Method for analyzing reliability of spaceflight solid rocket engine system

Abstract

The invention discloses an aerospace solid rocket engine system reliability analysis method, and relates to the technical field of solid engines. According to the method, the system reliability of the solid engine is calculated by adopting a Kriging-assisted Monte Carlo method. If the Kriging model does not meet the convergence criterion, predicting the response value of the candidate sample set by using the Kriging model, calculating the probability that each sample in the positive sample prediction set and the negative sample prediction set is wrongly predicted, and respectively defining the probability as a first probability and a second probability; and determining an optimal candidate sample and a corresponding response value according to the obtained probability, and then carrying out sequence updating on the Kriging model. And after the Kriging model meets a convergence criterion, inputting to-be-analyzed parameters related to the performance of the solid engine into the converged Kriging model to obtain time and thrust values predicted by the model. And finally, obtaining the system reliability of the solid engine by adopting a Monte Carlo method.

Description

Technical Field

[0001] The present application relates to the technical field of aerospace solid rocket engines, and in particular to a reliability analysis method for aerospace solid rocket engine systems. Background Art

[0002] A solid engine is a chemical rocket propulsion device that uses solid propellant. It has many advantages such as simple structure, easy use, ability to maintain combat readiness for a long time and high mass ratio. It is widely used in military fields such as missile weapons, launch vehicles and spacecraft.

[0003] Working time and working thrust are important performance indicators of aerospace solid rocket engines, and their reliability seriously affects the working reliability of aerospace solid rocket engines. However, due to the existence of multi-source uncertainties in the design, manufacturing and service links, the working time and working thrust performance parameters in actual operation deviate greatly from the design values, which ultimately leads to a sharp increase in the probability of performance failure of aerospace solid rocket engines.

[0004] Once it fails, it is very easy to cause accidents during ground tests and flights. Therefore, considering the impact of uncertainty factors on working time and working thrust, conducting reliability analysis of the performance failure system of aerospace solid rocket engines is an effective way to accurately evaluate the performance reliability of aerospace solid rocket engines, and provides an important reference for the high-reliability design of domestic aerospace solid rocket engine performance. Summary of the invention

[0005] The present application provides a method for analyzing the reliability of a space solid rocket engine system, which can efficiently and accurately analyze the reliability of the performance of a space solid rocket engine.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a method for analyzing the reliability of a space solid rocket engine system, the method comprising: Build a proxy model based on the initial sample set, obtain the t-th round candidate sample prediction set output by the t-th round Kriging model for the candidate sample set, and the t-th round candidate sample prediction set can be divided into: a positive sample prediction set and a negative sample prediction set according to the sign of the response value; According to the prediction set of candidate samples in round t, determine whether the accuracy of the Kriging model in round t meets the requirements; If the accuracy of the Kriging model in the tth round does not meet the requirements, the probability that each positive sample in the tth round positive sample prediction set may be a negative sample is calculated and defined as the first probability, and the probability that each negative sample in the tth round negative sample prediction set may be a positive sample is calculated and defined as the second probability; According to the first probability and the second probability of the tth round, determine the best candidate sample of the tth round from the candidate sample set, and calculate the response value corresponding to the best candidate sample; According to the best candidate sample in round t and the response value corresponding to the best candidate sample, the Kriging model in round t is updated to obtain the Kriging model in round t+1, where t is an integer greater than 0; If the accuracy of the t-th round Kriging model meets the requirements, the performance of the aerospace solid rocket engine is analyzed for failure based on the t-th round time prediction value and the t-th round thrust prediction value.

[0007] In the above technical solution, according to the prediction set of candidate samples in the tth round, the standard for judging whether the accuracy meets the requirements is as follows:

[0008] In the formula, is the accuracy threshold of the t-th round Kriging model, is the number of samples in the t-th round of negative sample prediction set, for The upper boundary of the confidence interval of for The upper boundary of the confidence interval of is the number of positive samples predicted as negative samples in the candidate sample prediction set in the tth round, The number of negative samples predicted as positive samples in the candidate sample prediction set for the tth round; if is less than or equal to the preset accuracy threshold, then the accuracy of the Kriging model in round t meets the requirements; if If it is greater than the preset accuracy threshold, the accuracy of the Kriging model in the tth round does not meet the requirements.

