CN115688316A - Multi-level reliability evaluation method for aircraft engine based on unit data recombination - Google Patents
Multi-level reliability evaluation method for aircraft engine based on unit data recombination Download PDFInfo
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Abstract
The invention discloses a method for evaluating the multi-level reliability of an aircraft engine based on unit data reorganization, which comprises the following steps: clearing the hierarchical relation and the logical relation between the whole engine system and each unit of the subsystem, the component and the part according to the structure of the engine and the fault tree; randomly sampling and combining the units according to the hierarchical relationship by a Monte Carlo method to form an engine complete machine system, and obtaining equivalent life data samples or mixed life data samples of the complete machine system according to the logic relationship through the life data of the units; the method utilizes the equivalent life data samples or the mixed life data samples of the whole system to estimate the probability distribution parameters of the system life by the existing method, thereby realizing the direct evaluation from the unit life samples to the system reliability. The evaluation method is suitable for various service life distribution types, and can evaluate the reliability of the aircraft engine more accurately, more stably and more effectively by using multi-level data on the premise of sufficient conservation.
Description
Technical Field
The invention belongs to the technical field of reliability evaluation of an aircraft engine system, and relates to a multi-level reliability evaluation method of an aircraft engine based on unit data recombination.
Background
Aeroengines are expensive in manufacturing cost, and system-level reliability tests are too high in cost and difficult to implement, and generally adopt pyramid tests, namely, a large number of part tests, a small number of part and subsystem tests and a small number of complete machine tests. However, how to make full use of such multi-level data to make an accurate evaluation of system reliability is still a complicated and unsolved problem.
The traditional complex system reliability synthesis method mainly comprises a confidence interval estimation method and a Bayes method. The estimation method using more confidence intervals is an approximate confidence limit method, and comprises an L-M method, an MML method, an SR method, a CMSR method and the like. In the aspect of the distribution form of the service life, the traditional method is mostly based on binomial distribution and exponential distribution, and domestic and foreign researches show that the three-parameter Weibull distribution has strong fitting capability, accords with the actual situation, and is more and more widely applied to the evaluation of the service life and the reliability of products. In terms of a system reliability model, the existing method adopts a series system reliability product model for a series system such as an aircraft engine, namely, the system reliability is equal to the product of the reliability of each unit. This model has a significant drawback: the cell reliability error is amplified in the form of a product to the system reliability evaluation result. The test data of subsystems and component parts of the complex mechanical system with high reliability and long service life like an aircraft engine are very limited, the unit reliability calculation error is large under the condition of a small sample, and the unit errors are finally accumulated on the system through multi-stage synthesis, so that the evaluation result is greatly deviated from the reality. Therefore, a more accurate, more robust and more effective multi-level reliability assessment method for an aircraft engine is needed.
Disclosure of Invention
Aiming at the defects that the traditional complex system reliability comprehensive method is limited to binomial distribution and exponential distribution, and the estimation accuracy is poor when the unit error is amplified in a product mode and the sample size is small by adopting a series system reliability product model, the invention provides the aircraft engine multi-level reliability estimation method based on unit data recombination.
The invention discloses a unit data recombination-based multi-level reliability evaluation method for an aircraft engine, which comprises the following steps:
step 1: according to the structure and the fault tree of the engine, a system boundary for evaluating the reliability of the engine is defined, and the hierarchical relationship and the logical relationship between the whole system of the engine and each unit of the subsystem, the component and the part are clarified;
step 2: randomly sampling and combining sub-systems, part assemblies and parts for obtaining life data in a test according to a hierarchical relationship by a Monte Carlo method to form an engine complete machine system, obtaining the life data of the engine complete machine system through the life data of each unit in the system according to a logical relationship, and forming an equivalent life data sample or a mixed life data sample of the complete machine system;
and 3, step 3: and (3) estimating the system life probability distribution parameters by using the equivalent life data samples or the mixed life data samples of the whole system in the step (2) through the existing method according to the data distribution type, so as to realize the direct evaluation from the unit life samples to the system reliability.
