A method and system for reliability evaluation of a repeatable system

Abstract

The application discloses a kind of reliability evaluation method and system of repeatable operation system, belong to system degradation modeling and reliability analysis field.For the stage-dependent degradation, competing failure and imperfect maintenance behavior of repeatable operation system in storage, operation and maintenance stage, a degradation model considering the competition of soft failure and hard failure is proposed.The method uses linear wiener process to describe natural degradation, based on environment stress-dependent poisson process to depict random and fixed impact, introduces safety impact threshold and damage impact cumulative threshold to distinguish impact type, uses maintenance termination threshold and target maintenance threshold to describe the influence of imperfect maintenance on system state and degradation rate.Sectional environment pressure function is constructed, and the reliability analytical expression of the system under multi-cycle operation is derived.The method of the application can accurately evaluate the reliability of repeatable operation system, compared with the model ignoring maintenance or stage difference, can avoid estimation bias, and provide a more reasonable basis for system maintenance decision.

Description

Technical Field

[0001] This invention belongs to the field of system degradation modeling and reliability analysis, and relates to degradation modeling and reliability estimation of repeatable systems, and particularly to a reliability assessment method and system for repeatable systems. Background Technology

[0002] Reusability refers to the process by which a system, after completing a mission cycle, undergoes evaluation, analysis, and maintenance, and then resumes its operational duties. Reusable launch vehicles are a typical example of reusable systems. Due to limitations in materials and manufacturing processes, as well as the requirement for high reliability, the first-stage propulsion system of a launch vehicle was, for a long time, a single-use system, essentially destroyed after use. However, the advent of the Falcon 9 in 2015 changed the development model of launch vehicles, and reusable propulsion systems have gradually become the mainstream in launch vehicle development.

[0003] The Falcon 9 operates in multiple states throughout a mission cycle. Depending on the mission, the system's operation can be divided into three phases: storage, operation, and maintenance. Generally, degradation occurs during both storage and operation. In the storage phase, degradation is primarily caused by chemical aging of materials or components, such as corrosion, oxidation, and deterioration. In the operation phase, degradation is mainly caused by functional aging during use, such as ablation, wear, and cracking. Degradation in both storage and operation phases contributes to failure, but due to differences in the system's operating environment, operating mode, and aging mechanisms at different stages, the degradation behavior exhibits a phase-related correlation. Simultaneously, external and internal shocks resulting from changes in the operating mode and environment during the operation phase also affect the system's degradation process. Shocks not only affect the system's degradation process but can also lead to sudden system failure; this random failure behavior caused by shocks during equipment use is defined as hard failure. Soft failures caused by system performance aging compete with hard failures caused by shocks and are therefore usually defined as competing failures. The maintenance phase primarily involves detecting and assessing the system's status and conducting targeted maintenance actions, such as repairs and component replacements, to extend the system's uptime. System maintenance actions simultaneously affect both soft and hard failure processes, restoring the system to its degraded state. Phase-dependent degradation, competing failures, and maintenance actions are typical characteristics of degradation in reusable systems, further increasing the difficulty of system degradation modeling and reliability analysis. Therefore, a reliability analysis and assessment method is urgently needed for reusable systems exhibiting imperfect maintenance processes and competing failure behaviors. Summary of the Invention

[0004] To address the shortcomings of existing research and solve the problems of degradation modeling and reliability analysis for reusable systems, this invention provides a reliability assessment method and system for reusable systems. It proposes a degradation model for reusable systems that simultaneously considers stage-dependent degradation, competitive failure, and maintenance behavior, analyzing the impact of the storage, operation, and maintenance stages, and constructing a stage-dependent degradation model with periodicity. Furthermore, this invention uses environmental pressure to describe stage differences, quantitatively analyzing its impact on shock intensity, shock probability, and degradation rate. It also considers the system's resistance to shocks, constructing a regional shock model to describe the effects of random and fixed shocks on the system, and using maintenance termination thresholds and maintenance target thresholds to analyze the system's maintenance behavior, considering the impact of the maintenance process on the system state and degradation rate.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a reliability assessment method for a reusable system, comprising the following steps: Obtain the degraded dataset from the system status monitoring sensor acquisition devices; Based on the system's working environment, working mode, and working status, establish model assumptions that consider stage dependency degradation, competition failure, and maintenance behavior; Based on the model assumptions, a natural degradation model and an impact degradation model of the system are constructed, and the natural degradation model and the impact degradation model are fused to obtain a comprehensive state degradation model of the system; based on the comprehensive state degradation model, the analytical calculation formula for the reliability of the system is derived. The model parameters are estimated by utilizing at least one of the following: material properties of system components, expert or experiential knowledge, and historical degradation data. Based on the comprehensive state degradation model and the model parameters, the reliability estimate of the system is calculated using the reliability analytical calculation formula.