[0009] In the above technical solution, the probability that each positive sample in the t-th round positive sample prediction subset may be a negative sample is calculated and defined as the first probability, and the formula is:

[0010] In the formula, is the probability that the positive sample in the t-th round positive sample prediction set may be a negative sample, and is defined as the first probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the Kriging model in the tth round, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0011] Calculate the probability that each negative sample in the t-th round of negative sample prediction set may be a positive sample and define it as the second probability. The formula is:

[0012] In the formula, The probability that a negative sample in the t-th round of negative sample prediction subset may be a positive sample is defined as the second probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0013] In the above technical solution, according to the first probability and the second probability of the tth round, the best candidate sample of the tth round and the response value corresponding to the best candidate sample are determined from the candidate sample set, and the specific contents are: Determine the learning function value of the tth round according to the first probability and the second probability of the tth round; Determine the maximum learning function value of the tth round from the learning function values ​​of the tth round; The sample corresponding to the maximum learning function value in the candidate sample set in the tth round is determined as the best candidate sample in the tth round, and the response value corresponding to the best candidate sample is calculated.

[0014] The learning function expression is:

[0015]

[0016] In the formula, is the learning function value of the tth round, is the first probability in round t, is the second probability in round t, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The cumulative distribution function of the normal distribution is, About The cumulative distribution function of the normal distribution is, is the difference between the first probability of the tth round and the second probability of the tth round. According to the tth round time prediction value and the tth round thrust prediction value, the reliability analysis of the aerospace solid rocket engine performance is carried out, including: If the t-th round time prediction value indicates that the working time performance of the aerospace solid rocket engine is failure or the t-th round thrust prediction value indicates that the working thrust performance of the aerospace solid rocket engine is failure, it is determined that the aerospace solid rocket engine performance is failure; If the t-th round time prediction value represents that the working time performance of the aerospace solid rocket engine is normal and the t-th round thrust prediction value represents that the working thrust performance of the aerospace solid rocket engine is normal, it is determined that the performance of the aerospace solid rocket engine has not failed.

[0017] In a second aspect, the present application provides a failure analysis device for aerospace solid rocket engine performance, the device comprising: An updating module is used to update the Kriging model according to the candidate samples in the tth round and the response values ​​corresponding to the candidate samples to obtain the Kriging model in the t+1th round, where t is an integer greater than 0; An acquisition module is used to acquire a t+1th round candidate sample response value prediction set output by the t+1th round Kriging model for the candidate sample set, including a positive sample prediction set and a negative sample prediction set; A judgment module, used to judge whether the accuracy of the Kriging model in the t+1th round meets the requirements according to the candidate sample prediction set in the t+1th round; A probability module, for determining the probability that each positive sample in the t+1th round of positive sample prediction set may be a negative sample if the accuracy of the t+1th round of Kriging model does not meet the requirements, and defining it as a first probability; determining the probability that each negative sample in the t+1th round of negative sample prediction set may be a positive sample, and defining it as a second probability; A screening module, used to determine the candidate sample for round t+1 from the candidate sample set according to the first probability and the second probability for round t+1, and determine the response value corresponding to the candidate sample for round t+1; A prediction module, for inputting the parameters to be analyzed of the aerospace solid rocket engine into the t+1th round Kriging model if the accuracy of the t+1th round Kriging model meets the requirements, and obtaining the t+1th round time prediction value and the t+1th round thrust prediction value output by the t+1th round Kriging model; An analysis module is used to perform failure analysis on the performance of the aerospace solid rocket engine based on the t+1th round time prediction value and the t+1th round thrust prediction value.

[0018] In a third aspect, the present application provides a computing device, including a memory and a processor; One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method as described in any one of the first aspects.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method as described in any one of the first aspects.

