LU101709B1 - Method and device for analyzing criticality of failure mode of numerical control equipment - Google Patents
Method and device for analyzing criticality of failure mode of numerical control equipment Download PDFInfo
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- LU101709B1 LU101709B1 LU101709A LU101709A LU101709B1 LU 101709 B1 LU101709 B1 LU 101709B1 LU 101709 A LU101709 A LU 101709A LU 101709 A LU101709 A LU 101709A LU 101709 B1 LU101709 B1 LU 101709B1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
Abstract
The present invention discloses a failure analysis method and device for numerical control quipment. The method comprises: step 1: determining failure subsystems constituting the numerical control equipment and a failure mode set according to the failure statistics data, and calculating a comprehensive effect relationship matrix between failure modes by using DEMATEL; step 2: obtaining an overall effect matrix according to the comprehensive effect relationship matrix, and obtaining a simplified overall effect matrix by setting corresponding thresholds; step 3: transforming the simplified overall effect matrix into an ANP network model and calculating an effect probability of each failure mode; and step 4: obtaining, based on the effect probability of each failure mode, the criticality of the failure mode of the numerical control equipment. The method and device can effectively solve the problem that the system failure modes having related failures affect probability calculation, and also eliminate the error caused by considering the effect of a failure mode as a fixed value in the existing criticality calculation. The method and device are helpful for technicians to accurately and clearly grasp the criticality of a failure mode of numerical control equipment, and to identify weak links in the system.
Description
1 | METHOD AND DEVICE FOR ANALYZING CRITICALITY OF FAILURE MODE | 4101709 ] OF NUMERICAL CONTROL EQUIPMENT ô Field of the Invention / The present invention belongs to the field of failure analysis, and particularly relates to a ; method and device for analyzing the criticality of a failure mode of numerical control | equipment. . Background of the Invention ; With the advent of the industry 4.0 era, the production of modern enterprises is increasingly automatic, and any failure in the production process may stop the entire line.
As a complex ; dynamic system that integrates light, machinery, electricity, software and people, numerical / control equipment plays an irreplaceable pillar role in production.
Once a failure occurs and | cannot be repaired in time, huge losses will be caused to the enterprise.
At the same time, with | the enhancement of composite functions and advancement of numerical control equipment, its ; structure has become increasingly complex, and “correlation” has become a common feature ; of failures.
Due to the existence of related relationships, failure of one subsystem may cause | failure of other parts of the system, causing domino effect, and forming failure sequences and | failure avalanches.
Therefore, in order to accurately obtain the weak links of failures and | identify key subsystems and key failure modes, it has important theoretical significance and | practical value to study the methods for analyzing the criticality of failure modes of numerical | control equipment in consideration of failure correlation according to the actual situations of | projects. | The criticality analysis of a failure mode is an important content of quantifying the | consequence (CA) caused by the failure mode based on FMEA analysis.
The criticality of the | failure mode reflects the criticality to the functionality and effectiveness of a product and to | the safety of an operator when the product fails.
In recent years, with the rapid development of failure mode effects and criticality analysis (FMECA) technology, FMECA has been widely used in the reliability research of numerical control equipment.
A combination mode of system failure causes is found by analyzing reliability function relationships between system functions and parts of a system, thereby specifically strengthening and improving the weak links of system failures, and reducing the risk caused by the failure mode.
However, as a complicated mechatronics product, the numerical control equipment has complicated DEE arelationships between failure modes, and these complicated relationships have great effects on LU101709 the numerical control equipment and should be fully considered.
In the process of sequencing | failure modes by using the existing FMECA, it is inevitable that only the severity of the | failure modes themselves is considered, but the relationships between the failure modes are Ë not considered.
Specifically, the criticality of a failure mode of numerical control equipment | in the existing FMECA is determined according to three factors: a failure mode frequency / ; ratio Ÿ, a failure mode effect probability B , and a component failure rate A.
The factor | ß here is usually a single value, which is subjectively given without considering the failure A correlation.
Therefore, it is an urgent technical problem to be solved by those skilled in the art © about how to obtain a correlation between failures and quantify the same, accurately acquire ; effect probabilities of failure modes, then obtain accurate criticality values of the failure | modes, and identify key subsystems and key failure modes of numerical control equipment. | Because the direct analysis on the correlation between failure modes has strong subjectivity / and uncertainty, a method of judging the correlation between failure modes with in virtue of 1 effects on a third party can be considered.