The unit data recombination-based multi-level reliability evaluation method for the aircraft engine at least has the following beneficial effects:
1) The method avoids the gradual integration of the reliability of the traditional method and the propagation and amplification of unit errors in the conversion process, and can more accurately, more stably and more effectively evaluate the reliability of the aircraft engine by using multi-level data on the premise of sufficient conservation.
2) The invention has strong universality, is suitable for various types of service life data, and can obtain ideal results for binomial distribution, exponential distribution, lognormal distribution and two-parameter and three-parameter Weibull distribution.
Drawings
FIG. 1 is a flow chart of a method for multi-level reliability assessment of an aircraft engine based on cell data reorganization.
Detailed Description
As shown in FIG. 1, the method for evaluating the multi-level reliability of the aeroengine based on the unit data reorganization comprises the following steps:
step 1: according to the structure and the fault tree of the engine, a system boundary for evaluating the reliability of the engine is defined, and the hierarchical relationship and the logical relationship between the whole system of the engine and each unit of a subsystem, a component and a part are cleared;
1) According to the service load environment of the aircraft engine, the functions, materials and design criteria of the parts, the parts are analyzed from the aspect of failure physics, the parts which have obvious influence on the reliability of the whole system and need to be considered, and the parts and the corresponding parts and subsystems thereof are screened out, and the system boundary is defined;
2) Dividing units influencing the reliability of the whole system into three levels of subsystems, part assemblies and parts, wherein each subsystem comprises a plurality of part assemblies, and each part assembly consists of a plurality of parts;
3) And drawing a pyramid reliability block diagram, and analyzing and expressing the hierarchical relationship and the logical relationship between the whole system and the composition units.
And 2, step: by means of a Monte Carlo method, a whole engine system is formed by randomly sampling and combining sub-systems, part assemblies and parts for obtaining life data in a test according to a hierarchical relationship, the life data of the whole engine system is obtained through the life data of all units in the system according to a logical relationship, and a peer-to-peer life data sample or a mixed life data sample of the whole engine system is formed, and the method specifically comprises the following steps:
step 2.1: according to the hierarchical relation of the engine, starting from the lowest level, counting and reconstructing the service life data of the component level unit, transmitting the service life information to the corresponding component, and obtaining the equivalent service life data sample of the component or the mixed service life data sample of the component, wherein the specific process comprises the following steps:
1) Data arrangement: service life data of all parts forming the component assemblies are guaranteed, and the data are reliable; the sample quantities of all parts do not need to be different too much, and the sample quantities which are too small need to be supplemented; for the parts with the same components, estimating the service life distribution parameters according to the service life test data of the parts, and then randomly sampling by a Monte Carlo method; each group of values is extracted, so that a life sample of the same type of unit is obtained and is used for participating in sample reconstruction;
2) Randomly extracting one part sample from each part sample forming the part assembly by adopting a Monte Carlo method, and combining the part assembly samples;
3) Obtaining the service life data of the part assembly sample according to the logical relationship between the part and the part assembly, wherein most units in the aircraft engine are in series connection, and the series system service life random variable is equal to the minimum sequence statistic of all unit service life random variables forming the system, so that the service life of the part assembly sample is the minimum value of the service life of the part; for a parallel system, the system life is the maximum order statistic of the unit life, and the maximum value of the part life is taken as the part life at the moment;
4) According to 2) and 3), sampling n times in a replacement mode to obtain n part component samples, and obtaining a group of equivalent life data samples of the part components obtained by recombining the life data of the parts; obviously, the number n of component parts that can be combined depends on the part number with the smallest sample size;
5) If the real life data of the component exists, combining the equivalent life data sample of the component with the real life data of the component to obtain a mixed life data sample of the component; when the test conditions of the part assembly and the part are consistent, the two data are directly mixed; the test loads and test environments of the actual middle assembly and the actual parts are different from time to time, and in this case, if the environment factor is known, the equivalent life data sample of the environment factor correction assembly is passed; if the environmental factor is unknown, the equivalent life data samples of the components are corrected according to the 6 sigma principle within a certain range according to the dispersion of the system life data, namely if the maximum distance between the real life data and the equivalent life data samples of the components is 6 times of the standard deviation of the equivalent life data samples of the components, the equivalent life data samples of all the components are translated to ensure that the maximum distance is 6 sigma.
