A method for analyzing the reliability of avionics products using multi-source interval data

Abstract

The present application relates to the technical field of data processing technology and discloses a method for analyzing the reliability of avionics products using multi-source interval data, comprising: summarizing and classifying the multi-source interval data of a specified avionics product to obtain multiple groups of interval data, substituting the multiple groups into an exponential distribution model, and constructing corresponding quality functions according to a unified identification framework; obtaining conflict coefficients and credibility coefficients between the quality functions corresponding to different groups of the interval data, and determining fusion weight coefficients of the respective quality functions; obtaining a weighted quality function according to the fusion weight coefficients, fusing the weighted quality functions based on the D-S evidence theory to obtain a fusion quality function, and transforming the fusion quality function according to the Pignistic probability transformation method; obtaining the reliability of the avionics product at any time according to the transformation result, fitting and obtaining a reliability curve with respect to time changes to perform reliability analysis.

Description

Technical Field

[0001] The present application relates to the technical field of data processing technology, and in particular to a method for analyzing the reliability of avionics products using multi-source interval data. Background Art

[0002] In recent years, my country's civil aircraft manufacturing industry has developed rapidly. Avionics equipment is a crucial component of airborne equipment, and its safety significantly impacts the overall safety of the aircraft. With the gradual localization of China's civil aircraft manufacturing, the localization of airborne avionics equipment is also being implemented.

[0003] Due to variations in equipment operating environments, tolerances in product design and production, test observation methods, and measurement errors, the resulting multi-source data may not represent precise point data, but rather a range—interval data. This is particularly evident in expert experience data, where precise estimates based on engineering experience are often difficult to make. To ensure data accuracy, only reasonable intervals are used. During reliability testing and product service, product failures may not be immediately observed, and only interval data can be obtained. Therefore, interval data is a crucial component of safety fundamentals.

[0004] However, it is currently difficult to conduct accurate and reliable reliability analysis of domestic avionics equipment using traditional reliability analysis methods; therefore, data from different sources (experiments, simulations, expert experience, etc.) can be used for data fusion to conduct more accurate reliability analysis of domestic avionics equipment and obtain more reliable analysis results. Summary of the Invention

[0005] In order to conduct more accurate reliability analysis of domestic avionics equipment and obtain more reliable analysis results, this application provides a method for analyzing the reliability of avionics products using multi-source interval data, which adopts the following technical solutions:

[0006] The method for analyzing the reliability of avionics products using multi-source interval data includes:

[0007] After summarizing and classifying the multi-source interval data of the specified avionics product, multiple groups of interval data are obtained, which are respectively substituted into the exponential distribution model, and the corresponding quality function is constructed according to the unified identification framework. ;

[0008] Obtain the conflict coefficient and credibility coefficient between the quality functions corresponding to the interval data of different groups, and determine the fusion weight coefficient of each quality function ;

[0009] Obtaining a weighted quality function according to the fusion weight coefficient, fusing the weighted quality function based on DS evidence theory to obtain a fusion quality function, and transforming the fusion quality function according to the Pignistic probability conversion method;

[0010] Obtaining the reliability of the avionics product at any time according to the conversion result, fitting and obtaining a reliability curve with respect to time variation to perform reliability analysis;

[0011] in, is the group number of the interval data.

[0012] Optionally, the multi-source interval data includes expert experience data, product service data, test data, simulation data and manual data;

[0013] The summary classification results include MTBF interval data and in Reliability interval data at the moment ;

[0014] in, is the lower limit of the MTBF interval data, is the upper limit of the MTBF interval data, is the lower limit of the reliability interval data, is the upper limit of the reliability interval data.

[0015] Optionally, the method of substituting into the exponential distribution model and constructing a corresponding quality function according to the unified recognition framework includes:

[0016] For group number Interval data of:

[0017] Substitute the upper and lower limit values ​​of the interval data into the exponential distribution model to obtain the reliability interval data of the specified avionics product. The upper and lower limits of

[0018] Construct the corresponding quality function based on the unified recognition framework ;

[0019]

[0020] in, is the lower limit of the reliability interval data, is the upper limit of the reliability interval data, 、 、 、 is the quality function The basic probability distribution for different subsets represents the operating status of the specified avionics products;

[0021] The operating status includes empty set, working, failed, working or failed.

[0022] Optionally, the method of obtaining the conflict coefficients and credibility coefficients between the quality functions corresponding to different groups of the interval data and determining the fusion weight coefficients of the quality functions includes:

[0023] The distance and similarity between two different quality functions are calculated using the Jouselme distance formula and the similarity coefficient formula respectively, which are used as conflict coefficients to measure the conflict between quality functions.

