A method and system for life prediction of a reusable rocket

Abstract

The application discloses a reusable rocket life prediction method and system, and relates to the technical field of life prediction, and comprises the following steps: according to the rocket fleet serviceability and reuse frequency requirement, an availability index system is constructed in combination with a top-level availability index; the rocket is qualitatively and quantitatively evaluated to obtain a maintainability evaluation result; based on the task demand and product characteristics of the rocket, test result analysis is carried out in combination with testability sub-indexes; a design load spectrum is constructed to carry out life estimation, and a life estimation result is obtained; multi-dimensional maintenance influence evaluation is carried out to obtain a reliability evaluation result; and a target life prediction result is obtained. The technical problems that, in the prior art, the life prediction dimension of the reusable rocket is not comprehensive, the coupling influences of reliability, maintainability and testability cannot be comprehensively considered, the prediction result precision is insufficient, and the practicality is limited are solved, and the technical effect that the rocket life prediction accuracy and availability are improved through multi-strategy evaluation and result integration is achieved.

Description

Technical Field

[0001] This invention relates to the field of lifespan prediction technology, specifically to a method and system for predicting the lifespan of a reusable rocket. Background Technology

[0002] Reusable rockets reduce space launch costs through recycling and reuse. To ensure economic efficiency, it is necessary to maximize the effective availability of rockets under high-density launch scenarios throughout the year. This places higher demands on the general quality characteristics of rockets. On the one hand, each system of the rocket must have a longer fault-free life, i.e., higher reliability / durability; on the other hand, the rocket should be easy to detect faults and quickly repair to shorten turnaround time. However, traditional expendable rocket development often focuses on the reliability of a single mission and lacks a holistic methodology for reusable scenarios. Existing technologies have shortcomings in life assessment, rapid fault diagnosis, and maintenance convenience, failing to meet the high availability requirements of reusable rockets. In particular, the limited number of test samples for aerospace products makes life assessment challenging due to the problem of small sample sizes and multiple failures; the high integration of complex rocket systems makes it difficult for traditional fault diagnosis methods to isolate faults in a timely and accurate manner; and the compact rocket structure and narrow maintenance access, coupled with a lack of systematic maintenance access design, prolong maintenance time.

[0003] Therefore, current technologies suffer from incomplete dimensions in predicting the lifespan of reusable rockets, and fail to integrate the coupled effects of reliability, maintainability, and testability, resulting in insufficient accuracy and limited practicality of the prediction results. Summary of the Invention

[0004] This application provides a method and system for predicting the lifespan of reusable rockets, which solves the technical problems in the prior art where the dimensions of reusable rocket lifespan prediction are not comprehensive, and the coupled effects of reliability, maintainability, and testability are not integrated, resulting in insufficient prediction accuracy and limited practicality. It achieves the technical effect of improving the accuracy and usability of rocket lifespan prediction through multi-strategy evaluation and result integration.

[0005] This application provides a method for predicting the lifespan of a reusable rocket. The method includes: decomposing the rocket's operational availability and reuse count requirements into top-level availability indicators to construct an availability indicator system covering the system level, subsystem level, and key component level. The availability indicator system includes reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators. Qualitative and quantitative assessments of the rocket are performed using the maintainability sub-indicators to obtain maintainability assessment results. Diagnostic strategies are identified based on the rocket's mission requirements and product characteristics. Rocket testing is conducted according to the diagnostic strategies, and test results are analyzed using the testability sub-indicators to obtain testability sub-indicator assessment results. A design load spectrum is constructed, and failure mechanism analysis and lifespan model construction are performed on the rocket. Lifespan estimation is performed based on the construction results to obtain lifespan estimation results. A reliability model is constructed based on the lifespan estimation results, and a multi-dimensional maintenance impact assessment is performed, including maintenance-free maintenance, preventative maintenance, repair maintenance, and component replacement. Integrated reliability analysis is performed using the reliability sub-indicators to obtain reliability assessment results. Finally, the maintainability assessment results, testability sub-indicator assessment results, lifespan estimation results, and reliability assessment results are integrated to obtain a target lifespan prediction result.

[0006] In a possible implementation, the life prediction method for a reusable rocket further includes the following steps: introducing a human posture model library and a maintenance access model, performing dynamic simulation of typical maintenance tasks, obtaining simulation results, conducting risk assessment in conjunction with the maintainability sub-indicators in the availability index system, refining design criteria based on the risk assessment results, obtaining maintenance accessibility design criteria, and feeding back the maintenance accessibility design criteria to designers for design optimization.

[0007] In a possible implementation, the life prediction method for a reusable rocket also performs the following processing: the maintenance accessibility design criteria include layout and accessibility criteria, easy-to-maintain structural design criteria, simplified maintenance process criteria, and diagnostic testing interface design criteria.

[0008] In a possible implementation, the reusable rocket life prediction method further performs the following processing: constructing a design load spectrum by integrating the rocket's full life-cycle load characteristics and typical operating conditions; identifying key failure mechanisms by combining failure mode influence analysis results; mapping and selecting a physical life model; and constructing a prediction link from the load spectrum to the life distribution. A pseudo-life distribution is obtained by combining the prediction link; the pseudo-life distribution is used as a priori life estimate, verified and updated by combining actual finite failure data to obtain a posterior life distribution, which is then used as the life estimation result.

[0009] In a possible implementation, the reusable rocket lifetime prediction method further performs the following processing: when a degradation data set is obtained, lifetime estimation is performed by extrapolating the performance degradation trend to obtain a pseudo lifetime distribution result; when a degradation data set is not obtained, lifetime estimation based on failure mechanism uncertainty is performed in conjunction with the prediction link to obtain a pseudo lifetime distribution.

