CN111080174A - Assembly quality risk assessment method, device and equipment based on interval probability - Google Patents

Assembly quality risk assessment method, device and equipment based on interval probability Download PDF

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CN111080174A
CN111080174A CN201911420164.9A CN201911420164A CN111080174A CN 111080174 A CN111080174 A CN 111080174A CN 201911420164 A CN201911420164 A CN 201911420164A CN 111080174 A CN111080174 A CN 111080174A
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欧阳林寒
郑伟
马义中
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Nanjing University of Aeronautics and Astronautics
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Abstract

The embodiment of the invention provides an assembling quality risk assessment method based on interval probability, which comprises the following steps: establishing a weight matrix W and a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein elements W in the weight matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l; element x in risk assessment matrix Rm njM in the list is the evaluator number, n is the serial number of the failure mode, and j is the serial number of the risk factor; obtaining the comprehensive weight of the failure mode through the weight matrix W, and obtaining a risk evaluation interval matrix through the risk evaluation matrix R; and calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and performing failure mode risk sequencing according to the interval comparison probability. A corresponding device and equipment are also provided. Embodiments of the inventionThe accuracy in the risk assessment of the assembly quality is improved.

Description

Assembly quality risk assessment method, device and equipment based on interval probability
Technical Field
The invention relates to the field of production risk assessment, in particular to an assembly quality risk assessment method based on interval probability, an assembly quality risk assessment device based on interval probability, assembly quality risk assessment equipment based on interval probability and a corresponding storage medium.
Background
FMEA is one of five tools for quality control of automobile industry, and is also an effective technology for preventing and controlling product failure. In the production process, potential failure modes related to production are identified in advance, corresponding preventive measures are made according to failure modes with higher risks, and the product failure risk can be effectively reduced. However, after studies by many scholars, FMEA has its inherent drawbacks in performing failure mode and risk assessment. Firstly, the expert evaluation has subjectivity and uncertainty, so that the evaluation result is not accurate enough; secondly, the risk factors of the failure modes are not weighted, so that the obtained risk priority number has the problem of a large number of repetitions; finally, the risk sequence numbers obtained by scoring the single value of the risk factor are not continuous, and certain information loss exists.
Taking a spark plug as an example, the spark plug is one of key parts of an automobile engine and is an ignition device of an automobile. As a device for igniting oil-gas mixed gas, the gas igniter is easy to damage when working in high-temperature and high-pressure environments for a long time. In addition, in the actual production process, the quality defects of the spark plug are hundreds, and each defect is difficult to monitor. Therefore, the quality requirement of the spark plug product is high, and the quality control difficulty is high. In the assembly quality control of the conventional spark plug, a post-inspection method is generally employed. The spark plug with quality problems is identified by fully inspecting the spark plug product or finished product, so that unqualified products are removed, and the purpose of quality control is achieved. However, as a mass-produced product, quality management based on inspection requires a large amount of manpower and material resources, and therefore preventive control is becoming an important point of quality control research in the assembly process of spark plugs.
Based on the above analysis, applying the conventional FMEA to the assembly process of a spark plug may result in a deviation in the risk assessment of the failure mode of the spark plug. Once the risk assessment result is inaccurate, the priority of the failure mode risks is unreasonable, and the preventive control measures made according to the failure mode risks cannot achieve the expected effect. Therefore, the application of the conventional FMEA in the assembling process of the spark plug is performed in combination with the knowledge and experience of the engineer, and has a great limitation.
Interpretation of terms:
FMEA, Failure Mode and Effects Analysis, potential Failure Mode and impact Analysis.
Disclosure of Invention
The invention aims to provide an assembling quality risk assessment method, device and equipment based on interval probability, so as to at least solve the problem that the assessment result is inaccurate due to the fact that the assembling quality risk assessment in the current production process is too dependent on subjective methods such as knowledge and experience.
In order to achieve the above object, in a first aspect of the present invention, there is provided an assembly quality risk assessment method based on interval probability, the method including:
establishing a weight matrix W and a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein elements W in the weight matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l; element x in risk assessment matrix Rm njM in the list is the evaluator number, n is the serial number of the failure mode, and j is the serial number of the risk factor;
obtaining the comprehensive weight of the failure mode through the weight matrix W, and obtaining a risk evaluation interval matrix through the risk evaluation matrix R;
and calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and performing failure mode risk sequencing according to the interval comparison probability.
