Comprehensive failure type single machine fault probability calculation method

Abstract

According to the satellite comprehensive failure type single machine fault probability calculation method, a single machine is divided into a random failure part and a loss failure part, and the random failure part needs to calculate a recoverable fault probability and a resident fault probability; and the loss failure part needs to calculate the permanent fault probability. The recoverable fault probability calculation step is as follows: for each single machine in each sample, the recoverable fault probability of the single machine is calculated respectively; when more than one single machine in the same sample has a recoverable fault, correcting the recoverable fault probability of the single machine by using the common cause factor; and when the single machine is reconstructed, calculating the self-recoverable fault probability of the reconstructed single machine so as to update the self-recoverable fault probability of the single machine. And the step of calculating the permanent fault probability is that different permanent fault probability calculation methods are selected according to the total number of times of permanent faults occurring in the sample, the reconstruction condition is considered, and the permanent fault probability is corrected by using a correction coefficient.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite single-machine risk quantitative assessment, and relates to a method for calculating the failure probability of a single-machine with comprehensive failure. Background Art

[0002] Accurately assessing the reliability of individual components is fundamental to assessing the reliability of the entire satellite. Satellites consist of numerous components with diverse failure mechanisms and relatively limited on-orbit data. Currently, only reliability assessment methods exist that analyze components with random failures or wear-out failures. Due to the complex composition and functionality of components with integrated failures, no comprehensive assessment method exists to calculate the overall failure probability of each component. Summary of the Invention

[0003] The technical problem solved by the present invention is to overcome the deficiencies of the prior art, propose a method for calculating the probability of single-machine failure of random failure, and improve the accuracy of satellite single-machine failure probability estimation.

[0004] The solution of the present invention is: a method for calculating a single machine failure of a comprehensive failure type, the method comprising the following steps:

[0005] S1. Extract all satellite failure units and group them into the same sample. Units from the same batch, model, and supplier are grouped into the same sample. Based on this, the existing sample is further divided. As of the current calculation time, units that have worked within the existing sample are grouped into the same sample, and units that have not worked are grouped into another sample.

[0006] S2. Divide each single machine into a random failure part and a wear-out failure part. Divide the random failure part within the same sample into the same sample, and divide the wear-out failure part within the same sample into another sample.

[0007] S3. For each failure part in the sample, calculate the recoverable failure probability of the random failure part as the recoverable failure probability of the single machine with comprehensive failure;

[0008] S4. Obtain telemetry parameters reflecting the degradation trend of the loss-of-function portion of the single machine in the sample. The telemetry parameters reflecting the degradation trend of the loss-of-function portion of the single machine in the sample have the same variation pattern. If the telemetry parameters monotonically increase or decrease over time, execute steps S5 and S6; otherwise, proceed to step S7.

[0009] S5. Calculate the reliability distribution of each random failure part and wear-out failure part of each single machine in the sample, and multiply the reliability distributions of the random failure part and the wear-out failure part of the single machine to obtain the reliability distribution of the comprehensive failure type single machine;

[0010] S6. Substitute the running time t0 of each single machine as of the current calculation time into the reliability distribution R(t) of the single machine with comprehensive failure, and calculate the reliability R(t0) of the single machine with comprehensive failure as of the current calculation time. Based on the reliability R(t0) of the single machine with comprehensive failure, calculate the resident failure probability of the single machine with comprehensive failure as of the current calculation time.

[0011] S7. Calculate the resident failure probability for each random failure part and wear-out failure part of each single machine in the sample, and sum the resident failure probabilities of the random failure part and the wear-out failure part of the single machine to obtain the resident failure probability of the comprehensive failure type single machine.

