A reliability evaluation method of aviation support system considering fault coupling and multi-dimensional resource constraints

Abstract

The application discloses a kind of reliability evaluation method of aviation support system considering fault coupling and multidimensional resource constraint, the method constructs the whole system closed loop link with aircraft state stock, support equipment state stock and fault coupling as core node, utilizes dynamic method to realize the overall planning of fault coupling and resource constraint process in time frame;By establishing the system dynamics model of transient load factor and equipment failure rate, the positive feedback deterioration cycle that aircraft failure leads to the increase of support equipment failure rate is quantified for the first time in dynamic framework, the problem that reliability is overestimated caused by traditional evaluation is solved, and the evolution under high intensity working condition is closer to the actual situation;Meanwhile, the step truncation logic of consumable spare parts is innovatively introduced, breaking the limitation that resources are only regarded as infinite in traditional queuing theory, accurately depicting the cliff-like maintenance paralysis caused by lack of parts, and providing a scientific calculation for the reliability of aviation support system.

Description

Technical Field

[0001] This invention belongs to the field of aviation support and maintenance engineering and system simulation technology, and relates to a reliability assessment method for aviation support systems. Specifically, it relates to a system reliability assessment method that comprehensively considers the bidirectional fault coupling between the aircraft and the aviation support system, as well as the multi-dimensional resource constraints of equipment and spare parts, under conditions of no or weak resupply. Background Technology

[0002] The aviation support system, comprised of aircraft and its support systems, is a complex and dynamic system with limited resources. Its continuous operational capability is highly dependent on its reliability. Under high-intensity mission environments without external resupply, existing reliability assessment methods have significant limitations. First, they neglect the failure coupling effects between systems. Traditional reliability models for aviation support systems often employ the assumption of unidirectional decoupling, failing to consider that numerous aircraft failures can lead to long-term overload operation of support equipment, thereby inducing accelerated equipment degradation and secondary failures. This neglect of positive feedback deterioration mechanisms often results in overly optimistic outcomes. Second, they lack a closed-loop characterization of consumable spare parts. Existing research does not consider spare parts as a non-renewable resource. In reality, once critical spare parts are depleted, the maintenance flow rate instantly drops to zero; existing research methods cannot reflect this precipitous shutdown effect.

[0003] In summary, existing technologies cannot integrate equipment overload degradation and spare parts consumption mechanisms, making it difficult to accurately predict system reliability under extreme conditions, and also unable to provide quantitative optimization basis for the configuration of cargo carrying within limited cabin capacity before departure. Summary of the Invention

[0004] This invention provides a reliability assessment method for aviation support systems comprised of aircraft and their support systems, considering fault coupling and multi-dimensional resource constraints. This method takes the reliability of the novel system as the core assessment objective, integrates fault feedback and resource constraints, constructs a stock and flow rate model of the aviation support system through system dynamics mechanisms, and introduces the impact logic of fault feedback and spare parts consumption to accurately characterize the system's paralysis effects under different states, ultimately achieving the reliability calculation of the aviation support system.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A reliability assessment method for an aviation support system that considers fault coupling and multidimensional resource constraints includes the following steps:

[0007] Step 1: Constructing a stock framework under the system dynamics mechanism:

[0008] The aviation support system is abstracted as a multimorphic coupled space comprising aircraft subsystems, support equipment subsystems, and consumable spare parts inventory subsystems. The state inventory of each subsystem is defined, and initial boundary parameters are set under conditions of no external resupply. The state inventory of the aircraft subsystem includes the aircraft's operational status. Aircraft fault inventory Aircraft maintenance inventory The status inventory of the equipment subsystem includes the inventory of equipment in good working order. Safeguard equipment failure inventory Ensure the stock of equipment awaiting repair. The status inventory of the consumable spare parts inventory subsystem includes the spare parts inventory level. Spare parts inventory ;

[0009] Step 2: Construct a flow rate model for the aviation support system:

[0010] Step 21: Extract the current aircraft fault inventory and the available inventory of support equipment, construct a coupling mechanism, and establish a transient load factor under the coupling mechanism. The coupling mechanism is manifested as follows: the initial fault backlog generated by the aircraft in the mission profile will be directly transformed into high-frequency and high-load operation requirements of the support equipment.

