CN110795696A - Risk priority number calculation method based on grey correlation - Google Patents

Risk priority number calculation method based on grey correlation Download PDF

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CN110795696A
CN110795696A CN201911036162.XA CN201911036162A CN110795696A CN 110795696 A CN110795696 A CN 110795696A CN 201911036162 A CN201911036162 A CN 201911036162A CN 110795696 A CN110795696 A CN 110795696A
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杨德真
陈继泽
任羿
冯强
王自力
孙博
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Abstract

The invention provides a gray correlation-based risk priority number calculation method, which comprises the following steps of: calculating a corrected fault mode detected difficulty level (DDR) score by means of refining the scoring problem and calculating each small problem by using a gray correlation algorithm; secondly, obtaining a corrected failure mode Occurrence Probability Rating (OPR) score by using a gray correlation algorithm through a detailed scoring problem; thirdly, obtaining a corrected fault mode severity grade (ESR) score by using a gray correlation algorithm through a detailed scoring problem; and fourthly, multiplying the corrected failure mode detection difficulty level (DDR) obtained in the first step to the third step, the failure mode occurrence probability level (OPR) and the failure mode severity level (ESR) score by three numbers to obtain a corrected failure mode risk priority number. Through the steps, the optimized product system fault mode risk priority number can be calculated, the fault mode needing to be processed preferentially can be selected conveniently in FMECA analysis, the fault finished product and the fault part are accurately positioned, the fault elimination optimization scheme is obtained, and the fault elimination time is saved.