[0024] The information entropy formula and standard deviation formula are used to measure the uncertainty of each quality function as a credibility coefficient to measure the credibility of the quality function itself;

[0025] The product of the conflict coefficient and the credibility coefficient is normalized to obtain the fusion weight coefficient of each quality function.

[0026] Optionally, the method for obtaining the conflict coefficient includes:

[0027] For quality function and the mass function Different arbitrary mass functions :

[0028] The Josselme distance formula and the similarity coefficient formula are used to calculate the quality function and any mass function The distance matrix between and similarity matrix ;

[0029] According to all the distance matrices and the similarity matrix Calculate the quality function The distance coefficient from all other mass functions and similarity coefficient ;

[0030] Normalize all corresponding products of all distance coefficients and all similarity coefficients to obtain the conflict coefficient As the support weight. Use the Josselme distance formula and similarity coefficient formula:

[0031]

[0032]

[0033] in, and Different group numbers 、 The corresponding mass function;

[0034] The D matrix is ​​defined as follows: ;

[0035] According to the above formula, the distance matrix is ​​calculated and similarity matrix ;

[0036]

[0037] Because it is generally believed that the smaller the conflict between a data and other data, the more reasonable the data is, so the following formula is used and , calculate the distance coefficient and similarity coefficient of each quality function with the rest of the quality functions, multiply them and normalize them to get the conflict coefficient as support weight.

[0038] Optionally, the method for obtaining the credibility coefficient includes:

[0039] Obtain information entropy value through information entropy formula ;

[0040] Get the standard deviation value using the standard deviation formula :

[0041] The information entropy value and the standard deviation The reciprocals are taken and normalized respectively, and the normalized results are multiplied and normalized again to obtain the credibility weight as the credibility weight.

[0042]

[0043]

[0044] in, To unify the recognition framework power set, A collection of propositions that constitute a unified recognition framework, Represents the subset in the recognition framework A proposition, It is a state in the unified recognition framework power set The number of elements contained; and It is the upper and lower bounds of the corresponding interval data itself;

[0045] Optionally, the method of obtaining a weighted quality function according to the fusion weight coefficient includes:

[0046] according to , obtain the weighted quality function ;

[0047] in, is the fusion weight of each quality function.

[0048] Optionally, the DS evidence theory includes:

[0049] DS evidence theory fusion formula:

[0050]

[0051] in, is the support of the corresponding proposition in the fused quality function, is the conflict coefficient, and are the support degrees of the corresponding propositions in the quality function before fusion.

[0052] Optionally, the Pignistic probability conversion formula is used to distribute the basic probability of the non-single-focus element subset to the single-focus element subset, that is, the quality function is converted into a Bayesian quality function.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] This application significantly improves the accuracy and comprehensiveness of avionics product reliability assessment through comprehensive processing and fusion analysis of multi-source interval data. First, through the classification modeling and quality function construction of multi-source interval data, the distribution characteristics of different data sources are effectively integrated, avoiding the limitations of single data source analysis. Secondly, the dynamic weight allocation mechanism based on the conflict coefficient and credibility coefficient can adaptively adjust the contribution of different quality functions, solving the fusion bias problem caused by evidence conflict in traditional methods. Through the fusion of DS evidence theory and Pignistic probability transformation, the reasonable transformation of uncertain information is achieved, so that the reliability analysis results retain the probabilistic characteristics of interval data and have clear engineering interpretability. The dynamic reliability curve finally generated intuitively reflects the reliability change trend of the product throughout its life cycle, providing a highly reliable quantitative basis for fault prediction and maintenance decisions of avionics products. This application has stronger robustness in complex data environments, and is particularly suitable for high-reliability product evaluation scenarios such as avionics equipment with multi-parameter coupling and diverse data sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the reliability analysis method of avionics products in the present invention.

[0056] Figure 2 It is a schematic diagram of the fusion rule of the quality function in the present invention.