[0011] Step 22: Construct a reliability degradation equation based on the transient load factor to obtain the load-modulated coupled fault flow rate, where:

[0012] The formula for calculating the transient load factor is as follows:

[0013]

[0014] In the formula, For aircraft malfunction inventory, The aircraft are in good condition and have been preserved.

[0015] The load-modulated coupled fault flow rate The calculation formula is as follows:

[0016]

[0017] In the formula, To ensure the equipment remains in good condition, To ensure the mean time between failures (MTBF) of equipment, To calculate the step size;

[0018] Step 3: Construct a maintenance flow rate model constrained by multi-dimensional resource coordination and truncation:

[0019] Step 31: Extract the current inventory of intact equipment. Combined with the concurrent protection capacity coefficient of a single protection device Determine the actual usable system assurance capability constraint boundaries;

[0020] Step 32: Simultaneously introduce a spare parts adequacy indicator variable based on a step function. When spare parts inventory 0 o'clock =0, otherwise =1; Calculate the aircraft repair flow rate based on load feedback, taking into account the system assurance capability constraints. :

[0021]

[0022] In the formula, This refers to the inventory of aircraft awaiting maintenance. Mean time to repair (MTBL) of an aircraft;

[0023] Step 4: System time-domain iteration and system reliability calculation:

[0024] Step 41: Set the simulation step size The total simulation duration is used to initialize the state inventory of each subsystem;

[0025] Step 42: Based on the established stock-flow differential equation system, calculate the current transient load factor sequentially within each step. The coupled fault flow rate modulated by load and the repair flow rate constrained by multidimensional resources;

[0026] Step 43: Using the calculated flow rates, update the aircraft status inventory, support equipment status inventory, and spare parts inventory through time-domain integration.

[0027] Step 44: Using the formula Calculate the system transient reliability at each simulation time point and obtain the system transient reliability curve, where This refers to the aircraft's intact condition during a momentary state. This represents the total number of aircraft in the system.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] 1. For the first time, this invention maps the complex aviation support system as a system dynamics polymorphic stock flow rate network in the continuous time domain, overcoming the limitations of traditional reliability assessment models that suffer from state discretization and lack of time-delay feedback. This invention innovatively constructs a closed-loop causal link for the entire system, with aircraft state stock, support equipment state stock, and fault coupling as core nodes. Utilizing a dynamic approach, it achieves comprehensive coordination of fault coupling and resource constraint processes within the time frame, providing a novel and scientific dynamic quantitative benchmark for the system reliability evolution of the aviation support system.

[0030] 2. By establishing a system dynamics model of transient load factor and equipment failure rate, this study quantifies for the first time in a dynamic framework the positive feedback deterioration cycle in which aircraft failures lead to an increase in the failure rate of support equipment. This solves the problem of overestimating reliability caused by traditional assessments and makes the evolution under high-intensity operating conditions more realistic. At the same time, it innovatively introduces the step cutoff logic of consumable spare parts, breaking the limitation of traditional queuing theory that considers resources as infinite. This accurately depicts the cliff-like maintenance paralysis caused by the lack of parts and provides a scientific calculation for the reliability of the aviation support system. Attached Figure Description

[0031] Figure 1 Flowchart for modeling the aviation support system;

[0032] Figure 2 A system dynamics diagram of the aviation support system;

[0033] Figure 3 A fault coupling diagram of the aircraft and support equipment;

[0034] Figure 4 This is a diagram showing the evolution of system reliability under scenario one.

[0035] Figure 5 This is a diagram showing the evolution of system reliability under scenario two.

[0036] Figure 6 This is a diagram showing the evolution of system reliability under scenario three. Detailed Implementation

[0037] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0038] This invention provides a reliability assessment method for aviation support systems that considers fault coupling and multidimensional resource constraints in continuous mission environments. The specific steps of the proposed method are described in detail below in conjunction with the stock-flow mechanism of system dynamics.

[0039] like Figure 1 As shown, the method mainly includes the following four steps:

[0040] Step 1: Constructing a stock framework under the system dynamics mechanism:

[0041] In this invention, an aviation support system consisting of a certain type of aircraft and its aviation support system is selected as the research object.

[0042] First, establish the system's state inventory, mainly including: the aircraft's operational status. Aircraft fault inventory Aircraft maintenance inventory Ensure the equipment is in good working order. Safeguard equipment failure inventory Ensure the stock of equipment awaiting repair. Spare parts inventory Spare parts inventory .