Optionally, obtaining the comprehensive weight of the failure mode through the weight matrix W includes:
the element W in WmlCarrying out standardization to obtain a corresponding standardized value;
converting the normalized value through an entropy weight to obtain a comprehensive weight β corresponding to the risk factor ll。
Optionally, the obtaining a risk assessment interval matrix through the risk assessment matrix R includes:
the element x in Rm njConversion to the interval form [ L (r) ]mL nj)，U(rmU nj)]Is shown, in which:
and L and U are respectively a lower limit value and an upper limit value of an interval of x determined by rough number theory.
Optionally, the calculating a risk sequence number interval matrix of the failure mode includes:
calculating the risk ranking by the formula:
risk rank 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
In a second aspect of the present invention, there is provided an assembly quality risk assessment apparatus based on interval probability, the apparatus comprising:
a weighting module used for establishing a weighting matrix W of risk factors in the failure mode of the evaluation object, wherein the element W in the weighting matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l;
a risk matrix module for establishing a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein the element x in the risk evaluation matrix Rm njM in the list is the evaluator number, n is the serial number of the failure mode, and j is the serial number of the risk factor;
the conversion module is used for obtaining the comprehensive weight of the failure mode through the weight matrix W and obtaining a risk assessment interval matrix through the risk assessment matrix R; and
and the ranking module is used for calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and ranking the risks of the failure mode according to the interval comparison probability.
Optionally, the obtaining the comprehensive weight of the failure mode through the weight matrix W includes:
the element W in WmlCarrying out standardization to obtain a corresponding standardized value;
converting the normalized value through an entropy weight to obtain a comprehensive weight β corresponding to the risk factor ll。
Optionally, the obtaining a risk assessment interval matrix through the risk assessment matrix R includes:
the element x in Rm njConversion to the interval form [ L (r) ]mL nj)，U(rmU nj)]Is shown, in which:
and L and U are respectively a lower limit value and an upper limit value of an interval of x determined by rough number theory.
Optionally, the calculating a risk sequence number interval matrix of the failure mode includes:
calculating the risk ranking by the formula:
risk rank 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
In a third aspect of the present invention, there is provided an assembly quality risk assessment apparatus based on interval probability, the apparatus comprising a data processing module; the data processing module comprises a memory and a processor;
the memory is to store program instructions;
the processor is configured to call the program instructions stored in the memory to implement the aforementioned interval probability-based assembly quality risk assessment method.
In a fourth aspect of the present invention, there is also provided a computer-readable storage medium having stored therein instructions that, when run on a computer, cause the computer to perform the aforementioned interval probability-based assembly quality risk assessment method.
According to the technical scheme, the risk factors of the failure modes are objectively weighted according to actual conditions, risk assessment is carried out on the failure modes, interval transformation is carried out on the score values, and risk ranking is carried out through interval probability comparison. The implementation method provided by the invention is simple to execute according to the standardized process of FMEA, and improves the accuracy of risk evaluation. The problems of strong subjectivity, inaccurate risk sequencing and the like in the traditional FMEA are solved, and enterprises can pay attention to the prevention work of the product failure mode, so that the product quality is improved, and the production cost is reduced.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a schematic diagram illustrating steps of an assembly quality risk assessment method based on interval probability according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an assembly quality risk assessment apparatus based on interval probability according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In the embodiments of the present invention, unless otherwise specified, the use of directional terms such as "upper, lower, top, and bottom" is generally used with respect to the orientation shown in the drawings or the positional relationship of the components with respect to each other in the vertical, or gravitational direction.
Fig. 1 is a schematic step diagram of an assembly quality risk assessment method based on interval probability according to an embodiment of the present invention, as shown in fig. 1. The embodiment of the invention provides an assembling quality risk assessment method based on interval probability, which comprises the following steps:
establishing a weight matrix W and a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein elements W in the weight matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l; element x in risk assessment matrix Rm njM in the list is the evaluator number, n is the serial number of the failure mode, and j is the serial number of the risk factor;
obtaining the comprehensive weight of the failure mode through the weight matrix W, and obtaining a risk evaluation interval matrix through the risk evaluation matrix R;
and calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and performing failure mode risk sequencing according to the interval comparison probability.
