A method, application, system and program product for determining a phase fusion threshold for new product reliability evaluation needs

Abstract

The present application relates to the field of reliability engineering, and relates to a method, application, system and program product for determining a fusion threshold for new product reliability evaluation requirements. The method comprises the following steps: constructing a feature set of a new product and an old product, and defining a similar element set; calculating the similarity and weight of the similar elements, and calculating the similarity of the new and old products based on the weight; establishing a minimum fusion cost mathematical model, and solving the fusion threshold; comparing the product similarity with the fusion threshold, and when the similarity is greater than the fusion threshold, the data of the similar products can be used for fusion, and the range of the usable similar products is determined. The present application provides clear indicators to determine the effectiveness of the fusion data of the new product and the similar products, increases the credibility of the fusion data, and can be widely applied to the reliability analysis of complex products.

Description

Technical Field

[0001] The present invention relates to the field of reliability engineering, and in particular to a method, application, system and program product for determining a convergence threshold for new product reliability assessment needs. Background Art

[0002] With the rapid development of industrial technology and the increasing complexity of products, the speed of new product design updates and iterations has significantly accelerated. This is particularly true in fields such as aerospace, automotive manufacturing, and electronic equipment. Many new products require long lifespans and high reliability. This makes field reliability data available in actual operating environments relatively scarce, resulting in a "multi-source, sparse" reliability data landscape. This data characteristic poses significant challenges to new product reliability assessment.

[0003] Traditional reliability assessment methods typically rely on complete experimental data or long-term field operation data for new products. However, in the absence of sufficient data, these methods cannot effectively assess the reliability of new products. To address this issue, researchers have begun to use data from similar products as a reference, comparing the characteristics of new products with similar products and fusing the data from similar products to indirectly infer the reliability of new products. However, existing methods have the following problems when fusing data from similar products:

[0004] 1. Blindly using similar data: The definition of similar products is broad, and the degree of similarity between different products can vary from 0.01 to 0.99. Product data with varying degrees of similarity can significantly impact reliability assessments. Using data from similar products can distort assessment results.

[0005] 2. Lack of quantitative fusion mechanism: Existing methods lack a systematic quantitative model when fusing similar product data, and are unable to effectively weigh the feature relationships between new products and similar products.

[0006] 3. The scope of similar products that can be used is unclear: During the fusion process, there are no clear indicators to judge the validity of similar product data, which limits the credibility of the fused data. Summary of the Invention

[0007] To address this issue, this paper proposes a method for determining a fusion threshold. This method constructs a mathematical model to quantify the relationship between similar products and new products, calculates similarity, and solves the fusion threshold. This method then clarifies the scope of similar products that can be used, effectively addressing the misuse of similar product data in multi-source sparse data environments.

[0008] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0009] A method for determining a range of similar products that can be used by a threshold of fusion, the method comprising the steps of:

[0010] 1) constructing a feature set of the new product and the old product, combining the characteristics with the same physical meaning into a similar element set;

[0011] 2) calculating the similarity and weight of the similar element, based on the quantity similarity Q n and the numerical similarity Q u to calculate the similarity Q of the new and old products;

[0012] 3) establishing a mathematical model for solving the threshold of fusion; the mathematical model is as follows:

[0013]

[0014] wherein φ: the value of the objective function, representing the sum of the fusion cost, the target is to minimize it; N={1,2}, M={1,2,…,n s}; D={d i |i∈N} is the set of original products and new products; V={v j |j∈M} is the set of product similar elements; η ij is the feature value of the product d i on the similar element v j ; is the adjusted feature value of the product; is the feature value of the fused product; c j is the unit feature deviation cost when the feature value of the similar element changes; ε j is the feature value deviation allowed range between the feature value of the fused product and the adjusted product feature value; ω j is the weight of the similar element in the total; γ is the threshold of fusion, representing the minimum utility value that meets the requirement of the similar product data for fusion use; δ is a small constant to avoid the denominator being zero;

[0015] 4) solving and verifying the mathematical model to obtain the threshold of fusion γ, comparing the size relationship between the similarity Q of the product and the threshold of fusion γ, when the similarity Q is greater than the threshold of fusion γ, the data of the similar product can be effectively used.