The present invention introduces a correlation | analysis method, that is, an analytic network process (ANP). ANP is a new decision method / ; formed in consideration of the correlation between elements on the basis of AHP.
The ANP : ; has the advantages of abandoning independent assumptions between the elements, fully considering the mutual effects between the elements in the hierarchy, quantifying the | correlation between the elements with a specific value, embodying the subjective information ] of a decision maker, and reflecting a lot of complex laws, including the correlation.
Therefore, | the use of ANP can combine qualitative and quantitative methods to meet the needs of . correlation analysis between failure modes of numerical control equipment. ] | When an ANP network model is constructed, it is difficult for experts to quantify the relationship between factors based on their own experience, and it is particularly difficult to 1 give an accurate evaluation on the indirect effects between the factors.
The decision-making ; and trial evaluation laboratory (DEMATEL) method can be used to construct and analyze a | network model including causal relationships between complex factors, thereby making up à for the shortcomings of the ANP method.
This method only requires experts to judge the 1 direct effects between the factors based on their own experience and related information, and ; to determine a comprehensive effect relationship matrix by calculating a direct effect matrix, É thereby expressing the complex relationships between the factors more accurately.
In addition, . | although the DEMATEL method can calculate the strength of effects between various factors, | the calculation process pays equal attention to the weight of each factor, and does not take | Dar a a a eeinto account the situation where the weights of the factors are different.
Therefore, the two LU101709 ] can be combined, wherein the DEMATEL method confirms the strength of effects between | | various factors, while the ANP method obtains the effect weight of each factor, and then obtains a blend weight, which can be used as an effect probability of each factor to lay a ; foundation for clarifying the criticality of the factor.
Therefore, the present invention is | intended to improve the existing FMECA method by using the DEMATEL/ANP method, and ] to study the criticality of failure modes of the numerical control equipment. : Summary of the Invention | In order to overcome the above shortcomings of the prior art, the present invention provides a ; method and device for analyzing the criticality of failure modes of numerical control : equipment.
Taking a machining center as an example, a failure mode criticality analysis | method based on FMECA (Failure mode effects and criticality analysis DEMATEL ] (Decision making and trial evaluation laboratory)/ ANP (Network analytic hierarchy process) is determined in virtue of failure mechanism analysis to acquire an effect probability of a 2 failure mode of the numerical control equipment and obtain an accurate criticality of the E failure mode of the numerical control equipment, thereby providing a basis for identifying È weak links in reliability, analyzing a failure effect mechanism and improving the reliability. ; In order to achieve the above objectives, the present invention adopts the following technical . solutions: | A method for analyzing the criticality of a failure mode of numerical control equipment, . including the following steps: : Step 1: determining failure subsystems constituting the numerical control equipment and a | failure mode set according to the failure statistics data, and calculating a comprehensive effect ; relationship matrix between failure modes by using a DEMATEL method; Step 2: obtaining an overall effect matrix according to the comprehensive effect relationship . matrix, and obtaining a simplified overall effect matrix by setting corresponding thresholds; | Step 3: transforming the simplified overall effect matrix into an ANP network model and . calculating an effect probability of each failure mode; and ; | Step 4: obtaining, based on the effect probability of each failure mode, the criticality of the ; failure mode of the numerical control equipment. ! Further, step 1 includes: Step 1.1: determining failure subsystems constituting the numerical control equipment and a . failure mode set according to the failure statistics data; |]
Step 1.2: constructing a direct effect matrix between failure modes; and LU101709 ! Step 1.3: calculating a comprehensive effect matrix according to the direct effect matrix. | Further, step 2 includes: Step 2.1: calculating an overall effect matrix based on the comprehensive effect relationship matrix; and ; ; Step 2.2: determining a simplified overall effect matrix according to the effect matrix, | including: setting the overall effect matrix # and the simplified overall effect matrix 4, | ‘ and supposing | H'=[h,lyunl j =1,2,L ,n . h, = (G@G=1,2,L ,n; 7j =1,2,L ,n) , Wherein hy denotes the degree of direct and indirect effects of the failure mode ! on the , failure mode J after considering the effects of the failure modes on themselves, # } represents the number of all failure modes, and À isa given threshold; Further, step 3 includes: . Step 3.1: constructing an ANP network model of failure modes of the numerical control ; equipment by using Super Decision software; : Step 3.2: determining an unweightedsupermatrix; | Step 3.3: constructing a weighted supermatrix; ; Step 3.4: solving a limit matrix to obtain a weight matrix of each failure mode; and ÿ Step 3.5: calculating a blend weight matrix according to the overall effect matrix and the | weight matrix, and normalizing the blend weight matrix to obtain the effect probability of ; ; each failure mode. . Further, step 4 includes:obtaining, based on the effect probability of each failure mode, the | ; criticality of the failure mode of the numerical control equipment. | According to a second objective of the present invention, the present invention also provides a ; . criticality analysis device for numerical control equipment, including a memory, a processor, : and a computer program stored on the memory and executable on the processor, wherein . when the processor executes the program, the failure analysis method for the numerical . control equipment is implemented. ; According to a third objective of the present invention, the present invention also provides a | | computer-readable storage medium, storing a computer program thereon, wherein when the / | program is executed by a processor, the failure analysis method for the numerical control .