Therefore, objective weighting is carried out on the risk factors of the failure modes according to actual conditions, risk assessment is carried out on the failure modes, interval transformation is carried out on the score values, and risk sequencing is carried out through interval probability comparison. The implementation method provided by the invention is simple to execute according to the standardized process of FMEA, and improves the accuracy of risk evaluation. The problems of strong subjectivity, inaccurate risk sequencing and the like in the traditional FMEA are solved, and enterprises can pay attention to the prevention work of the product failure mode, so that the product quality is improved, and the production cost is reduced.
Specifically, the Risk factors (RF, Risk Factor) selected by the conventional FMEA are failure severity (S), frequency (O) and detection (D), and assignment and evaluation thereof have certain subjectivity. The method provided by the embodiment of the invention can reduce the influence of different evaluators on the evaluation result. The existing data are converted into interval numbers through a rough number theory and are given, more evaluation information is covered, the defect that information loss is possibly caused by single value risk evaluation is avoided, and the complexity and uncertainty of an evaluation scene are adapted better. And finally, sequencing is carried out through interval comparison probability, so that the decision problem can be described more accurately, and the comparison method has more scientificity and applicability.
In an embodiment provided by the present invention, the obtaining the comprehensive weight of the failure mode through the weight matrix W includes: the element W in WmlNormalizing to obtain corresponding normalized value, and converting the normalized value by entropy weight to obtain comprehensive weight β corresponding to risk factor ll. The method specifically comprises the following steps: the element W in WmlAnd (4) carrying out standardization to obtain corresponding standardized values, wherein the evaluators are preferably technical experts in the field, and each evaluator gives an evaluation value to the risk factors. Standardizing the evaluation value to obtain a corresponding standardized value; calculating the average value C of each expert evaluationjAnd normalized to αjWherein
Figure BDA0002352165090000061
Converting the standardized value through entropy weight to obtain corresponding comprehensive weight value βj(ii) a The method comprises the following steps:
1) calculating index value proportion p of ith expert to jth risk factorij；
Figure BDA0002352165090000071
2) Calculating an entropy value e of the jth risk factorj；
Figure BDA0002352165090000072
3) Calculating the entropy weight w of the jth indexj；
Figure BDA0002352165090000073
4) Calculate the composite weight β for the jth indexj；
Figure BDA0002352165090000074
Here, the integrated weight value βjI.e. the comprehensive weight corresponding to the risk factor j.
The method adopts the entropy weight method to objectively weight, and has the advantage of solving the problem that the risk priority number has a large number of repetition numbers due to the fact that the traditional FMEA lacks of failure mode risk factor weight evaluation.
In an embodiment provided by the present invention, the obtaining a risk assessment interval matrix through a risk assessment matrix R includes: the element x in Rm njConversion to the interval form [ L (r) ]mL nj)，U(rmU nj)]Is shown, in which: and L and U are respectively a lower limit value and an upper limit value of an interval of x determined by rough number theory. The specific calculation steps are as follows:
let R ═ { X ═ X1,X2,X3…XnIs a scored set of questions by n experts, where X is1<X2<X3…<XnAnd Y is any one of the objects in U, then XiUpper approximation of (I)*(X), lower approximation I*(X) and boundary region B (X)i) The definition is as follows:
I*(X)＝∪{Y∈U|R(Y)≥Xi}
I*(X)＝∪{Y∈U|R(Y)≤Xi}
B(Xi)＝∪{Y∈U|R(Y)<Xi}∪{Y∈U|R(Y)>Xi}
Xiupper limit U ofiAnd a lower limit LiThe definition is as follows:
Figure BDA0002352165090000081
Figure BDA0002352165090000082
wherein N isU、NLAre each XiThe number of elements in the upper and lower approximation sets.
Specific values are exemplified as follows:
for example, expert DM4For failure mode FM4Is 9, the approximate set is I*(X) {9,10,10,10}, with the lower approximation set being I*Since (X) {8,9}, the risk score interval for severity is [8.5, 9.75 ]]。
After obtaining the evaluation intervals of all experts for the risk factors, respectively taking the interval upper limit mean value and the interval lower limit mean value of the failure mode risk factors as final interval evaluation values, and expressing the severity (S), the frequency (O) and the detection degree (D) of each failure mode in the evaluation by adopting intervals.
