Cubesat improved FMEA method based on analytic hierarchy process

Abstract

The invention discloses a cubic satellite improved FMEA method based on an analytic hierarchy process, and the method comprises the steps: building an FMEA expert team according to the overall task and subsystem characteristics of a cubic satellite; performing subsystem-level FMEA (failure mode and effect analysis); performing expert subjective weight omega = [omega1, omega2 and omega3] calculation by adopting an analytic hierarchy process and combining expert experience information according to the sod risk factors of the fault modes of the devices; and performing tangent space objective average weight correction according to a judgment matrix given by multiple experts and a subjective weight value obtained by calculation to obtain a comprehensive risk factor, a statistical function fault mode risk factor and a subsystem level risk factor of each device fault mode. And calculating the FMEA fault mode of the whole satellite. According to the method, the characteristics of the cubesat are combined, the quantitative objective expert database and the analytic hierarchy process are adopted for comprehensive processing of various component level fault modes and functional fault modes, on one hand, potential risk components and brought consequences can be rapidly positioned, and on the other hand, FMEA analysis results of subsystems and the whole satellite can be scientifically obtained.

Description

Technical Field

[0001] The present invention relates to a quantitative analysis method in the field of aerospace quality control technology, in particular to an improved FMEA method for cubic satellites based on an expert database and a hierarchical analysis method. Background Art

[0002] In the field of cubic satellite research and development and even aerospace quality control, failure mode and effects analysis (FMEA) is widely cited as an effective method. Its traditional analysis steps are to identify and summarize various failure modes at the level of each single board and single machine based on the designer's experience to complete the work. The disadvantage of this step is that it can only perform qualitative analysis on some potential failure modes of single machines and single boards, and cannot quantify and locate specific risk components. At the same time, there is no intuitive method to deal with the failure mode identification at the subsystem level and the whole satellite. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention proposes an improved FMEA method for cubic satellites based on the hierarchical analysis method. In combination with the characteristics of cubic satellites, a quantitative and objective expert library and the hierarchical analysis method are used to comprehensively process various component-level failure modes and functional failure modes. On the one hand, potential risk components can be quickly located and the consequences brought about can be analyzed. At the same time, the FMEA analysis results of subsystems and the entire satellite can be scientifically obtained.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to an improved FMEA method for cubic satellites based on an expert database and a hierarchical analysis method, comprising:

[0001] Step 1: According to the overall mission and subsystem characteristics of the CubeSat, a FMEA expert team is formed, with the satellite chief designer Z and n Z Personnel, Chief Designer of Subsystems F F Person, single machine supervisor D total n D People and single board designer B together B composed of people.

[0002] Step 2: Subsystem level FMEA analysis.

[0003] Step 3: Using the analytic hierarchy process, the expert subjective weight ω = [ω1 ω2 ω3] is calculated for the SOD risk factor of each device failure mode in combination with expert experience information.

[0004] Step 4: Perform objective average weight correction in the tangent space based on the judgment matrix given by multiple experts and the calculated subjective weight values ​​to obtain the comprehensive risk factor of each device failure mode, the statistical function failure mode risk factor and the subsystem-level risk factor.

[0005] Step 5: Calculate the FMEA failure mode of the entire star. Technical Effects

[0006] The present invention uses a quantitative objective expert database and hierarchical analysis method to comprehensively process various component-level failure modes and functional failure modes, and corrects the subjective scoring results of experts by objective average weight in the tangent space, fully combining the rich experience and objective evaluation of experts to give risk assessment results. Compared with the existing technology, the present invention can quickly locate potential risk components and analyze the risk consequences brought about, and can scientifically obtain FMEA analysis results for subsystems and the entire star. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flow chart of the present invention;

[0008] Figure 2 Schematic diagram of key steps;

[0009] Figure 3 This is a diagram showing the ranking of the comprehensive risk factors and device failure modes in the embodiment. DETAILED DESCRIPTION

[0010] like Figure 1 and Figure 2 As shown, this embodiment relates to an improved FMEA method for a cubic satellite based on an expert database and a hierarchical analysis method, including:

[0011] Step 1: According to the overall mission and subsystem characteristics of the CubeSat, a FMEA expert team is formed, with the satellite chief designer Z and n Z Personnel, Chief Designer of Subsystems F F Person, single machine supervisor D total n D People and single board designer B together B composed of people.

[0012] Step 2: Subsystem level FMEA analysis.

[0013] Step 2.1, FMEA analysis starts from the subsystem level. This embodiment takes the important measurement and control subsystem as an example. After consultation by the chief designer F of the measurement and control and related subsystems, the single machine supervisor D and the single board designer B, n potential failure modes of the measurement and control subsystem are determined: FM1, FM2, ..., FMn.