[0012] Preferably, the reliability distribution calculation step of each random failure part of a single machine is as follows:

[0013] According to the total number of resident faults that occurred in the sample where the random failure part of the single machine is located as of the current calculation time, different resident fault probability calculation methods are selected to calculate the reliability distribution R of the random failure part of the single machine in the sample 随机 (t);

[0014] Preferably, when the total number of resident faults of the random failure part of all single machines in the sample at the current calculation time is 0, the reliability distribution R of the random failure part of the single machine is calculated using the small sample analysis model without failure 随机 (t);

[0015] When the total number of resident faults of the random failure part of all single machines in the sample as of the current calculation time t0 is 1-2, the reliability distribution R of the random failure part of the single machine is calculated using the timed truncation model. 随机 (t);

[0016] When the cumulative number of resident failures of all single machines in the sample at the current calculation time is greater than or equal to 3, the reliability distribution R of the random failure part of the single machine is calculated using the non-parametric estimation model. 随机 (t).

[0017] Preferably, in step S5, the reliability distribution R of the failure part of a single machine is calculated using the log-normal model. 耗损 (t).

[0018] Preferably, in step S7, a stress intensity model is used to calculate the resident failure probability value of the failure part of a single machine. The resident failure probability formula of a single machine with a comprehensive failure type is as follows:

[0019] The probability of resident failure of a single machine with comprehensive failure type =

[0020] (1-R 随机 (t)) + (failure probability value calculated by stress intensity model).

[0021] Preferably, in step S5, the formula for the resident failure probability of a single machine of the single machine comprehensive failure type is as follows:

[0022] The probability of resident failure of a single machine with comprehensive failure = 1-R 随机 (t)*R 耗损 (t).

[0023] Preferably, the corrected resident failure probability of a single machine is: λ1*resident failure probability of a single machine of comprehensive failure type, wherein λ1 is a correction coefficient.

[0024] Preferably, when no reconstruction occurs on a single machine, the correction coefficient λ1 = 1; when reconstruction can effectively solve or improve the working condition of the on-orbit single machine, the correction coefficient λ1 = 0.5 to 1; when the reconstruction plan worsens the working condition of the specified single machine or increases the load, the correction coefficient λ1 = 1 to 1.5.

[0025] Preferably, the steps for calculating the recoverable partial failure of each single machine in the same random sample are as follows:

[0026] S9.1. For each random failure component of each single machine in each sample, calculate the probability of recoverable failure of the random failure component of the single machine;

[0027] S9.2. When more than one random failure unit in the same sample has experienced a recoverable failure as of the current calculation time, the probability of recoverable failure of the random failure unit is modified using the common cause factor;

[0028] S9.3. When the random failure portion of a single machine has been reconstructed up to the current calculation time, the probability of recovering the random failure portion of the single machine after the reconstruction is calculated to update the probability of recovering the random failure portion of the single machine.

[0029] Preferably, the probability of partially recoverable failure of a single machine with random failure is as follows:

[0030]

[0031] Where MTBF is the mean time between failures, and MTTR is the mean time to repair.

[0032] Preferably, the method for correcting the probability of partially recoverable failures of random failures of a single machine using common cause factors is as follows:

[0033] Corrected probability of recoverable partial failure of a single machine random failure =

[0034] Probability of self-recoverable failure of random failure of a single machine before correction + β

[0035] *The sum of the recoverable failure probabilities of other random failure parts in the sample to which the random failure part belongs

[0036] Among them, β is the common cause factor.

[0037] Preferably, considering the reconstruction measures of a single machine, the probability of recoverable failure of a random failure of a single machine is updated as:

[0038]

[0039] Where, MTBF 重构后 MTTR is the mean time between failures of the random failure parts after reconstruction. 重构后 is the average repair time of the randomly failed parts after reconstruction.

[0040] The beneficial effects of the present invention compared with the prior art are:

[0041] (1) The present invention considers different components of a single machine of comprehensive failure type respectively, adopts different calculation methods to obtain the failure probability of each component, and integrates the calculation results through a certain method to finally obtain the failure probability of the single machine.