In an embodiment of the present invention, in step S4, the calculating a risk sequence number interval matrix of the failure mode includes:
calculating the risk ranking interval by:
risk sequence interval of 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
Firstly, calculating an RPN interval of each failure mode by using a Risk sequence Number (RPN, Risk Priority Number) formula, wherein the calculation result is shown in the following table 1; then, obtaining a probability value through pairwise comparison of the interval RPNs, wherein the result is shown in a table 2; and finally, sorting the failure mode risks according to the compared probability values.
Failure mode FM1 FM2 FM3 FM4 FM5
RPN ** ** [31.43,47.58] [31.75,41.92] **
TABLE 1 failure mode interval RPN values
For the calculation of the interval probability, the following is exemplified:
as can be seen from Table 1, FM3And FM4Respectively, are [31.43,47.58 ]]And [31.75,41.92]According to the calculation formula of interval probabilityKnowing:
Figure BDA0002352165090000091
according to the interval probability theory, since 0.665>0.5, therefore FM3Has a higher risk priority than FM4. Accordingly, other interval probability results are shown in table 2 below:
Figure BDA0002352165090000092
TABLE 2 failure mode RPN interval comparison probability
From the interval probability theory, when the probability value generated by comparing the interval a with the interval B is greater than 0.5, the interval a > the interval B can be determined. Therefore, according to the calculation results of the interval probabilities in table 2, the risk priorities of the failure modes are ranked as:
FM3>FM4>FM1>FM2>FM5
according to the failure mode risk priority ranking, the failure mode with larger influence in the assembly process can be positioned by an FMEA expert group, so that more resources are invested to carry out risk control on the specific failure mode. For example, for a selected failure mode, corresponding risk control measures are established, and resources should be invested preferentially in the more risky failure mode.
Fig. 2 is a schematic structural diagram of an assembly quality risk assessment apparatus based on interval probability according to an embodiment of the present invention, as shown in fig. 2. In an embodiment provided by the present invention, there is also provided an assembly quality risk assessment apparatus based on interval probability, the apparatus including:
a weighting module used for establishing a weighting matrix W of risk factors in the failure mode of the evaluation object, wherein the element W in the weighting matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l;
a risk matrix module for establishing a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein the element x in the risk evaluation matrix Rm njM in (1) is an evaluationThe serial number, n is the serial number of the failure mode, and j is the serial number of the risk factor;
the conversion module is used for obtaining the comprehensive weight of the failure mode through the weight matrix W and obtaining a risk assessment interval matrix through the risk assessment matrix R; and
and the ranking module is used for calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and ranking the risks of the failure mode according to the interval comparison probability.
As an optional implementation manner, the obtaining the comprehensive weight of the failure mode through the weight matrix W includes:
the element W in WmlCarrying out standardization to obtain a corresponding standardized value;
converting the normalized value through an entropy weight to obtain a comprehensive weight β corresponding to the risk factor ll。
As an optional implementation manner, the obtaining a risk assessment interval matrix through the risk assessment matrix R includes:
the element x in Rm njConversion to the interval form [ L (r) ]mL nj)，U(rmU nj)]Is shown, in which:
and L and U are respectively a lower limit value and an upper limit value of an interval of x determined by rough number theory.
As an optional implementation, the calculating a risk sequence number interval matrix of the failure mode includes:
calculating the risk ranking by the formula:
risk rank 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
The technical details and advantages of the device are referred to the methods described above and will not be repeated here.
In an embodiment provided by the invention, the invention further provides assembling quality risk assessment equipment based on the interval probability, and the equipment comprises a data processing module; the data processing module comprises a memory and a processor;
the memory is to store program instructions; the processor is configured to call the program instructions stored in the memory to implement the aforementioned interval probability-based assembly quality risk assessment method. The data processing module has the functions of numerical calculation and logical operation, and at least comprises a central processing unit CPU with data processing capability, a random access memory RAM, a read-only memory ROM, various I/O ports, an interrupt system and the like. The data processing module may be, for example, a single chip, a chip, or a processor, and the like, which are commonly used hardware, and in a more common case, the data processing module is a processor of an intelligent terminal or a PC. Here, the apparatus may be an existing controller in a production management system or a production analysis system, and the implemented function is a sub-function of the controller, and the specific form may be a piece of software code in a hardware running environment depending on the existing controller.