[0010] In a possible implementation, the reusable rocket life prediction method further performs the following processes: establishing a correlation model between failure modes and test signals to obtain an initial test set; simplifying the initial test set to obtain a simplified test set; constructing a diagnostic tree and a fault dictionary; traversing the simplified test set through correlation analysis to perform fault detection analysis; guiding fault isolation based on the diagnostic tree and fault dictionary; iteratively optimizing the test based on the fault detection rate and isolation rate obtained from the analysis to obtain a diagnostic strategy set; and matching the diagnostic strategy set with the mission requirements and product characteristics to obtain a diagnostic strategy.

[0011] In a possible implementation, the life prediction method for a reusable rocket further performs the following processing: for preventive maintenance, the reliability difference before and after maintenance is calculated by adjusting the life model parameters; for corrective maintenance, the overall reliability is obtained by resetting the life of replacement parts and calculating the reliability contribution of each replacement part; for component replacement, the reliability is recalculated by resetting the life curve assessment time to zero.

[0012] This application also provides a life prediction system for reusable rockets. The system includes: an index decomposition module, used to decompose indicators based on the rocket's fleet integrity rate and reuse count requirements, combined with top-level availability indicators, to construct an availability index system covering the system level, subsystem level, and key component level. The availability index system includes reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators; a maintainability assessment module, used to perform qualitative and quantitative assessments of the rocket based on the maintainability sub-indicators to obtain maintainability assessment results; and a test result analysis module, used to identify diagnostic strategies based on the rocket's mission requirements and product characteristics, conduct rocket testing according to the diagnostic strategies, and combine the testability sub-indicators... The system comprises the following modules: a test result analysis module to obtain testability sub-index evaluation results; a life estimation module to construct a design load spectrum, perform failure mechanism analysis and life model construction for the rocket, and estimate life based on the construction results; a maintenance impact assessment module to construct a reliability model based on the life estimation results, and conduct multi-dimensional maintenance impact assessments including maintenance-free maintenance, preventive maintenance, repair maintenance, and component replacement, and integrate reliability sub-indexes for reliability analysis to obtain reliability evaluation results; and a life prediction result acquisition module to integrate the maintainability evaluation results, testability sub-index evaluation results, life estimation results, and reliability evaluation results to obtain target life prediction results.

[0013] This application proposes a method and system for predicting the lifespan of reusable rockets. Based on the rocket's operational availability and reuse requirements, and combined with top-level availability indicators, a usability index system is constructed. Qualitative and quantitative assessments of the rocket are conducted to obtain maintainability assessment results. Based on the rocket's mission requirements and product characteristics, test results are analyzed in conjunction with testability sub-indicators. A design load spectrum is constructed to estimate the lifespan, yielding lifespan estimation results. Multi-dimensional maintenance impact assessments are performed to obtain reliability assessment results. Finally, target lifespan prediction results are obtained. This addresses the technical problems in existing technologies where reusable rocket lifespan prediction lacks comprehensive dimensions and fails to integrate the coupled effects of reliability, maintainability, and testability, resulting in insufficient prediction accuracy and limited practicality. The method achieves the technical effect of improving the accuracy and usability of rocket lifespan prediction through multi-strategy evaluation and result integration. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 This is a schematic flowchart of a method for predicting the lifespan of a reusable rocket, provided as an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of a life prediction system for a reusable rocket, provided as an embodiment of this application.

[0017] Figure labeling: Index decomposition module 10, maintainability assessment module 20, test result analysis module 30, life estimation module 40, maintenance impact assessment module 50, life prediction result acquisition module 60. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0019] This application provides a method for predicting the lifespan of a reusable rocket, such as... Figure 1 As shown, the method includes: Step S100: Based on the rocket's operational integrity rate and reuse times requirements, and combined with the top-level availability indicators, the indicators are decomposed to construct an availability indicator system covering the system level, subsystem level, and key component level. The availability indicator system includes reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators.

[0020] Preferably, an airborne sensor network is deployed in key components such as the rocket engine, structure, avionics, and thermal protection to collect vibration signals, pressure and temperature signals, strain signals, displacement and position signals, and electrical parameters in real time. After the rocket is recovered, the fault codes and performance parameters of the self-inspection reports of each intelligent unit are read through ground support equipment, and ultrasonic flaw detectors are used to detect structural cracks, endoscopes are used to observe internal engine damage, and high-precision calibration equipment is used to calibrate the sensors. Rocket fleet availability rate is a system-level availability indicator, referring to the proportion of rockets that are ready to perform missions at any given time out of the total number of rockets. It directly reflects the rapid response and sustained operational capability of the rocket fleet. Reusability count refers to the number of times a single rocket is expected or designed to safely perform flight missions during its lifespan. Combining top-level availability indicators with hierarchical decomposition, a system of availability indicators covering the system level, subsystem level, and critical component level is constructed. System-level indicators correspond to the entire rocket level; for example, mission reliability is the probability that a rocket will not experience a failure leading to mission failure during a single flight mission; mean time to recovery (MTR) is the average time required for the rocket to recover from a faulty state to a usable state; and launch preparation time is the time required from rocket recovery to the next launch readiness. Subsystem-level indicators involve allocating system-level indicators to engine systems, structural systems, control systems, avionics systems, etc., to meet the overall mission reliability requirements of the rocket. Critical component-level indicators further decompose subsystem-level indicators to specific critical components, such as turbopumps, valves, propellant tanks, and blade actuators, to meet the reliability requirements of these critical components.