[0020] It can be seen from the above technical solution that the present application has at least the following beneficial effects: The present application provides a method for analyzing the reliability of a space solid rocket engine system. In the method, if the accuracy of the Kriging model does not meet the requirements during the training process, the Kriging model is updated again for the candidate sample prediction set output by the Kriging model for the candidate sample set. Specifically, the candidate sample prediction set includes a positive sample prediction set and a negative sample prediction set. The probability that each positive sample in the positive sample prediction set may be a negative sample is determined and defined as a first probability. The probability that each negative sample in the negative sample prediction set may be a positive sample is determined and defined as a second probability. Based on the first probability and the second probability, the best candidate sample and the response value corresponding to the best candidate sample are determined from the candidate sample set. The best candidate sample and the response value are used to update the above Kriging model until the accuracy of the Kriging model meets the requirements. After the accuracy of the Kriging model meets the requirements, the parameters to be analyzed related to the performance of the space solid rocket engine can be input into the Kriging model to obtain the time prediction value and thrust prediction value output by the Kriging model. According to the time prediction value and the thrust prediction value, the reliability analysis of the performance of the space solid rocket engine is performed. It can be seen that this method can realize the analysis of performance failure of aerospace solid rocket engines, provide an effective evaluation method for the performance reliability of aerospace solid rocket engines, and provide an effective reference for the high-reliability design of aerospace solid rocket engine performance.

[0021] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of features or beneficial effects means that specific technical features, technical solutions or beneficial effects are included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be realized without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in a specific embodiment that does not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a space solid rocket engine provided in an embodiment of the present application; Figure 2 A flow chart of a method for analyzing performance failure of aerospace solid rocket engines provided in an embodiment of the present application; Figure 3 A schematic diagram of a device for analyzing performance failure of aerospace solid rocket engines provided in an embodiment of the present application; Figure 4 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The terms "first", "second", "third", etc. in the specification of this application and the accompanying drawings are used to distinguish different objects rather than to limit a specific order.

[0024] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0025] like Figure 1 As shown in FIG. 1 , the figure is a schematic diagram of a space solid rocket engine provided by an embodiment of the present application. It can be seen from the figure that the space solid rocket engine includes a data set of front wing length 11, front wing circumscribed circle diameter 12, rear wing length 13, rear wing circumscribed circle diameter 14, internal ballistics-combustion chamber maximum pressure, front wing inclination angle 15, rear wing inclination angle 16, wing width 17, first-stage charge reference burning rate, and expansion ratio.

[0026] The design of the above parameters will affect the performance of aerospace solid rocket engines, such as the working time performance and working thrust performance of aerospace solid rocket engines. If the working time performance and working thrust performance do not meet the requirements, the performance of aerospace solid rocket engines will fail. Once the performance of aerospace solid rocket engines fails, unexpected accidents will occur. Therefore, it is necessary to evaluate the performance reliability of aerospace solid rocket engines, so as to provide effective guidance for the design of aerospace solid rocket engines and reduce the failure of aerospace solid rocket engines.

[0027] In this application, a method for analyzing the reliability of aerospace solid rocket engine system is proposed to solve the problems that the normal temperature working time and normal temperature average thrust of aerospace solid rocket engines under the influence of multi-source uncertainties deviate from their corresponding design values, resulting in reduced reliability of aerospace solid rocket engine performance and lack of reliability evaluation methods. The method first establishes a series system reliability model of working time and working thrust; secondly, the initial Kriging models of working time and thrust performance are established respectively; then, considering the situation that the Kriging model has prediction errors during the prediction process, an improved learning function is proposed, and the learning function is used to select sample points from the candidate sample pool, and the initial (or previously updated) Kriging model is updated based on the selected sample points and their corresponding response values ​​until the accuracy of the Kriging model meets the requirements; finally, based on the established series system Kriging model, the Monte Carlo method is used to calculate the performance reliability of aerospace solid rocket engines. Compared with other implementation schemes, the present invention significantly improves the calculation efficiency while achieving accurate evaluation of the performance reliability of aerospace solid rocket engines.

[0028] In order to make the technical solution of the present application clearer and easier to understand, the technical solution of the present application is described in detail below in conjunction with the accompanying drawings. Figure 2 As shown in the figure, this figure is a flow chart of a system reliability analysis method for aerospace solid rocket engine thrust performance and working time provided by the present application, the method comprising: S201, constructing an initial sample set and a candidate sample set respectively.

[0029] The sample set includes multiple samples and the response values ​​corresponding to the samples. The initial sample set is a small-scale sample library generated by Latin hypercube sampling according to the distribution of parameters such as the front wing length of the sample; the candidate sample set is a large-scale sample library generated by Latin hypercube sampling according to the distribution of parameters such as the front wing length of the sample.