[0043] Step 2: Construct a flow rate model for the aviation support system:

[0044] This step aims to accurately quantify the hidden two-way fault coupling mechanism between the aircraft and support equipment through a system dynamics model.

[0045] In traditional reliability models, aircraft and support equipment failures are typically assumed to be independent random events. However, under high-intensity continuous operational conditions, significant load transfer and positive feedback degradation effects exist between the aircraft and support equipment. Figure 3 As shown, the core coupling mechanism manifests as follows: the initial backlog of faults generated by the aircraft in the mission profile directly translates into high-frequency, high-load operational demands on support equipment. When the maintenance demands within the system approach or exceed the rated concurrent support capacity of a single piece of equipment, the system's transient load factor will surge dramatically. This continuous, excessive physical load disrupts the inherent basic degradation trajectory of the support equipment, inducing accelerated degradation effects such as thermal stress overload and accelerated mechanical wear, causing the probability of secondary faults in the support equipment to increase exponentially. Consequently, the sharp reduction in the available stock of support equipment will physically limit the overall aggregated maintenance flow rate of the system, further exacerbating the backlog of aircraft awaiting repair.

[0046] Based on the definition in step 1, a three-loop coupled system dynamics flow diagram is constructed. The three loops include the aircraft state loop, the support equipment state loop, and the spare parts supply and demand loop. The core calculation logic of steps 21 and 22 above is realized through the interaction of these three loops, as detailed below:

[0047] (1) Aircraft status loop: describes the process of an aircraft going from "good" to "faulty" and then being restored to "good" through "repair".

[0048] 1) Fault Flow Rate: In system dynamics models, the fault flow rate is defined as the number of individuals that migrate from an "intact state" to a "fault state" per unit time. Let... Let be the fault probability density function. Let be the reliability function (i.e., the probability that the device is still alive at time t), then

[0049]

[0050] According to the mathematical definition of reliability, instantaneous failure rate for:

[0051]

[0052] Therefore, at the simulation time The fault flow rate equation is:

[0053]

[0054] in, This represents the current stock of aircraft that are in good working order.

[0055] Taking the exponential distribution as an example, we define... If the failure rate follows an exponential distribution, then:

[0056]

[0057] In the formula, Mean time between failures (MTBF) of the aircraft;

[0058] The transformed flow rate equation is:

[0059]

[0060] In the formula, The aircraft is in good condition and still exists. Mean time between failures (MTBF) of an aircraft To calculate the step size.

[0061] 2) Fault location flow rate: describes the time required to locate faults in an aircraft. Traditional maintenance models often neglect the time consumed in the fault diagnosis process.

[0062] Analogous to the fault flow rate, the transformed fault location flow rate equation is:

[0063]

[0064] In the formula, The number of faulty aircraft. Mean time to fault location for aircraft.

[0065] (2) Equipment status protection loop (including the physical implementation of steps 21 and 22): describes the process of ensuring that the equipment goes from "good" to "fault" and then is restored to "good" through "repair". This loop focuses on degradation at the equipment level and strictly excludes interference from human factors.

[0066] 1) Failure Flow Rate: Load-Modulated Coupled Failure Flow Rate (corresponding to steps 21 and 22): In the system dynamics model, the failure flow rate of support equipment is defined as the number of individuals that migrate from an "intact state" to a "failed state" per unit time. To quantify the failure coupling effect, it is necessary to calculate the failure flow rate of support equipment due to high-load operation. As the number of aircraft failures increases, the failure rate of support equipment due to high-load operation increases. Its calculation depends not only on MTBF but also on the number of aircraft failures. The calculation formula is as follows:

[0067]

[0068] In the formula, To ensure the equipment remains in good condition, To ensure the mean time between failures (MTBF) of equipment, To calculate the step size, The load factor is defined as follows:

[0069]

[0070] 2) Fault location step flow rate: describes the fault location time of the equipment. Traditional maintenance models often ignore the time consumption of the fault diagnosis process.

[0071] Analogous to fault flow rate, the transformed flow rate equation is:

[0072]

[0073] In the formula, The number of backup devices for failures; Mean time to locate fault.