[0014] Step 2.2: Since the CubeSat uses the special PC104 architecture, the specific implementation of its subsystem level is basically completed at the single machine (single board) level. Therefore, in this embodiment, taking an actual measurement and control single machine carried by a CubeSat as an example, the various fault modes and labels identified are shown in Table 1.

[0015] Table 1 FMEA table and failure mode number of cubic satellite measurement and control subsystem

[0016] Step 2.3: Based on the failure mode numbers in Table 1, the FMEA expert team scores each type of failure according to the three dimensions of severity s (serious), occurrence o (occur), and detectability d (detect). The scoring criteria are shown in Table 2.

[0017] Table 2 CubeSat FMEA scoring criteria

[0018] For the FMEA expert team composed of the chief designer F of measurement and control and related subsystems, the single machine supervisor D and the single board designer B, the failure mode analysis at the component level mainly refers to the opinions of the experienced single machine supervisor D and the single board designer B. The three types of experts can be set as weights (a F =0.2,a D =0.4,a B =0.4), so that the SOD scores of the three types of experts can be comprehensively averaged.

[0019] In this embodiment, taking severity s as an example, its calculation method is: First, check the FM1 information in Table 1 to find out that FM1 is a short-circuit fault of the phase compensation capacitor C56; its potential fault cause is the influence of electricity / heat / stress, or the quality problem of the device; the impact on the current level product will cause the amplification factor to be 0 and the servo gain to decrease; the impact on the upper level product is that the loop gain of the whole machine is reduced and the stability is deteriorated. The expert scoring results are: 1 chief designer of the subsystem, score s F =5.10; 1 stand-alone supervisor, score s D =4.90; 3 single board designers, score s D1 =4.95,s D2 =5.30,s D3 =5.12. After calculation, it can be obtained that the short-circuit fault severity s of FM1 compensation capacitor C56 is 5.03. Other calculations are similar to this, and the scoring results of all fault modes are obtained.

[0020] Table 3 FMEA scoring table for the CubeSat measurement and control subsystem

[0021] Step 3: Use the analytic hierarchy process to calculate the expert subjective weight ω = [ω1 ω2 ω3] of the SOD risk factor of each device failure mode.

[0022] Step 3.1, get advice from experts, get the importance comparison of the three risk factors of SOD, and construct the judgment matrix E = [e ii' ]| 3×3 The scale basis of the judgment matrix is ​​shown in Table 4.

[0023] Table 4 Judgment matrix scale basis

[0024] Step 3.2: Calculate the consistency ratio C R Determine the consistency of pairwise comparison matrices

[0025] When C R <0.1, the judgment matrix is ​​considered to meet the consistency requirements. I =(λ max -n) / (n-1) is the consistency index, λ max is the maximum eigenvalue of matrix E, n is its order, R I is the random consistency index, which can be obtained from Table 5.

[0026] Table 5 Consistency index

[0027] Step 3.3, weight calculation: Use the characteristic root method to calculate the relative weight EW = λ of each indicator relative to the previous layer element from the judgment matrix max W, where: W is the feature vector, and the corresponding indicator weight is obtained after normalization.

[0028] In this embodiment, it is assumed that the expert constructs the judgment matrix according to the scale table 4 Calculate the maximum eigenvalue λ max =3.0055, get C I =0.0055, C R =0.0028<0.1, so the judgment matrix given by the experts meets the consistency requirements.

[0029] The relative weight eigenvector W is further obtained by the eigenvalue method, and after normalization, it is finally obtained ω=[0.8902 0.4132 0.1918].

[0030] Step 4: Perform objective average weight correction in the tangent space based on the judgment matrix given by multiple experts and the calculated subjective weight values ​​to obtain the comprehensive risk factor of each device failure mode, the statistical function failure mode risk factor and the subsystem-level risk factor.

[0031] Step 4.1: First, based on step 3, record the judgment matrix E obtained from the subjective scores of any expert i ,i=1,...,n, n is the number of experts.

[0032] In order to avoid the judgment matrix bias caused by subjective reasons of multiple experts scoring, which affects the objectivity of the comprehensive risk factor, the tangent space average matrix calculation method is used here to find the average judgment matrix Among them: SO(κ) represents the κ-dimensional matrix tangent space, d(E,E i ) represents two judgment matrices E, E i The tangent space distance, that is, under this definition, the sum of the squares of the distances from the average judgment matrix to the subjective scoring judgment matrices given by all experts is minimized.