[0042] (2) When using telemetry parameters to calculate the probability of permanent failure, calculations can be performed separately according to the characteristics of different telemetry data. In particular, when there are too few telemetry parameters to form a sample and there is no obvious degradation trend over time, the stress intensity model can be selected to calculate the probability of permanent failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is the calculation process of the single-machine failure probability of comprehensive failure.

[0044] Figure 2 Specific process for calculating the probability of resident failure. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the embodiments.

[0046] The definition of a comprehensive failure type single machine refers to some single machines that have the characteristics of random failure and wear-out failure at the same time.

[0047] like Figure 1 As shown, the present invention provides a method for calculating a single machine failure of a comprehensive failure type, the method comprising the following steps:

[0048] S1. Extract all satellite failure units and group them into the same sample. Units from the same batch, model, and supplier are grouped into the same sample. Based on this, the existing sample is further divided. As of the current calculation time, units that have worked within the existing sample are grouped into the same sample, and units that have not worked are grouped into another sample.

[0049] S2. Divide each single machine into a random failure part and a wear-out failure part. Divide the random failure part within the same sample into the same sample, and divide the wear-out failure part within the same sample into another sample.

[0050] S3. For each failure part in the sample, calculate the recoverable failure probability of the random failure part as the recoverable failure probability of the single machine with comprehensive failure;

[0051] S4. Obtain telemetry parameters reflecting the degradation trend of the loss-of-function portion of the single machine in the sample. The telemetry parameters reflecting the degradation trend of the loss-of-function portion of the single machine in the sample have the same variation pattern. If the telemetry parameters monotonically increase or decrease over time, execute steps S5 and S6; otherwise, proceed to step S7.

[0052] S5. Calculate the reliability distribution R for each random failure part and wear failure part of each single machine in the sample. 耗损 (t), multiply the reliability distribution of the random failure part of a single machine and the failure part of wear and tear to obtain the reliability distribution of the single machine with comprehensive failure;

[0053] S6. Substitute the running time t0 of each single machine as of the current calculation time into the reliability distribution R(t) of the single machine with comprehensive failure, and calculate the reliability R(t0) of the single machine with comprehensive failure as of the current calculation time. Based on the reliability R(t0) of the single machine with comprehensive failure, calculate the resident failure probability of the single machine with comprehensive failure as of the current calculation time.

[0054] S7. Calculate the resident failure probability for each random failure part and wear-out failure part of each single machine in the sample, and sum the resident failure probabilities of the random failure part and the wear-out failure part of the single machine to obtain the resident failure probability of the comprehensive failure type single machine.

[0055] (1) Random failure part

[0056] 1) Calculation of recoverable failure probability of random failure part

[0057] The probability of recoverable failure is mainly calculated based on the recoverable failures in quality problems and their corresponding interruption time.

[0058] For each random failure part of each single machine in each sample, calculate the probability of recoverable failure of the random failure part of the single machine;

[0059] When more than one random failure unit in the same sample has experienced recoverable failures up to the current calculation time, the probability of recoverable failures of the random failure unit is corrected using the common cause factor.

[0060] When the random failure part of a single machine has been reconstructed up to the current calculation time, the probability of recovering the random failure part of the single machine after the reconstruction is calculated to update the probability of recovering the random failure part of the single machine.

[0061] The specific calculation method is as follows:

[0062] A. Based on the recoverable fault data of random failure components of a single unit with comprehensive failures, calculate the availability of each random failure component during on-orbit operation and use it as the probability of the random failure component itself being recoverable. Therefore, in the factory state, the calculation formula for the probability of the random failure component itself being recoverable is:

[0063]

[0064] Where MTBF is the mean time between failures, and MTTR is the mean time to repair.