[0006] Furthermore, the model assumptions include: The system's life cycle includes a storage phase, an operation phase, and a maintenance phase. The system degrades during both the storage and operation phases, and the operation phase is further divided into ground and air states. During the storage phase, only soft failure modes exist, while during the operation phase, both soft and hard failure modes exist. The system is subjected to two types of impacts: random impacts and fixed impacts. Random impacts occur at any time during the operation phase, while fixed impacts occur at fixed times during the transition between ground and air states during the operation phase. The system undergoes maintenance at the end of each cycle, and the maintenance process affects the system's degradation state and degradation rate. Set maintenance thresholds for soft failure processes and hard failure processes for the system. When the system degradation state exceeds the maintenance threshold, the system is determined to have failed. Set target maintenance thresholds for soft failure processes and hard failure processes for the system. The system state after maintenance must be less than the target maintenance threshold.

[0007] Furthermore, the natural degradation model is constructed as follows: a linear Wiener process is used to describe the natural degradation of the system during the storage and operation phases. The degradation rate is proportional to the environmental pressure on the system, and the environmental pressure is a piecewise function in different phases and states.

[0008] Furthermore, the impact degradation model is constructed as follows: the arrival probability of random impacts is described based on the Poisson distribution, and the Poisson distribution parameters are related to the environmental stress; the intensity of random impacts follows a normal distribution, and its expectation and variance are related to the environmental stress; the intensity of fixed impacts follows a normal distribution; according to the system's resistance to impacts, random impacts are divided into safe impacts and damage impacts. Safe impacts do not affect the degradation state, while damage impacts do affect the degradation state; when the impact intensity exceeds a preset damage threshold, it is determined to be a damage impact; the hard failure of the system adopts a cumulative impact model, and the system experiences hard failure when the cumulative damage impact intensity exceeds the hard failure threshold.

[0009] Furthermore, the maintenance behavior is imperfect maintenance, the system state after maintenance is proportional to the target maintenance threshold, and the degradation rate conversion coefficient and maintenance state conversion coefficient are related to the number of maintenance cycles.

[0010] Furthermore, the construction of the comprehensive state degradation model specifically includes: based on the number of maintenance cycles the system has undergone and the current stage, establishing expressions for the degradation rate and Poisson process parameters of the system in the storage stage, the ground state in the operation stage, and the air state in the operation stage, respectively, and combining the natural degradation component and the impact degradation component to obtain the system state. The comprehensive state degradation model is as follows:

[0011] in, For the first System status after the last maintenance. For the first One work cycle time, The rate of ground condition degradation. The rate of degradation of the air state. Total system uptime The number of impacts that caused the damage. , As a conversion factor between impact strength and system damage, For the first Impact strength of ground condition 1 after the second maintenance. For the first Impact intensity in the air after the second maintenance. k The sequence number of the impact. for The impact strength of fixed impact 2 after the second maintenance. for The impact strength of fixed impact 1 after the second maintenance. For the first Impact strength of ground condition 2 after the second maintenance. For the first Impact strength in storage state after the first maintenance The number of damage impacts occurring within a given time period. and Indicates the system at the 1st Storage phase duration after the first maintenance in the next work cycle. , and These represent the time spent in the ground, air, and ground states, respectively.

