Thermal test cycle index determination method for satellite fault influence degree

Abstract

According to the thermal test cycle index determination method oriented to the satellite fault influence degree, the satellite thermal test cycle index is determined, and compared with an existing test scheme making method, the method has the main advantages that faults of different severity levels can be fully screened through the cycle index determination method, and the accuracy of the test scheme making method is improved; the risk of in-orbit operation of the spacecraft is reduced; and (3) the cycle index is determined through the test method, various factors such as reliability and test expenditure / time can be considered, the test cost is controlled, the test scheme is more practical, and satellite purchasers or customers are more satisfied.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of spacecraft environmental testing technology, and in particular to a method for determining the number of thermal test cycles based on the impact of satellite failures. Background Art

[0002] Spacecraft ground testing is an indispensable and critical step in the satellite development process. It is crucial for spacecraft performance verification and reliability assurance, and has the greatest impact on development time and cost. Currently, spacecraft thermal vacuum testing (also known as vacuum thermal cycle testing) and atmospheric pressure thermal cycle testing (collectively referred to as spacecraft thermal testing in this project) are the most important tests for determining the effectiveness of spacecraft ground environmental testing. They also expose the highest proportion of failures during spacecraft ground environmental testing, accounting for approximately 80%. However, they also incur the highest proportion of financial and time costs. Traditional spacecraft ground thermal testing cycles typically last around two months, accounting for over 30% of the total development cycle, and costs range from hundreds of thousands to millions of yuan, representing approximately 20%-30% of the total development cost. Therefore, optimizing ground thermal testing has become a bottleneck restricting the efficient development of satellites.

[0003] The cost and effectiveness of thermal vacuum and thermal cycling tests for stress screening depend primarily on the number of cycles, temperature range, hold times at high and low temperatures, ramp rates, and vacuum level. Generally, the vacuum level remains constant during testing, and optimization focuses on thermal test conditions such as the number of cycles and temperature range. A higher number of cycles and a wider temperature range increase the probability of fault exposure, reducing the risk of on-orbit satellite operations. However, this also increases testing time and costs, and excessive fatigue and wear can lead to unnecessary damage to the product, shortening its useful life. Conversely, too few cycles and a narrow temperature range can fail to detect defects, posing a risk after launch and increasing on-orbit operational risks. In short, critical thermal test parameters conflict with the high reliability and low cost requirements of satellites. This is particularly true for large-scale constellations, where the "scale-efficiency-reliability" paradox exists. Therefore, determining thermal test conditions such as the number of cycles and temperature range to achieve optimal environmental stress screening while minimizing testing time and cost is a key factor in achieving both high efficiency and high reliability in satellite development.

[0004] Current test effectiveness assessments only consider the number of failures, ignoring their severity and impact on the constellation's mission objectives. For example, if a high-reliability satellite ground test reveals 95% failures, if the 5% that escape include a fatal failure (such as a solar wing failure to deploy), the mission may still fail even if the test "meets the target." On the other hand, for low-cost commercial satellites, even if the ground screening rate is only 80%, the test can still be considered effective if the escape failure has a negligible impact on the mission. This "one-size-fits-all" assessment approach can easily lead to uncontrolled risks for high-importance missions and excessive testing for low-cost missions. Summary of the Invention

[0005] In view of this, the present invention provides a method for determining the number of thermal test cycles for the impact degree of satellite failures, so as to solve the technical defects existing in the prior art.