[0074] (3) Spare parts supply and demand loop: The spare parts supply and demand loop is the link connecting the maintenance needs of aircraft or support equipment. In order to reflect the timeliness of maintenance support, the consumption rate of spare parts is directly driven by the aircraft repair flow rate, which is the core calculation logic of this invention.

[0075] To accurately calculate the spare parts availability rate, the actual repair flow rate after limitations must first be calculated. Assume that a single repair requires a certain number of spare parts. (number of flights / flights), the calculation formula is as follows:

[0076]

[0077] Step 3: Construct a maintenance flow rate model constrained by multi-dimensional resource coordination and truncation:

[0078] This step aims to transform the qualitative causal relationships between various elements of the system into quantitative mathematical equations. The flow rate equation system constructed in this invention not only includes the traditional repair process, but also innovatively introduces load feedback of the protection equipment and inventory constraints of spare parts.

[0079] 1) Aircraft repair flow rate based on load feedback:

[0080] The aircraft repair flow rate is constrained by MTTR, the number of support equipment, and spare parts inventory. The calculation formula is as follows:

[0081]

[0082] In the formula, This refers to the concurrent support capacity coefficient of a single support device, which is the number of aircraft malfunctions that one device can handle simultaneously. Mean time to repair aircraft The step function for spare parts adequacy is defined as follows:

[0083]

[0084] 2) Ensure equipment repair flow rate:

[0085] The repair flow rate of the equipment is affected The constraints on the quantity of guaranteed equipment and the inventory of spare parts are calculated using the following formula:

[0086]

[0087] In the formula, To ensure the average repair time of equipment, This is the step function for spare parts adequacy, defined as above.

[0088] Step 4: System time-domain iteration and reliability calculation of any system:

[0089] In this step, the simulation step size and total simulation duration are set, and the parameters are initialized. Based on historical data or task scenarios, the model parameters are determined, as shown in Table 1.

[0090] Table 1 Model Parameters

[0091]

[0092] Using a system dynamics engine, the aforementioned system of differential equations containing feedback loops is solved, where the system reliability of the aviation support system is defined. The definition is as follows:

[0093]

[0094] In the formula, This refers to the aircraft's intact condition during a momentary state. This represents the initial number of aircraft.

[0095] Compared to traditional reliability assessments that only output steady-state probability values, this invention can output a time-domain evolution measure of system transient reliability, transforming the abstract single-point reliability index into a concrete, continuously changing dynamic trajectory over time. This dynamic reliability index accurately characterizes the nonlinear decay process of a system subjected to high-intensity continuous deployment loads and internal fault coupling impacts, given a given initial amount of support equipment and spare parts carried. This result not only clearly defines the critical time point at which system reliability falls below the safety threshold, but also provides a quantitative benchmark with clear physical boundaries and temporal characteristics for optimizing material loading within the aircraft's limited cabin capacity and for the dynamic scheduling of support resources during mission execution.

[0096] Example:

[0097] To verify the effectiveness and scientific validity of the method proposed in this invention, this embodiment uses the system dynamics engine of Anylogic software to set a set of initial boundary parameters for typical tasks. For different task environments and resource boundaries, three progressive simulation scenarios are constructed, and specific implementation and calculation are carried out according to the four core steps of this invention.

[0098] Step 1: Constructing a stock framework under the system dynamics mechanism:

[0099] This step defines the spatiotemporal boundaries of the aviation support system, first initializing the state inventory of each subsystem. For comparative verification, this embodiment simultaneously initializes the boundary parameters for three different operating scenarios:

[0100] (1) Scenario 1: Unconstrained resource redundancy:

[0101] The boundary conditions are set as shown in Table 2. The initial stock of the support equipment is set to be extremely abundant, and the stock of spare parts is sufficient to meet the replenishment needs of the aircraft during continuous missions. This scenario aims to simulate the assumption of sufficient resources in traditional evaluation methods.

[0102] Table 2 Scenario 1 Model Parameter Settings

[0103]

[0104] (2) Scenario 2: High-intensity operation with limited equipment redundancy:

[0105] The boundary conditions are set as shown in Table 3. The high sortie rate feature is introduced to increase the basic aircraft failure flow rate. The initial good stock of support equipment is set to be limited, and spare parts are still sufficient during the current observation period. This scenario aims to verify the core advantages of the fault coupling model of the present invention.