[0016] As preferred, the step 1) comprises the following steps:

[0017] a) the margin equation of the system is recorded as m=g(z1,z2,…,z ns ), and the feature set is composed of each parameter in the margin equation as a characteristic element;

[0018] b) the feature set of the new product is defined as: A={z1,z2,…,z n}

[0019] c) the feature set of the old product is defined as: A' = {z1', z2',..., z n′ ′}

[0020] d) the features with the same physical meaning in the two feature sets are combined to form a similar element, and the set of similar elements is set as: U = {u1, u2,..·, u ns}。

[0021] As preferred, the calculation formula of the similarity of the similar elements in step 2) is as follows:

[0022]

[0023] The calculation formula of the relative sensitivity is as follows:

[0024]

[0025] The calculation formula of the relative sensitivity weight is as follows:

[0026]

[0027] The calculation formula of the product similarity is as follows:

[0028]

[0029] As preferred, the objective function minΦ in the mathematical model is replaced by to determine the optimal threshold, and the objective function is set as:

[0030]

[0031] The constraint condition is:

[0032]

[0033] wherein: x i and y i are the known data of the new product and the old product in the similar element; a i and b i are the adjusted data of each other; η i is the data of the fusion product, ω1 and ω2 are the weight proportions of the new product and the old product in the fusion product, c i and d i are the weights of x i and y i occupying the performance margin m.

[0034] As preferred, ω1 = ω2 = 0.5 is set.

[0035]

[0036] Furthermore, the present invention also provides a method for evaluating whether data of similar products can be used in a sparse data environment.

[0037] Furthermore, the present invention also provides a system for determining the fusion threshold and verifying whether similar product data can be used. The system implements the method described above, including:

[0038] Feature set construction module is used to extract characteristic parameters from new and old products; characteristics with the same physical meaning are grouped into similar element sets;

[0039] The similarity and weight calculation module is used to calculate the similarity of each similar element; based on the characteristic parameters of the similar elements and their weights, calculate the overall similarity Q between the new product and the old product;

[0040] Mathematical modeling module, used to build a mathematical model for solving the fusion threshold,

[0041] The solution and verification module is used to solve the mathematical model and obtain the optimal fusion threshold γ; compare the size relationship between product similarity Q and fusion threshold γ; only when Q>γ can the data of similar products be used for fusion;

[0042] The estimation and output module is used to output the results; display the similarity Q, fusion threshold γ and characteristic parameters of the adjusted data.

[0043] Furthermore, the present invention also provides a computer-readable storage medium having a computer program or instruction stored thereon, which implements the method when the computer program or instruction is executed by a processor.

[0044] Furthermore, the present invention also provides a computer program product, comprising a computer program or instructions, which implement the method when executed by a processor.

[0045] The present invention adopts the above technical solution, quantifies the similarity and introduces the concept of a fusion utility function to solve the fusion threshold and further determine the range of similar products that can be used.

[0046] In addition, the present invention proposes a mathematical model of minimum fusion cost based on the objective function, clarifies key parameters such as similar element eigenvalue deviation, weight distribution, and fusion utility, and solves the optimal fusion threshold through mathematical optimization methods, providing a quantitative theoretical basis for reliability assessment needs.

[0047] Furthermore, this invention is suitable for evaluating the scope of use of similar products, especially when there are design improvements or generational changes between new and similar products. It can flexibly adapt to different product characteristics and application scenarios, providing a universal evaluation framework for multi-domain engineering applications. By minimizing the fusion cost objective function, this invention optimizes resource utilization efficiency during the fusion of new and old product data while ensuring similarity fusion accuracy, thereby improving the accuracy and reliability of the collected data.

[0048] Through the above technical effects, the present invention solves the problem of insufficient accuracy of traditional methods using similar product data in sparse data environments, and provides an efficient, accurate and scientific prior condition for the reliability evaluation of complex products, which has important theoretical value and practical application significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The distribution of new and old products and the distribution of integrated products.