[0021] Preferably, the availability index system is a hierarchical set of technical parameters, including reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators. Specifically, it includes reliability parameters characterizing fault-free capability, maintainability parameters characterizing repairability, and testability parameters characterizing condition monitorability. The reliability sub-indicator refers to the product's ability to perform its intended function without failure under specified conditions and within a specified time. Technical parameters include mean time between failures (MTBF) / mileage, reliability, failure rate, and fatigue life, representing the average operating time between two consecutive failures of the rocket product, the probability of completing the intended function within a specified time, the probability of failure per unit time after the product has not yet failed, and the number of cycles a structural component can withstand under alternating loads until cracks or fractures occur, respectively. The maintainability sub-indicator refers to the product's ability to be repaired under specified maintenance conditions and according to specified methods. The ability to maintain or restore to a specified state includes technical parameters such as mean repair time, mean preventive maintenance time, maintenance man-hour rate, and maximum repair time. These represent the average direct repair time required to eliminate a fault, the average time required for each preventive maintenance, the maintenance man-hours required per unit of working hour, and the upper limit of time required to complete a specified proportion of repair tasks, respectively. Testability sub-indicators refer to the design characteristics of a product that can determine its working state and isolate its internal faults in a timely and accurate manner. Technical parameters include fault detection rate, fault isolation rate, and fault detection time / isolation time. These represent the ratio of the number of faults correctly detected using the specified method to the total number of faults occurring within the same time period, the ratio of the number of detected faults correctly isolated to the specified ambiguity within a specified time period to the total number of faults detected within the same time period, and the time elapsed from the occurrence of a fault to its detection / isolation.

[0022] Furthermore, step S100 also includes introducing a human posture model library and a maintenance channel model, performing dynamic simulation of typical maintenance tasks, obtaining simulation results, conducting risk assessment in conjunction with the maintainability sub-indicators in the availability index system, refining design criteria based on the risk assessment results, obtaining maintenance accessibility design criteria, and feeding back the maintenance accessibility design criteria to designers for design optimization.

[0023] Furthermore, step S100 also includes the maintenance accessibility design criteria including layout and accessibility criteria, easy-to-maintain structure design criteria, simplified maintenance process criteria, and diagnostic testing interface design criteria.

[0024] Preferably, the human posture model library is a digital human body model database containing human body dimensions at different percentiles, joint degrees of freedom, range of motion, and mechanical properties. This database is used to simulate the body shape and operational capabilities of real maintenance personnel in a virtual environment, assessing whether maintenance personnel can reach and operate target components with the necessary postures. The maintenance access model is a digital 3D model of the rocket's structural layout, focusing particularly on the physical path from external access ports to internal maintenance target points. It defines geometric constraints such as obstacles, spatial dimensions, and opening sizes along the path. Combined with the human posture model, it verifies whether maintenance tools and personnel's hands, arms, and even their entire bodies can smoothly traverse the path to reach the maintenance location. In the virtual prototype environment, typical maintenance tasks are dynamically simulated. This involves driving the human posture model and simulating the entire maintenance operation process according to pre-defined standardized maintenance procedures, such as replacing engine igniters and overhauling turbopump seals. This simulation includes, but is not limited to, accessibility verification, operational space verification, and ergonomic verification. The simulation results are then output, including the virtual time required to complete each sub-task, maintenance personnel posture data, interference data between the human body, tools, and rocket structure, and visibility data of the maintenance target.

[0025] Preferably, the simulation results are compared and analyzed with the maintainability sub-indicators in the availability index system. If the simulation shows that a certain operation takes too long, it will lead to the average repair time exceeding the standard. If the simulation shows that the operation requires an extremely difficult posture or excessive force, it will lead to an increase in maintenance man-hours and a safety risk of personnel injury. If the simulation finds that the tool cannot be operated effectively or there is a collision, it may lead to task failure or secondary damage, which is assessed as a risk to task success. Based on the risk assessment results, maintenance accessibility design principles are summarized, including layout and accessibility principles, easy-to-maintain structure design principles, simplified maintenance process principles, and diagnostic and testing interface design principles. For example, critical maintenance points must be within the natural extension range of the arm, quick-release latches should be used instead of bolts, disassembling part A should not require disassembling part B first, and test interfaces should face the inspection port and have no obstructions on the surface. By introducing a human posture model library and a maintenance access model, typical maintenance tasks are dynamically simulated to identify bottlenecks such as spatial interference, operational difficulty, and safety risks in maintenance operations. Based on the simulation results and combined with maintainability indicators, a quantitative risk assessment is conducted, ultimately refining specific design principles, including layout and accessibility, easy-to-maintain structural design, simplified maintenance processes, and diagnostic testing interface design. The refined maintenance accessibility design principles are then used as formal inputs to provide feedback to designers for optimization. For example, the location and shape of inspection ports can be modified, the installation orientation of components can be adjusted, the internal layout of equipment compartments can be optimized, and more suitable connection methods can be selected. This ensures that the product can be maintained, repaired, and replaced efficiently, safely, and economically after manufacturing, thereby improving the efficiency and safety of rocket maintenance support from the source.

[0026] Step S200: Combine the maintainability sub-indicators to perform qualitative and quantitative evaluation of the rocket and obtain maintainability evaluation results.

