Construction method of SD model for long-distance voyage of far sea with repairable spare parts

Abstract

The application relates to a construction method of a long-haul sea voyage life-repairable spare parts carrying demand SD model, which is constructed according to the demand of life-repairable spare parts of a long-haul sea voyage of a carrier-based aircraft, selects the number of carrying spare parts, the number of in-repair spare parts, the number of consumed spare parts, the number of fault spare parts, the number of life-expired spare parts and the number of repaired spare parts in a life-repairable spare parts supply system as dynamic variables, establishes a causal relationship diagram according to the relationship among the six factors and the relationship among other factors related to each factor, combines the dynamic variables and symbols organically according to the causal relationship diagram and the feedback mechanism of the spare parts supply guarantee during the voyage, and establishes a long-haul sea voyage life-repairable spare parts supply system flow diagram, that is, the long-haul sea voyage life-repairable spare parts carrying demand SD model. The life-repairable spare parts carrying demand quantity predicted by the model is high in accuracy.

Description

Technical Field

[0001] This invention belongs to the field of aviation maintenance and support technology. Specifically, it relates to a method for constructing a SD model of the carrying requirements of repairable aircraft parts with long service life for long-range maritime operations. Background Technology

[0002] Carrier-based aircraft are the most important rapid strategic strike weapons on an aircraft carrier. With the successive commissioning of new aircraft carriers, long-range, long-endurance missions will become one of the main mission types for carrier strike groups. The time an aircraft carrier spends at sea will increase from one or two months to about six months, and the material support mode will shift from short-term onboard support to long-term onboard support. Long-range, long-endurance missions for carrier-based aircraft are characterized by their long duration, high intensity, and harsh environment, resulting in highly complex factors influencing material consumption. Furthermore, there is a lack of relevant data samples, and estimates can only be made based on routine consumption patterns. Therefore, predicting the material carrying requirements under long-range, long-endurance mission conditions is quite difficult, inevitably affecting material support work under these conditions. To facilitate the rapid development of long-range, long-endurance combat training capabilities, it is urgent to conduct research on predicting the material carrying requirements under long-range, long-endurance mission conditions for carrier-based aircraft.

[0003] Currently, methods for predicting the demand for aircraft materials carried on board include regression analysis, time series forecasting, grey forecasting, and BP neural network models. However, these methods are all based on historical consumption data and consider too few influencing factors; most methods even consider only one factor. Clearly, existing research has not deeply analyzed the key elements of the aircraft material supply system and the interrelationships between them, resulting in demand models that cannot accurately reflect the actual consumption patterns of aircraft materials during long-range, long-duration maritime operations. Furthermore, carrier-based aircraft have not yet performed long-range, long-duration maritime missions, thus lacking corresponding aircraft material consumption data. Therefore, existing research literature and data foundation cannot meet the needs of predicting the demand for aircraft materials carried on board carrier-based aircraft during long-range, long-duration maritime operations. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a method for constructing a SD model of the carrying demand for repairable aircraft parts with service life during long-range maritime operations. The SD model constructed by this method accurately predicts the carrying demand for repairable aircraft parts with service life, thereby improving the supply guarantee level of repairable aircraft parts with service life for carrier-based aircraft during long-range maritime operations.

[0005] To achieve the above objectives, this invention provides a method for constructing a SD model of the carrying requirements for repairable aircraft parts with long service life for long-range maritime operations, which includes the following steps:

[0006] S1, Assumption:

[0007] (1) Only repairable aircraft parts that have experienced failures or have potential failures are considered; other aircraft parts can meet the field requirements.

[0008] (2) Only the lifespan of the aircraft materials installed during the mission is considered, and the lifespan of the aircraft materials stored in the cabins of the aircraft carrier is not considered;

[0009] (3) During the mission, each piece of aircraft material is subject to occasional failures, and the failure rate is a fixed constant.

[0010] (4) The failures of aircraft materials are random failures, and the failures are independent, with the number of failures following a Poisson distribution;

[0011] (5) Without considering the repair of multiple parts, multiple parts can be repaired at the same time, with a repair rate of 0.7~0.95. If they cannot be repaired, they are scrapped.

[0012] (6) The duration of long-range maritime missions is 26 weeks ± 1 week;

[0013] (7) The impact of economic factors on the support of aviation materials for long-range maritime voyages is not considered;

[0014] Six factors affecting the consumption of the long-range, long-duration, repairable aircraft parts supply system were selected as dynamic variables of the long-range, long-duration, long-duration, long-duration, long-duration, long-life ...