[0012] Furthermore, the derivation of the analytical reliability calculation formula includes: deriving the soft failure reliability and hard failure reliability of the system in a single working cycle and multiple working cycles in stages, and considering the impact of maintenance behavior on the initial value of the system state and the degradation rate, to obtain the overall system reliability expression.

[0013] Secondly, the present invention provides a reliability assessment system for a repeatable system, characterized in that it includes: The data acquisition module is used to acquire the degraded dataset of the status monitoring sensor acquisition devices deployed in the system; The model building module is used to establish model assumptions that take into account stage dependency degradation, competition failure, and maintenance behavior, based on the system's working environment, working mode, and working status. The model building module is used to construct a natural degradation model and an impact degradation model of the system based on the model assumptions, and to fuse the natural degradation model and the impact degradation model to obtain a comprehensive state degradation model of the system, and to derive the analytical calculation formula for the reliability of the system based on the comprehensive state degradation model. The parameter estimation module is used to estimate model parameters by utilizing at least one of the following: material properties of system components, expert or experiential knowledge, and historical degradation data. The reliability assessment module is used to calculate the system's reliability estimate based on the comprehensive state degradation model and the model parameters, using the reliability analytical calculation formula.

[0014] Thirdly, the present invention also provides a computer device, including a processor and a memory, wherein the memory is used to store a computer executable program, the processor reads part or all of the computer executable program from the memory and executes it, and the processor can implement the above-mentioned reliability assessment method for a reusable system when executing part or all of the computer executable program.

[0015] A computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, enables the aforementioned reliability assessment method for a reusable system.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: This invention addresses the reliability assessment needs of reusable systems. Considering external shocks, competitive failures, and maintenance behaviors during the degradation process, it constructs a reliability analysis model for reusable systems based on a linear degradation function and a composite Poisson process. The model incorporates the impact of maintenance behaviors on the soft and hard failure processes of the system, derives and analyzes the system reliability estimation results, and experimental results show that the proposed model can accurately estimate system reliability, improving the efficiency and economy of assurance. Attached Figure Description

[0017] To facilitate technical personnel's understanding of the implementation process of this invention, the accompanying drawings are described below.

[0018] Figure 1 This is a flowchart for reliability estimation of repeatable systems that takes into account imperfect maintenance processes and competing failure behaviors; Figure 2 This is a graph showing the reliability estimation results of the proposed model; Figure 3 This is a comparison chart of the reliability estimation results of different models; Figure 4 This is a parameter sensitivity analysis diagram for the model; Detailed Implementation The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] The purpose of this invention is to construct a degradation model for repeatable systems with imperfect maintenance behavior and competing failure modes in order to obtain their reliability estimation results.

[0020] To make the model proposed in this invention easier for those skilled in the art to understand, the following will describe in detail the construction process and implementation method of the degradation model of repeatable systems under imperfect maintenance behavior and competing failure modes.

[0021] Figure 1 This is a flowchart illustrating the staged two-variable degradation device reliability and remaining life estimation proposed in this invention, taking a reusable vehicle system as an example. Figure 1 The present invention provides a method for estimating the reliability and lifespan of a phased two-variable degradation device, comprising: Step 1: Collect equipment degradation datasets based on monitoring sensors ; Step 2: Propose corresponding model assumptions based on the actual situation to simplify modeling and computational complexity; Step 3: Construct a natural degradation model for the system using a linear degradation model, and an impact degradation model based on the Poisson distribution assumption. These are then combined to obtain a comprehensive system state degradation model. Using the constructed comprehensive state degradation model, the analytical formula for calculating the system's reliability is derived. Step 4: Estimate the model parameters using the material properties of system components, expert or experiential knowledge, historical degradation data, etc. Step 5: Combine the obtained degradation model with the system parameters to calculate the system reliability estimate.

[0022] Based on the above technical solution, the specific implementation steps will be further explained.

[0023] Step 1: Collect multi-source degradation data from the system using condition monitoring sensors deployed on the system, and perform data preprocessing to obtain the equipment degradation dataset. .