[0027] Preferably, the rocket is qualitatively and quantitatively evaluated through maintainability sub-indicators to objectively measure the merits of the rocket design in terms of maintainability. Specifically, qualitative evaluation refers to analyzing the rocket's design characteristics, structure, and configuration to determine whether it is conducive to maintenance. This includes analyzing whether maintenance personnel's hands, arms, or tools can see, approach, and touch the parts that need maintenance; whether the location of access ports and hatches is reasonable; whether external or internal replaceable units are used; whether the connections between components are easy to separate; whether the maintenance operation space is sufficient; whether it can avoid putting maintenance personnel in forced, awkward, or dangerous postures; and whether testing and diagnostic support is available. The qualitative evaluation results are then output, listing the items that conform to or do not conform to good maintainability design, and pointing out specific design defects and improvement measures. Quantitative assessment refers to assigning specific numerical indicators to maintainability based on mathematical models, historical data, and predictions, making them predictable, comparable, and verifiable. This includes predictions of mean repair time, mean preventative maintenance time, maintenance man-hour rate, and maximum repair time, ultimately outputting quantifiable assessment results. For example, predicting that the average repair time for this type of rocket is 4.5 hours, 95% of preventative maintenance tasks can be completed within 12 hours, and the average preventative maintenance time after each flight mission is 50 hours. The final maintainability assessment results are used to determine whether the design meets the top-level requirements of "rocket fleet integrity" and "rapid reuse."

[0028] Step S300: Identify diagnostic strategies based on the rocket's mission requirements and product characteristics, conduct rocket testing according to the diagnostic strategies, analyze the test results in conjunction with testability sub-indicators, and obtain the testability sub-indicator evaluation results.

[0029] Preferably, the rocket's mission requirements refer to the flight mission profile, environmental conditions, reliability requirements, and relaunch preparation time requirements that the rocket needs to complete. Product characteristics refer to the functional principles, interface relationships, and potential failure modes of each subsystem and component of the rocket. Then, based on the rocket's mission requirements and product characteristics, a diagnostic strategy is identified. Specifically, failure mode, impact, and diagnostic analysis are performed, systematically listing all possible failure modes, such as sensor drift, valve jamming, and structural cracks. Then, signal-failure correlation analysis is performed to determine the physical signal changes caused by each failure mode, such as voltage anomalies, pressure drops, vibration spectrum changes, and temperature increases. Next, the best measurement points for acquiring key signals are selected on the rocket's physical structure and circuitry, and the most efficient test process is formulated. Finally, a diagnostic strategy is output, including a test point layout diagram, a diagnostic tree / flowchart, and a list of required test equipment. After the rocket is recovered, it undergoes testing according to the diagnostic strategy. This includes applying stimuli at designated test points using specified equipment, collecting data, executing diagnostic procedures, and obtaining test results. The test results are then analyzed using testability sub-indicators, which are compared and analyzed with preset testability sub-indicators to obtain testability sub-indicator evaluation results. These sub-indicators include fault detection rate, fault isolation rate, and fault detection / isolation time. For example, under the current diagnostic strategy, the rocket's fault detection rate is 92%, the fault isolation rate is 85%, and the average fault detection time is 15 minutes.

[0030] Furthermore, step S300 also includes step S310, establishing a correlation model between fault modes and test signals to obtain an initial test set, and simplifying the initial test set to obtain a simplified test set; step S320, constructing a diagnostic tree and a fault dictionary; step S330, traversing the simplified test set through correlation analysis to perform fault detection analysis, guiding fault isolation based on the diagnostic tree and fault dictionary, and performing test iteration optimization based on the fault detection rate and isolation rate obtained from the analysis to obtain a diagnostic strategy set; step S340, matching the diagnostic strategy set based on the task requirements and product characteristics to obtain a diagnostic strategy.

[0031] Preferably, a correlation matrix between failure modes and test signals is established as an association model. Failure modes may include sensor M output voltage drift and valve N stuck in the closed position. Test signals may include measuring sensor M output voltage, sending an opening command to valve N and monitoring downstream pressure, and performing X-ray scanning to check for structural cracks. Failure modes are listed using failure mode influence analysis. Matrix elements indicate whether there is a correlation between the corresponding failure mode and the test signal. All test signals are combined to form an initial test set, which includes all theoretically feasible tests. A greedy algorithm is used to simplify and optimize the initial test set to determine the smallest test subset, ensuring that each failure mode is covered by at least the tests in that subset, thus obtaining a simplified test set.

[0032] Preferably, a diagnostic tree and a fault dictionary are constructed. The diagnostic tree is a flowchart based on logical judgment. Starting from the root node, it guides maintenance personnel to different branches based on the pass / fail test results. Through orderly testing, the fault unit at the leaf node is finally reached, and the fault is quickly located with the fewest average test steps. The fault dictionary is a symptom-fault lookup table that records all possible combinations of test results after the test is applied and the most likely cause of the fault for each combination. Computer simulation is used to model the detection process using a simplified test set when all fault modes occur. Based on information entropy and correlation coefficient indicators, the theoretical fault detection rate is calculated by analyzing the correlation matrix. Similarly, simulation is used to model the proportion of detected faults that can be successfully isolated to a designated repair unit when using a diagnostic tree or fault dictionary, thus calculating the theoretical fault isolation rate. Based on the obtained fault detection and isolation rates, iterative optimization of the test is performed, including optimizing the test strategy using diagnostic tree algorithms and the fault dictionary. If the calculated FDR / FIR does not meet the requirements, it may be necessary to add new, more discriminative tests to the simplified test set, rebuild the diagnostic tree, adjust the test order, or update the fault dictionary to include more refined fault modes, thereby generating multiple alternative diagnostic strategies to form a diagnostic strategy set. Finally, the diagnostic strategy set is matched based on task requirements and product characteristics to obtain the diagnostic strategy.