Step 2.2: and (4) integrating the life data step by step upwards according to the step 2.1 until an equivalent life data sample or a mixed life data sample of the whole system is obtained.
For example, for a series system of 10 success-or-failure type elements each with a reliability of 0.99, each of the elements was subjected to 30 tests, the number of failures was 0,0,0,1, 0. Assuming that one of 30 samples of each element is randomly extracted, 10 extracted elements can form a sample of a system, and if any one element in the system fails, the system fails. And (4) randomly extracting 30 system samples in a place-back manner, wherein the failure times are 2 times, and the corresponding system reliability can be calculated.
The method can also be conveniently used in the occasions where the service life of the element is subjected to exponential and log-normal distribution and two-parameter and three-parameter Weibull distribution. For example, a rotor system comprising a disk, a blade, and a pair of bearings, the disk, blade, and bearing life obeying a three parameter weibull distribution with parameters W (2.5, 1600, 1200), W (2.0, 2500, 1250), and W (1.5, 4000, 1400), the following element test data were obtained by simulation:
a wheel disc: 2916.81,3556.17,2469.61,1327.78,2000.86,2543.84,1780.31,2133.04,2256.64,2676.14;
a blade: 4173.82,3599.62,5334.78,5273.03,3964.35,2272.88,2807.52,4361.39,3388.10,2799.64,3705.09,2709.31,1553.68,3277.68,2318.39;
bearing: 3851.66,2950.24,1924.31,6167.94,3464.53,5042.58,5437.47,1500.82,4177.06,2271.23.
And (3) fitting by adopting a correlation coefficient method to obtain a bearing distribution parameter W (1.79, 3420.71, 699.77), and generating a group of other bearing service life data according to simulation: 2746.59,5875.28,1921.16,2138.93,4564.06,1959.69,3199.40,4355.54,7427.48,5937.16.
Randomly extracting data from the four elements respectively, wherein the minimum life is the system life, the minimum sample size of the four elements is 10, and a group of system 'equivalent life samples' is obtained by randomly extracting 10 times in a replacement mode: 1780.31,1500.82,2256.64,2133.04,2256.64,1921.16,2256.64,1780.31,2271.23,1780.31.
And 3, step 3: and (3) estimating system life probability distribution parameters by using the equivalent life data samples or the mixed life data samples of the whole machine system in the step (2) through the existing method according to the data distribution type, so that the direct evaluation from the unit life samples to the system reliability is realized, and the error propagation is avoided. The method specifically comprises the following steps:
1) Determining the service life data distribution of the whole machine according to experience, wherein common distribution types comprise binomial distribution, exponential distribution, normal distribution, lognormal distribution and two-parameter and three-parameter Weibull distribution, and different distribution types have corresponding distribution inspection methods for verifying whether the selected distribution type is proper or not;
2) According to the distribution type of the life data, the existing related method is adopted to carry out distribution parameter estimation by utilizing equivalent life data samples or mixed life data samples of the whole machine system, and then the reliability R of the whole machine system is estimated;
3) Repeating the steps for multiple times by means of the Bootstrap method idea to obtain the reliability R of a plurality of complete machine systems, carrying out interval estimation on the reliability of the complete machine systems according to the reliability R, and calculating the confidence lower limit R of the reliability under the confidence gamma by the following formula L ：
For the serial system composed of 10 success-failure type elements in the step 2, repeating the above process for multiple times, such as 10000 times, the mean value of the system reliability can be calculated to be 0.9344, and the standard deviation is calculated to be 0.0448, so that the lower confidence limit of the system reliability can be obtained, and the reliability of the system is 0.8607 at 95% confidence.