[0015] S2. Analyze the operation mechanism of aircraft material supply guarantee during the flight, and establish a causal relationship diagram of the long-range, repairable aircraft material supply system based on the relationship between the number of aircraft materials carried, the number of aircraft materials under repair, the amount of aircraft materials consumed, the number of faulty aircraft materials, the number of aircraft materials that have reached the end of their service life, and the number of aircraft materials to be repaired, as well as the relationship between each factor and other factors related to each factor.

[0016] S3. Based on the cause-and-effect diagram and combined with the feedback mechanism for aircraft material supply during operation, the dynamic variables and symbols of the long-range, repairable aircraft material supply system are organically combined to establish a flow graph for the long-range, repairable aircraft material supply system, namely, the SD model of the long-range, repairable aircraft material carrying demand. The dynamic equations of the SD model of the long-range, repairable aircraft material carrying demand are as follows:

[0017] The quantity of carry-on aircraft parts = INTEG(DELAY1I(repaired aircraft parts quantity, repair cycle, 0) - aircraft parts consumption, demand), the unit is: pieces; the demand is the quantity of carry-on aircraft parts that needs to be predicted. The replenishment of carry-on aircraft parts quantity by the repaired aircraft parts quantity is delayed due to the repair cycle, the unit of repair cycle is: weeks;

[0018] The quantity of aircraft parts under repair = INTEG(material consumption - quantity of repaired aircraft parts - quantity of unrepaired aircraft parts, 0), with the unit being: pieces; the quantity of aircraft parts under repair on the aircraft carrier is set to zero before the mission is executed;

[0019] Quantity of repaired aircraft parts = INTEGER (Quantity of aircraft parts under repair * Repair rate), unit: pieces;

[0020] The repair rate depends on the repair level. The relationship between the repair rate and the repair level is represented by a table function. The value between 0 and 1 represents multiple repair level levels, and different repair level levels correspond one-to-one with different repair rates.

[0021] The cumulative number of scrapped aircraft parts is expressed as: Cumulative number of scrapped aircraft parts = INTEG(number of scrapped aircraft parts, 0), with the unit being: pieces; The number of scrapped aircraft parts is the number of unrepaired aircraft parts, which is the difference between the number of aircraft parts under repair and the number of repaired aircraft parts;

[0022] Test1 data, which follows a Poisson distribution, is generated based on the number of faulty aircraft parts, and Test2 data is generated based on the number of aircraft parts that have reached the end of their service life. The sum of the two is used as the aircraft part consumption pattern. Test1 data uses the RANDOM POISSON function to simulate the fault situation, and the mean is the number of faulty aircraft parts. Test2 data uses the PULSE TRAIN function to simulate the aircraft parts reaching the end of their service life, and it is assumed that the end of service life has been determined.

[0023] Number of faulty aircraft parts = Planned flight time * Number of aircraft parts installed * Failure rate * Repair cycle * Environmental correction factor, unit: piece; Where, Number of aircraft parts installed = Number installed per aircraft * Aircraft capacity;

[0024] Aircraft material support good rate A Represented as:

[0025] (1)

[0026] in, E i For the first i The cumulative shortage of a particular aircraft material represents the expected shortage of that aircraft material when the carried inventory is equal to the demand. The cumulative shortage is calculated as follows: Cumulative shortage = INTEG(shortage, 0) and Shortage = IF THEN ELSE(Carried Aircraft Material Quantity < 0, ABS(Carried Aircraft Material Quantity) / 26, 0). N For aircraft strength, b i For the first i The number of aircraft materials installed.

[0027] Preferably, in step S2, when constructing the causal relationship diagram, other variables related to the number of faulty aircraft parts include the number of installed aircraft parts, planned flight time, and failure rate. Other variables related to both aircraft parts consumption and the number of repaired aircraft parts are the repair rate.

[0028] Preferably, in step S3, the symbol is an arrow.

[0029] Preferably, in step S3, a value between 0 and 1 is set to represent four levels of repair quality: poor, medium, good, and excellent.

[0030] If the aircraft carrier maintenance department is rated as "Good", then:

[0031] The maintenance level is set to 0.75, and the maintenance level is calculated as: WITH LOOKUP(0.75,[(0,0)-(10,10)],(0.25,0.7),(0.5,0.85),(0.75,0.9),(1,0.95)).