[0065] B. Correction of recoverable fault probability considering software reconstruction

[0066] If the random failure part has a recoverable failure again after reconstruction, the recoverable failure probability of itself needs to be corrected. The recoverable failure probability of the random failure part of a single machine is updated to:

[0067]

[0068] Where, MTBF 重构后 MTTR is the mean time between failures after partial reconstruction due to random failures. 重构后 is the average repair time after partial reconstruction of random failures.

[0069] If the random failure part has no quality problems after reconstruction, the probability of recoverable failure is corrected to 0 (MTTR 重构后 is 0).

[0070] C. Probability of partially recoverable failures considering common causes of random failures

[0071] In the same sample, random failure parts with the same structure and working environment often have common cause failures. Therefore, for the recoverable failure probability of the random failure part, the common cause failure part must also be comprehensively considered and the common cause factor should be used to make corrections based on the divided sample results.

[0072] The method to correct the probability of self-recoverable failure of random failure part by using common cause factor is:

[0073] Corrected probability of recoverable random failure part =

[0074] Probability of self-recoverable failure of random failure part before correction + β

[0075] *The sum of the recoverable failure probabilities of other random failure parts in the sample to which the random failure part belongs

[0076] Where β is the common cause factor. In practical applications, expert experience generally gives the value of the common cause factor β between 0.001 and 0.10.

[0077] Preferably, the single machine type and sample situation to which the random failure part belongs can be further considered, and the value of β is as follows.

[0078]

[0079] Similarly, by comprehensively considering the common cause failure part, the recoverable failure probability of the random failure part itself can be substituted into the recoverable failure probability of the random failure part itself before correction to calculate the recoverable failure probability after considering the common cause.

[0080] 2) Calculation of the resident failure probability of the random failure part

[0081] The resident failure probability for random failures is calculated primarily based on the resident failures identified in quality issues, combined with on-orbit baselines and supplier data. Due to the varying number of actual resident failures within each sample, the analysis method employed varies. The on-orbit baseline refers to the satellite's current on-orbit operational status at the time of calculation.

[0082] like Figure 2 As shown, the calculation steps of the resident failure probability of each random failure part of each single machine are:

[0083] According to the total number of resident faults that occurred in the sample where the random failure part of the single machine is located as of the current calculation time, different resident fault probability calculation methods are selected to calculate the reliability distribution R of the random failure part of the single machine in the sample 随机 (t);

[0084] When the total number of resident faults of the random failure part of all single machines in the sample as of the current calculation time is 0, the zero-failure small sample analysis model is used to calculate the reliability distribution R of the random failure part of the single machine 随机 (t);

[0085] When the total number of resident faults of the random failure part of all single machines in the sample as of the current calculation time is 1-2, the reliability distribution R of the random failure part of the single machine is calculated using the timed truncation model. 随机 (t);

[0086] When the cumulative number of resident failures of all single machines in the sample at the current calculation time is greater than or equal to 3, the reliability distribution R of the random failure part of the single machine is calculated using the non-parametric estimation model. 随机 (t).

[0087] A. Calculation of Resident Failure Probability by Zero-Failure Data Analysis Model

[0088] If the random failure part of all single machines in the sample has not experienced any permanent failure so far, it can be regarded as a small sample with no failure. The structure of the no-failure data is shown in the following table.

[0089] Table 1. Data structure of small sample with no failure

[0090] Group number i Truncation time T The number of samples in group i, n Number of unfailed 1 <![CDATA[T1]]> <![CDATA[n1]]> <![CDATA[S1]]> 2 <![CDATA[T2]]> <![CDATA[n2]]> <![CDATA[S2]]> 3 … … … 4 T n S

[0091] According to the definition of the multi-layer Bayes method and Bayes theorem, under square loss, the multi-layer failure probability p of the i-th group of samples is ihB The calculation of is as follows:

[0092]

[0093] Where s i is the number of samples in group i that have not failed, c is the recommended value of hyperparameter engineering 5, and the failure probability p of the samples in group i can be obtained according to the above formula. ihB Estimated value.