[0106] Table 3 Scenario 2 Model Parameter Settings

[0107]

[0108] (3) Scenario 3: Spare parts and components depleted:

[0109] The boundary conditions are set as shown in Table 4, approximating the real unreplenished extreme survival environment. Based on Scenario 2, strict initial hard constraints are imposed on the spare parts inventory. This scenario aims to demonstrate the original step cutoff logic and critical reliability capability of the system.

[0110] Table 4 Scenario 3 Model Parameter Settings

[0111]

[0112] Step 2: Construct a flow rate model for the aviation support system:

[0113] Within the simulation time domain, the system extracts the aircraft fault inventory at each step. With available inventory of protective equipment Calculate the load factor at each instant. And adjust the fault rate of the protection equipment accordingly.

[0114] (1) Scenario 1: Due to the large MTBF of the aircraft and the sufficient number of support equipment, the support equipment can handle the aircraft failure in a timely manner. The number of faulty aircraft is extremely low, the load factor is small, and the equipment is not impacted by the over-limit load.

[0115] (2) Scenario 2 and Scenario 3: Due to the availability of only 4 support devices, high-intensity deployment leads to a rapid accumulation of aircraft malfunctions. The system calculates a rapid increase in the transient load factor, and according to the formula:

[0116]

[0117] The load-modulated failure rate of support equipment increases exponentially, accurately simulating the failure coupling deterioration cycle of aircraft failure backlog dragging down support equipment.

[0118] Step 3: Construct a maintenance flow rate model constrained by multi-dimensional resource coordination and truncation:

[0119] This step calculates the actual repair flow rate under physical constraints based on the concurrent capacity of the support equipment and the real-time inventory of spare parts.

[0120] (1) Scenario 1 and Scenario 2: Since the spare parts inventory is set to be sufficient, the spare parts sufficiency step function based on the spare parts inventory is used. It is always equal to 1, and the actual repair flow rate is only constrained by the number of guaranteed devices and MTTR.

[0121] (2) Scenario 3: In the early stage of evolution, as the maintenance flow rate continues to drive the consumption of spare parts, when the simulation progresses to the critical node, the spare parts inventory is exhausted and returns to zero. At this time, the spare parts sufficiency step function The mutation changes to 0, according to the formula:

[0122]

[0123] The actual repair flow rate was forcibly truncated to zero. The physical blockage of the maintenance channel caused the system to enter a state of precipitous paralysis.

[0124] Step 4: System time-domain iteration and system reliability calculation:

[0125] Based on the established stock-flow equations, numerical integral evolution calculations are performed in the time domain to obtain the system's transient reliability. curve.

[0126] (1) Calculation results for scenario one:

[0127] Figure 4 The figure shows the transient reliability of the aviation support system calculated by this invention under scenario one. The temporal evolution trajectory.

[0128] Depend on Figure 4 It can be seen that during the simulation observation period (0~100 hours), the system reliability evolution exhibits typical high-level dynamic equilibrium and discrete step characteristics. In the initial stage of the simulation (approximately 0~10 hours), driven by the aircraft's basic random failure rate, the system reliability changes from the initial ideal boundary... 1 shows the expected slight decrease. Subsequently, the evolution curve enters a plateau period exhibiting obvious step-like fluctuations. This step-like feature accurately maps the pulse-like perturbations of discrete failure events and maintenance recovery events on the continuous time axis.

[0129] Because this scenario assumes a high degree of redundancy in the existing equipment and a consistently low spare parts inventory, under these ideal boundary conditions, the equipment does not experience excessive maintenance loads and the fault coupling mechanism is not triggered. Simultaneously, the actual repair flow rate can efficiently offset the aircraft fault flow rate without hysteresis. Therefore, the system's transient reliability... Although there were localized sharp drops due to random malfunctions of individual aircraft, the overall system was consistently maintained within a high stability range of 96% to 100%.

[0130] comprehensive Figure 4 The evolution results show that the system reliability line did not experience truncation or paralysis in the entire time domain. The benchmark simulation curve verifies that under the constraint of absolutely abundant guarantee resources, the system has extremely strong robustness against discrete fault disturbances, and provides a highly confident ideal reference benchmark for the performance degradation comparison of subsequent constrained operating conditions (Scenario 2 and Scenario 3).