[0033] Step S400: Construct the design load spectrum, perform failure mechanism analysis and life model construction on the rocket, estimate the life based on the construction results, and obtain the life estimation results.

[0034] Step S400 further includes step S410, which involves constructing a design load spectrum by integrating the full life cycle load characteristics and typical operating conditions of the rocket, identifying key failure mechanisms by combining the failure mode influence analysis results, mapping and selecting a physical lifetime model, and constructing a prediction link from the load spectrum to the lifetime distribution; step S420, which involves obtaining a pseudo lifetime distribution by combining the prediction link; and step S430, which involves using the pseudo lifetime distribution as a priori lifetime estimate, verifying and updating it by combining it with actual finite failure data, obtaining a posterior lifetime distribution, and using the posterior lifetime distribution as the lifetime estimation result.

[0035] Preferably, the full life cycle load characteristics refer to all physical loads that the rocket experiences throughout its entire life cycle, from launch, flight, interstage separation, reentry, landing to post-recovery transportation and testing, including acceleration, vibration, shock, acoustic vibration, thermal cycling, pressure fluctuations, etc. Typical operating conditions refer to the typical stages that constitute a complete mission, such as ignition, maximum dynamic pressure, interstage separation, atmospheric reentry, landing retro-propulsion, etc. By combining the full life cycle load characteristics and typical operating conditions of the rocket, a design load spectrum is constructed, that is, the load characteristics of the entire life cycle are compiled into a standardized load time history that can be used for design and analysis according to the occurrence sequence, amplitude, frequency and duration of typical operating conditions. Failure Mode and Effects Analysis (FMEA) is used to identify all potential failure modes of a product, analyze their consequences, and determine their severity. Failure mechanisms are physical, chemical, or biological processes that lead to material or product failure, such as fatigue fracture, creep, corrosion, wear, and aging. They serve as a bridge between "load" and "failure." In the life estimation process, failure thresholds such as crack length and wear amount are determined based on material tolerance limits or experimental data. The FMEA results are then combined to identify key failure mechanisms and map them to a Physical Lifetime Model (PSM). The PSM is a physical equation based on the failure mechanism, describing the process of damage accumulation until failure under specific loads. Examples of PSMs include the Paris model based on crack propagation or the SN curve. A mathematical model is then established to map the load spectrum to the life distribution to obtain the prediction chain: design load spectrum → stress / strain / temperature field analysis → apply the PSM to calculate damage → accumulate damage until failure → predict the theoretical life of individual components.

[0036] Preferably, due to the dispersion of material properties, manufacturing processes, and loads themselves, the prediction link is run multiple times using a Monte Carlo simulation with equal probability methods to obtain a lifetime prediction distribution. This is then combined with the prediction link to obtain a pseudo-life distribution. The Monte Carlo simulation is set to 10,000 iterations to obtain robust lifetime statistics. The pseudo-life distribution refers to a lifetime distribution predicted entirely based on physical models and theoretical analysis, without verification by actual experimental data. This pseudo-life distribution is used as a priori lifetime estimate and verified and updated using actual finite failure data. This actual finite failure data comes from ground tests, early flight tests, or limited failure data from similar components, and may even include operational data from non-failed components. Specifically, ground test data provides data on crack initiation, propagation, and eventual failure under simulated loads. Flight test and on-orbit data are load data recorded by airborne sensors under real flight conditions, including… Actual damage data such as structural cracks, corrosion, and ablation, as well as performance degradation data such as engine thrust and specific impulse compared between the first flight and subsequent flights, are used. Bayesian statistical methods or least squares methods are employed to combine the prior distribution with the likelihood function implied by the actual observation data. It is assumed that the prior distribution of turbine disk fatigue life is obtained through physical models and Monte Carlo simulations. Failure data of five turbine disk samples obtained through ground tests are used as finite failure data. The life of the turbine disk follows a Weibull distribution, and a likelihood function is established and Bayesian updates are performed. The parameters of the prior Weibull distribution are combined with the likelihood information provided by the observation data to solve for the parameters of the posterior distribution, resulting in a more realistic posterior distribution, i.e., the posterior life distribution. Finally, the posterior life distribution is used as the life estimation result. For example, the lower confidence limit or expected value of the posterior life distribution is taken as the predicted life, thereby improving the accuracy and confidence level of life prediction.

[0037] Furthermore, step S420 also includes step S421, when the degradation data set is obtained, lifetime estimation is performed by extrapolating the performance degradation trend to obtain a pseudo lifetime distribution result; step S422, when the degradation data set is not obtained, failure mechanism uncertainty lifetime estimation is performed in combination with the predicted link to obtain a pseudo lifetime distribution.

[0038] Preferably, the degradation data set includes crack length, wear amount, etc. When the degradation data set is obtained, curve fitting is performed on the obtained performance degradation data set to establish a degradation trajectory model. Then, the fitted degradation trend curve is extended forward until it intersects with the failure threshold. Since the degradation trajectory of each individual is different and there are measurement errors, the lifetime estimated by extrapolating the performance degradation trend is not a fixed value. By statistically analyzing the uncertainty of the degradation trajectory of all individuals, the overall lifetime distribution of the entire product is predicted, i.e., the pseudo lifetime distribution result. When the degradation data set is not obtained, the lifetime estimation of failure mechanism uncertainty is combined with the prediction link, i.e., uncertainty quantification is introduced, such as load uncertainty, material property uncertainty, model parameter uncertainty, and manufacturing process uncertainty. Monte Carlo simulation with equal probability method is used to randomly sample from the distribution of the above-mentioned uncertainties and substitute them into the prediction link for tens of thousands of simulation calculations. Each time, a theoretical lifetime value is obtained, and then statistical analysis is performed to obtain the pseudo lifetime distribution.