[0006] According to a first aspect of the present invention, a method for determining the number of thermal test cycles for the impact degree of satellite failure is provided, comprising:

[0007] Step 1: Determine the fault severity coefficient E of the on-orbit thermal fault i under different cycle times of the satellite. i and failure frequency F i , and the fault severity coefficient E of fault j in the ground thermal cycle test j and failure frequency F j ;

[0008] Step 2: Severity coefficient E based on different cycle numbers i , Fault frequency F i , Fault severity coefficient E j and failure frequency F j , generate the revised comprehensive test effectiveness calculation formula;

[0009] Step 3: Construct a failure distribution probability model that characterizes the relationship between the effectiveness of the comprehensive test and the number of cycles, wherein the exponential coefficient of the failure distribution probability model is based on the severity coefficient E under different cycle numbers. i , Fault frequency F i , Fault severity coefficient E j , Fault frequency F j and comprehensive trial validity determination;

[0010] Step 4: construct a multi-objective optimization function, wherein the multi-objective optimization function includes the fault failure distribution probability model and preset constraints;

[0011] Step 5: Solve the multi-objective optimization function to determine the minimum number of cycles that meets the effectiveness of the comprehensive test.

[0012] In some embodiments, the modified comprehensive test effectiveness calculation formula includes:

[0013]

[0014] Among them, ITE represents the integrated test effectiveness, m is the total number of fault types in the thermal cycling test, and n is the total number of early thermal-induced failures on orbit.

[0015] In some embodiments, the fault failure distribution probability model includes:

[0016] ITE(N)=1-e-λN

[0017] Where N is the number of cycles of the thermal cycle test, and λ is the exponential coefficient of the failure distribution probability model.

[0018] In some embodiments, the multi-objective optimization function includes:

[0019] Objective function:

[0020] Constraints: ΔT∈[T min ,T max ]

[0021] Among them, ITE min represents the comprehensive effectiveness threshold, T min Indicates the minimum temperature threshold, T max Indicates the maximum temperature threshold, and ΔT indicates the preset temperature range requirement.

[0022] In some embodiments, the algorithm for solving the multi-objective optimization function includes:

[0023] The NSGA-II algorithm is used to generate the Pareto front solution set, and the final output is the minimum number of cycles under satisfactory comprehensive test effectiveness.

[0024] In some embodiments, the method for determining the number of thermal test cycles for the impact degree of satellite failure further includes:

[0025] Step 6: Perform a test based on the determined minimum number of cycles to obtain the latest fault severity coefficient E of the rail thermal fault i. i and failure frequency F i , and the fault severity coefficient E of fault j in the ground thermal cycle test j and failure frequency F j , and based on the latest fault severity factor E i , Fault frequency F i , Fault severity coefficient E j and failure frequency F j , updating the fault failure distribution probability model.

[0026] At least one embodiment of the present invention can determine the number of satellite thermal test cycles. Compared with the existing test plan formulation method, the main advantages are: ② The method for determining the number of cycles can fully screen faults of different severity levels, reducing the risk of spacecraft on-orbit operation; ③ The number of cycles determined by the test method can take into account multiple factors such as reliability and test funds / time, control the test cost, make the test plan more practical, and make satellite purchasers or customers more satisfied. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a method for determining the number of thermal test cycles for the impact degree of satellite failures provided by the present invention;

[0028] Figure 2 The present invention provides a method for determining the number of thermal test cycles for determining the degree of impact of satellite failures, and provides a numerical relationship curve between the number of thermal test cycles and the corresponding comprehensive effectiveness value. DETAILED DESCRIPTION

[0029] The following description sets forth many specific details to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0030] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items. The modifications of "one" and "a plurality" mentioned in this disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0031] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0032] First, the terms involved in one or more embodiments of this specification are explained.

[0033] NSGA-II algorithm: Nondominated Sorting Genetic Algorithm II, fast nondominated sorting genetic algorithm II.

[0034] See also Figure 1 , Figure 1A flowchart of a method for determining the number of thermal test cycles for the impact degree of satellite failures provided in accordance with some embodiments of this specification is shown, specifically including:

[0035] Step 1: Determine the fault severity coefficient E of the on-orbit thermal fault i under different cycle times of the satellite. i and failure frequency F i , and the fault severity coefficient E of fault j in the ground thermal cycle test j and failure frequency F j .