[0033] In this embodiment, the tangent space distance between two judgment matrices is defined as d(E, E i )=||log(E)-log(E i )||, so the tangent space average matrix calculation can be written as Among them: exp and log are matrix exponential and matrix logarithm operations.

[0034] After calculation, the average judgment matrix can be obtained And update the expert subjective weight ω = [ω1 ω2ω3] to the objective average correction weight of the tangent space The corrected weights of the SOD risk factors of each device failure mode are calculated. And record.

[0035] The comprehensive risk factor of each device failure mode is calculated as The final comprehensive risk factors and rankings of device failure modes are shown in Table 6.

[0036] Table 6 Comprehensive risk factors and rankings of device failure modes

[0037] From the above table, we can see that for single-machine measurement and control products, each component contributes differently to the risk. Among them, the FM18 feedback resistor has the greatest impact and ranks first. This means that in the actual space environment, due to electrical / thermal / stress effects or device quality problems, the resistance output of this level of product will deviate from the positive value, causing the upper-level product to not work normally. We should pay special attention to this risk.

[0038] Step 4.2: According to Table 1, after obtaining the comprehensive risk factor of component failure mode, the functional failure mode risk factor is calculated according to reasonable criteria to simplify the FMEA analysis at the subsystem level.

[0039] According to the component failure categories in Table 1, they are roughly divided into four categories: open circuit, short circuit, parameter drift, and others. Based on the device grade information provided by the component supplier, the probability distribution of the above four types of failure modes is obtained. Here, it is assumed that f k ,f d ,f p ,f q , the four types of failure modes cover all failure causes of the functional item, so the functional failure mode can be calculated as follows: Wherein i=1,2,3,...,18, indicating 18 functional items.

[0040] Step 4.3, calculate the fault risk factor value of the measurement and control subsystem level. First, based on the expert opinions, obtain the importance comparison judgment matrix E of the 18 functional items. f =[e jj' ]| 18×18 After that, the consistency judgment is performed, and the characteristic root method E f W=λ max W obtains the eigenvector of the judgment matrix, and after normalization, the corresponding risk factor indicator weight ω is obtained j ,j=1,2,3,...,18; calculate the failure risk factor of the measurement and control subsystem

[0041] Step 5: Calculate the FMEA failure mode of the entire star.

[0042] Step 5.1: First, sort out the satellite subsystems. In this embodiment, the cubic satellite subsystems include: structure subsystem, satellite service subsystem, measurement and control subsystem, power subsystem, control subsystem, load subsystem, and thermal control subsystem. After analyzing and summarizing each subsystem, the failure modes of the entire satellite are summarized as shown in Table 7.

[0043] Table 7 Whole Star FMEA Form

[0044] Step 5.2: Calculate the risk factor value of the whole satellite failure. Based on the expert opinions, the comparison judgment matrix E of the importance of the seven subsystems is obtained. t =[e kk' ]| 7×7 After that, the consistency judgment is performed, and the characteristic root method E t W=λ max W obtains the eigenvector of the judgment matrix, and after normalization, the corresponding risk factor indicator weight ω is obtained k ,k=1,2,3,...,7; calculate the whole satellite failure risk factor

[0045] In order to verify the effectiveness of this method, after specific actual experiments, step 2 is repeated and the composition of experts is changed. Assuming that the FMEA expert team consists of chief designer F, single machine supervisor D and single board designer B, the three types of experts can be set as weighted (a F =0.4,a D =0.2,a B =0.4), 1 chief designer of the specific subsystem, 2 single machine supervisors, and 2 single board designers gave the scoring results of all fault modes under the same conditions, as shown in Table 8.

[0046] Table 8 FMEA scoring table of the CubeSat measurement and control subsystem after replacing experts

[0047] After the normalization processing in step 3 and the correction of the objective average weight of the cutting space in step 4, the comprehensive risk factor of each device failure mode is calculated as follows: The ranking is shown in Table 9.

[0048] Table 9 Comprehensive risk factors and rankings of device failure modes after replacing experts

[0049] It can be seen that after the experts were replaced, the risk ranking of the single-machine level measurement and control products remained basically unchanged compared with Table 6, such as Figure 3 , the FM18 feedback resistor is still accurately located as having the greatest impact, ranking first. Similarly, the risks of other component failure modes are also accurately located, indicating that in the actual space environment, due to electrical / thermal / stress effects or component quality problems, the output of this level of product will deviate from the positive value, causing the upper level product to not work normally. This risk should be paid special attention to;

[0050] Compared with the existing technology, this method combines the characteristics of cubic satellites, adopts a quantitative objective expert database and hierarchical analysis method to comprehensively process various component-level failure modes and functional failure modes, and corrects the subjective scoring results of experts by objective average weight in the tangent space. It fully combines the rich experience and objective evaluation of experts. On the one hand, it can quickly locate potential risk components and analyze the consequences brought about, and at the same time, it can scientifically obtain the FMEA analysis results of subsystems and the entire satellite.