[0024] Step 2: Based on the degradation characteristics of the equipment degradation data, and considering the system's working environment, operating mode, and working status, propose a reasonable model description to simplify the modeling complexity. Specific assumptions are explained below: 1) Reusable delivery systems exist in three phases: operation, storage, and maintenance. System performance degrades during both the operation and storage phases. For high-reliability systems, the performance degradation during the storage phase cannot be ignored; therefore, the system degradation is the result of the cumulative effect of both phases. Here, we use... and Indicates the system at the 1st The duration of the storage and operation phases in the next work cycle after the first maintenance. and This indicates the duration of the system's first operation after leaving the factory. , and These represent the time spent in the ground, air, and ground states, respectively.

[0025] 2) Reusable launch systems experience relatively mild environmental conditions during storage, resulting in a continuous and stable degradation process. However, during operation, the environmental conditions change drastically due to events such as launch, reentry, and landing. Therefore, the operation phase can be further divided into ground and air states, with the environmental stress generally considered to be greater in the air state than on the ground state. It is generally assumed that the environmental pressure experienced by a reusable launch system is a piecewise function throughout its operational cycle. Its environmental pressure during storage is The environmental pressure at ground level is The ambient pressure in the air is .

[0026] 3) Use a linear Wiener process to describe the natural degradation of the system during the storage and operation phases, i.e. .in Indicates the initial value of the system state. Let be the drift coefficient of the Wiener process. The degradation rate of the system varies significantly at different stages and under different operating conditions. Generally, the harsher the operating environment, the faster the degradation. To describe this phenomenon, the coefficients of the given linear degradation model are proportional to the environmental stress on the system, i.e. . The proportionality coefficient is a normally distributed random variable, i.e. The soft failure threshold for reusable launch systems during both the storage and operation phases is expressed as... It remains unchanged during the degradation process.

[0027] 4) In storage mode, the equipment is assumed to be immune to external impacts. Therefore, storage mode only exhibits soft failure modes, while operational mode exhibits both soft and hard failure modes. Furthermore, reusable vehicle systems experience both random and fixed impact modes. Random impacts can occur at any time during operation. Fixed impacts can only occur at specific moments in the system's operation, specifically during the transition between ground and air states.

[0028] 5) The probability of random impact arrival follows a Poisson distribution, but the probability of system impact occurrence is affected by operating conditions and environmental stress. Generally, the probability of impact occurrence and the impact intensity are higher under harsh environmental conditions (high stress). To describe the influence of environmental stress on impact, the Poisson distribution parameters are described in this invention as being dependent on environmental stress, with the following transformation factors: , and Impact strength Described as a normally distributed random variable. To describe the effect of environmental stress on impact intensity, the expected value of the impact intensity distribution is also used. With variance This is expressed as dependent on environmental stress, i.e. , and This is the conversion factor between mean and variance. Fixed shock. The impact on the system is similar to that of random shocks; the intensity of the shock also follows a normal distribution, i.e. The conversion factor between impact strength and system damage is: .

[0029] 6) The system's hard failure is described using a cumulative impact model, meaning the system fails when the cumulative impact intensity exceeds a given threshold. We assume that impacts only affect the system's degradation state. Considering the system's resistance to external impacts, random impacts can be categorized into safe impacts and damaging impacts. Safe impacts have no effect on the system's degradation. Damaging impacts, however, affect the system's degradation state. An impact threshold is used. To distinguish between safety and damage impact. When the cumulative damage impact intensity of the system reaches the failure threshold. The system experienced a hard failure.

[0030] 7) A complete system cycle consists of one storage and one running phase. This indicates that the system undergoes maintenance at the end of each cycle (which can be understood as planned maintenance). However, after multiple cycles, considering the overall maintenance costs (maintenance time, personnel, and components), parts of the system may no longer be worth maintaining, at which point the system is considered to have failed. To describe this phenomenon, a system maintenance threshold exists for a given system. and . The maintenance threshold representing the soft failure process of the system. This represents the maintenance threshold for a system's hard failure process. When the system's degradation exceeds this threshold, the system is no longer worth maintaining and is defined as a failure. There are also requirements for the system's post-maintenance state; the post-maintenance system state must be less than the target maintenance threshold. and .