equipment is implemented.
LU101709 ' ; Beneficial effects of the invention: ‘ By integrating the three methods FMECA/ DEMATE/ ANP, the present invention provides a © new method for analyzing the criticality of a failure mode of numerical control equipment. © This method can effectively solve the problem that the system failure modes having related + failures affect probability calculation, and also eliminates the error caused by considering the 0 effect of a failure mode as a fixed value in the existing criticality calculation.
This method can : provide reference for similar system failure analysis, and also lay a foundation for reliability ; modeling evaluation and reliability growth technologies for numerical control equipment. | | Brief Description of the Drawing . The accompanying drawing constituting a part of the present application is used for providing ; ‘ a further understanding of the present application, and the schematic embodiments of the | present application and the description thereof are used for interpreting the present application, rather than constituting improper limitations to the present application. / Fig. 1 is a flowchart of a failure analysis method according to the present invention. } ; Detailed Deseription of Embodiments Ë It should be pointed out that the following detailed descriptions are all exemplary and aim to | further illustrate the present application.
Unless otherwise specified, all technical and : scientific terms used in the descriptions have the same meanings generally understood by { those of ordinary skill in the art of the present application. / . It should be noted that the terms used herein are merely for describing specific embodiments, | but are not intended to limit exemplary embodiments according to the present application.
As | | used herein, unless otherwise explicitly pointed out by the context, the singular form is also 1 intended to include the plural form.
In addition, it should also be understood that when the Ë terms “include” and/or “comprise” are used in the specification, they indicate features, steps, . operations, devices, components and/or their combination.
It should be noted that the embodiments in the present application and the features in the / embodiments can be combined with each other without conflicts. / Embodiment 1 | This embodiment discloses a failure analysis method fornumerical control equipment.
As | | shown in Fig. 1, the method includes the following steps: |
Step 1: determining failure subsystems constituting the numerical control equipment LU101709 ; and a failure mode set according to the failure statistics data, and calculating a | comprehensive effect relationship matrix between failure modes by using a DEMATEL . method; . Step 1.1: determining failure subsystems constituting the numerical control equipment and a | failure mode set according to the failure statistics data; | / Taking a machining center of a series of numerical control equipment products as the research | object, a series of machining center failure data acquired is analyzed and sorted to determine ; main failure subsystems, namely a mechanical system, an electrical system and auxiliary .