[0057] Figure 3 It is a curve showing the time variation of the reliability of a certain avionics product in the present invention. DETAILED DESCRIPTION

[0058] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0059] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0060] The present application discloses a method for analyzing the reliability of avionics products using multi-source interval data. Figure 1 ,include:

[0061] After summarizing and classifying the multi-source interval data of the specified avionics product, multiple groups of interval data are obtained, which are respectively substituted into the exponential distribution model, and the corresponding quality function is constructed according to the unified identification framework. ;

[0062] Obtain the conflict coefficient and credibility coefficient between the quality functions corresponding to the interval data of different groups, and determine the fusion weight coefficient of each quality function ;

[0063] Obtaining a weighted quality function according to the fusion weight coefficient, fusing the weighted quality function based on DS evidence theory to obtain a fusion quality function, and transforming the fusion quality function according to the Pignistic probability conversion method;

[0064] Obtaining the reliability of the avionics product at any time according to the conversion result, fitting and obtaining a reliability curve with respect to time variation to perform reliability analysis;

[0065] in, is the group sequence number of the interval data.

[0066] This application significantly improves the accuracy and comprehensiveness of avionics product reliability assessment through comprehensive processing and fusion analysis of multi-source interval data. First, through the classification modeling and quality function construction of multi-source interval data, the distribution characteristics of different data sources are effectively integrated, avoiding the limitations of single data source analysis. Secondly, the dynamic weight allocation mechanism based on the conflict coefficient and credibility coefficient can adaptively adjust the contribution of different quality functions, solving the fusion bias problem caused by evidence conflict in traditional methods. Through the fusion of DS evidence theory and Pignistic probability transformation, the reasonable transformation of uncertain information is achieved, so that the reliability analysis results retain the probabilistic characteristics of interval data and have clear engineering interpretability. The dynamic reliability curve finally generated intuitively reflects the reliability change trend of the product throughout its life cycle, providing a highly reliable quantitative basis for fault prediction and maintenance decisions of avionics products. This application has stronger robustness in complex data environments, and is particularly suitable for high-reliability product evaluation scenarios such as avionics equipment with multi-parameter coupling and diverse data sources.

[0067] In this embodiment of the present application, the multi-source interval data includes expert experience data, product service data, test data, simulation data and manual data; the summary classification results include MTBF interval data and in Reliability interval data at the moment ;MTBF interval data is mean time between failures interval data.

[0068] in, is the lower limit of the MTBF interval data, is the upper limit of the MTBF interval data, is the lower limit of the reliability interval data, is the upper limit of the reliability interval data.

[0069] In this embodiment, the MTBF interval data and reliability interval data of the LED lamp model CREEXPE2-R4 obtained from simulation are as follows:

[0070] Interval data 1 is MTBF interval data (17520h, 26280h);

[0071] Interval data 2 is MTBF interval data (21000h, 43000h);

[0072] Interval data 3 is Reliability interval data at 20,000 hours (0.6065, 0.6703, 2000);

[0073] Interval data 4 is the MTBF interval data of similar products (30000h, 40000h).

[0074] The interval data Substituting the upper and lower limit values ​​into the exponential distribution model, the upper and lower limit values ​​of the reliability interval data of the specified avionics product are obtained accordingly; is the lower limit of the reliability interval data, is the upper limit of the reliability interval data;

[0075] when hour, ;

[0076] when hour, ;

[0077] when hour, , and the solution is , we can further get ;

[0078] when hour, ;

[0079] In this way, the reliability interval data corresponding to the four interval data can be obtained as follows:

[0080] , , and ;

[0081] After that, substitute the unified recognition framework to construct the corresponding quality function :

[0082]

[0083] in, 、 、 、 is the quality function The basic probability distribution of different subsets, i.e., propositions, respectively represents the operating status of the specified avionics product; the operating status includes empty set, working, failure, working or failure;

[0084] From a set theory perspective, a unified identification framework Θ is a set consisting of all possible mutually exclusive and complete elements. For example, in this paper, the identification framework Θ for a product's status is {working, failed}. These two subsets form a complete set (completeness), covering all possible outcomes. Furthermore, the elements "working" and "failed" are mutually exclusive (mutual exclusivity), meaning they cannot occur simultaneously.

[0085] A unified identification framework clearly defines all possible outcomes of the problem under consideration. By defining the identification framework, it clarifies the scope of evidence theory, avoids the inclusion of irrelevant information, and provides a clear boundary for subsequent evidence analysis and integration. The quality function is a core concept in evidence theory. It is defined based on the identification framework and assigns basic probabilities to subsets of the identification framework, which represent different propositions or hypotheses.

[0086] Please refer to Table 1 below for details.

[0087] Table 1: Quality function results corresponding to interval data

[0088]

[0089] Then, the distance and similarity between two different quality functions are calculated using the Jousellme distance formula and the similarity coefficient formula respectively, which are used as conflict coefficients to measure the conflict between quality functions.