[0031] 8) Although system maintenance occurs, the system rarely returns to a completely new state after maintenance. Therefore, maintenance can only extend the system's operational lifespan to a certain extent. This paper describes the post-maintenance state of the system as proportional to the target maintenance threshold, i.e. , . To maintain the state values ​​of the system after the soft failure process, To maintain the state values ​​after the hard failure process, This is a proportionality coefficient. Simultaneously, it is a degradation rate conversion coefficient. State transition scaling factor Related to the number of maintenance cycles, i.e. , ,in , This is a hyperparameter.

[0032] Based on the above description, the probability of different types of impacts occurring can be obtained as follows:

[0033] in This is the distribution function of impact intensity, which is also related to environmental pressure. Impact strength, its representative . The probability of a security breach occurring varies depending on the system's operating state. , and . , and These represent the probabilities of safe impact occurrence during the storage phase, the ground-based state during the operation phase, and the air-based state during the operation phase, respectively. Similarly... The probability of damage occurring due to impact is represented by... , and At this point, the impact of the reusable launch system at different state stages can be understood as having parameters as follows: Two independent Poisson processes. The parameters of the Poisson distribution are represented by... , and .

[0034] Step 3: Based on the above description, obtain the system's degradation rate and Poisson distribution impact parameters in the ground state during storage and operation phases, and in the air state during operation phase, respectively: Storage stage: This corresponds to the Poisson process parameters. The expected value and the root mean square deviation of the impact strength are respectively and .

[0035] Ground Phase (Operating State): This phase describes the Poisson process parameters that determine the occurrence of the impact. The expected value and the root mean square deviation of the impact strength are respectively and .

[0036] The airborne phase of the operational state: This phase describes the Poisson process parameters that describe the occurrence of the impact. The expected value and the root mean square deviation of the impact strength are respectively and .

[0037] Using the above analysis results, after experiencing The comprehensive state degradation model of the system after the first maintenance can be expressed as:

[0038] Experienced The cumulative impact degradation model of the system after the first maintenance can be expressed as:

[0039] in and For those who have experienced The system's degraded state after the last maintenance. and These are the initial values ​​of the system state after maintenance. Considering the impact regions of the system, the impacts that cause damage to the system follow a truncated normal distribution, and their mean and variance can be expressed as:

[0040] In the formula and Let these be the probability density function and cumulative distribution function of the standard normal distribution, respectively. Similarly, the mean and variance of the truncated normal distribution of the impact intensity under ground and flight conditions during the operational phase can be obtained. , This represents the lower boundary probability after transformation. The lower boundary distribution after transformation, The root mean square of the truncated distribution for storage conditions. The mean of the truncated distribution for storage status.

[0041] Based on the system's degradation stages and degradation model, taking the first working cycle as an example, the system's lifetime prediction results are derived under different scenarios: During the storage phase, i.e., system runtime hour:

[0042] in This represents the reliability distribution of the system's soft failure process. Reliability analysis representing the hard failure process of a system. Root mean square deviation of the proportionality coefficient Probability of damage from impact during storage Number of impacts that caused damage.

[0043] During the ground-based operational phase, i.e., the system's operating time... hour:

[0044] The initial value of the system's degenerate state. The root mean square error of the ground state truncated distribution. The mean of the truncated distribution of ground conditions. The root mean square deviation of the proportionality coefficient. The mean of the truncated distribution is the storage state. The duration of the storage state, Environmental pressure at ground level. The environmental pressure during storage, where the probability of ground-level damage and impact occurs during operation. It can be represented as:

[0045] This represents the probability of ground-condition damage from impact. Impact intensity distribution; During the operational phase in the air state, i.e., the system runtime hour:

[0046]

[0047] in, The average impact value is fixed. To fix the impact variance; This is the soft failure threshold; This represents the initial cumulative impact value; the probability of damage impact occurring during flight conditions in the operational phase. The probability of a fixed impact being a safe impact or a damaging impact. , It can be represented as:

[0048] When the system is operating in the second operational phase, the ground state, i.e., the system runtime hour:

[0049]

[0050] Similar to the derivation process above, considering the maintenance activities of the system, when the system enters its second working cycle, i.e., the system runtime... The reliability of the system can be expressed as:

[0051] In the formula This represents the system's reliability during the first operating cycle, and its calculation formula is shown above. However, it's important to note the threshold value in the above formula. and Need to be replaced with threshold and . This indicates the system reliability during the storage phase of the second working cycle. It's important to note that the initial state values ​​and initial cumulative damage values ​​of the system need to consider the impact of maintenance actions. This represents the probability of ground-level damage impact occurring. For the atmospheric pressure in the air; Duration of the first ground state; The duration of the aerial state.