[0051] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.

Claims

1. An improved FMEA method for cubic satellite based on expert database and hierarchical analysis method, characterized in that: include: Step 1: According to the overall mission and subsystem characteristics of the CubeSat, a FMEA expert team is formed, with the satellite chief designer Z and n Z Personnel, Chief Designer of Subsystems F F Person, single machine supervisor D total n D People and single board designer B together B People make up; Step 2: Subsystem level FMEA analysis; Step 3: Using the analytic hierarchy process, the expert subjective weight ω = [ω1 ω2 ω3] is calculated for the SOD risk factor of each device failure mode in combination with expert experience information; Step 4: Perform objective average weight correction in the tangent space based on the judgment matrix given by multiple experts and the calculated subjective weight values ​​to obtain the comprehensive risk factor of each device failure mode, the statistical function failure mode risk factor and the subsystem-level risk factor; Step 5: Calculate the FMEA failure mode of the entire star.

2. The improved FMEA method for cubic satellites based on expert database and analytic hierarchy process according to claim 1 is characterized in that: The step 2 specifically includes: Step 2.1, FMEA analysis starts from the subsystem level. This embodiment takes the important measurement and control subsystem as an example. After consultation between the chief designer F of the measurement and control and related subsystems, the single machine supervisor D and the single board designer B, n potential failure modes of the measurement and control subsystem are determined: FM1, FM2, ..., FMn; Step 2.2: Since the CubeSat uses the special PC104 architecture, the specific implementation of the subsystem level is basically completed at the single machine level; Step 2.3: Based on the failure mode number, the FMEA expert team scores each type of failure in terms of severity, occurrence, and detectability.

3. The improved FMEA method for cubic satellites based on expert database and hierarchical analysis method according to claim 1 is characterized in that: The step 3 specifically includes: Step 3.1, get advice from experts, get the importance comparison of the three risk factors of SOD, and construct the judgment matrix E = [e ii' ] 3×3 ; Step 3.2: Calculate the consistency ratio C R Determine the consistency of pairwise comparison matrices When C R <0.1, the judgment matrix is ​​considered to meet the consistency requirements, where: C I =(λ max -n) / (n-1) is the consistency index, λ max is the maximum eigenvalue of matrix E, n is its order, R I is a random consistency indicator; Step 3.3, weight calculation: Use the characteristic root method to calculate the relative weight EW = λ of each indicator relative to the previous layer element from the judgment matrix max W, where: W is the feature vector, and the corresponding indicator weight is obtained after normalization.

4. The improved FMEA method for cubic satellites based on expert database and hierarchical analysis method according to claim 1 is characterized in that: The step 4 specifically includes: Step 4.1: First, based on step 3, record the judgment matrix E obtained from the subjective scores of any expert i ,i=1,...,n, n is the number of experts; Step 4.2: After obtaining the comprehensive risk factors of component failure modes, calculate the functional failure mode risk factors according to reasonable criteria to simplify the FMEA analysis at the subsystem level; Step 4.3, calculate the failure risk factor value of the measurement and control subsystem: First, based on the expert opinions, obtain the importance comparison judgment matrix E of the 18 functional items f =[e jj' ] 18×18 After that, the consistency judgment is performed, and the characteristic root method E f W=λ max W obtains the eigenvector of the judgment matrix, and after normalization, the corresponding risk factor indicator weight ω is obtained j ,j=1,2,3,...,18; calculate the failure risk factor of the measurement and control subsystem 5. The improved FMEA method for cubic satellites based on expert database and hierarchical analysis method according to claim 1 is characterized in that: The step 5 specifically includes: Step 5.1, first sort out the subsystems of the whole satellite. In this embodiment, the subsystems of the cubic satellite include: structure subsystem, satellite service subsystem, measurement and control subsystem, power subsystem, control subsystem, load subsystem, thermal control subsystem, and analyze and summarize each subsystem; Step 5.2: Calculate the risk factor value of the whole satellite failure, and obtain the comparison judgment matrix E of the importance of the seven subsystems according to the expert opinions. t =[e kk' ] 7×7 After that, the consistency judgment is performed, and the characteristic root method E t W=λ max W obtains the eigenvector of the judgment matrix, and after normalization, the corresponding risk factor indicator weight ω is obtained k ,k=1,2,3,...,7; calculate the whole satellite failure risk factor

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