[0030] In the embodiment of the present application, the samples used include: sample front wing length, sample front wing circumscribed circle diameter, sample rear wing length, sample rear wing circumscribed circle diameter, sample front wing inclination angle, rear wing inclination angle, sample wing width, sample first-stage charge reference burning rate, and sample internal trajectory combustion chamber maximum pressure. The response value includes a sample time response value and a sample thrust response value.

[0031] Among them, the sample time response value is obtained by subtracting the time threshold from the working time corresponding to the sample. If the obtained value is greater than or equal to 0, it indicates that the aerospace solid rocket engine corresponding to the sample has not failed in terms of working time performance. The sample thrust response value is obtained by subtracting the thrust threshold from the working thrust corresponding to the sample. If the obtained value is greater than or equal to 0, it indicates that the aerospace solid rocket engine corresponding to the sample feature has not failed in terms of working thrust performance. In this way, based on the positive or negative output results of the Kriging model obtained after training, it can be determined whether the aerospace solid rocket engine performance corresponding to the parameters input to the model is failed or not.

[0032] In this scheme, the sample set can be constructed by Latin hypercube sampling.

[0033] S202: Construct a proxy model based on the initial sample set.

[0034] In the embodiment of the present application, the proxy model can be a Kriging model. The initial Kriging model includes two categories. The first category is a Kriging model for working time, which is used to predict working time, and the second category is a Kriging model for working thrust, which is used to predict working thrust. The construction method and principle of the Kriging model in the above two methods are similar. For the sake of ease of description, the Kriging model whose output parameters include working time and working thrust is taken as an example for introduction. The initial Kriging model is constructed based on the samples in the initial sample set and their corresponding response values.

[0035] S203: Predict the candidate sample set through the proxy model and classify them according to the signs of the predicted response values.

[0036] After the proxy model of work time and work thrust is constructed, the candidate sample pool prediction set corresponding to the candidate sample is calculated. According to the positive or negative prediction response value, it can be divided into two sets: positive sample set and negative sample set.

[0037] S204: Determine whether the accuracy of the t-th round Kriging model meets the requirements.

[0038] Determine whether the accuracy of the Kriging model in the tth round meets the requirements. If the accuracy of the Kriging model in the tth round does not meet the requirements, execute S205. If the accuracy of the Kriging model in the tth round meets the requirements, execute S208.

[0039] Wherein, t is an integer greater than 0. When t=2, the t-th round Kriging model is the second-round Kriging model, and the initial Kriging model is the first-round Kriging model. The parameter t is introduced here only to more clearly introduce the cyclic judgment process, and then more clearly introduce the technical solution of the present application.

[0040] The following describes the process of how to determine whether the accuracy of the t-th round Kriging model meets the requirements.

[0041] First, based on the output of the t-round Kriging model for the candidate sample set, the t-round candidate sample prediction set is judged according to the following formula:

[0042] In the formula, is the accuracy threshold of the t-th round Kriging model, is the number of samples in the t-th round of negative sample prediction set, for The upper boundary of the confidence interval of for The upper boundary of the confidence interval of The number of samples that should be positive in the candidate sample prediction set in the tth round and are predicted to be negative samples, The number of samples that should be negative in the candidate sample prediction set for the tth round and are predicted to be positive samples.

[0043] If the is less than or equal to the preset accuracy threshold, then the accuracy of the Kriging model in round t meets the requirements; if If it is greater than the preset accuracy threshold, the accuracy of the Kriging model in the tth round does not meet the requirements.

[0044] The following introduces and determination process.

[0045] The prediction error of the Kriging model is caused by the samples that should be positive samples but are predicted as negative samples, and the samples that should be negative samples but are predicted as positive samples. The uncertain samples are defined as follows:

[0046] In the formula, It indicates that the sample is in the fault domain and is also in the safe domain predicted by the Kriging model. That is, the sample should be a negative sample but is predicted as a positive sample. It indicates that the sample is in the safety domain and is also in the fault domain predicted by the Kriging model. That is, the sample should be a positive sample but is predicted as a negative sample. The response values ​​of time and thrust predicted by the Kriging model; The variance of the response values ​​of time and thrust predicted by the Kriging model. Factors for constructing confidence intervals, such as , with a confidence interval of 95%. Considering the statistical characteristics of the Kriging model, and The probability that each sample in has an incorrect symbol prediction can be calculated as:

[0047] In the formula, For The probability of an incorrect prediction, where Indicates the fault domain, Indicates a security domain. The response values ​​of time and thrust of a sample predicted by the Kriging model; The variance corresponding to the response value of time and thrust of a sample predicted by the Kriging model.