[0050] Figure 2 This is a schematic diagram of a seed source circuit for an application example of the present invention. DETAILED DESCRIPTION

[0051] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0052] First, the present invention proposes a method for solving the fusion threshold: establishing a minimum fusion cost model to solve the fusion threshold. s Similar elements, let N = {1,2}, M = {1,2,…,n s}, let the set D = {d i |i∈N} is the set of original products and new products, V={v j |j∈M} is the set of product similarity elements, η ij For product d i In similar element v j The eigenvalues ​​on is the adjusted characteristic value of the product, is the characteristic value of the integrated product, c j The unit cost generated when the similar element characteristic value changes is set, and the deviation between the characteristic value of the integrated product and the adjusted product characteristic value is set within an acceptable range of ε j In, ω j As the weight of the similar element in the population, the following mathematical model can be established:

[0053]

[0054] The integration utility function is introduced. The integration utility function reflects the closeness between the similarity parameters of the new and old products after adjustment and the similarity parameters of the integrated products. A threshold γ is set. The relationship between the integration utility function and the threshold is used to determine whether the expected degree of integration is achieved. The integration utility function is defined as:

[0055]

[0056] Here, δ=0.0001 is set to avoid the situation where the denominator in the above formula is 0.

[0057] The soft consistent weighted fusion model based on fusion utility constraint is obtained as follows:

[0058]

[0059] Based on the above model, the present invention further provides a method for solving the fusion threshold and determining the range of similar products that can be used. The method includes the following steps:

[0060] 1) Construct feature sets for new and old products, and group features with the same physical meaning into similar element sets;

[0061] 2) Calculate the similarity and weight of similar elements, and calculate the similarity Q between new and old products based on the feature values ​​and weights;

[0062] 3) Establish a mathematical model for solving the fusion threshold; the mathematical model is shown above;

[0063] 4) Based on the case study, the mathematical model is solved and verified to obtain the fusion threshold γ. The relationship between the similarity Q of the products and the fusion threshold γ is compared. Only when the similarity Q is greater than the fusion threshold γ can the similar product data be used for fusion.

[0064] Wherein: step 1) includes the following steps:

[0065] a) Write the margin equation of the system as A feature set is formed by taking each parameter in the margin equation as a feature element;

[0066] b) Define the feature set of the new product as: A = {z1, z2, ..., z n};

[0067] c) The feature set of the old product is defined as: A′={z1′,z2′,...,z n′ '};

[0068] d) Combine the characteristics with the same physical meaning in the two characteristic sets to form similar elements, and set the similar element set as:

[0069] The calculation formula of similarity of similar elements in step 2) is as follows:

[0070]

[0071] The relative sensitivity calculation formula is as follows:

[0072]

[0073] The relative sensitivity weight calculation formula is as follows:

[0074]

[0075] The formula for calculating product similarity is as follows:

[0076]

[0077] Furthermore, in step 3), the objective function is a linear algorithm, which means that the cost of the deviation between the data of the fused product and the data of the new and old products is the smallest. However, in the actual working environment, it is difficult for the data of the new and old products to be completely consistent with the data of the fused product. Therefore, it is only necessary to adjust the data of the new and old products a i and b i Integration with product data η i The deviation is set within an acceptable range of ε i Within, set the integration threshold γ, requiring the integration utility function to be no less than the integration threshold. Since the value of γ is usually unknown, and when it is necessary to find a threshold that minimizes the unit threshold cost, the objective function minΦ can be simply replaced by To determine the optimal threshold, the objective function can be set as:

[0078]

[0079] The constraints are:

[0080]

[0081] x i and y i is the known data of new products and old products in the similar element; a i and b i are the adjusted data of each; η is the data of the integrated product, ω1 and ω2 are the weight ratios of the new and old products in the integrated product, and here we simply set ω1=ω2=0.5; c i and d i is x i and yi The weight of the performance margin m.

[0082] in,

[0083]

[0084] In the present invention, sparse data of a seed source circuit of a laser generator is used to solve the fusion threshold. The schematic diagram of a typical seed source circuit is shown in FIG. Figure 2 Its margin equation is:

[0085]

[0086] Wherein k1=R3 / (R3+R5), k2=R6 / (R6+R8).