Description

Risk priority number calculation method based on grey correlation
Technical Field
The invention provides a risk priority number calculation method based on gray correlation, which is a method for obtaining a corrected risk priority number of a fault mode by integrating a plurality of corrected three-level evaluation scores after considering a problem correlation weight and an expert score weight and multiplying the three evaluation scores to obtain the corrected risk priority number of the fault mode by grading each score of a fault mode severity grade (ESR), a fault mode occurrence probability grade (OPR) and a fault mode detection difficulty grade (DDR) to be more detailed and accurate when performing fault mode influence and hazard analysis (FMECA) on a product system. The method belongs to the technical field of reliability engineering.
Background
Failure Mode impact Analysis (FMECA), a systematic technical method for finding problems, identifies, analyzes and judges failures and their expressions (i.e. Failure modes) that may exist in a system under the support of certain basic data and rules, calculates the Risk Priority (RPN) of the Failure Mode according to the occurrence probability, detection degree and severity of the failures after analyzing the possible effects and consequences of each Failure Mode one by one, determines weak links and key parts of the system that are prone to Failure according to the risk priority, and proposes suggestions and measures for maintenance, improvement and control on the relevant parts of the system.
Through investigation, in the conventional method for calculating the risk priority number, experts are asked to score the occurrence probability, the detection degree and the severity of the fault mode, the scores are 1-10, and finally the three scores are multiplied to obtain the risk priority number. The method is convenient and fast, and has good resolution when processing the fault mode with large grading difference. However, when the probability, the detection degree and the severity of the failure mode are similar or one score is far from the other two scores, the method has poor resolution. Therefore, it is necessary to adopt measures such as refining the scoring criterion, calculating each sub-score weight, cooperating with the expert scoring weight, and calculating the corrected risk priority number by adopting a gray correlation method. By the method, the resolution accuracy of the risk priority number to different fault modes can be remarkably improved, the fault mode needing priority processing can be conveniently selected in FMECA analysis, a fault finished product and a fault part are accurately positioned, an optimized fault elimination scheme is obtained, and the fault elimination time is saved.
Disclosure of Invention
Objects of the invention
Aiming at the current situation that score fluctuation is strong due to the fact that the score range of risk priority calculation in the current fault mode influence analysis is too large, scoring standard is not detailed enough, scoring is inaccurate, and expert scores are difficult to coordinate when being divergent, the method can calculate each sub-score weight by using a gray correlation method through refining the scoring criterion, and obtain more accurate risk priority by cooperating with the expert scoring weight. By the method, the fault mode needing to be processed preferentially can be selected in FMECA analysis, and after the fault finished product and the fault part are accurately positioned, an optimized fault elimination scheme is obtained, so that the fault elimination time is saved.
(II) technical scheme
The invention relates to a risk priority number calculation method based on gray correlation, which is a method for firstly refining three evaluations, namely a fault mode detection difficulty level (DDR), a fault mode occurrence probability level (OPR) and a fault mode severity level (ESR), into a plurality of minor problems, asking a plurality of experts to grade each fault mode of a product according to each minor problem, calculating the weight of each minor problem by using a gray correlation method after grading, giving a weight to each expert grade, obtaining the grade after the three evaluations are respectively corrected by weighting calculation, and multiplying the grades to obtain the corrected risk priority number. As shown in fig. 1, the method is divided into four steps:
step one, calculating a corrected fault mode detected difficulty level (DDR) score by means of refining the scoring problem and calculating each minor problem by using a grey correlation algorithm. It comprises 9 sub-steps:
(1) continuously dividing the problem of the fault mode detected difficulty level (DDR) score into n small problems, wherein each problem is scored to be 1-m (m represents the worst condition), the number n of the problems and the score m are selected by a user, generally, n is not less than three, and m is not more than five;
(2) please score each question with p experts to obtain a score matrix (X'1,X′2…X′p)；
(3) Taking the maximum value of each column of the fractional matrix to form a reference data column, and recording as follows: x'0＝(X′0(1)，X′0(2)…X′0(n)), calculating the difference between each expert score in the original matrix and the corresponding element in the reference number sequence to obtain a correction matrix (X)1,X2…Xp)；