If the conventional L-M method is adopted, the system reliability point estimation value is 0.9344, and the reliability under 95% confidence is 0.8062.
The exact value of the system reliability is easily calculated to be 0.9044. Compared with the L-M method, the method provided by the invention has consistent point estimation values, and the reliability confidence lower limit under 95% confidence is more accurate. The success-failure system is subjected to a simulation test with a sample size of 30 for multiple times, the confidence lower limit of the reliability of the system at 95% confidence obtained by calculation by the method is between 0.63 and 0.90, and the confidence lower limit is superior to the estimation result of the L-M method, so that the method is proved to be sufficiently conservative and effective.
The lower confidence limit of reliability at 95% confidence for 1500 hours of operation was evaluated for the example of the subsystem in step 2. According to the system 'equivalent life sample', the distribution parameter is obtained as W (4.78, 1333.16 and 768.05) by a correlation coefficient method, and the reliability of 1500 hours of operation is calculated to be 0.9445. The 'system test' is repeatedly carried out for a plurality of times to obtain 10000 'equivalent life samples' of the system, and the average value of the reliability after 1500-hour running is 0.86, the standard deviation is 0.085, and the confidence lower limit of the reliability is 0.7197.
If the reliability of the wheel disc, the blade and the bearing at 1500 hours is respectively calculated to be 0.9019, 0.9652 and 0.9284 after the distribution parameters are fitted by adopting a traditional method, and a system reliability point estimation value can be obtained to be 0.7503 by adopting a series system model. The equivalent test times of the system are further calculated to be 10, the equivalent failure times are 2.4968, and the confidence lower limit of the reliability under the 95% confidence coefficient is 0.3444.
The system reliability can be easily calculated to be accurately solved to 0.9675 according to the real distribution of each element, so that the method provided by the invention has a greatly better evaluation result than the traditional method under the condition of a small sample. For larger samples, such as 30 samples, the method has a mean reliability value of 0.9174, a standard deviation of 0.038, a lower confidence limit of 0.8547, a traditional reliability point estimation value of 0.9103, and a lower confidence limit of 0.7340, and the method also has obvious advantages.
Claims (5)
1. The method for evaluating the multi-level reliability of the aircraft engine based on unit data recombination is characterized by comprising the following steps:
step 1: according to the structure and the fault tree of the engine, a system boundary for evaluating the reliability of the engine is defined, and the hierarchical relationship and the logical relationship between the whole system of the engine and each unit of the subsystem, the component and the part are clarified;
and 2, step: randomly sampling and combining sub-systems, part assemblies and parts for obtaining life data in a test according to a hierarchical relationship by a Monte Carlo method to form an engine complete machine system, obtaining the life data of the engine complete machine system through the life data of each unit in the system according to a logical relationship, and forming an equivalent life data sample or a mixed life data sample of the complete machine system;
and step 3: and (3) estimating the system life probability distribution parameters by using the equivalent life data samples or the mixed life data samples of the whole system in the step (2) through the existing method according to the data distribution type, so as to realize the direct evaluation from the unit life samples to the system reliability.
2. The method for evaluating the multi-level reliability of the aircraft engine based on the unit data reorganization as claimed in claim 1, wherein the step 1 of clarifying the hierarchical relationship and the logical relationship among the whole engine, the subsystems, the components and the parts comprises:
1) According to the service load environment of the aircraft engine, the functions, materials and design criteria of the parts, the parts are analyzed from the aspect of failure physics, the parts which have obvious influence on the reliability of the whole system and need to be considered and the corresponding part assemblies and subsystems thereof are screened out, and the system boundary is defined;
2) Dividing units influencing the reliability of the whole system into three levels of subsystems, part assemblies and parts, wherein each subsystem comprises a plurality of part assemblies, and each part assembly consists of a plurality of parts;
3) And drawing a pyramid reliability block diagram, and analyzing and expressing the hierarchical relationship and the logical relationship between the whole system and the composition units.