[0039] Step S500: Based on the life estimation results, a reliability model is constructed, and a multi-dimensional maintenance impact assessment is performed, including maintenance-free maintenance, preventive maintenance, repair maintenance, and component replacement. The reliability sub-indicators are combined to perform an integrated reliability analysis to obtain the reliability assessment results.

[0040] Preferably, a mathematical model describing the relationship between the overall rocket reliability and time is constructed based on the lifespan estimation results. This model serves as the reliability model, and a multi-dimensional maintenance impact assessment is then conducted, including maintenance-free operation, preventive maintenance, restorative maintenance, and component replacement. Specifically, maintenance-free operation serves as the baseline scenario, assuming that the rocket undergoes no maintenance after its first use and is directly deployed to the next mission. The reliability decreases monotonically with the number of missions, following the initial reliability model. Preventive maintenance refers to proactive maintenance activities performed according to plan or condition before component failure, aiming to prevent failures. This includes preventive maintenance such as maintenance, calibration, and tightening, which is considered to optimize the product's operating environment or condition. Restorative maintenance refers to maintenance performed after component failure to restore its function. Component replacement is a special case of restorative maintenance, emphasizing the planned replacement of high-value, long-life critical components. An integrated reliability analysis is conducted by combining reliability sub-indicators, clarifying the specific effects of different maintenance strategies on improving reliability. Ultimately, a reliability assessment result is obtained, ensuring the economy and safety of reusable rockets while meeting the requirements for rocket integrity and reuse count.

[0041] Preferably, by establishing a reliability block diagram or Markov model of the rocket in reliability simulation software and assigning a life distribution to each component, the reliability changes under different maintenance strategies are dynamically simulated. Specifically, for the post-mission maintenance strategy, the system starts with 100% reliability. As the number of flights increases, the failure probability of the valve and other components accumulates, and the system reliability monotonically decreases at an increasingly faster rate, resulting in a smooth, continuously declining reliability curve. For the periodic preventive maintenance strategy, whenever the number of flights reaches a multiple of 5, the "age" of the valve is forcibly reset to zero in the simulation. Its reliability instantly recovers to 100%, resulting in a sawtooth-shaped reliability curve. After each replacement, the reliability jumps significantly, then continues to decrease with the number of flights. The overall level of the curve is much higher than that of the "no maintenance" strategy. For the post-failure maintenance strategy, if a valve failure is detected during simulation, it is immediately replaced to reset its lifespan. The occurrence of failure is random and depends on its lifespan distribution, resulting in a reliability change curve that jumps at random moments. The magnitude of the jump is related to the number of failed valves and their importance in the system. Its average reliability level is between that of "no maintenance" and "preventive maintenance".

[0042] Furthermore, step S500 also includes step S510, for preventive maintenance, calculating the reliability difference before and after maintenance by adjusting the life model parameters; step S520, for corrective maintenance, obtaining the overall reliability by resetting the life of replacement parts and calculating the reliability contribution of each replacement part; and step S530, for component replacement, recalculating the reliability by resetting the life curve assessment time to zero.

[0043] Preferably, in the reliability model, when preventive maintenance is performed, the life model parameters of the component are reset or refreshed depending on the maintenance effect. A thorough preventive maintenance may reduce the effective age of the component by a certain proportion. For example, after maintenance, the reliability curve of the component does not return to the starting point, but to a position equivalent to 70% of its age. By comparing the improvement in reliability before and after maintenance, the effectiveness of the maintenance measure is quantified. In the simulation, when a component fails and is repaired, the component's life clock is reset to zero, and its reliability curve is recalculated from the initial state. The overall reliability is the result of the combined effect of all components. The simulation tracks the state of each component after maintenance and calculates its contribution to the overall reliability of the system at that moment in real time. Finally, these contributions are accumulated to obtain the overall reliability. Similar to the life reset in restorative maintenance, when a component is replaced when it reaches its planned replacement life, the new component restores its reliability to the initial state, that is, the reliability is recalculated. Assuming a certain type of reusable rocket has a design life of 50 launches, the method of this invention predicts its safe reuse life to be 40 flights with a confidence level of 90%, and gives the key conditions for achieving 40 flights under a predictive maintenance strategy; the failure mode and threshold of the turbopump are also given, with a turbopump blade crack length of 5mm considered as failure, and the degradation data is extrapolated to predict that it will reach this threshold on the 30th flight, thereby determining its life contribution.

[0044] Step S600: Integrate the maintainability assessment results, testability sub-indicator assessment results, life estimation results, and reliability assessment results to obtain the target life prediction results.

[0045] Preferably, the life estimation result is based on the theoretical life of the physical model and data, the reliability assessment result is the dynamic reliability evolution after the introduction of maintenance strategies, the maintainability assessment result is the assessment of the maintenance support capabilities required to achieve reliability, and the testability sub-index assessment result is the assessment of the rocket's condition monitoring and fault diagnosis capabilities. Then, the maintainability assessment result, the testability sub-index assessment result, the life estimation result, and the reliability assessment result are integrated to obtain the target life prediction result. For example, under predictive maintenance, based on the current testability level, the safe reuse life of the rocket is 40 flights, along with the confidence level and key conditions of this life value, thereby ensuring the improvement of the accuracy and availability of rocket life prediction.