[0131] (2) Calculation results for scenario two:

[0132] Figure 5 The figure shows the transient reliability of the aviation support system calculated by this invention under scenario two. The temporal evolution trajectory.

[0133] During the simulation period (0-100 hours), the system did not converge to a high-level dynamic equilibrium as it would under resource-sufficient conditions. Instead, it exhibited typical nonlinear degradation characteristics, including a step-like drop in the early stage followed by continuous monotonic consumption in the middle and later stages. The specific evolution mechanism is analyzed as follows:

[0134] Initial drop during the early stages of evolution (approximately 0-10 hours): The aircraft's basic failure rate surges significantly due to a sharp increase in high-intensity mission loads. Because the redundancy of support equipment is severely limited in this scenario, maintenance access is immediately congested. The transient load factor rapidly climbs and exceeds the normal rated threshold, leading to a decrease in system transient reliability. From the initial boundary A sharp initial drop occurred, quickly falling to around 90%.

[0135] The continuous decay and depletion during the mid-to-late evolutionary period (approximately 10-100 hours): As the time domain is further extended, the curve exhibits a continuous and irreversible smooth decay trend. The evolutionary trajectory during this period precisely confirms the core fault coupling and deterioration mechanism of this invention: the long-term backlog of aircraft faults causes limited support equipment to bear excessive maintenance loads for extended periods; the coupled fault flow rate of support equipment, modulated by this high load factor, experiences a nonlinear surge, leading to accelerated degradation of support equipment and large-scale maintenance. The sharp reduction in available equipment inventory conversely stifles the actual aircraft repair flow rate, and the system is completely trapped in a positive feedback loop of aircraft backlog dragging down equipment, and equipment downtime restricting aircraft maintenance.

[0136] By the end of the simulation period, the system transient reliability The load feedback mechanism has continued to decline to around 84%, and the decline trend shows no signs of bottoming out. This simulation result strongly demonstrates that under resource-constrained, high-intensity task profiles, if the traditional decoupling model is used to ignore secondary failures induced by equipment overload, it will severely mask the underlying vulnerability of the system and overestimate its actual protection effectiveness. The load feedback mechanism constructed in this invention successfully quantifies this hidden system-level risk.

[0137] (3) Calculation results for scenario three:

[0138] Figure 6 The figure shows the transient reliability of the aviation support system calculated by this invention under scenario three. The temporal evolution trajectory.

[0139] In the initial stage of simulation operation, the overall system reliability remained in a relatively high range above 0.8, with a gradual decline trend. This stage indicates that, under initial operating conditions, the system's support equipment had a high availability rate and smooth turnover, and spare parts inventory levels were sufficient. At this time, the system's internal positive control loop could efficiently respond to routine aircraft malfunctions and material consumption, ensuring the smooth operation of the support links, and physical losses had not yet had a substantial impact on the overall system performance.

[0140] As the simulation progressed, the overall reliability curve exhibited a significant nonlinear accelerated degradation characteristic (reliability dropped sharply from approximately 0.8 to around 0.2). Based on the dynamic mechanism, in this continuous operation scenario, the accumulation of aircraft failure events led to a large consumption of spare parts, with inventory rapidly approaching or even falling below the safety threshold. Simultaneously, the maintenance equipment, due to high-frequency and high-load operation, experienced an increased failure rate or was occupied for extended periods. The intertwined delays of waiting for spare parts replenishment and waiting for equipment scheduling activated a negative reinforcement loop within the system, causing the system's repair capability to lag significantly behind the equipment wear rate, resulting in a precipitous drop in reliability.

[0141] As the simulation progresses, the rate of decay of the overall reliability curve gradually slows down, eventually dropping to an extremely low level of around 0.1. This low steady-state value objectively reflects that the support system has fallen into a deep resource bottleneck. At this point, critical spare parts are basically exhausted, available support equipment is severely insufficient, the system can only maintain extremely limited emergency operation, and routine aviation support operations can hardly be carried out effectively.

[0142] In summary, the simulation results of Scenario 3 profoundly reveal that, even after removing other interfering factors, the dynamic consumption and shortage of equipment and spare parts alone can lead to a non-linear and sharp decline in the effectiveness of the aircraft aviation support system. This embodiment further demonstrates the accuracy of the model of this invention in capturing the strong coupling correlation and delayed feedback between "aircraft-support system-spare parts," providing an intuitive quantitative basis for optimizing spare parts carrying capacity and equipment scheduling strategies.