[0087] Table 1 Variable distribution parameters

[0088] Variable Name Mean Standard Deviation Variable Name Mean Standard Deviation <![CDATA[R SENSE ]]> 0.0936 Ω 3e-4 Ω <![CDATA[R′ SENSE ]]> 0.0923 Ω 3e-4 Ω <![CDATA[R3]]> 1203.0 Ω 0.24 Ω [R'3] 1200.8 Ω 0.24 Ω [R5] 5.470 kΩ 48 Ω <![CDATA[R′5]]> 5.459 kΩ 45 Ω [R6] 1.510 kΩ 7 Ω <![CDATA[R′6]]> 1.498 kΩ 9 Ω [R7, R8] 1.312 kΩ 12.4 Ω [R'7, R'8] 1.296 kΩ 11.8 Ω <![CDATA[R9,R 10 ]]> 2.63 kΩ 2.8 Ω <![CDATA[R′9,R′ 10 ]]> 2.6 kΩ 3.2 Ω <![CDATA[U SEED ]]> 2258 mV 31 mV <![CDATA[U′ SEED ]]> 2200 mV 29 mV <![CDATA[U P ]]> 452 mV 0.4 mV U' P ]] 459 mV 0.5 mV <![CDATA[U N ]]> 45 mV 4.5 mV <![CDATA[U′ N ]]> 46 mV 5.2 mV

[0089] According to the similarity calculation formula of similar elements, the similarity of each similar element is calculated as shown in the following table

[0090] Table 2 Similarity calculation results of similar elements

[0091] <![CDATA[相似元(u i )]]> <![CDATA[相似度q(u i )]]> <![CDATA[相似元(u i )]]> <![CDATA[相似度q(u i )]]> <![CDATA[相似元(u i )]]> similarity q(u i )]]> <![CDATA[u1]]> 0.9930 <![CDATA[u2]]> 0.9991 u3 0.9990 u4 0.9960 <![CDATA[u5]]> 0.9939 u6 0.9939 <![CDATA[u7]]> 0.9943 <![CDATA[u8]]> 0.9943 u9 0.9870 u 10 ]]> 0.9923 <![CDATA[u 11 ]]> 0.9890 <![CDATA[u 12 ]]> 1

[0092] According to the calculation formula of relative sensitivity and relative sensitivity weight, the calculation results of the two are as follows:

[0093] Table 3 Relative sensitivity calculation results

[0094] Variable Name <S1> <![CDATA[S2]]> <![CDATA[S3]]> <![CDATA[S4]]> Value 4.6935 3.8473 3.8473 2.4452 Variable Name <![CDATA[S5]]> <![CDATA[S6]]> [S7] <![CDATA[S8]]> Value 0.9971 2.4452 1.2099 2.2069 Variable Name <![CDATA[S9]]> <![CDATA[S 10 ]]> <![CDATA[S 11 ]]> <![CDATA[S 12 <!-- 8 -->]]> Value 3.4168 1.4301 0.1535 3.6935

[0095] Table 4 Calculation results of relative sensitivity weights

[0096] Variable Name ​ <![CDATA[ω2]]> <![CDATA[ω3]]> <![CDATA[ω4]]> Value 0.1545 0.1266 0.1266 0.0805 Variable Name <![CDATA[ω5]]> <![CDATA[ω6]]> <![CDATA[ω7]]>

[0078] ω8 Value 0.0328 0.0805 0.0398 0.0726 Variable Name <![CDATA[ω9]]> <![CDATA[ω 10 ]]> <![CDATA[ω 11 ]]> <![CDATA[ω 12 ]]> Value 0.1124 0.0471 0.0051 0.1216

[0097] According to the mean data given in the case and the calculation formula proposed above, the product similarity Q = 0.9952 is obtained.

[0098] The mean data is also used to verify the proposed mathematical model, that is, x = [0.0936, 1203, 5470, 1510, 1312, 1312, 2630, 2630, 2.258, 0.452, 0.045, 2.5]; y = [0.0923, 1200.8, 5459, 1498, 1296, 1296, 2600, 2600, 2.2, 0.459, 0.046, 2.5].

[0099] The above data and mathematical model are encoded in MATLAB. Since γ is unknown, the traversal method is used to solve the value of γ. The range of γ is set to 0 to 1, and the step size is 0.01. After solving, the minimum objective function value is 8.24×10 5 , the corresponding γ value is 0.92, a i and b i as follows:

[0100] Table 5 Mathematical model results under original data

[0101] i <![CDATA[a i ]]> <![CDATA[b i ]]> 1 0.093127 0.091834 2 1209.076 1206.865 3 5442.374 5431.429 4 1502.374 1505.566 5 1305.374 1302.545 6 1305.374 1302.545 7 2616.717 2613.131 8 2616.717 2613.131 9 2.223788 2.211111 10 0.454283 0.456682 11 0.044773 0.045768 12 2.487374 2.487374

[0102] It is found that the product similarity Q = 0.9952 > γ = 0.92, that is, the data of existing new products and similar products can be used for fusion analysis to obtain the reliability evaluation results of new products.