[0046] In the above text, refer to Figure 1 A method for predicting the lifespan of a reusable rocket according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A life prediction system for a reusable rocket according to an embodiment of the present invention is described.

[0047] According to an embodiment of the present invention, a life prediction system for reusable rockets addresses the technical problems in existing technologies where reusable rocket life prediction lacks comprehensive dimensions and fails to integrate the coupled effects of reliability, maintainability, and testability, resulting in insufficient prediction accuracy and limited practicality. The system achieves the technical effect of improving the accuracy and usability of rocket life prediction through multi-strategy evaluation and result integration. Figure 2 As shown, a life prediction system for a reusable rocket includes: an index decomposition module 10, a maintainability assessment module 20, a test result analysis module 30, a life estimation module 40, a maintenance impact assessment module 50, and a life prediction result acquisition module 60.

[0048] The indicator decomposition module 10 is used to decompose indicators based on the rocket's operational availability and reuse requirements, combined with top-level availability indicators, to construct an availability indicator system covering the system level, subsystem level, and key component level. This availability indicator system includes reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators. The maintainability assessment module 20 is used to perform qualitative and quantitative assessments of the rocket based on the maintainability sub-indicators to obtain maintainability assessment results. The test result analysis module 30 is used to identify diagnostic strategies based on the rocket's mission requirements and product characteristics, conduct rocket testing according to the diagnostic strategies, and analyze the test results based on the testability sub-indicators to obtain testability sub-indicators. The system comprises the following modules: a target life assessment module 40, a life estimation module 50, and a maintenance impact assessment module 60. The maintenance impact assessment module 50 is used to construct a reliability model based on the life estimation results and to perform multi-dimensional maintenance impact assessments including maintenance-free maintenance, preventative maintenance, repair maintenance, and component replacement. It also integrates reliability sub-indicators to conduct reliability analysis and obtain reliability assessment results. Finally, the life prediction result acquisition module 60 integrates the maintainability assessment results, testability sub-indicator assessment results, life estimation results, and reliability assessment results to obtain the target life prediction result.

[0049] The specific configuration of the indicator decomposition module 10 will be described in detail below. The indicator decomposition module 10 further includes: introducing a human posture model library and a maintenance channel model, performing dynamic simulation of typical maintenance tasks, obtaining simulation results, conducting risk assessment in conjunction with the maintainability sub-indicators in the availability indicator system, refining design criteria based on the risk assessment results, obtaining maintenance accessibility design criteria, and feeding back the maintenance accessibility design criteria to designers for design optimization.

[0050] The specific configuration of the indicator decomposition module 10 will be described in detail below. The indicator decomposition module 10 further includes: the maintenance accessibility design criteria include layout and accessibility criteria, easy-to-maintain structure design criteria, simplified maintenance process criteria, and diagnostic testing interface design criteria.

[0051] The specific configuration of the lifetime estimation module 40 will be described in detail below. The lifetime estimation module 40 further includes: constructing a design load spectrum by comprehensively considering the rocket's full life-cycle load characteristics and typical operating conditions; identifying key failure mechanisms by combining the results of failure mode influence analysis; mapping and selecting a physical lifetime model; and constructing a prediction link from the load spectrum to the lifetime distribution. Based on the prediction link, a pseudo-life distribution is obtained. This pseudo-life distribution is used as a priori lifetime estimate, verified and updated using actual finite failure data to obtain a posterior lifetime distribution, which is then used as the lifetime estimation result.

[0052] The specific configuration of the lifetime estimation module 40 will be described in detail below. The lifetime estimation module 40 further includes: when a degradation data set is acquired, lifetime estimation is performed by extrapolating the performance degradation trend to obtain a pseudo lifetime distribution result; when a degradation data set is not acquired, lifetime estimation based on the failure mechanism uncertainty is performed in conjunction with the predicted link to obtain a pseudo lifetime distribution.

[0053] The specific configuration of the test result analysis module 30 will be described in detail below. The test result analysis module 30 further includes: establishing a correlation model between fault modes and test signals to obtain an initial test set; simplifying the initial test set to obtain a simplified test set; constructing a diagnostic tree and a fault dictionary; traversing the simplified test set through correlation analysis to perform fault detection analysis; guiding fault isolation based on the diagnostic tree and fault dictionary; performing iterative optimization of the test based on the fault detection rate and isolation rate obtained from the analysis to obtain a diagnostic strategy set; and matching the diagnostic strategy set with the task requirements and product characteristics to obtain a diagnostic strategy.

[0054] The specific configuration of the maintenance impact assessment module 50 will be described in detail below. The maintenance impact assessment module 50 further includes: for preventive maintenance, calculating the reliability difference before and after maintenance by adjusting the life model parameters; for corrective maintenance, obtaining the overall reliability by resetting the life of replacement parts and calculating the reliability contribution of each replacement part; for component replacement, recalculating the reliability by resetting the life curve assessment time to zero.