[0094] According to the weighted least squares estimation method, the Weibull double parameter estimates β and η are obtained as follows:

[0095]

[0096] η=expμ

[0097] Where:

[0098]

[0099] Where:

[0100]

[0101] Where ω i is the weight value, T i is the censoring time of group i, k is the number of groups, n i is the number of samples in group i.

[0102] x i and y i The formula is as follows:

[0103]

[0104] Where p ihB is the failure probability of the i-th group of samples, T i is the censoring time of group i.

[0105] Substituting the obtained Weibull parameter point estimate into the reliability formula, we can obtain the reliability R of the original sample that obeys the Weibull sampling function. 随机- No failure (t), as shown below:

[0106]

[0107] Substituting the running time t0 of the single machine up to the current calculation time, we can get the resident failure probability of the single machine under this sample as 1-R 随机- No failure (t0).

[0108] B. Calculation of Resident Failure Probability with Timed Truncation Model

[0109] For the random failure samples with less than three but more than zero resident faults, the timed truncation test method can be used to calculate the reliability distribution parameters and further obtain the resident fault probability.

[0110] According to the reliability index model R = e -λt , parameter θ is the mean failure time, which is the inverse of the failure rate λ, and the formula is as follows:

[0111]

[0112] Where, T r is the sum of the running time of all the machines in the sample, and r is the sum of the number of resident faults that occur in the sample.

[0113] Combined with the reliability function, it is as follows:

[0114]

[0115] Substituting the running time t0 of a single machine up to the current calculation time into the above formula, we can obtain the probability of resident failure of the random failure part of the sample without resident failure as 1-R 随机- Timed truncation (t0).

[0116] C. Nonparametric estimation model for calculating resident failure probability

[0117] For the random failure samples that have more than three resident faults, the non-parametric estimation method can be used to calculate the reliability distribution parameters and further obtain the resident fault probability.

[0118] The total sample size is N, the number of failed (abnormal) samples is n, and the number of truncated test samples is Nn. Using the failure / abnormal information of the sample, calculate the reliability of 1, 2, ..., n failed (abnormal) units.

[0119] The reliability of one failed unit is:

[0120] The reliability of failed units 2 is:

[0121] …

[0122] The reliability of n failed units is:

[0123] The nonparametric estimated probability of random failure partial reliability is expressed in terms of operating time (days). The Weibull distribution is fitted to the curve to calculate the shape and life parameters of the Weibull distribution. The results of the nonparametric analysis of the sample are logarithmized twice and fitted with the minimum variance method.

[0124] Through fitting, the slope of the straight line is a and the intercept is b, so the two parameter values ​​of the Weibull distribution can be obtained as follows:

[0125] β′=a

[0126]

[0127] The reliability calculation formula is as follows:

[0128]

[0129] Substituting the running time t0 of a single machine up to the current calculation time into the above formula, we can obtain the probability of resident failure of the random failure part of the sample without resident failure as 1-R 随机- Nonparametric estimation (t0).

[0130] (2) Consumption failure part

[0131] The resident failure probability of the consumption failure part is mainly calculated based on the telemetry data of a single machine and the corresponding threshold.

[0132] 1) Lognormal model

[0133] When the telemetry parameters increase or decrease monotonically over time, the lognormal model can be used for calculation.

[0134] Using the log-normal model, the reliability distribution R of the failure part of a single machine is calculated. 耗损 (t), the specific steps are as follows:

[0135] Select a telemetry parameter that reflects the degradation trend of the failure part of the single machine in the sample as the candidate telemetry parameter, and use the lognormal model to calculate the measured value y of the candidate telemetry parameter. i and its corresponding stand-alone running time t i Perform fitting, i = 1 ~ n, the candidate telemetry parameter fitting formula is as follows:

[0136]

[0137] Among them, b0 and b1 are fitting parameters, is the error of the random variable of standard normal distribution; n is the number of telemetry parameters in the sample;

