: A weight distribution model, wherein the weight distribution model includes a plurality of samples to be evaluated and a plurality of data evaluation indicators, and an original matrix G is established according to the plurality of samples to be evaluated and the plurality of data evaluation indicators; A factor acquisition module, the factor acquisition module is used to perform dimensionless processing on the original matrix G to obtain an indicator matrix; obtain a correlation coefficient according to the original matrix G; obtain a standard deviation according to the indicator matrix; the correlation coefficient represents the conflict intensity, and the standard deviation represents the contrast intensity; A weight acquisition module, the weight acquisition module is used to acquire a standard strength matrix according to the conflict strength and the contrast strength, perform weight allocation on the standard conflict strength and the standard contrast strength in the standard strength matrix and calculate information entropy, and calculate the weights of the standard conflict strength and the standard contrast strength according to the information entropy; An evaluation module is used to determine the machine tool to be evaluated, obtain the characteristics and corresponding characteristic values ​​representing various data evaluation indicators, divide the characteristic values ​​into intervals and obtain the correlation function; and obtain the comprehensive correlation degree according to the weight calculation of the standard conflict intensity and the standard comparison intensity and draw a machine tool correlation table.

Images