equipment. There are multiple failure modes under each subsystem. Due to complex mutual : effect relationships between the failures of the failure subsystems, a multi-level complex ’ network structure can be constructed. ; Step 1.2: constructing a direct effect matrix Ÿ between failure modes , Y = (95) men (1) ; is the number of times the failure mode ! affects the failure mode / ; when i=j , , Vy =0 ; # represents the number of all failure modes; Step 1.3: calculating a comprehensive effect matrix ! The direct effect matrix Ÿ between all the failure modes is normalized to obtain a normalized ; matrix À : In the formula: is the number of times the failure mode ! affects the failure mode J , and } | M represents the number of all failure modes; | A comprehensive effect matrix 7 is calculated T=Bm(X +X ++ -X*) =X (IX) | In the formula: /is a unit matrix, À is the normalized matrix, X“ denotes the indirect | effect of the failure mode ! onthe ¥ phase of the failure mode J, wherein i denotes the ; i failure mode related to other failure modes, and denotes the / failure mode related to ’ other failure modes, i=L2L,mj=L2L,mA=LZL,n | Step 2: obtaining am overall effect matrix according to the comprehensive effect ;
relationship matrix, and obtaining a simplified overall effect matrix by setting LU101709 Ë corresponding thresholds; | Step 2.1: calculating an overall effect matrix j The comprehensive effect matrix /'can only clearly reflect the mutual effect relationship | between different failure modes and the degree, but does not consider the effects of the failure } | modes on themselves, so the overall effect relationship reflecting the failure modes needs to ] : be calculated; ' The overall effect matrix His calculated by the formula: Ë | H=T+1=[h],., (4) L In the formula: / is the unit matrix, h, denotes the degree of direct and indirect effects of ; the failure mode / on the failure mode / after considering the effects of the failure modes | on themselves, and ” represents the number of all failure modes; f Step 2.2: determining a simplified overall effect matrix ; | Through the overall effect matrix X, a simplified overall effect matrix H' can be | / determined, supposing : . H‘=[hylyaot, j =1,2,L ,n 5) 7 The value of "vis obtained according to the following formula , 4 1,74 (=12L ,mj=12L ,n) . ns © | dis a given threshold, and the magnitude of A directly affects the composition of the | overall influence matrix; for the failure modes with a small ” value, simplification is not | | required, and A=0 is set; . #u denotes whether the failure mode has an effect on the failure mode J under the given . threshold À ; if hy > 4 , it indicates an effect, and the value of "vis 1; if hy SA , it ’ indicates no effect, and the value of hy is 0; ; Step 3: transforming the simplified overall effect matrix into an ANP network model : / and calculating an effect probability of each failure mode; | Step 3.1: constructing an ANP network model | The simplified overall effect matrix /' is transformed into an ANP network structure, and | the model is constructed using Super Decision software.
The control layer is the machining © center, and the network layer includes three failure subsystems (equivalent to three element | |groups) that affect the reliability of the machining center, including ” failure modes LU101709 } (equivalent to the elements in the element groups). The failure subsystems have mutual effect © relationships, so all failure modes also have mutual effect relationships. 0 Step 3.2: determining an unweightedsupermatrix 0 Itis assumed that #72 Pa are control layer elements of the ANP model, and #F:L E. are | network layer elements, wherein the elements él 82 1=12L00 in Æ are based on the | control layer elements PS =LZL ,m) and the elements “#4=L2L 2) in the network layer ; element 5, and compared according to the effects of the elements in the element group E } e 2 . . . WR IL y VO" . | on “#, thereby obtaining a judgment matrix.
Weight vectors =" ” ” ”"’ ’ are obtained | according to the characteristic root method.
For k=12L " the above steps are repeated to 0 obtain a matrix Wi; shown in formula 7. | I'M ML M | Here, the column vectors of the matrix "i are sorting vectors of the effects of the elements | enenloe, | veal €, . E, | mins Cm in Ei on the elements “7%? “min Li 1f does not have effects on the | elements in E, ;=0. For i=L2L.mj=L2L ,n , the above steps are repeated to obtain a | a terion ? . supermatrix =" under the criterion fs fl 5 M ML M | ANP questionnaires are designed by using a 1-9 scaling method according to the ANP É network relationship model, and filled by a group of reliability staff in the machining center. | | After the questionnaires are collected, the results are input into the Super Decision software. | An unweightedsupermatrix W,is obtained according to formula (8). | Step 3.3: constructing a weighted supermatrix | Based on the main criterion Ps and the second criterion E, , the two element groups are . compared in pairs, a judgment matrix Gi is established, G is normalized, normalized | feature vectors (8:82, 83» »8n) are obtained, and a weight matrix G, reflecting the | VE a eee EE ee
9 . ‘anshi Ds ; LU101709 | relationships between the elements under “s is obtained by the same way.
The weighted | supermatrix Wis obtained by multiplying G, with the unweightedsupermatrix W, ; In this study, it can be considered that there are three element groups: a mechanical system, an . electrical system, and auxiliary equipment.