Claims

1. A reliability assessment method for an aviation support system considering fault coupling and multidimensional resource constraints, characterized in that... The method includes the following steps: Step 1: Constructing a stock framework under the system dynamics mechanism: The aviation support system is abstracted into a multi-state coupled space that includes aircraft subsystems, support equipment subsystems and consumable spare parts inventory subsystems. The state inventory of each subsystem is defined, and the initial boundary parameters are set under the condition of no external supply. Step 2: Construct a flow rate model for the aviation support system: Step 21: Extract the current aircraft fault inventory and the available inventory of support equipment, construct a coupling mechanism, and establish a transient load factor under the coupling mechanism; Step 22: Construct a reliability degradation equation based on the transient load factor to obtain the coupled fault flow rate modulated by the load; Step 3: Construct a maintenance flow rate model constrained by multi-dimensional resource coordination and truncation: Step 31: Extract the current inventory of intact equipment. Combined with the concurrent protection capacity coefficient of a single protection device Determine the actual usable system assurance capability constraint boundaries; Step 32: Simultaneously introduce a spare parts adequacy indicator variable based on a step function. When spare parts inventory 0 o'clock =0, otherwise =1; Calculate the aircraft repair flow rate based on load feedback, taking into account the system assurance capability constraints. ; Step 4: System time-domain iteration and system reliability calculation: Step 41: Set the simulation step size The total simulation duration is used to initialize the state inventory of each subsystem; Step 42: Based on the established stock-flow differential equation system, calculate the current transient load factor sequentially within each step. The coupled fault flow rate modulated by load and the repair flow rate constrained by multidimensional resources; Step 43: Using the calculated flow rates, update the aircraft status inventory, support equipment status inventory, and spare parts inventory through time-domain integration. Step 44: Calculate the transient reliability of the system at each simulation time and obtain the transient reliability curve of the system.

2. The reliability assessment method for aviation support systems considering fault coupling and multidimensional resource constraints according to claim 1, characterized in that... In step 1, the state inventory of the aircraft subsystem includes the aircraft's intact inventory. Aircraft fault inventory Aircraft maintenance inventory ; Ensuring the state inventory of the equipment subsystem includes ensuring the equipment is in good working order. Safeguard equipment failure inventory Ensure the stock of equipment awaiting repair. ; The status inventory of the consumable spare parts inventory subsystem includes the spare parts inventory. Spare parts inventory .

3. The reliability assessment method for aviation support systems considering fault coupling and multidimensional resource constraints according to claim 1, characterized in that... In step 21, the coupling mechanism manifests itself as follows: the initial fault backlog generated by the aircraft in the mission profile will be directly transformed into high-frequency, high-load operation requirements for support equipment.

4. The reliability assessment method for aviation support systems considering fault coupling and multidimensional resource constraints according to claim 1, characterized in that... In step 22, the transient load factor is calculated using the following formula: In the formula, For aircraft malfunction inventory, The aircraft is in good condition.

5. The reliability assessment method for aviation support systems considering fault coupling and multidimensional resource constraints according to claim 1, characterized in that... In step 22, the load-modulated coupled fault flow rate The calculation formula is as follows: In the formula, To ensure the equipment remains in good condition, To ensure the mean time between failures (MTBF) of equipment, To calculate the step size.

6. The reliability assessment method for an aviation support system considering fault coupling and multidimensional resource constraints as described in claim 1, characterized in that... In step 32, the aircraft repair flow rate The calculation formula is as follows: In the formula, This refers to the inventory of aircraft awaiting maintenance. Mean time to repair aircraft This is a step function for the adequacy of spare parts.

7. The reliability assessment method for aviation support systems considering fault coupling and multidimensional resource constraints according to claim 1, characterized in that... The The definition of is: In the formula, This refers to the inventory of spare parts.

8. The reliability assessment method for an aviation support system considering fault coupling and multidimensional resource constraints according to claim 1, characterized in that... In step 44, the system transient reliability The calculation formula is as follows: in, This refers to the aircraft's intact condition during the instantaneous phase. This represents the total number of aircraft in the system.

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