[0048] according to , and The definition of can be calculated by the bootstrap confidence estimation method and Specifically, from Generated Sample (correspondingly, from Generated samples) with replacement, and then calculate the , and calculate The mean This process needs to be repeated Second-rate, The values ​​are sorted in ascending order. and The confidence intervals are:

[0049]

[0050] In the formula, express The confidence interval of express Confidence interval of . and For sorting value The order can be calculated as:

[0051] In the formula, is the number of replications to bootstrap confidence estimates; It is used to determine and In some examples, and The values ​​can be set to 1000 and 0.05 respectively.

[0052] It should be noted that the method for judging whether the accuracy of the initial Kriging model meets the requirements is similar to the method described above and will not be repeated here.

[0053] S205. In the tth round, the probability of incorrect prediction of the response values ​​in the two sets is calculated respectively.

[0054] Calculate the probability that each positive sample in the t-th round positive sample prediction subset may be a negative sample and define it as the first probability. The formula is:

[0055] In the formula, is the probability that the positive sample in the t-th round positive sample prediction set may be a negative sample, and is defined as the first probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the Kriging model in the tth round, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0056] Calculate the probability that each negative sample in the t-th round of negative sample prediction set may be a positive sample and define it as the second probability. The formula is:

[0057] In the formula, The probability that each negative sample in the t-th round of negative sample prediction set may be a positive sample is defined as the second probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0058] S206. In the tth round, the learning function value is calculated according to the probability of the wrong prediction corresponding to the response values ​​in the two sets, the best candidate sample is selected from the candidate sample pool, and the response value corresponding to the best candidate sample is calculated.

[0059] Determine the learning function value of the tth round according to the first probability and the second probability of the tth round; Determine the maximum learning function value of the tth round from the learning function values ​​of the tth round; The sample corresponding to the maximum learning function value in the candidate sample set in the tth round is determined as the best candidate sample in the tth round, and the response value corresponding to the best candidate sample is calculated.

[0060] The learning function expression is:

[0061]

[0062] In the formula, is the learning value of the t-th round of learning function, is the first probability in round t, is the second probability in round t, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The cumulative distribution function of the normal distribution is, About The cumulative distribution function of the normal distribution is, is the difference between the first probability in round t and the second probability in round t.

[0063] S207, adding the best candidate sample obtained in the tth round and the response value corresponding to the best candidate sample to the initial sample set, updating the tth round Kriging model, and obtaining the t+1th round Kriging model.

[0064] After obtaining the best candidate sample in the tth round and the response value corresponding to the best candidate sample in the tth round, the tth round Kriging model can be updated using the candidate sample in the tth round and the response value corresponding to the candidate sample in the tth round to obtain the t+1th round Kriging model. Then return to S204 to re-judge whether the accuracy of the t+1th round Kriging model meets the requirements.

[0065] S208. Perform reliability analysis on the performance of aerospace solid rocket engines based on the t-th round time prediction value and the t-th round thrust prediction value.

[0066] If the accuracy of the t-th round Kriging model meets the requirements, the parameters to be analyzed related to the performance of the aerospace solid rocket engine are input into the t-th round Kriging model to obtain the t-th round time prediction value and the t-th round thrust prediction value output by the t-th round Kriging model. Among them, the parameters to be analyzed include: front wing length, front wing circumscribed circle diameter, rear wing length, rear wing circumscribed circle diameter, front wing inclination angle, rear wing inclination angle, wing width, first-stage charge reference burning rate and expansion ratio.

[0067] If the t-th round time prediction value represents that the working time performance of the space solid rocket engine is failed or the t-th round thrust prediction value represents that the working thrust performance of the space solid rocket engine is failed, then it is determined that the performance of the space solid rocket engine has failed; if the t-th round time prediction value represents that the working time performance of the space solid rocket engine is normal and the t-th round thrust prediction value represents that the working thrust performance of the space solid rocket engine is normal, then it is determined that the performance of the space solid rocket engine has not failed.