The weighted supermatrix is obtained by . calculation. ° Step 3.4: solving a limit matrix to obtain weight values of the failure modes | In order to reveal the correlation between the elements more clearly, the weighted supermatrix | W needs to be stabilized, that is, the weighted supermatrix 7 is calculated.
The / column ‘ of % is a relative limit sequence of respective elements in the network layer for the . elements / , that is, weights of respective elements relative to a decision target.
Ê w= lm (1/ NY 7) . Now pi (10) . This study has only one target and three element groups, and the weights of respective elements relative to the target, that is, the weights of respective failure modes of the . machining center are the values of each column of the obtained stability limit supermatrix. | Step 3.5: obtaining a blend weight matrix . A blend weight matrix Z is obtained by using a blend importance calculation method given ; by Tamura according to formula (11), wherein # is the overall effect matrix obtained in | step 2.1, and W is the weight matrix of each failure mode. | Z is normalized to obtain a normalized blend weight value, that is, an effect probability BP, | of each failure mode. | Step 4: obtaining, based on the effect probability of each failure mode, the criticality of û the failure mode of the numerical control equipment; | . The criticality of failure modes of the machining center is a part of the criticality of the | machining center.
The criticality of the J failure mode of the machining center on a severity a level is as shown in formula (12). | C,=a,xp,xA4, (12) |. In the formula: J =L2Ln ‚and ” is the total number of failure modes of the machining |center; “ is a frequency ratio of the failure modes, which is the ratio of the frequency of the HUT01709 / J failure mode of the machining center to the frequency of all possible failure modes of the ; machining center; PB is the effect probability of a failure mode, and is obtained from the | above step; A is a basic failure rate of the failure mode / , and is an average failure rate | obtained through field experiments, and its calculation formula is: È CU | is the total number of failures in the failure mode within a specified time, and 47 | is the cumulative working time of the failure mode J within the specified time and can be | obtained by statistics. | Through the above steps, the criticality of each failure mode of the machining center can be i obtained. By determining the subsystem of each failure mode, the failure criticality of each l failure subsystem can also be obtained by summation, so that key failure subsystems are | determined. E Embodiment 2 ’ This embodiment aims to provide a computing device. ; A failure analysis device for numerical control equipment, including a memory, a processor, | and a computer program stored on the memory and executable on the processor, wherein É when the processor executes the program, the following steps are implemented, including: | Step 1: determining failure subsystems constituting the numerical control equipment and a : failure mode set according to the failure statistics data, and calculating a comprehensive effect | relationship matrix between failure modes by using a DEMATEL method; à Step 2: obtaining an overall effect matrix according to the comprehensive effect relationship | matrix, and obtaining a simplified overall effect matrix by setting corresponding thresholds; | Step 3: transforming the simplified overall effect matrix into an ANP network model and | calculating an effect probability of each failure mode; and | Step 4: obtaining, based on the effect probability of each failure mode, the criticality of the | failure mode of the numerical control equipment. ; Embodiment 3 À
This embodiment aims to provide a computer-readable storage medium. LU101709 ; A computer-readable storage medium, storing a computer program that, when executed by a | processor, the following steps are implemented: ; Step 1: determining failure subsystems constituting the numerical control equipment and a ! failure mode set according to the failure statistics data,and calculating a comprehensive effect | relationship matrix between failure modes by using a DEMATEL method; | Step 2: obtaining an overall effect matrix according to the comprehensive effect relationship È matrix, and obtaining a simplified overall effect matrix by setting corresponding thresholds; | Step 3: transforming the simplified overall effect matrix into an ANP network model and : calculating an effect probability of each failure mode; and É Step 4: obtaining, based on the effect probability of each failure mode, the criticality of the | failure mode of the numerical control equipment. 5 The steps involved in the devices of the second and third embodiments correspond to the first : embodiment of the method. For specific implementation, reference may be made to the ’ relevant description of the first embodiment. The term “computer-readable storage medium” 3 should be understood as a single medium or multiple mediums including one or more } instruction sets, and should also be understood as any medium capable of storing, coding or É. bearing an instruction set executed by a processor and causing the processor to perform any of | the methods of the present invention. © By integrating the three methods FMECA/ DEMATE/ANP, the present invention provides a | | new criticality analysis method for numerical control equipment. This method can effectively | solve the problem that the failure modes having related failures affect probability calculation, | and also eliminates the error caused by considering the effect of a failure mode as a fixed 2 value in the existing criticality calculation. This method can provide reference for similar | system failure analysis, and also lay a foundation for reliability modeling evaluation and | reliability growth technologies for numerical control equipment. ; It should be appreciated by those skilled in the art that the modules or steps of the present È invention can be implemented by a general computer device, alternatively, can be A implemented by program codes executable by a computing device, and thus can be stored in a | storage device and executed by the computing device, or in some cases, the modules or steps | are respectively fabricated into individual integrated circuit modules, or a plurality of modules ; or steps of them are fabricated into a single integrated circuit module. The present invention is | not limited to any particular combination of hardware and software. |
Although the specific embodiments of the present invention are described above in Lu101709 ! combination with the accompanying drawing, the protection scope of the present invention is | not limited thereto.