[0138] Substitute the candidate telemetry parameter boundary value of each single machine into the candidate telemetry parameter fitting formula and calculate the single machine running time corresponding to the boundary value. The single machine running time corresponding to the boundary value is defined as the life of the wear-out failure part of the corresponding single machine. i , i=1~n;

[0139] Age based on the lifespan of all the units in the sample that have failed due to wear and tear i , the mean μ′ and standard deviation σ′ of the failure life of the single machine in the sample are calculated as follows:

[0140]

[0141] This results in a reliability model:

[0142]

[0143] The reliability model of the mean μ′ and standard deviation σ′ of the life of the failure part of the sample unit is:

[0144]

[0145] 2) Stress intensity model

[0146] For telemetry parameters that have no obvious degradation trend over time (no monotonically increasing or decreasing trend), the stress intensity model can be used to calculate the probability of permanent failure.

[0147] Select a telemetry parameter that reflects the degradation trend of the failure part of the single machine in the sample as a candidate telemetry parameter;

[0148] Obtain a sampling sequence of candidate telemetry parameters for a single machine;

[0149] Calculate the mean of the candidate telemetry parameter sampling sequence and standard deviation S x :

[0150]

[0151] Among them, M is the length of the candidate telemetry parameter sampling sequence, x m is the mth sampling value in the candidate telemetry parameter sampling sequence.

[0152] If the candidate telemetry parameter has a design lower limit, the first coefficient K1 is calculated based on the design lower limit L:

[0153]

[0154] According to the first coefficient K1, the given confidence level γ, and the candidate telemetry parameter sampling sequence length M, the normal distribution bilateral tolerance coefficient table QJ1384-88 is consulted to obtain the shortest interval [a1, b1] containing K1 and the p corresponding to a1 and b1. (11) ,p (12) ; Then check the normal distribution quantile table GB4086 and find the value corresponding to p (11) The corresponding normal distribution quantile With p (12) The corresponding normal distribution quantile

[0155] Using linear interpolation, we can get Look up the normal distribution table to get the p1 formula:

[0156]

[0157] The NormalDist function is used to calculate the cumulative distribution value of the normal distribution;

[0158] If the candidate telemetry parameter has a design upper limit, the second coefficient K2 is calculated based on the design upper limit U:

[0159]

[0160] According to the second coefficient K2 and the given confidence level γ, the candidate telemetry parameter sampling sequence length M, the normal distribution bilateral tolerance coefficient table QJ1384-88 is consulted to obtain the shortest interval [a2, b2] containing K2 and the p corresponding to a2 and b2. (21) ,p (22) ; Then check the normal distribution quantile table GB4086 and find the value corresponding to p (21) The corresponding normal distribution quantile With p (22) The corresponding normal distribution quantile

[0161] Using linear interpolation, we can get Look up the normal distribution table to get the p2 formula:

[0162]

[0163] The NormalDist function is used to calculate the cumulative distribution value of the normal distribution;

[0164] If the candidate telemetry parameter has both a lower design limit and an upper design limit, then:

[0165] Reliability of the failed part: R 耗损 =1-(p1+p2).

[0166] If the candidate telemetry parameter only has a design lower limit, then:

[0167] Reliability of the failed part: R 耗损 =1-p1.

[0168] If the candidate telemetry parameters only have design upper limits, then:

[0169] Reliability of the failed part: R 耗损 =1-p2.

[0170] The probability of permanent failure of the wear-out failure part is: 1-R 耗损 .

[0171] (3) Comprehensive calculation of the resident failure probability of a single machine

[0172] The recoverable failure probability of a single machine with comprehensive failure is obtained from the recoverable failure probability of the random part.