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] (1) The SD model for the carrying demand of long-duration, repairable aircraft parts for long-range maritime operations constructed by the method of this invention is stable and reliable. It has good simulation effect on the process of supporting aircraft parts carried by carrier-based aircraft for long-range maritime operations on a macroscopic level, and accurately predicts the carrying demand of aircraft parts. Under the current situation where the consumption pattern of aircraft parts for long-range maritime operations of carrier-based aircraft is unclear and the consumption data samples are lacking, the method of this invention provides a scientific and feasible simulation model for predicting the demand of repairable aircraft parts for long-range maritime operations of carrier-based aircraft. This model has high reference value for predicting the demand of other aircraft parts under long-range maritime conditions.

[0034] (2) This invention analyzes the parameters of the SD model of the demand for long-duration, repairable aircraft materials carried by carriers in the open sea. Based on the simulation of the supply and demand process of carried aircraft materials, it proposes strategies such as improving the maintenance factors of aircraft materials, improving the maintainability and reliability of aircraft materials, and improving the sealing and packaging standards. Through various technical and management approaches, it further improves the supply guarantee level of long-duration, repairable aircraft materials for carrier-based aircraft in the open sea.

[0035] (3) The SD model for the carrying requirements of long-range, repairable aircraft materials for long-term sea operations constructed by the method of the present invention makes appropriate assumptions about the supply system of long-range, repairable aircraft materials for long-term sea operations. When it is used in future actual support work, it is necessary to adjust the model parameters or even expand and refine the model based on actual ship-based maintenance capabilities, aircraft strength, flight missions and other factors so that the model conforms to the actual support work. Attached Figure Description

[0036] Figure 1This is a schematic diagram of the causal relationship established in the SD model method for carrying repairable aircraft materials for long-range maritime operations as described in this embodiment of the invention.

[0037] Figure 2 This is a flow chart of the long-range, long-duration, repairable aircraft parts supply system established in the SD model method for carrying demand of repairable aircraft parts for long-duration, long-duration, long-life operations described in this embodiment of the invention.

[0038] Figure 3 This is a schematic diagram of the maintenance level table function according to an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram comparing the quantity of aircraft parts under repair and the quantity of aircraft parts to be repaired under extreme conditions in an embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram illustrating the cumulative number of scrapped aircraft materials under extreme conditions in an embodiment of the present invention.

[0041] Figure 6 This is a schematic diagram illustrating the quantity of carried aviation materials under extreme conditions in an embodiment of the present invention.

[0042] Figure 7 This is a schematic diagram illustrating the cumulative shortage number under extreme conditions in an embodiment of the present invention;

[0043] Figure 8 This is a schematic diagram illustrating the good condition rate of aircraft material support under extreme conditions in an embodiment of the present invention.

[0044] Figure 9 This is a schematic diagram of interference under sensitivity testing according to an embodiment of the present invention;

[0045] Figure 10 This is a schematic diagram showing the quantity of aircraft parts under repair under different sensitivity tests according to an embodiment of the present invention;

[0046] Figure 11 This is a schematic diagram illustrating the quantity of carried aviation materials under different sensitivity tests according to embodiments of the present invention;

[0047] Figure 12 This is a schematic diagram illustrating the simulation results of the quantity of different aviation materials carried in an embodiment of the present invention;

[0048] Figure 13 This is a schematic diagram of the simulation results of the quantity of different aviation materials carried after the modification of the present invention embodiment;

[0049] Figure 14 This is a schematic diagram illustrating the simulation results of the number of aircraft materials carried under different maintenance levels according to an embodiment of the present invention;

[0050] Figure 15 This is a schematic diagram illustrating the simulation results of the number of aircraft materials carried during different repair cycles according to an embodiment of the present invention;

[0051] Figure 16 This is a schematic diagram illustrating the simulation results of the number of carried aircraft materials under the environmental correction factor in an embodiment of the present invention. Detailed Implementation

[0052] The present invention will now be described in detail through exemplary embodiments. However, it should be understood that, without further description, elements, structures, and features in one embodiment may be advantageously incorporated into other embodiments.