[0036] Based on fault tree analysis and failure mode and effect analysis, the statistical analysis results and satellite fault severity level assessment results can be sorted out according to the historical failure modes of the satellite. The fault j exposed by the satellite in the ground thermal cycle test and the thermally induced fault i exposed by the satellite in the early operation of the satellite on orbit (i.e., the fault related to the thermal environment, which should have been exposed in the ground thermal cycle test but escaped and was exposed in the early stage of the satellite on orbit) can be statistically analyzed to determine the fault severity coefficient E of the satellite on-orbit thermally induced fault i and the ground thermal cycle test fault j. i and E j , and the fault frequency F i and F j The fault severity coefficient E corresponding to different fault severity levels i and E j Based on the results of the failure mode and effect analysis, four levels of severity can be defined and quantified, and the values ​​can be selected according to the recommendations in Table 1, such as solar wing failure (Level I, E1 = 0.9) and sensor offset (Level III, E2 = 0.5).

[0037] Table 1

[0038] grade describe Fault severity coefficient range E Catastrophic (I) Mission completely failed 0.8～1.0 Criticality (II) Partial failure of the task 0.5～0.8 Non-major (III) Performance is degraded but recoverable 0.2～0.5 Negligible (IV) No significant effect 0～0.2

[0039] Note: For the same single machine, if the fault name is the same and the feedback to the ground test is the same, then it is considered as the same type of fault. i and F j The frequency of failures of the same type.

[0040] Step 2: Severity coefficient E based on different cycle numbers i , Fault frequency F i , Fault severity coefficient E j and failure frequency F j , generate the revised comprehensive test effectiveness calculation formula.

[0041] Similarly, in some other optional implementations, a modified formula for calculating the escape rate of the comprehensive test can be generated. The sum of the escape rate and the effectiveness is 1, so either one can be selected for processing.

[0042] Step 3: Construct a failure distribution probability model that characterizes the relationship between the effectiveness of the comprehensive test and the number of cycles, wherein the exponential coefficient of the failure distribution probability model is based on the severity coefficient E under different cycle numbers. i , Fault frequency F i , Fault severity coefficient E j , Fault frequency F j and comprehensive test effectiveness determination.

[0043] Step 4: construct a multi-objective optimization function, which includes the fault failure distribution probability model and preset constraints.

[0044] Step 5: Solve the multi-objective optimization function to determine the minimum number of cycles that meets the effectiveness of the comprehensive test.

[0045] In some optional implementations, the revised comprehensive test effectiveness calculation formula includes:

[0046]

[0047] Among them, ITE represents the integrated test effectiveness, m is the total number of fault types in the thermal cycling test, and n is the total number of early thermal-induced failures on orbit.

[0048] Similarly, in some other optional implementations, the revised calculation formula for the comprehensive test escape rate FE includes:

[0049]

[0050] Where FE represents the comprehensive test effectiveness, m is the total number of fault types in the thermal cycle test, and n is the total number of early thermal-induced failures on orbit.

[0051] In some optional implementations, the fault failure distribution probability model includes:

[0052] ITE(N)=1-e -λN

[0053] Where N is the number of cycles of the thermal cycle test, and λ is the exponential coefficient of the failure distribution probability model.

[0054] In some optional implementations, the multi-objective optimization function includes:

[0055] Objective function:

[0056] Constraints: ΔT∈[T min ,T max ]

[0057] Among them, ITEmin represents the comprehensive effectiveness threshold, T min Indicates the minimum temperature threshold, T max Indicates the maximum temperature threshold, and ΔT indicates the preset temperature range requirement.

[0058] In some optional implementations, the algorithm for solving the multi-objective optimization function includes:

[0059] The NSGA-II algorithm is used to generate the Pareto front solution set, and the final output is the minimum number of cycles under satisfactory comprehensive test effectiveness.

[0060] In some optional implementations, the method for determining the number of thermal test cycles for the impact degree of satellite failure further includes:

[0061] Step 6: Perform a test based on the determined minimum number of cycles to obtain the latest fault severity coefficient E of the rail thermal fault i. i and failure frequency F i , and the fault severity coefficient E of fault j in the ground thermal cycle test j and failure frequency F j , and based on the latest fault severity factor E i , Fault frequency F i , Fault severity coefficient E j and failure frequency F j , updating the fault failure distribution probability model.