[0052] If the system exists The system reliability calculation formula for a single maintenance process can be expressed as:

[0053] Step 4: Estimate the model parameters using the material properties of system components, expert or experiential knowledge, historical degradation data, etc.

[0054] in For parameter vectors.

[0055] Using the obtained degradation model and system parameters, the reliability estimate of the system is obtained through the reliability analytical calculation formula.

[0056] Step 5: To fully demonstrate the effectiveness of the present invention, the following verification is performed using a degradation dataset of a three-cycle repeatable system.

[0057] The reliability estimation results using the proposed model are compared with the reliability results based on Monte Carlo simulation, for example... Figure 2 As shown in the figure, the curves of the simulation results and the analytical results are in good agreement, which fully demonstrates the effectiveness of the model proposed in this paper.

[0058] Figure 3The paper illustrates the changes in system reliability across three cycles. In the first cycle, during the storage phase, the system reliability remains relatively stable due to the slow degradation rate. As the system transitions to the operational phase, reliability gradually decreases over time. At the end of the first cycle, the system reliability experiences a sudden shift as some devices reach the maintenance termination threshold. In the second cycle, due to maintenance, both soft and hard failure states are below the target maintenance threshold, thus maintaining stable reliability during the storage phase. However, upon transitioning to the operational phase, system reliability rapidly declines over time due to the accelerated degradation rate after maintenance. The reliability changes in the third cycle are similar to those in the first and second cycles and are not described here. The figures show that the reliability curves of the proposed model largely overlap with those of the model without maintenance during the first cycle. However, because the comparative model lacks maintenance, the system reliability continues to decline during the storage phase after entering the second cycle. The comparative results demonstrate that maintenance actions influence system reliability, slowing down the reliability decline and extending the system's operational lifespan. The reliability curve of the model proposed in this paper is higher than that of the comparative models 1 and 2. The comparison results indicate that using a single-state model to analyze a repeatable system will lead to either an overly optimistic or overly conservative estimation of system reliability. Both will result in an underestimated system reliability, leading to premature maintenance and increased costs.

[0059] Figure 4 Reflects the shock occurrence probability conversion factor Regarding the impact on system reliability, the figure shows that changes in the conversion factor have a greater impact on system reliability than environmental stress. This indicates that the more sensitive the system is to environmental pressure, the greater the probability of shocks occurring, leading to faster system degradation.

[0060] Based on the above method, the present invention can also provide a reliability assessment system for a reusable system, comprising: The data acquisition module is used to acquire the degraded dataset of the status monitoring sensor acquisition devices deployed in the system; The model building module is used to establish model assumptions that take into account stage dependency degradation, competition failure, and maintenance behavior, based on the system's working environment, working mode, and working status. The model building module is used to construct a natural degradation model and an impact degradation model of the system based on the model assumptions, and to fuse the natural degradation model and the impact degradation model to obtain a comprehensive state degradation model of the system, and to derive the analytical calculation formula for the reliability of the system based on the comprehensive state degradation model. The parameter estimation module is used to estimate model parameters by utilizing at least one of the following: material properties of system components, expert or experiential knowledge, and historical degradation data. The reliability assessment module is used to calculate the system's reliability estimate based on the comprehensive state degradation model and the model parameters, using the reliability analytical calculation formula.

[0061] The model assumptions in the model building module include: the system's life cycle includes a storage phase, an operation phase, and a maintenance phase; the system degrades during both the storage and operation phases, and the operation phase is further divided into ground state and air state; only soft failure modes exist during the storage phase, while both soft and hard failure modes exist during the operation phase; the system is subjected to two impact modes: random impact and fixed impact; the system undergoes maintenance at the end of each cycle, and the maintenance behavior affects the system's degradation state and degradation rate.