[0173] When the telemetry parameters do not have a monotonically increasing or decreasing relationship over time, the stress intensity model is used for the wear and tear failure part. The formula for the resident failure probability of a single machine in the single machine comprehensive failure category is as follows:

[0174] Resident failure probability of a single machine with comprehensive failure

[0175] =(1-R 随机 (t)) + (failure probability value calculated by stress intensity model)

[0176] When the telemetry parameters increase or decrease monotonically over time, the loss failure part adopts the lognormal model. The formula for the resident failure probability of a single machine in the single machine comprehensive failure category is as follows:

[0177] The probability of resident failure of a single machine with comprehensive failure = 1-R 随机 (t)*R 耗损 (t).

[0178] Considering the impact of single machine reconstruction, the correction coefficient λ1 is added to correct the resident failure probability of the comprehensive failure type single machine. The corrected resident failure probability of the single machine is: λ1*resident failure probability of the comprehensive failure type single machine.

[0179] Where λ1 is the correction factor. When no reconstruction is performed on a single unit, the correction factor is 0 and λ1 = 1. When reconstruction can effectively resolve or improve the operating status of the single unit on orbit, the correction factor is 0.5 to 1. When the reconstruction solution worsens the operating condition of the specified single unit or increases the load, the correction factor is 1 to 1.5.

[0180] Example of Stress Intensity Model Calculation

[0181] Calculate the sample mean of the telemetry parameter and standard deviation S x get:

[0182]

[0183] Select γ = 0.95, when n = 70, the range of K in the normal distribution bilateral tolerance coefficient table QJ1384-88 is 1.36499-5.09007, so when the K1 value exceeds this range, the reliability can be approximated to 0.99998.

[0184] Design upper and lower limits U and L according to telemetry thresholds. Calculate the first coefficient K1 and the second coefficient K2:

[0185]

[0186] Get the shortest interval [a1, b1] containing K1 and the p corresponding to a1 and b1 (11) ,p (12) ; Then check the normal distribution quantile table GB4086 and find the value corresponding to p (11) The corresponding normal distribution quantile With p (12) The corresponding normal distribution quantile

[0187] Using linear interpolation, we can get Look up the normal distribution table to get the p1 formula:

[0188]

[0189] Get the reliability confidence lower limit: R 耗损 =1-p1=0.998197268.

[0190] Get the probability of resident failure: 1-R 耗损 =0.001803.

[0191] Assuming that only the failure part of this single machine exists, considering that reconstruction can improve the working state of the single machine, the correction coefficient λ1=0.8 is selected, and then the corrected resident failure probability is obtained: λ1(1-R 耗损 )=0.001442.

[0192] Although the present invention has been disclosed above in terms of preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solutions of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the scope of protection of the technical solutions of the present invention.

Claims

1. A method for calculating single machine failure of comprehensive failure type, characterized by The steps include: S1. Extract all satellites’ comprehensive failure units, and classify units of the same batch, model, and supplier into the same sample. On this basis, divide the existing samples again. As of the current calculation time, the units that have worked in the existing samples are classified into the same sample, and the units that have not worked are classified into another sample. S2, divide each single machine into a random failure part and a wear and tear failure part, and divide the random failure part in the same sample into the same sample again, and divide the wear and tear failure part in the same sample into another sample; S3. For each failure part in each sample, calculate the recoverable failure probability of the random failure part as the recoverable failure probability of the single machine with comprehensive failure; S4, obtaining telemetry parameters reflecting the degradation trend of the loss-of-function part of the single machine in the sample, wherein the telemetry parameters reflecting the degradation trend of the loss-of-function part of the single machine in the sample have the same change rule, and when the telemetry parameters monotonically increase or decrease over time, execute steps S5 and S6, otherwise, proceed to step S7; S5. Calculate the reliability distribution of each random failure part and wear failure part of each single machine in the sample, and multiply the reliability distribution of the random failure part and the wear failure part of the single machine to obtain the reliability distribution of the comprehensive failure type single machine; S6. Substitute the running time t0 of each single machine up to the current calculation time into the reliability distribution R(t) of the single machine of the comprehensive failure class, calculate the reliability R(t0) of the single machine of the comprehensive failure class up to the current calculation time, and calculate the resident failure probability of the single machine of the comprehensive failure class up to the current calculation time according to the reliability R(t0) of the single machine of the comprehensive failure class; S7. Calculate the resident failure probability for each random failure part and wear failure part of each single machine in the sample, and sum the resident failure probabilities of the random failure part and the wear failure part of the single machine to obtain the resident failure probability of the comprehensive failure type single machine.