It should be understood by those skilled in the art that various | modifications or variations could be made by those skilled in the art based on the technical 2 solution of the present invention without any creative effort, and these modifications or Ë variations shall fall into the protection scope of the present invention. |
Claims (7)
1. A method for analyzing the criticality of a failure mode of numerical control equipment, / comprising the following steps: step 1: determining failure subsystems constituting the numerical control equipment and a : failure mode set according to the failure statistics data, and calculating a comprehensive effect relationship matrix between failure modes by using a DEMATEL method; g step 2: obtaining an overall effect matrix according to the comprehensive effect relationship | matrix, and obtaining a simplified overall effect matrix by setting corresponding thresholds; | step 3: transforming the simplified overall effect matrix into an ANP network model and | calculating an effect probability of each failure mode; and É step 4: obtaining, based on the effect probability of each failure mode, the criticality of the | failure mode of the numerical control equipment. :
2. The method for analyzing the criticality of a failure mode of numerical control equipment ! according to claim 1, wherein step 1 comprises: . step 1.1: determining failure subsystems constituting the numerical control equipment and a Ê failure mode set according to the failure statistics data; Ë step 1.2: constructing a direct effect matrix between failure subsystems; and ; step 1.3: calculating a comprehensive effect matrix according to the direct effect matrix. ;
3. The method for analyzing the criticality of a failure mode of numerical control equipment | according to claim 2, wherein step 2 comprises: | step 2.1: calculating an overall effect matrix based on the comprehensive effect relationship | matrix; and } step2.2: determining a simplified overall effect matrix according to the effect matrix, | comprising: setting the overall effect matrix # and the simplified overall effect matrix 4, | and supposing | H'=[h;l,xn>1 j =1,2,L ,n | hele WA Ge 12. m f=L2L 4m) | wherein hy denotes the degree of direct and indirect effects of the failure mode ? on the | failure mode J after considering the effects of the failure modes on themselves, ” | represents the number of all failure modes, and À is a given threshold; i TE
4. The method for analyzing the criticality of a failure mode of numerical control equipment | y101709 | according to claim 2, wherein step 3 comprises: step 3.1: constructing an ANP network model by using Super Decision software; : step 3.2: determining an unweightedsupermatrix; / step 3.3: constructing a weighted supermatrix; step 3.4: solving a limit matrix to obtain a weight matrix of each failure subsystem; and / step 3.5: calculating a blend weight matrix according to the overall effect matrix and the | weight matrix, and normalizing the blend weight matrix to obtain the effect probability of each failure mode. /
5. The method for analyzing the criticality of a failure mode of numerical control equipment | according to claim 1, wherein step 4 comprises: obtaining, based on the effect probability of | each failure mode, the criticality of the failure mode of the numerical control equipment. /
6. A device for analyzing the criticality of a failure mode of numerical control equipment, 1 comprising a memory, a processor, and a computer program stored on the memory and ] executable on the processor, wherein when the processor executes the program, the method | for analyzing the criticality of a failure mode of numerical control equipment according to any | one of claims 1-5 is implemented. |
7. A computer-readable storage medium, storing a computer program thereon, wherein ' when the program is executed by a processor, the method for analyzing the criticality of a | failure mode of numerical control equipment according to any one of claims 1-5 is | implemented. | Seman a aa a ET
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