[0062] When constructing the natural degradation model, the model building module uses a linear Wiener process to describe the natural degradation of the system during the storage and operation phases. The degradation rate is proportional to the environmental pressure experienced by the system.

[0063] When constructing the impact degradation model, the model building module describes the arrival probability of random impacts based on the Poisson distribution, and sets the intensity of random and fixed impacts to follow a normal distribution related to environmental stress.

[0064] The model building module is also used to classify random shocks into safe shocks and damaging shocks based on the system's resistance to shocks, and to set the cumulative shock model for hard failure of the system.

[0065] The model assumptions in the model building module also include: setting maintenance thresholds for soft failure processes and hard failure processes, as well as target maintenance thresholds for soft failure processes and hard failure processes; the maintenance behavior is imperfect maintenance, the system state after maintenance is proportional to the target maintenance threshold, and the degradation rate conversion coefficient and maintenance state conversion coefficient are related to the number of maintenance cycles.

[0066] When constructing the comprehensive state degradation model, the model building module establishes the degradation rate and Poisson process parameter expressions for the system in the storage stage, the ground state in the operation stage, and the air state in the operation stage, based on the number of maintenance cycles the system has undergone and the current stage. The system state is obtained by integrating the natural degradation component and the impact degradation component.

[0067] When calculating the reliability estimate, the reliability assessment module derives the soft failure reliability and hard failure reliability of the system in stages within a single working cycle and multiple working cycles, and considers the impact of maintenance behavior on the initial value of the system state and the degradation rate to obtain the overall system reliability.

[0068] On the other hand, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the reliability assessment method for a reusable system described in the present invention.

[0069] The present invention can also provide a computer device, including a processor and a memory, wherein the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and the processor can implement the reliability assessment method of the reusable system described in the present invention when executing the computer executable program.

[0070] The computer device may be a laptop, tablet, desktop computer, or workstation.

[0071] The processor can be a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or an off-the-shelf programmable gate array (FPGA).

[0072] The memory described in this invention can be an internal storage unit of a laptop, tablet, desktop computer, or workstation, such as memory or hard disk; or it can be an external storage unit, such as a portable hard disk or flash memory card.

[0073] Computer-readable storage media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media can include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. Random access memory can include resistive random access memory (ReRAM) and dynamic random access memory (DRAM).

[0074] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A reliability assessment method for a reusable system, characterized in that, Includes the following steps: Obtain the degraded dataset from the system status monitoring sensor acquisition devices; Based on the system's working environment, working mode, and working status, establish model assumptions that consider stage dependency degradation, competition failure, and maintenance behavior; Based on the model assumptions, a natural degradation model and an impact degradation model of the system are constructed, and the natural degradation model and the impact degradation model are fused to obtain a comprehensive state degradation model of the system; based on the comprehensive state degradation model, the analytical calculation formula for the reliability of the system is derived. The model parameters are estimated by utilizing at least one of the following: material properties of system components, expert or experiential knowledge, and historical degradation data. Based on the comprehensive state degradation model and the model parameters, the reliability estimate of the system is calculated using the reliability analytical calculation formula.

2. The reliability assessment method for a reusable system according to claim 1, characterized in that, The model assumptions include: The system's life cycle includes a storage phase, an operation phase, and a maintenance phase. The system degrades during both the storage and operation phases, and the operation phase is further divided into ground and air states. During the storage phase, only soft failure modes exist, while during the operation phase, both soft and hard failure modes exist. The system is subjected to two types of impacts: random impacts and fixed impacts. Random impacts occur at any time during the operation phase, while fixed impacts occur at fixed times during the transition between ground and air states during the operation phase. The system undergoes maintenance at the end of each cycle, and the maintenance process affects the system's degradation state and degradation rate. Set maintenance thresholds for soft failure processes and hard failure processes for the system. When the system degradation state exceeds the maintenance threshold, the system is determined to have failed. Set target maintenance thresholds for soft failure processes and hard failure processes for the system. The system state after maintenance must be less than the target maintenance threshold.