2. The method for calculating a single machine failure of a comprehensive failure type according to claim 1, characterized in that: The reliability distribution calculation steps of each single machine random failure part are as follows: According to the total number of resident faults that have occurred in the sample where the random failure part of the single machine is located up to the current calculation time, different resident fault probability calculation methods are selected to calculate the reliability distribution R of the random failure part of the single machine in the sample 随机 (t).

3. The method for calculating a single machine failure of a comprehensive failure type according to claim 2, characterized in that: When the total number of resident failures of the random failure part of all single machines in the sample up to the current calculation time is 0, the reliability distribution R of the random failure part of the single machine is calculated using the small sample analysis model without failure 随机 (t); When the total number of resident failures of the random failure part of all single machines in the sample up to the current calculation time is 1-2, the reliability distribution R of the random failure part of the single machine is calculated using the timed truncation model. 随机 (t); When the cumulative number of permanent failures of all single machines in the sample as of the current calculation time is greater than or equal to 3, the reliability distribution R of the random failure part of the single machine is calculated using the non-parametric estimation model. 随机 (t).

4. The method for calculating a single machine failure of a comprehensive failure type according to claim 1, characterized in that: In step S5, the reliability distribution R of the failure part of a single machine is calculated using the log-normal model. 耗损 (t).

5. The method for calculating a single machine failure of a comprehensive failure type according to claim 1, characterized in that: In step S7, the stress intensity model is used to calculate the resident failure probability value of the failure part of the single machine. The resident failure probability formula of the single machine with comprehensive failure is as follows: The probability of resident failure of a single machine with comprehensive failure type = (1-R 随机 (t))+(failure probability value calculated by stress intensity model).

6. The method for calculating a single machine failure of a comprehensive failure type according to claim 1, characterized in that: In step S5, the formula for the resident fault probability of a single machine of the single machine comprehensive failure type is as follows: The probability of permanent failure of a single machine with comprehensive failure = 1-R 随机 (t)*R 耗损 (t).

7. The method for calculating the probability of single-machine failure of satellite comprehensive failure according to claim 1 is characterized by: The corrected resident failure probability of a single machine is: λ1*resident failure probability of a single machine of comprehensive failure type, wherein λ1 is a correction coefficient.

8. The method for calculating the probability of single-machine failure of satellite comprehensive failure according to claim 7 is characterized by: When the single machine has not been reconstructed, the correction coefficient λ1=1; when the reconstruction can effectively solve or improve the working condition of the single machine on orbit, the correction coefficient λ1=0.5~1; when the reconstruction plan worsens the working condition of the specified single machine or increases the load, the correction coefficient λ1=1~1.

5.

9. The method for calculating a single machine failure of a comprehensive failure type according to claim 1, characterized in that: The steps for calculating the partially recoverable faults of random failures of each single machine in the same random sample are as follows: S9.

1. For each random failure part of each single machine in each sample, calculate the probability of recoverable failure of the random failure part of the single machine; S9.

2. When more than one single random failure part in the same sample has experienced recoverable failures up to the current calculation time, the probability of recoverable failures of the single random failure part is corrected using the common cause factor; S9.

3. When the random failure part of a single machine has been reconstructed up to the current calculation time, the probability of recoverable failure of the random failure part of the single machine after the reconstruction is calculated to update the probability of recoverable failure of the random failure part of the single machine.

Images