In an embodiment provided by the present invention, a computer-readable storage medium is further provided, where instructions are stored, and when the instructions are executed on a computer, the instructions cause the computer to execute the aforementioned assembling quality risk assessment method based on interval probability.
The following specific calculation steps of the assembling quality risk assessment method based on interval probability provided by the invention are described through a complete implementation case:
the first step is as follows: and calculating the comprehensive weight of the failure mode by an entropy weight method. Wherein the raw data of the failure mode is obtained by the following method: a team of FMEA experts, denoted DM, consisting of m engineers was constructed1,DM2,…DMm(ii) a Identification of n potential spark plug assembly-related events by a team of FMEA expertsFailure mode, denoted FM1,FM2,…,FMn(ii) a Establishing the risk factors listed by the expert needs, such as: and transforming the subjective weight to obtain the comprehensive weight according to an entropy weight calculation formula by using a table with the severity, the frequency and the detection degree as rows.
The second step is that: calculating a risk factor evaluation value interval of the failure mode
As mentioned before, the expert DM4For failure mode FM4Is 9, the approximate set is I*(X) {9,10,10,10}, with the lower approximation set being I*Since (X) {8,9}, the risk score interval for severity is [8.5, 9.75 ]]。
After obtaining the evaluation intervals of all experts for the risk factors, respectively taking the interval upper limit mean value and the interval lower limit mean value of the failure mode risk factors as final interval evaluation values
Severity (S), frequency (O) and detection (D) for each failure mode are represented by intervals, such as failure mode FM4The severity (S), frequency (O) and probing degree (D) of (A) are: [8.94,9.76]、[2.9，4.5]、[6.75，7.58]。
The third step: calculating failure mode risk ordinal number
In an embodiment of the present invention, in step S4, the calculating a risk sequence number interval matrix of the failure mode includes:
calculating the risk ranking by the formula:
risk rank 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
The fourth step: ranking failure mode risks according to interval probability comparison
Calculating the RPN interval of each failure mode by using a Risk sequence Number (RPN) formula according to the failure mode Risk factor comprehensive weight and the Risk factor interval evaluation value obtained in the first step and the second step, wherein the calculation result is shown in the table 1; then, obtaining a probability value through pairwise comparison of the interval RPNs, wherein the result is shown in the table 2; and finally, sorting the failure mode risks according to the compared probability values. For the calculation of the interval probability, the following is exemplified: as can be seen from Table 1, FM3And FM4Respectively, are [31.43,47.58 ]]And [31.75,41.92]According to the calculation formula of the interval probability, the following results are obtained:
Figure BDA0002352165090000121
according to the interval probability theory, since 0.665>0.5, therefore FM3Has a higher risk priority than FM4. Accordingly, other interval probability results are seen in table 2, supra:
from the interval probability theory, when the probability value generated by comparing the interval a with the interval B is greater than 0.5, the interval a > the interval B can be determined. Therefore, according to the calculation results of the interval probabilities in table 2, the risk priorities of the failure modes are ranked as:
FM3>FM4>FM1>FM2>FM5
to facilitate understanding by those skilled in the art, the interval probability theory in this appendix:
assuming two intervals
Figure BDA0002352165090000131
The number of intervals
Figure BDA0002352165090000132
Number of intervals
Figure BDA0002352165090000133
With a high probability of
Figure BDA0002352165090000134
Expressed as:
Figure BDA0002352165090000135
when in use
Figure BDA0002352165090000136
When it is, the number of sections is indicated
Figure BDA0002352165090000137
Number of intervals
Figure BDA0002352165090000138
Is large. Otherwise, the number of intervals
Figure BDA0002352165090000139
Number of intervals
Figure BDA00023521650900001310
Is small.
According to the failure mode risk priority ranking, the failure mode with larger influence in the assembly process can be positioned by an FMEA expert group, so that more resources are invested to carry out risk control on the specific failure mode. For example, for a selected failure mode, corresponding risk control measures are established, and resources should be invested preferentially in the more risky failure mode.