[0103] The above is a description of the embodiments of the present invention. The above description of the disclosed embodiments will enable professionals in the field to implement or use the present invention. Various modifications to these embodiments will be apparent to professionals in the field. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a fusion threshold and clarifying a range of similar products that can be used, the method comprising the following steps: 1) Construct feature sets for new and old products, and group features with the same physical meaning into similar element sets; 2) Calculate the similarity and weight of similar elements, and calculate the similarity Q between new and old products based on the feature values ​​and weights; 3) Establish a mathematical model for solving the fusion threshold; the mathematical model is as follows: Where φ: the value of the objective function, which represents the sum of the fusion costs, and the goal is to minimize it; there are n new and old products. s similar elements; let N = {1,2}, M = {1,2,…,n s }; D = {d i |i∈N} is the set of original products and new products; V={v j |j∈M} is the set of product similarity elements; η ij For product d i In similar element v j The eigenvalues ​​on ; is the adjusted characteristic value of the product; is the characteristic value of the integrated product; c j is the unit feature deviation cost when the similar element feature value changes; ε j is the permissible range of characteristic value deviation between the characteristic value of the integrated product and the characteristic value of the adjusted product; ω j is the weight of the similar element in the population; γ is the fusion threshold, which represents the minimum utility value required for similar product data to be fused and used; δ is a minimum constant to avoid the denominator being zero; 4) Solve and verify the mathematical model to obtain the fusion threshold γ, and compare the size relationship between the product similarity Q and the fusion threshold γ. When the similarity Q is greater than the fusion threshold γ, the data of the similar products can be used to obtain the fused product.

2. The method according to claim 1, characterized in that Step 1) includes the following steps: a) Let the margin equation of the system be m=g(z1,z2,…,z ns ), each parameter in the margin equation is used as a characteristic element to form a feature set; b) Define the feature set of the new product as: A = {z1, z2, ..., z n }; c) The feature set of the old product is defined as: A′={z1′,z2′,...,z n″ }; d) Combine the characteristics with the same physical meaning in the two characteristic sets to form similar elements, and set the similar element set as:

3. The method according to claim 2, characterized in that The calculation formula of similarity of similar elements in step 2) is as follows: The relative sensitivity calculation formula is as follows: The relative sensitivity weight calculation formula is as follows: The formula for calculating product similarity is as follows:

4. The method according to claim 3, characterized in that In the mathematical model, the objective function minΦ is replaced by To determine the optimal threshold, set the objective function to: The constraints are: Where: x i and y i is the known data of new products and old products in the similar element; a i and b i are the adjusted data respectively; η i is the data of the integrated product, ω1 and ω2 are the weight ratios of the new and old products in the integrated product, c i and d i is x i and y i The weight of the performance margin m.

5. The method according to claim 4, characterized in that Set ω1=ω2=0.

5.

6. The method according to claim 4, characterized in that 7. The method according to any one of claims 1 to 6 is used to determine product similarity and the fusion threshold for integrating new and old products.

8. A system for determining the fusion threshold and whether similar product data can be used, characterized in that: The system implements the method according to any one of claims 1 to 6, including: a) Feature set construction module, used to extract characteristic parameters from new and old products; combining characteristics with the same physical meaning into similar element sets; b) Similarity and weight calculation module, used to calculate the similarity of each similar element; based on the similar element characteristic parameters and their weights, calculate the overall similarity Q between the new product and the old product; c) Mathematical modeling module, used to build a mathematical model for solving the fusion threshold, d) The solution and verification module is used to solve the mathematical model and obtain the optimal fusion threshold γ; compare the size relationship between the product similarity Q and the fusion threshold γ; only when Q>γ can the data of similar products be used for fusion; e) Estimation and output module, used to verify the results; display the similarity Q, fusion threshold γ and characteristic parameters of the adjusted data.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images