3. The method for evaluating the multi-level reliability of the aircraft engine based on the unit data reorganization as claimed in claim 1, wherein the forming of the equivalent system life test samples in the step 2 is specifically as follows:
step 2.1: according to the hierarchical relation of the engine, starting from the lowest level, counting and reconstructing the service life data of the component level units, transmitting the service life information to the corresponding component, and obtaining the equivalent service life data samples of the component or the mixed service life data samples of the component;
step 2.2: and (4) integrating the life data step by step upwards according to the step 2.1 until an equivalent life data sample or a mixed life data sample of the whole system is obtained.
4. The method for evaluating the multi-level reliability of the aeroengine based on the unit data reorganization as claimed in claim 3, wherein the step 2.1 is specifically as follows:
1) Data arrangement: service life data of all parts forming the component assemblies are guaranteed, and the data are reliable; the sample quantities of all parts do not need to be different too much, and the sample quantities which are too small need to be supplemented; for parts with the same components, estimating service life distribution parameters according to service life test data of the parts, and then randomly sampling by a Monte Carlo method; each group of values is extracted, so that a life sample of the same type of unit is obtained and is used for participating in sample reconstruction;
2) Randomly extracting one part sample from each part sample forming the part assembly by adopting a Monte Carlo method, and combining the part assembly samples;
3) Obtaining the service life data of the part assembly sample according to the logical relationship between the parts and the part assembly, wherein most units in the aircraft engine are in a series connection relationship, and the series connection system service life random variable is equal to the minimum order statistic of all unit service life random variables forming the system, so that the service life of the part assembly sample is the minimum value of the service life of the parts; for a parallel system, the system life is the maximum order statistic of the unit life, and the maximum value of the part life is taken as the part life at the moment;
4) According to 2) and 3), sampling n times in a replacement mode to obtain n part component samples, and obtaining a group of equivalent life data samples of the part components obtained by recombining the life data of the parts; obviously, the number n of the combined part assemblies depends on the part number with the minimum sample amount in the part assembly;
5) If the real life data of the component exists, combining the equivalent life data sample of the component with the real life data of the component to obtain a mixed life data sample of the component; when the test conditions of the part assemblies and the parts are consistent, the two data are directly mixed; the test loads and test environments of the actual middle assembly and the actual parts are different from time to time, and in this case, if the environment factor is known, the equivalent life data sample of the environment factor correction assembly is passed; if the environmental factor is unknown, the equivalent life data samples of the components are corrected according to the 6 sigma principle within a certain range according to the dispersion of the system life data, namely if the maximum distance between the real life data and the equivalent life data samples of the components is 6 times of the standard deviation of the equivalent life data samples of the components, the equivalent life data samples of all the components are translated to ensure that the maximum distance is 6 sigma.
5. The method for evaluating the multi-level reliability of the aircraft engine based on the unit data reorganization as claimed in claim 1, wherein the step 3 is specifically as follows:
1) Determining the service life data distribution of the whole machine according to experience, wherein different distribution types have corresponding distribution inspection methods for verifying whether the selected distribution type is proper;
2) According to the distribution type of the life data, the existing method is adopted to carry out distribution parameter estimation by utilizing equivalent life data samples or mixed life data samples of the whole machine system, and then the reliability R of the whole machine system is estimated;
3) Repeating the steps for multiple times by means of the Bootstrap method idea to obtain the reliability R of a plurality of complete machine systems, carrying out interval estimation on the reliability of the complete machine systems according to the reliability R, and calculating the confidence lower limit R of the reliability under the confidence gamma by the following formula L ：
In the formula
s R Respectively the mean value, standard deviation and mu of the reliability R of the whole system 1-γ Is the 1-gamma quantile of a standard normal distribution.
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| CN116307859B (en) * | 2023-02-27 | 2023-12-29 | 中国人民解放军92942部队 | Reliability distribution method comprehensively utilizing common probability method and CMSR method |
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