[0055] The lifespan prediction system for a reusable rocket provided in this embodiment of the invention can execute the lifespan prediction method for a reusable rocket provided in this embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method. The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for predicting the lifespan of a reusable rocket, characterized in that, The method includes: Based on the rocket's operational integrity rate and reuse requirements, and combined with the top-level availability indicators, an availability indicator system covering the system level, subsystem level, and key component level is constructed. The availability indicator system includes reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators. The rocket is qualitatively and quantitatively evaluated by combining the aforementioned maintainability sub-indicators to obtain maintainability evaluation results. Based on the mission requirements and product characteristics of the rocket, a diagnostic strategy is identified, rocket testing is conducted according to the diagnostic strategy, and the test results are analyzed in combination with test performance sub-indicators to obtain the evaluation results of the test performance sub-indicators. The design load spectrum is constructed, the failure mechanism analysis and life model construction of the rocket are carried out, and the life is estimated based on the construction results to obtain the life estimation results; Based on the life estimation results, a reliability model is constructed, and a multi-dimensional maintenance impact assessment is performed, including maintenance-free maintenance, preventive maintenance, repair maintenance, and component replacement. Combined with reliability sub-indicators, an integrated reliability analysis is conducted to obtain the reliability assessment results. By integrating the maintainability assessment results, testability sub-indicator assessment results, life estimation results, and reliability assessment results, the target life prediction results are obtained.

2. The method for predicting the lifespan of a reusable rocket as described in claim 1, characterized in that, By introducing a human posture model library and a maintenance access model, dynamic simulations are performed on typical maintenance tasks to obtain simulation results. Risk assessment is then conducted by combining the maintainability sub-indicators in the availability index system. Based on the risk assessment results, design criteria are extracted to obtain maintenance accessibility design criteria, which are then fed back to designers for design optimization.

3. The method for predicting the lifespan of a reusable rocket as described in claim 2, characterized in that, The maintenance accessibility design criteria include layout and accessibility criteria, easy-to-maintain structural design criteria, simplified maintenance process criteria, and diagnostic and testing interface design criteria.

4. The method for predicting the lifespan of a reusable rocket as described in claim 1, characterized in that, A design load spectrum is constructed, a failure mechanism analysis and life model are built for the rocket, and a life estimate is performed based on the construction results. The life estimate results include: Based on the full life cycle load characteristics and typical operating conditions of the rocket, a design load spectrum is constructed. Key failure mechanisms are identified by combining the failure mode influence analysis results, and a physical lifetime model is selected to construct a prediction link from the load spectrum to the lifetime distribution. By combining the predicted links, a pseudo-lifetime distribution is obtained; The pseudo-lifetime distribution is used as a priori lifetime estimate, and then verified and updated by combining it with actual finite failure data to obtain a posterior lifetime distribution, which is then used as the lifetime estimate result.

5. The method for predicting the lifespan of a reusable rocket as described in claim 4, characterized in that, include: When a set of degraded data is obtained, lifetime estimation is performed by extrapolating the performance degradation trend to obtain pseudo lifetime distribution results. When the degradation data set is not available, the failure mechanism uncertainty lifetime estimation is performed by combining the predicted link to obtain the pseudo lifetime distribution.

6. The method for predicting the lifespan of a reusable rocket as described in claim 1, characterized in that, Based on the rocket's mission requirements and product characteristics, diagnostic strategies are identified. Rocket testing is then conducted according to these strategies. Test results are analyzed in conjunction with testability sub-indicators to obtain evaluation results for these sub-indicators, including: Establish a correlation model between fault modes and test signals to obtain an initial test set, and then simplify the initial test set to obtain a simplified test set. Construct a diagnostic tree and a fault dictionary; Fault detection analysis is performed by traversing the simplified test set through correlation analysis. Fault isolation is guided by diagnostic tree and fault dictionary. Test iteration optimization is carried out based on the fault detection rate and isolation rate obtained from the analysis to obtain a set of diagnostic strategies. The diagnostic strategy set is matched based on the task requirements and product characteristics to obtain a diagnostic strategy.

7. The method for predicting the lifespan of a reusable rocket as described in claim 1, characterized in that, Based on the lifespan estimation results, a reliability model is constructed, and a multi-dimensional maintenance impact assessment is performed, including maintenance-free maintenance, preventative maintenance, repair maintenance, and component replacement. An integrated reliability analysis is then conducted using reliability sub-indices to obtain the reliability assessment results, including: For preventative maintenance, the reliability difference before and after maintenance is calculated by adjusting the life model parameters; For corrective repairs, the overall reliability is obtained by resetting the lifespan of replacement parts and calculating the reliability contribution of each replacement part, and then summing them up. For component replacement, the life curve assessment time is reset to zero and the reliability is recalculated.

8. A lifespan prediction system for a reusable rocket, characterized in that, The system is used to implement the life prediction method for a reusable rocket according to any one of claims 1 to 7, the system comprising: The indicator decomposition module is used to decompose indicators based on the rocket's integrity rate and reuse times requirements, combined with top-level availability indicators, to construct an availability indicator system covering the system level, subsystem level, and key component level. The availability indicator system includes reliability sub-indicators, maintainability sub-indicators, and testability sub-indicators. The maintainability assessment module is used to perform qualitative and quantitative assessments of the rocket by combining the maintainability sub-indicators, and to obtain maintainability assessment results. The test result analysis module is used to identify diagnostic strategies based on the rocket's mission requirements and product characteristics, conduct rocket tests according to the diagnostic strategies, and analyze test results in combination with test performance sub-indicators to obtain test performance sub-indicator evaluation results. The life estimation module is used to construct the design load spectrum, perform failure mechanism analysis and life model construction on the rocket, and perform life estimation based on the construction results to obtain the life estimation results. The maintenance impact assessment module is used to construct a reliability model based on the life estimation results, and to conduct a multi-dimensional maintenance impact assessment of maintenance-free, preventive maintenance, repair maintenance and component replacement. It also integrates reliability sub-indicators to perform reliability analysis and obtain reliability assessment results. The life prediction result acquisition module is used to integrate the maintainability assessment results, testability sub-indicator assessment results, life estimation results, and reliability assessment results to obtain the target life prediction result.

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