3. The reliability assessment method for a reusable system according to claim 2, characterized in that, The natural degradation model is constructed as follows: a linear Wiener process is used to describe the natural degradation of the system during the storage and operation phases. The degradation rate is proportional to the environmental pressure on the system, and the environmental pressure is a piecewise function in different phases and states.

4. The reliability assessment method for a reusable system according to claim 2, characterized in that, The impact degradation model is constructed as follows: the arrival probability of random impacts is described based on the Poisson distribution, and the Poisson distribution parameters are related to the environmental stress; the intensity of random impacts follows a normal distribution, and its expectation and variance are related to the environmental stress; the intensity of fixed impacts also follows a normal distribution; based on the system's resistance to impacts, random impacts are divided into safe impacts and damaging impacts. Safe impacts do not affect the degradation state, while damaging impacts do. When the impact intensity exceeds a preset damage threshold, it is determined to be a damaging impact; the hard failure of the system adopts a cumulative impact model, and the system experiences hard failure when the cumulative damage impact intensity exceeds the hard failure threshold.

5. The reliability assessment method for a reusable system according to claim 2, characterized in that, The maintenance behavior is imperfect maintenance. The system state after maintenance is proportional to the target maintenance threshold, and the degradation rate conversion coefficient and maintenance state conversion coefficient are related to the number of maintenance cycles.

6. The reliability assessment method for a reusable system according to claim 2, characterized in that, The construction of the comprehensive state degradation model specifically includes: based on the number of maintenance cycles the system has undergone and the current stage, establishing expressions for the degradation rate and Poisson process parameters of the system in the storage stage, the ground state in the operation stage, and the air state in the operation stage, respectively, and combining the natural degradation component and the impact degradation component to obtain the system state. The comprehensive state degradation model is as follows: in, For the first System status after the last maintenance. For the first One work cycle time, This represents the rate of ground condition degradation. The rate of degradation of the air state. Total system uptime The number of impacts that caused the damage. , As a conversion factor between impact strength and system damage, For the first Impact strength of ground condition 1 after the second maintenance. For the first Impact intensity in the air after the second maintenance. k The sequence number of the impact. for The impact strength of fixed impact 2 after the second maintenance. for The impact strength of fixed impact 1 after the second maintenance. For the first Impact strength of ground condition 2 after the second maintenance. For the first Impact strength in storage state after the first maintenance The number of damage impacts occurring within a given time period. and Indicates the system at the 1st Storage phase duration after the first maintenance in the next work cycle. , and These represent the time spent in the ground, air, and ground states, respectively.

7. The reliability assessment method for a reusable system according to claim 6, characterized in that, The derivation of the analytical reliability calculation formula includes: deriving the soft failure reliability and hard failure reliability of the system in a single working cycle and multiple working cycles in stages, and considering the impact of maintenance behavior on the initial value of the system state and the degradation rate, to obtain the overall system reliability expression.

8. A reliability assessment system for a repeatable system, characterized in that, include: The data acquisition module is used to acquire the degraded datasets from the status monitoring sensor acquisition devices deployed in the system; The model building module is used to establish model assumptions that take into account stage dependency degradation, competition failure, and maintenance behavior, based on the system's working environment, working mode, and working status. The model building module is used to construct a natural degradation model and an impact degradation model of the system based on the model assumptions, and to fuse the natural degradation model and the impact degradation model to obtain a comprehensive state degradation model of the system, and to derive the analytical calculation formula for the reliability of the system based on the comprehensive state degradation model. The parameter estimation module is used to estimate model parameters by utilizing at least one of the following: material properties of system components, expert or experiential knowledge, and historical degradation data. The reliability assessment module is used to calculate the system's reliability estimate based on the comprehensive state degradation model and the model parameters, using the reliability analytical calculation formula.

9. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer-executable program, the processor reading part or all of the computer-executable program from the memory and executing it, and the processor executing part or all of the computer-executable program is able to implement the reliability assessment method of the reusable system according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, enables the reliability assessment method for a reusable system as described in any one of claims 1-7.

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