According to the technical scheme, the entropy weight method, the rough number theory and the interval ordering are provided for processing the evaluation data, the influence of subjective factors in the evaluation data is reduced, the scientificity and the objectivity of the evaluation result are improved, and the evaluation result is acted on the actual production, so that corresponding quality control measures are made, and the purposes of improving the product quality and reducing the production cost are achieved.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as disclosed in the embodiments of the present invention as long as it does not depart from the spirit of the embodiments of the present invention.

Claims (10)

1. An assembly quality risk assessment method based on interval probability is characterized by comprising the following steps:
establishing a weight matrix W and a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein elements W in the weight matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l; element x in risk assessment matrix Rm njM in the list is the evaluator number, n is the serial number of the failure mode, and j is the serial number of the risk factor;
obtaining the comprehensive weight of the failure mode through the weight matrix W, and obtaining a risk evaluation interval matrix through the risk evaluation matrix R;
and calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and performing failure mode risk sequencing according to the interval comparison probability.
2. The method of claim 1, wherein deriving the composite weight of the failure mode from the weight matrix W comprises:
the element W in WmlCarrying out standardization to obtain a corresponding standardized value;
converting the normalized value through an entropy weight to obtain a comprehensive weight β corresponding to the risk factor ll。
3. The method of claim 1, wherein obtaining a risk assessment interval matrix from the risk assessment matrix R comprises:
the element x in Rm njConversion to the interval form [ L (r) ]mL nj)，U(rmU nj)]Is shown, in which:
and L and U are respectively a lower limit value and an upper limit value of an interval of x determined by rough number theory.
4. The method of claim 1, wherein computing the risk order interval matrix for failure modes comprises:
calculating the risk ranking by the formula:
risk rank 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
5. An assembly quality risk assessment device based on interval probability, the device comprising:
a weighting module used for establishing a weighting matrix W of risk factors in the failure mode of the evaluation object, wherein the element W in the weighting matrix WmlThe evaluator with the number m represents a weight value preset by the risk factor with the sequence number l;
a risk matrix module for establishing a risk evaluation matrix R of risk factors in failure modes of the evaluation object, wherein the element x in the risk evaluation matrix Rm njM in the list is the evaluator number, n is the serial number of the failure mode, and j is the serial number of the risk factor;
the conversion module is used for obtaining the comprehensive weight of the failure mode through the weight matrix W and obtaining a risk assessment interval matrix through the risk assessment matrix R; and
and the ranking module is used for calculating a risk sequence number interval matrix of the failure mode based on the obtained comprehensive weight and the risk assessment interval matrix, and ranking the risks of the failure mode according to the interval comparison probability.
6. The apparatus of claim 5, wherein the deriving the synthetic weight of the failure mode from the weight matrix W comprises:
the element W in WmlCarrying out standardization to obtain a corresponding standardized value;
converting the normalized value through an entropy weight to obtain a comprehensive weight β corresponding to the risk factor ll。
7. The apparatus of claim 5, wherein the deriving a risk assessment interval matrix from the risk assessment matrix R comprises:
the element x in Rm njConversion to the interval form [ L (r) ]mL nj)，U(rmU nj)]Is shown, in which:
and L and U are respectively a lower limit value and an upper limit value of an interval of x determined by rough number theory.
8. The apparatus of claim 5, wherein the computing the risk order interval matrix for failure modes comprises:
calculating the risk ranking by the formula:
risk rank 3(RF1×W1)+3(RF2×W2)+…+3(RFj×Wj)；
The calculation results obtained were:
risk sequence interval [ [ R ]1 L，R1 U][R2 L，R2 U]…[Rj L，Rj U]]，
Wherein R isj LAnd Rj URespectively representing the upper limit and the lower limit of the risk sequence number interval of the jth failure mode.
9. An assembling quality risk assessment device based on interval probability is characterized by comprising a data processing module; the data processing module comprises a memory and a processor;
the memory is to store program instructions;
the processor is configured to call the program instructions stored in the memory to implement the interval probability based assembly quality risk assessment method of any one of claims 1 to 4.
10. A computer-readable storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the interval probability-based assembly quality risk assessment method according to any one of claims 1 to 4.
CN201911420164.9A 2019-12-31 2019-12-31 Assembly quality risk assessment method, device and equipment based on interval probability Pending CN111080174A (en)

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