(4) Then, the maximum value and the minimum value of all data in the correction matrix are calculated, and the association coefficient zeta of each score is calculated according to the maximum value and the minimum valuei(k) Wherein (i ═ 1 to p, and k ═ 1 to n);
(5) the correlation coefficient r of each minor problem can be obtained by calculating the arithmetic mean of all fractional correlation coefficients of the minor problems0k；
(6) In order to ensure that the final weight sum is 1, normalizing the correlation coefficients of the minor problems until the sum of all the correlation coefficients is 1, wherein the correlation coefficient at the moment is the weight r 'of each minor problem'0k；
(7) The scores of all the small questions are weighted and summed to obtain the preliminary score G of each experti；
(8) In order to ensure that the distribution of the final scores is 1-10 as same as the original standard, range modification is carried out on the preliminary scores to obtain the score G 'corrected by each expert'i；
(9) Since the system grasping degree of each expert is different, the weighting average G' of each expert score can be calculated by adding weights in the comprehensive evaluation.
Through the steps, the fault mode detected difficulty level (DDR) score corrected by using the gray correlation algorithm can be obtained.
Step two, according to the method of 9 sub-steps in the step one, through the detailed scoring problem, the grey correlation algorithm is used for obtaining the revised failure mode occurrence probability grade (OPR) score;
step three, according to the method of 9 sub-steps in the step one, through the thinning scoring problem, a grey correlation algorithm is used for obtaining a corrected fault mode severity grade (ESR) score;
and step four, multiplying the corrected failure mode detected difficulty level (DDR), the failure mode occurrence probability level (OPR) and the failure mode severity level (ESR) score obtained in the step one to the step three to obtain a corrected failure mode risk priority number.
Wherein, the step of 'continuously dividing into n minor problems' in the substep (1) is to provide a plurality of minor problems which can be scored against the difficulty level (DDR) of the detection of the barrier mode, such as 'how to visually detect level', 'how to design BIT detection', 'how to design error-proofing measures', etc.;
wherein "correlation coefficient r of each sub-question" described in step one sub-step (5)0k", which represents the degree of association between the small question and the large question, the larger the numerical value, the higher the degree of association, and thus the weight can be calculated as the total score.
Wherein, the final score distribution in the substep (8) is the same as the original standard, which means that the range of the initial score is 1-m, but the range of the original standard score is 1-10, so the range of the initial score is modified.
Through the steps, the optimized product system fault mode risk priority number can be calculated.
(III) advantages and effects
The invention has the following advantages and effects:
(1) the grading calculation of each grade can be more accurate when the influence analysis of the fault mode of the product system is carried out;
(2) the influence of the understanding degree of each expert on the scoring weight can be effectively adjusted when multiple experts score;
(3) the faults can be accurately sequenced according to risks, and the fault sequencing sequence is optimized.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention relates to a risk priority number calculation method based on gray correlation, which is a method for firstly refining three evaluations, namely a fault mode detection difficulty level (DDR), a fault mode occurrence probability level (OPR) and a fault mode severity level (ESR), into a plurality of minor problems, asking a plurality of experts to grade each fault mode of a product according to each minor problem, calculating the weight of each minor problem by using a gray correlation method after grading, giving a weight to each expert grade, obtaining the grade after the three evaluations are respectively corrected by weighting calculation, and multiplying the grades to obtain the corrected risk priority number. As shown in fig. 1, the specific implementation is as follows:
the method comprises the following steps: and calculating the corrected fault mode detected difficulty level (DDR) score by using a mode of calculating each small problem by using a gray correlation algorithm through the detailed scoring problem. It comprises 9 sub-steps:
1) aiming at the difficulty level (DDR) of the fault mode detection of the current product, the scoring problem is continuously divided into n (the value range of n is a positive integer) small problems, each problem is scored to be 1-m (the value range of m represents the worst condition, and the value range of m is a positive integer);
2) asking p experts to score each small problem and sorting the scores into a score matrix;
Figure BDA0002251556950000041
3) the maximum value of each column of the fractional matrix is taken to form a reference data column which is recorded as
X′0＝(X′0(1)，X′0(2)…X′0(n)) (2)
Calculating the difference between each expert score in the original matrix and the corresponding element in the reference sequence, and sorting out the correction matrix (X)1,X2…Xp) The method is as in formula (3)