[0068] Specifically, it can be determined whether the predicted value of the t-th round time and the predicted value of the t-th round thrust are less than 0. If they are less than 0, it indicates that the aerospace solid rocket engine corresponding to the parameter to be analyzed has failed and needs to be further optimized. If they are not less than 0, it indicates that the solid performance engine corresponding to the parameter has not failed, which can provide an effective reference for subsequent design.

[0069] Examples of parameters to be analyzed are shown in Table 1 below.

[0070] Table 1:

[0071] In the embodiment of the present application, for a series system of aerospace solid rocket engine performance failure events, the failure probability can be expressed by the following formula:

[0072] in, It can be the parameters to be analyzed of aerospace solid rocket engines, For x k The state function of a failure event, Pr Indicates an event The probability of occurrence is expressed in this formula, in the embodiment of the present application k=2, corresponding to the working time performance and working thrust performance respectively. In this application, failure of the working thrust and working time to meet the specified requirements is a failure event. Once a failure event occurs in either of the two, the series system composed of the working thrust performance and the working time performance will fail.

[0073] The comparison of the number of function calls based on the parameters in the aerospace solid rocket engine case and the results of the Monte Carlo method (MCS) is shown in the following table.

[0074] Table 2:

[0075] It can be seen that in the technical solution of the present application, the number of function calls is significantly less than that of the MCS method, which is reduced by 98.8%. In the process of calculating the failure probability of the series system, the solution provided by the embodiment of the present application improves the calculation efficiency while also ensuring the calculation accuracy.

[0076] Based on the above description, the technical solution of the present application fully considers the influence of uncertainty factors such as geometric errors of aerospace solid rocket engines on working time and working thrust, establishes a performance reliability model of aerospace solid rocket engine series system, and proposes a reliability analysis method for aerospace solid rocket engine system. This method comprehensively considers the prediction errors through the known information in the model construction process, constructs an improved expected risk learning function, and realizes the efficient and accurate evaluation of the performance reliability of the series system composed of the working thrust performance and working time performance of aerospace solid rocket engines. At the same time, this method is also suitable for aerospace solid rocket engine performance functions with a high degree of nonlinearity. Compared with the component reliability analysis method based on the Monte Carlo method, the present invention carries out performance reliability analysis from the perspective of the series system, which can better meet the actual situation; at the same time, this method has low calculation cost and high accuracy.

[0077] Combination of the above Figure 1 to Figure 2 The method for analyzing the performance failure of a fixed engine provided in an embodiment of the present application is introduced in detail. The device provided in the embodiment of the present application will be introduced below in conjunction with the accompanying drawings.

[0078] like Figure 3 As shown, this figure is a schematic diagram of a fixed engine performance failure analysis device provided by an embodiment of the present application, and the device includes: An updating module 301 is used to update the t-th round Kriging model according to the t-th round candidate samples and the response values ​​corresponding to the candidate samples to obtain the t+1-th round Kriging model, where t is an integer greater than 0; An acquisition module 302 is used to acquire a t+1th round candidate sample prediction set output by the t+1th round Kriging model for the candidate sample set, wherein the t+1th round candidate sample prediction set includes a t+1th round positive sample prediction set and a t+1th round negative sample prediction set; A judgment module 303 is used to judge whether the accuracy of the Kriging model in the t+1th round meets the requirements according to the candidate sample prediction set in the t+1th round; The probability module 304 is used to determine the probability that each positive sample in the t-th round positive sample prediction set may be a negative sample if the accuracy of the t+1-th round Kriging model does not meet the requirements, and define it as a first probability; determine the probability that each negative sample in the t-th round negative sample prediction set may be a positive sample, and define it as a second probability; A screening module 305 is used to determine the best candidate sample in the t+1th round from the candidate sample set according to the t+1th round first probability and the t+1th round second probability, and determine the response value corresponding to the tth round best candidate sample; A prediction module 306 is used for inputting the parameters to be analyzed of the aerospace solid rocket engine into the t+1th round Kriging model if the accuracy of the t+1th round Kriging model meets the requirements, and obtaining the t+1th round time prediction value and the t+1th round thrust prediction value output by the t+1th round Kriging model; The analysis module 307 is used to perform a performance failure analysis on the aerospace solid rocket engine according to the t+1th round time prediction value and the t+1th round thrust prediction value.