[0090] The information entropy formula and standard deviation formula are used to measure the uncertainty of each quality function as a credibility coefficient to measure the credibility of the quality function itself;

[0091] The product of the conflict coefficient and the credibility coefficient is normalized to obtain the fusion weight coefficient of each quality function.

[0092] Specifically, the method for obtaining the conflict coefficient includes:

[0093] For quality function and the mass function Different arbitrary mass functions :

[0094] The Josselme distance formula and the similarity coefficient formula are used to calculate the quality function and any mass function The distance matrix between and similarity matrix ;

[0095] According to all the distance matrices and the similarity matrix Calculate the quality function The distance coefficient from all other mass functions and similarity coefficient ;

[0096] Normalize all corresponding products of all distance coefficients and all similarity coefficients to obtain the conflict coefficient as support weight.

[0097] Use the Jouselme distance formula and similarity coefficient formula:

[0098]

[0099]

[0100] in, and Different group numbers 、 The corresponding mass function;

[0101] The D matrix is ​​defined as follows: ;

[0102] According to the above formula, taking the 20,000-hour regional data 3 as an example, the distance matrix is ​​calculated and similarity matrix ;

[0103]

[0104]

[0105] Because it is generally believed that the smaller the conflict between a data and other data, the more reasonable the data is, so the following formula is used and , calculate the distance coefficient between each mass function and the rest of the mass functions and similarity coefficient , multiply each other and then normalize to get the conflict coefficient As the support weight, is the total number of interval data.

[0106] On the other hand, the method for obtaining the credibility coefficient includes:

[0107] Obtain information entropy value through information entropy formula ;

[0108] Get the standard deviation value using the standard deviation formula :

[0109] The information entropy value and the standard deviation The reciprocals are taken and normalized respectively, and the normalized results are multiplied and normalized again to obtain the credibility weight as the credibility weight.

[0110] Specifically:

[0111]

[0112]

[0113] in, To unify the recognition framework power set, A collection of propositions that constitute a unified recognition framework, Represents the subset in the recognition framework A proposition, It is a state in the unified recognition framework power set The number of elements contained; and It corresponds to the upper and lower bounds of the interval data itself; all interval data need to calculate the standard deviation, but since there are two types of interval data in the case, the two types of interval data need to be converted into the same type before calculation. In this article, the reliability interval data is converted into MTBF interval data before calculation.

[0114] Conversion process: Find the reliability interval data and , and then take the reciprocal to get the MTBF interval data.

[0115] Before calculation, the original interval data must be uniformly converted into the MTBF interval form and then the standard deviation is calculated. The uncertainty and standard deviation of each interval data are calculated using the above formula;

[0116] Table 2: Information entropy of quality function and standard deviation of interval data

[0117]

[0118] It is generally believed that the smaller the uncertainty of a data itself, the better the quality of the data. Therefore, the calculated information entropy and standard deviation are normalized after taking the inverse respectively, and then the normalized results are multiplied and normalized again as the credibility weight.

[0119] Optionally, the method of obtaining a weighted quality function according to the fusion weight coefficient includes:

[0120] according to , obtain the weighted quality function ;

[0121] in, is the fusion weight of each quality function.

[0122] Then use the DS evidence theory fusion formula: , the weighted quality function is fused; Figure 2 The figure shows a schematic diagram of the fusion principle.

[0123] in, is the support of the corresponding proposition in the fused quality function, is the conflict coefficient, which plays a normalization role in the formula. and are the support degrees of the corresponding propositions in the quality function before fusion.

[0124] Using the Pignistic probability conversion formula: , assigning the basic probabilities of the non-monofocal subsets to the monofocal subsets, thus converting the quality function into a Bayesian quality function. The result of fusion according to the combination rules of evidence theory is still a quality function, which contains the basic probability distribution of each subset within the recognition framework, not a strict probability. Therefore, it is necessary to use the Pignistic probability transformation to convert the basic probability distribution into a probability to facilitate the next step of reliability analysis.

[0125] After Pignistic probability conversion and , respectively represent the reliability and failure rate of the product;

[0126] Mean time between failures of products following exponential distribution and characteristic life Equal to its failure rate The reciprocal of

[0127]

[0128] Among them, the characteristic life is the usage time corresponding to when the product reliability drops to 0.368.

[0129] Using the conversion results from the previous step, we can determine the reliability of the avionics product at a specific moment. By applying the above steps at different moments, we can determine the reliability of the product at different times. Repeating the above steps at a large number of moments will yield a large amount of product reliability data. Using curve fitting, we can then generate a time-varying reliability curve.