[0062] One of the embodiments of this specification can determine the number of satellite thermal test cycles. Compared with the existing test plan formulation method, the main advantages are: ② The method for determining the number of cycles can fully screen faults of different severity levels, reducing the risk of spacecraft on-orbit operation; ③ The number of cycles determined by the test method can take into account multiple factors such as reliability and test funds / time, control the test cost, make the test plan more practical, and make satellite purchasers or customers more satisfied.

[0063] The present invention is further described below by a specific embodiment:

[0064] The object of analysis is a navigation satellite, and thermal cycle test conditions are now being formulated for the satellite.

[0065] according to Figure 1 The specific steps of this method are as follows:

[0066] Step 1: Fault severity quantification model: Based on fault tree analysis (FTA) and failure mode and effect analysis (FMEA), the statistical analysis results and satellite fault severity level assessment results are sorted out according to the historical failure modes of the satellite. The fault j exposed by the satellite in the ground thermal cycle test and the thermally induced fault i exposed by the satellite in the early operation of the satellite are statistically analyzed. Based on the FMEA results, the fault severity coefficient E of the satellite on-orbit thermally induced fault i and the ground thermal cycle test fault j is determined. i and E j , and the fault frequency F i and F j , select the value according to the recommendations in Table 1. Taking a certain acceptance-level thermal cycle test as an example, when the temperature range is 61℃~-24℃ and the number of cycles n is 12, the value results are shown in Table 2.

[0067] Table 2

[0068] On-orbit fault Fault severity <![CDATA[Fault severity coefficient E i > <![CDATA[Failure frequency F i > On-orbit failure 1 Can be ignored 0 5 On-orbit failure 2 Criticality 0.8 1 On-orbit failure 3 Non-major 0.2 3 … … … … On-orbit failure 6 Non-major 0.2 2

[0069] Table 3

[0070] On-orbit failure Fault severity level <![CDATA[Fault severity coefficient E j > <![CDATA[Failure frequency F j > Ground Fault 1 Non-major 0 3 Ground Fault 2 Can be ignored 0.2 4 On-orbit failure 3 Criticality 0.8 2 … … … … Ground Fault 35 Can be ignored 0 1

[0071] Step 2: Modify the traditional test effectiveness evaluation model. Based on the physical definition of test effectiveness, the fault severity coefficient E when the number of cycles is 12 in Tables 2 and 3 is converted to i and E j , and the failure frequency F i and F j , substituting the effectiveness ITE formula in step 2, we can get the test effectiveness ITE corresponding to the acceptance level thermal cycling test when the number of cycles is 12:

[0072]

[0073] Similarly, the test effectiveness ITE corresponding to other cycle numbers can also be obtained through steps 1 and 2. The results are summarized in Table 4 below, which shows the corresponding values ​​of the test effectiveness ITE and the number of cycles for a certain acceptance level thermal cycling test:

[0074] Table 4

[0075] Number of cycles n ITE 1 36.2％ 2 59.3％ 3 74.1％ 4 83.5％ 5 89.5％ 6 93.3％ 7 95.7％ 8 97.3％ 9 98.3％ 10 98.9％ 11 99.3％ 12 99.5％ 13 99.7％ 14 99.8％ 15 99.9％ 16 99.9％ 17 100.0％ 18 100.0％

[0076] Step 3: Establish a relationship model between comprehensive test effectiveness and cycle number, and establish a physical model based on the failure distribution probability model as follows:

[0077] ITE(N)=1-e -λn

[0078] The number of thermal cycling test cycles and the corresponding comprehensive effectiveness values ​​in Table 4 obtained in step 2 are brought into the numerical model to obtain the numerical relationship curve between the number of thermal cycling test cycles and the corresponding comprehensive effectiveness values ​​as shown in the figure below: Figure 2 As shown, by fitting with the nonlinear least squares method, it can be determined that the initial value of λ is 0.45.