Xi(k)＝X′0(k)-X′i(k) (i＝1～p,k＝1～n) (3)
4) Then calculating the maximum value of all data in the correction matrix
Figure BDA0002251556950000051
And minimum valueAnd calculating a correlation coefficient ζ of each score by the formula (4)i(k) Where ρ represents a resolution coefficient;
Figure BDA0002251556950000053
5) the correlation coefficient r of each minor problem can be obtained by calculating the arithmetic mean of all fractional correlation coefficients of the minor problems through the formula (5)0k；
6) In order to ensure that the final weight sum is 1, normalizing the correlation coefficients of the minor problems through a formula (6) until the sum of all the correlation coefficients is 1, wherein the correlation coefficient at the moment is the weight r 'of each minor problem'0k；
Figure BDA0002251556950000055
7) The scores of all the small questions are weighted and summed through a formula (7) to obtain the preliminary score G of each experti；
Figure BDA0002251556950000056
8) In order to ensure that the distribution of the final scores is 1-10 as same as the original standard, the range of the preliminary scores is modified through a formula (8), and the scores G 'corrected by each expert are obtained'i；
Figure BDA0002251556950000057
9) Because of the different mastery degree of each expert on the system,weights may be added to the overall evaluation, and a weighted average G' of the expert scores is found by equation (9), where WkThe weight value of each expert. The score G' is GD of a fault mode detected difficulty level (DDR) score after the current product is corrected.
Figure BDA0002251556950000058
Step two, according to the method of 9 sub-steps in the step one, a gray correlation algorithm is used for obtaining a corrected failure mode occurrence probability grade (OPR) score GO through a detailed scoring problem;
step three, according to the method of 9 sub-steps in the step one, through the thinning scoring problem, a gray correlation algorithm is used for obtaining a corrected failure mode severity grade (ESR) score GE;
and step four, multiplying the corrected failure mode detection difficulty level (DDR), the failure mode occurrence probability level (OPR) and the failure mode severity level (ESR) scores obtained in the step one to the step three by using a formula (10) to obtain the corrected risk priority number of the failure mode.
G＝GD*GO*GE (10)
The "resolution coefficient ρ" in the step one sub-step 4) is a coefficient for differentiating the fractional correlation coefficients, and is between 0 and 1, and the smaller the numerical value is, the larger the difference between the correlation coefficients is. Usually 0.5 is selected as a general value for calculation.
The use of the method is shown next as an example.
Please give a score to 7 relevant professional designers (P1, P2, P3, … …, P7), and take an example of the detected difficulty level (DDR) score of the failure mode of the "dual channel failure" of a certain electromechanical control system, we will go on to subdivide 3 questions to ask questions in detail.
① Q1 how does the level of visual detection?
② Q2 how does BIT detection design?
③ Q3 how do error proofing measures design?
Each small problem can be divided into 1-3 points, wherein 1 point is the best, and 3 points are the worst. The scoring table is shown in table 1 below:
TABLE 1 initial scoring table
P1 P2 P3 P4 P5 P6 P7
Q1 1 2 2 3 2 3 3
Q2 2 2 1 2 3 3 1
Q3 2 3 2 1 1 1 1
From the table matrix, a reference data column X 'is derived'0The original matrix is subtracted from the reference data column to obtain a modified score data matrix (2,3,2,3,3, 3) as shown in table 2:
TABLE 2 modified scoring data matrix
P1 P2 P3 P4 P5 P6 P7
Q1 1 1 0 0 1 0 0
Q2 0 1 1 1 0 0 2
Q3 0 0 0 2 2 2 2
The maximum value and the minimum value of the correction matrix are respectively
Figure BDA0002251556950000061
Using equation (4), the correlation coefficient for each score is calculated, where we choose the resolution coefficient ρ to be 0.5, which can be:
Figure BDA0002251556950000062
ζ5(1)＝0.5，ζ6(1)＝1，ζ7(1)＝1
after all correlation coefficients are calculated, a correlation coefficient matrix can be obtained as shown in table 3:
TABLE 3 correlation coefficient matrix
P1 P2 P3 P4 P5 P6 P7
Q1 0.5 0.5 1 1 0.5 1 1
Q2 1 0.5 0.5 0.5 1 1 0.33
Q3 1 1 1 0.33 0.33 0.33 0.33
After obtaining the correlation coefficient table, we can calculate the correlation coefficients of the three problems as
Figure BDA0002251556950000071
I.e., the question Q1 is a relatively more important question, the weight should be increased.
Then, the weights of the small questions are calculated by the formula (6) as
Assuming that all experts know the conditions of the system consistently, the scores are not required to be subjected to weight adjustment, and then the fault mode detection difficulty level (DDR) score of the fault mode of ' the pressure servo valve brake pressure is less than the rated pressure ' can be obtained through the formula (7) -the formula (9), and the final score is G ' which is 5.41. By using the method, the risk priority number of the fault mode corrected by using the gray correlation method can be obtained by calculating the respective scores of the process fault mode severity level (ESR) and the fault mode occurrence probability level (OPR) and then multiplying the scores.