[0079] Optionally, the judgment module 303 may determine the convergence value by the following formula:

[0080] in, is the accuracy threshold of the t-th round Kriging model, is the number of samples in the t-th round of negative sample prediction set, for The upper boundary of the confidence interval of for The upper boundary of the confidence interval of The number of samples that should be positive in the candidate sample prediction set in the tth round and are predicted to be negative samples, The number of samples that should be negative in the candidate sample prediction set in the tth round and are predicted to be positive samples; is less than or equal to the preset accuracy threshold, then the result of the Kriging model accuracy of the tth round is obtained to meet the requirements; if If the value is greater than the preset accuracy threshold, the result is that the accuracy of the Kriging model in the tth round does not meet the requirements.

[0081] Optionally, the probability module 304 is specifically used to determine the probability that the positive sample in the t-th round positive sample prediction set may be a negative sample by the following formula, and define it as a first probability:

[0082] in, is the probability that the positive sample in the t-th round positive sample prediction set may be a negative sample, and is defined as the first probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0083] Optionally, the probability module 304 is specifically used to determine the probability that each negative sample in the t-th round negative sample prediction set may be a positive sample by the following formula, and define it as a second probability:

[0084] in, The probability that each negative sample in the t-th round of negative sample prediction set may be a positive sample is defined as the second probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0085] The screening module 305 is specifically used to determine the t-th round learning function value based on the t-th round first probability and the t-th round second probability; determine the t-th round maximum learning function value from the t-th round learning function values; determine the sample corresponding to the t-th round maximum learning value in the candidate sample set as the best candidate sample for the t-th round, and then determine the response value corresponding to the t-th round best candidate sample.

[0086] The screening module 305 is specifically used to determine the value of the learning function in the tth round by using the following formula:

[0087]

[0088] in, is the learning function value of the tth round, is the first probability in round t, is the second probability in round t, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The cumulative distribution function of the normal distribution is, About Cumulative distribution function of the normal distribution.

[0089] Optionally, the analysis module 307 is specifically used to determine that the performance of the aerospace solid rocket engine has failed if the t-th round time prediction value indicates that the working time performance of the aerospace solid rocket engine is failed or the t-th round thrust prediction value indicates that the working thrust performance of the aerospace solid rocket engine is failed; if the t-th round time prediction value indicates that the working time performance of the aerospace solid rocket engine is normal and the t-th round thrust prediction value indicates that the working thrust performance of the aerospace solid rocket engine is normal, then determine that the performance of the aerospace solid rocket engine has not failed.

[0090] The fixed engine failure analysis device according to the embodiment of the present application may correspond to the method described in the embodiment of the present application, and the above-mentioned other operations and / or functions of each module / unit of the fixed engine failure analysis device are respectively to achieve Figure 2 For the sake of brevity, the corresponding processes of each method in the illustrated embodiment are not described in detail here.

[0091] The present application also provides a computing device, such as Figure 4 As shown, this figure is a schematic diagram of a computing device provided in an embodiment of the present application, such as Figure 4 As shown, computing device 400 includes bus 401, processor 402, communication interface 403 and memory 404. Processor 402, memory 404 and communication interface 403 communicate with each other via bus 401.

[0092] The bus 401 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0093] The processor 402 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0094] The communication interface 403 is used for communicating with the outside.

[0095] The memory 404 may include a volatile memory, such as a random access memory (RAM). The memory 404 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0096] The memory 404 stores executable codes, and the processor 402 executes the executable codes to perform the aforementioned analysis method of fixed engine failure.

[0097] Specifically, in implementing Figure 3 In the case of the embodiment shown, and Figure 3 When each module or unit of the fixed engine failure analysis device described in the embodiment is implemented by software, the execution Figure 3 The software or program code required for the functions of each module / unit in the system may be partially or completely stored in the memory 404. The processor 402 executes the program code corresponding to each unit stored in the memory 404 to perform the above-mentioned analysis method for fixed engine failure.

[0098] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state hard disk). The computer-readable storage medium includes instructions that instruct the computing device to execute the above-mentioned fixed engine failure analysis method.

[0099] The embodiment of the present application further provides a computer program product, which includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part.

[0100] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer or data center to another website, computer or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0101] When the computer program product is executed by a computer, the computer executes any one of the aforementioned methods for analyzing a stationary engine failure. The computer program product may be a software installation package, and when any one of the aforementioned methods for analyzing a stationary engine failure needs to be used, the computer program product may be downloaded and executed on a computer.