[0130] like Figure 3 As shown in , according to the changes in the reliability curve, it can be analyzed that the median life of the LED lamp is 25871h, the characteristic life is 28651h, and its reliability at some moments is shown in Table 3 below.

[0131] Table 3: Reliability of LED lights at certain times

[0132]

[0133] Through the product reliability curve, we can obtain the time corresponding to the reliability of 0.368, which is the MTBF of the product.

[0134] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for analyzing the reliability of avionics products using multi-source interval data, characterized in that: include: After summarizing and classifying the multi-source interval data of the specified avionics product, multiple groups of interval data are obtained, which are respectively substituted into the exponential distribution model, and the corresponding quality function is constructed according to the unified identification framework. ; Obtain the conflict coefficient and credibility coefficient between the quality functions corresponding to the interval data of different groups, and determine the fusion weight coefficient of each quality function ; Obtaining a weighted quality function according to the fusion weight coefficient, fusing the weighted quality function based on DS evidence theory to obtain a fusion quality function, and transforming the fusion quality function according to the Pignistic probability conversion method; Obtaining the reliability of the avionics product at any time according to the conversion result, fitting and obtaining a reliability curve with respect to time variation to perform reliability analysis; in, is the group serial number of the interval data; The method of obtaining the conflict coefficients and credibility coefficients between the quality functions corresponding to the interval data of different groups and determining the fusion weight coefficients of the quality functions includes: The distance and similarity between two different quality functions are calculated using the Jouselme distance formula and the similarity coefficient formula respectively, which are used as conflict coefficients to measure the conflict between quality functions. The information entropy formula and standard deviation formula are used to measure the uncertainty of each quality function as a credibility coefficient to measure the credibility of the quality function itself; The product of the conflict coefficient and the credibility coefficient is normalized to obtain the fusion weight coefficient of each quality function.

2. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 1, characterized in that: The multi-source interval data includes expert experience data, product service data, test data, simulation data and manual data; The summary classification results include MTBF interval data and in Reliability interval data at the moment ; in, is the lower limit of the MTBF interval data, is the upper limit of the MTBF interval data, is the lower limit of the reliability interval data, is the upper limit of the reliability interval data.

3. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 1, characterized in that: The method of substituting into the exponential distribution model and constructing the corresponding quality function according to the unified recognition framework includes: For group number Interval data of: Substituting the upper and lower limit values ​​of the interval data into an exponential distribution model to obtain the upper and lower limit values ​​of the reliability interval data of the specified avionics product; Construct the corresponding quality function based on the unified recognition framework ; in, is the lower limit of the reliability interval data, is the upper limit of the reliability interval data, 、 、 、 is the quality function The basic probability distribution for different subsets represents the operating status of the specified avionics products; The operating status includes empty set, working, failed, working or failed.

4. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 1, characterized in that: The method for obtaining the conflict coefficient includes: For quality function and the mass function Different arbitrary mass functions : The Josselme distance formula and the similarity coefficient formula are used to calculate the quality function and any mass function The distance matrix between and similarity matrix ; According to all the distance matrices and the similarity matrix Calculate the quality function The distance coefficient from all other mass functions and similarity coefficient ; Normalize all corresponding products of all distance coefficients and all similarity coefficients to obtain the conflict coefficient as support weight.

5. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 3, characterized in that: The method for obtaining the credibility coefficient includes: Obtain information entropy value through information entropy formula ; Get the standard deviation value using the standard deviation formula : The information entropy value and the standard deviation The reciprocals are taken and normalized respectively, and the normalized results are multiplied and normalized again to obtain the credibility weight as the credibility weight.

6. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 1, characterized in that: The method for obtaining a weighted quality function according to the fusion weight coefficient includes: according to , obtain the weighted quality function ; in, is the fusion weight of each quality function.

7. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 6, characterized in that: The DS evidence theory includes: DS evidence theory fusion formula: in, is the support of the corresponding proposition in the fused quality function, is the conflict coefficient, and are the support degrees of the corresponding propositions in the quality function before fusion.

8. The method for analyzing the reliability of avionics products using multi-source interval data according to claim 7, characterized in that: The method for converting the fusion quality function according to the Pignistic probability conversion method includes: The Pignistic probability conversion formula is used to assign the basic probability of the non-single-focus element subset to the single-focus element subset, and the quality function is converted into a Bayesian quality function.

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