Claims (1)

1. A risk priority number calculation method based on gray correlation is a method for obtaining a corrected risk priority number of a fault mode by sorting more detailed and accurate scores of each score of a fault mode severity grade (ESR), a fault mode occurrence probability grade (OPR) and a fault mode detection difficulty grade (DDR) when fault mode influence and hazard analysis (FMECA) is carried out on a product system and scoring the scores by a plurality of experts, then calculating the correlation weight among the small questions by using a gray correlation method, integrating the problem correlation weight and the expert scoring weight to obtain corrected three-grade evaluation scores, and multiplying the three-grade evaluation scores to obtain the corrected risk priority number of the fault mode, and is characterized by comprising the following steps of:
step one, calculating a corrected fault mode detected difficulty level (DDR) score by means of refining the scoring problem and calculating each minor problem by using a grey correlation algorithm. It comprises 9 sub-steps:
(1) continuously dividing the problem of the fault mode detected difficulty level (DDR) score into n small problems, wherein each problem is scored to be 1-m (m represents the worst condition), the number n of the problems and the score m are selected by a user, generally, n is not less than three, and m is not more than five;
(2) please score each question with p experts to obtain a score matrix (X'1,X′2…X′p)；
(3) Taking the maximum value of each column of the fractional matrix to form a reference data column, and recording as follows: x'0＝(X′0(1)，X′0(2)…X′0(n)), calculating the difference between each expert score in the original matrix and the corresponding element in the reference number sequence to obtain a correction matrix (X)1,X2…Xp)；
(4) Then, the maximum value and the minimum value of all data in the correction matrix are calculated, and the association coefficient zeta of each score is calculated according to the maximum value and the minimum valuei(k) Wherein (i ═ 1 to p, and k ═ 1 to n);
(5) the correlation coefficient r of each minor problem can be obtained by calculating the arithmetic mean of all fractional correlation coefficients of the minor problems0k；
(6) In order to ensure that the final weight sum is 1, normalizing the correlation coefficients of the minor problems until the sum of all the correlation coefficients is 1, wherein the correlation coefficient at the moment is the weight r 'of each minor problem'0k；
(7) For each minute questionWeighting and summing the scores of the questions to obtain the preliminary score G of each experti；
(8) In order to ensure that the distribution of the final scores is 1-10 as same as the original standard, range modification is carried out on the preliminary scores to obtain the score G 'corrected by each expert'i；
(9) Since the system grasping degree of each expert is different, the weighting average G' of each expert score can be calculated by adding weights in the comprehensive evaluation.
Through the steps, the fault mode detected difficulty level (DDR) score corrected by using the gray correlation algorithm can be obtained.
Step two, according to the method of 9 sub-steps in the step one, through the detailed scoring problem, the grey correlation algorithm is used for obtaining the revised failure mode occurrence probability grade (OPR) score;
step three, according to the method of 9 sub-steps in the step one, through the thinning scoring problem, a grey correlation algorithm is used for obtaining a corrected fault mode severity grade (ESR) score;
and step four, multiplying the corrected failure mode detected difficulty level (DDR), the failure mode occurrence probability level (OPR) and the failure mode severity level (ESR) score obtained in the step one to the step three to obtain a corrected failure mode risk priority number.
Through the steps, the optimized product system fault mode risk priority number can be calculated, the fault mode needing to be processed preferentially can be selected conveniently in FMECA analysis, a fault elimination optimization scheme is obtained after fault finished products and fault parts are accurately positioned, and fault elimination time is saved.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111475893A (en) * 2020-03-27 2020-07-31 北京航空航天大学 Spatial fault field model construction method based on product three-dimensional model
CN111950238A (en) * 2020-07-30 2020-11-17 禾多科技（北京）有限公司 Automatic driving fault score table generation method and device and electronic equipment
CN113688224A (en) * 2021-10-26 2021-11-23 成都飞机工业（集团）有限责任公司 Self-adaptive processing method for complex equipment delivery problem based on grey correlation

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111475893A (en) * 2020-03-27 2020-07-31 北京航空航天大学 Spatial fault field model construction method based on product three-dimensional model
CN111475893B (en) * 2020-03-27 2021-06-08 北京航空航天大学 Spatial fault field model construction method based on product three-dimensional model
CN111950238A (en) * 2020-07-30 2020-11-17 禾多科技（北京）有限公司 Automatic driving fault score table generation method and device and electronic equipment
CN113688224A (en) * 2021-10-26 2021-11-23 成都飞机工业（集团）有限责任公司 Self-adaptive processing method for complex equipment delivery problem based on grey correlation

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