[0079] Where N is the number of cycles of the thermal cycling test, λ is the exponential coefficient of the failure distribution probability model, and the initial value of λ can be based on the historical failure data and cycle statistics in step 2 (including the severity coefficient E of the fault j and the on-orbit thermal failure i corresponding to different cycle numbers N in the satellite thermal cycling test). i and E j , and the fault frequency F i and F j ), determined by maximum likelihood estimation or nonlinear least squares fitting.

[0080] Step 4: Multi-objective optimization function construction: Construct a multi-objective optimization model. If the task requires a threshold ITE min is 0.95, then the objective function and constraints are as follows:

[0081] Objective function:

[0082] Constraints: ΔT∈[-24℃,61℃]

[0083] Step 5: Multi-objective optimization solution: Use a suitable multi-objective solution algorithm, such as the NSGA-II algorithm, to generate a Pareto frontier solution set. The final output is the minimum number of cycles of 7 when the comprehensive test effectiveness is 0.95.

[0084] Step 6: Conduct experiments according to the determined number of cycles, and then update the model based on the experimental data and on-orbit data, such as using a Bayesian dynamic update model.

[0085] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of the present invention. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for determining the number of thermal test cycles for the impact of satellite failures, characterized in that: include: Step 1: Determine the fault severity coefficient E of the on-orbit thermal fault i under different cycle times of the satellite. i and failure frequency F i , and the fault severity coefficient E of fault j in the ground thermal cycle test j and failure frequency F j ; Step 2: Severity coefficient E based on different cycle numbers i , Fault frequency F i , Fault severity coefficient E j and failure frequency F j , generate the revised comprehensive test effectiveness calculation formula; Step 3: Construct a failure distribution probability model that characterizes the relationship between the effectiveness of the comprehensive test and the number of cycles, wherein the exponential coefficient of the failure distribution probability model is based on the severity coefficient E under different cycle numbers. i , Fault frequency F i , Fault severity coefficient E j , Fault frequency F j and comprehensive trial effectiveness determination; Step 4: construct a multi-objective optimization function, wherein the multi-objective optimization function includes the fault failure distribution probability model and preset constraints; Step 5: Solve the multi-objective optimization function to determine the minimum number of cycles that meets the effectiveness of the comprehensive test.

2. The method according to claim 1, characterized in that The revised comprehensive test effectiveness calculation formula includes: Among them, ITE represents the integrated test effectiveness, m is the total number of fault types in the thermal cycling test, and n is the total number of early thermal-induced failures on orbit.

3. The method according to claim 2, characterized in that The fault failure distribution probability model includes: ITE(N)=1-e -λN Where N is the number of cycles of the thermal cycling test, and λ is the exponential coefficient of the failure distribution probability model.

4. The method according to claim 3, characterized in that The multi-objective optimization function includes: Objective function: Constraints: ΔT∈[T min ,T max ] Among them, ITE min represents the comprehensive effectiveness threshold, T min Indicates the minimum temperature threshold, T max Indicates the maximum temperature threshold, and ΔT indicates the preset temperature range requirement.

5. The method according to claim 1, wherein The solution algorithm of the multi-objective optimization function includes: The NSGA-II algorithm is used to generate the Pareto front solution set, and the final output is the minimum number of cycles under satisfactory comprehensive test effectiveness.

6. The method according to claim 1, characterized in that The method for determining the number of thermal test cycles for the impact degree of satellite failures also includes: Step 6: Perform a test based on the determined minimum number of cycles to obtain the latest fault severity coefficient E of the rail thermal fault i. i and failure frequency F i , and the fault severity coefficient E of fault j in the ground thermal cycle test j and failure frequency F j , and based on the latest fault severity factor E i , Fault frequency F i , Fault severity coefficient E j and failure frequency F j , updating the fault failure distribution probability model.

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