[0102] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0103] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. A method for analyzing the reliability of a space solid rocket engine system, characterized in that: The method comprises: Through the t-th round of Kriging model, the prediction output results corresponding to the candidate samples are calculated; the obtained prediction output results are divided into two categories according to the signs: the prediction set of positive samples and the prediction set of negative samples; According to the prediction output results of the candidate samples in round t, determine whether the accuracy of the Kriging model in round t meets the requirements; If the accuracy of the Kriging model in the tth round does not meet the requirements, the probability that each positive sample in the tth round positive sample prediction set may be a negative sample is calculated and defined as the first probability; the probability that each negative sample in the tth round negative sample prediction set may be a positive sample is calculated and defined as the second probability; According to the first probability and the second probability of the tth round, determine the best candidate sample of the tth round from the candidate sample set, and the response value corresponding to the best candidate sample; According to the best candidate sample in round t and the response value corresponding to the best candidate sample, the Kriging model in round t is updated to obtain the Kriging model in round t+1, where t is an integer greater than 0; If the accuracy of the t-th round Kriging model meets the requirements, the reliability analysis of the aerospace solid engine performance is carried out according to the t-th round time prediction value and the t-th round thrust prediction value.

2. The method according to claim 1, characterized in that According to the predicted set of candidate sample response values ​​in round t, determine whether the accuracy of the Kriging model in round t meets the requirements: In the formula, is the accuracy threshold of the t-th round Kriging model, is the number of samples in the t-th round of negative sample prediction set, for The upper boundary of the confidence interval of for The upper boundary of the confidence interval of The number of samples that should be positive in the candidate sample prediction set in the tth round and are predicted to be negative samples, The number of samples that should be negative in the candidate sample prediction set in the tth round and are predicted to be positive samples; if is less than or equal to the preset accuracy threshold, then the accuracy of the Kriging model in round t meets the requirements; if If it is greater than the preset accuracy threshold, the accuracy of the Kriging model in the tth round does not meet the requirements.

3. The method according to claim 1, characterized in that Calculate the probability that each positive sample in the t-th round positive sample prediction subset may be a negative sample and define it as the first probability. The calculation formula is: in, is the probability that the positive sample in the t-th round positive sample prediction subset may be a negative sample, and is defined as the first probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the Kriging model in the tth round, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

4. The method according to claim 1, characterized in that Calculate the probability that each negative sample in the t-th round of negative sample prediction subset may be a positive sample and define it as the second probability. The calculation formula is: in, is the probability that the negative sample in the t-th round negative sample prediction subset may be a positive sample, and is defined as the second probability, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The probability density function of the normal distribution is, About Cumulative distribution function of the normal distribution.

5. The method according to claim 1, characterized in that According to the first probability and the second probability of the tth round, determining the best candidate sample of the tth round from the candidate sample set, and the response value corresponding to the best candidate sample, including: Determine the learning function value of the tth round according to the first probability and the second probability of the tth round; Determine the maximum learning function value of the tth round from the learning function values ​​of the tth round; The sample corresponding to the maximum learning function value in the candidate sample set in the tth round is determined as the best candidate sample in the tth round, and the response value corresponding to the best candidate sample is calculated.

6. The method according to claim 5, characterized in that The learning function expression is: In the formula, is the learning value of the t-th round of learning function, is the first probability in round t, is the second probability in round t, is the variance predicted by the Kriging model in the tth round, is the response value predicted by the t-th round Kriging model, About The cumulative distribution function of the normal distribution is, About The cumulative distribution function of the normal distribution is, is the difference between the first probability in round t and the second probability in round t.

7. The method according to claim 1, characterized in that According to the predicted value of the time and thrust of the tth round, the reliability analysis of the performance of the aerospace solid rocket engine is carried out, including: If the t-th round time prediction value indicates that the working time performance of the aerospace solid rocket engine is failure or the t-th round thrust prediction value indicates that the working thrust performance of the aerospace solid rocket engine is failure, it is determined that the aerospace solid rocket engine performance is failure; If the t-th round time prediction value represents that the working time performance of the aerospace solid rocket engine is normal and the t-th round thrust prediction value represents that the working thrust performance of the aerospace solid rocket engine is normal, it is determined that the performance of the aerospace solid rocket engine has not failed.

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