[0053] This invention provides a method for constructing a SD model of the carrying demand for repairable aircraft parts during long-range, long-duration sea operations. When constructing the model, the influence of multiple factors on the prediction of aircraft part carrying demand is considered. System factors related to the prediction of aircraft part carrying demand are selected, while irrelevant system factors are eliminated. The impact of factors such as end of service life, failures, and maintenance is also considered. The constructed model is stable and reliable, and provides good simulation results of the aircraft part carrying support process for carrier-based aircraft during long-range, long-duration sea operations on a macroscopic scale, with accurate prediction of the amount of aircraft part carried. The method includes the following steps:

[0054] S1, Assumption:

[0055] (1) Only repairable aircraft parts that have experienced failures or have potential failures are considered; other aircraft parts can meet the field requirements.

[0056] (2) Only the lifespan of the aircraft materials installed during the mission is considered, and the lifespan of the aircraft materials stored in the cabins of the aircraft carrier is not considered;

[0057] (3) During the mission, each piece of aircraft material is subject to occasional failures, and the failure rate is a fixed constant.

[0058] (4) The failures of aircraft materials are random failures, and the failures are independent, with the number of failures following a Poisson distribution;

[0059] (5) Without considering the repair of multiple parts, multiple parts can be repaired at the same time, with a repair rate of 0.7~0.95. If they cannot be repaired, they are scrapped.

[0060] (6) The duration of long-range maritime missions is 26 weeks ± 1 week;

[0061] (7) The impact of economic factors on the support of aviation materials for long-range voyages at sea is not considered.

[0062] Six factors affecting the long-range, long-duration, repairable aircraft parts supply system were selected as dynamic variables for the long-range, long-duration, long-duration, long-duration, long-life, and long-life, long-life, long-repairable aircraft parts supply system. Among them, the number of carried aircraft parts refers to the number of repairable aircraft parts stored in the aircraft carrier's aircraft parts warehouse.

[0063] It should be noted that the repair rate can be set according to actual needs, and can be 0.7, 0.75, 0.8, 0.85, 0.9, or 0.95, etc. Similarly, the duration of long-range maritime missions is not fixed and will be slightly adjusted according to the actual mission, and can be 25 weeks, 27 weeks, or 26 weeks.

[0064] S2. Analyze the operational mechanism of aircraft material supply support during voyages. Based on the relationships between the quantity of aircraft materials carried, the quantity of aircraft materials under repair, the consumption of aircraft materials, the quantity of faulty aircraft materials, the quantity of aircraft materials nearing the end of their service life, and the quantity of aircraft materials to be repaired, as well as the relationships between each factor and other factors related to each factor, establish a causal relationship diagram for the long-range, long-duration, repairable aircraft material supply system (see...). Figure 1 ).

[0065] Specifically, see [link to relevant documentation] Figure 1 When constructing the causal relationship diagram, other factors related to the number of faulty aircraft parts include the number of aircraft parts installed, planned flight time and failure rate. Other factors related to both aircraft parts consumption and the number of repaired aircraft parts are the repair rate.

[0066] See also Figure 1 When constructing the causal relationship diagram, a time delay symbol was added to the causal chain between the quantity of repaired aircraft parts and the quantity of carried aircraft parts to represent the maintenance cycle (i.e., repair cycle) of the faulty aircraft parts.

[0067] When carrier-based aircraft experience malfunctions or reach the end of their service life, spare parts are retrieved from the carrier's spare parts storage to replace and repair the faulty aircraft. This process consumes spare parts, reducing the amount of spare parts carried on board while increasing the amount of spare parts under repair. Faulty spare parts are repaired by maintenance personnel, and the repaired spare parts become usable and are transferred to the carrier's spare parts storage to replenish the carried spare parts. Conversely, spare parts that cannot be repaired are designated as scrapped spare parts and withdrawn from circulation.

[0068] S3. Based on the cause-and-effect diagram and combined with the feedback mechanism for aircraft material supply during operation, the dynamic variables and symbols of the long-range, repairable aircraft material supply system are organically combined to establish a flow graph for the long-range, repairable aircraft material supply system, namely, the SD model of the long-range, repairable aircraft material carrying demand. The dynamic equations of the SD model of the long-range, repairable aircraft material carrying demand are as follows:

[0069] The quantity of carry-on aircraft parts = INTEG(DELAY1I(repaired aircraft parts quantity, repair cycle, 0) - aircraft parts consumption, demand), the unit is: pieces; the demand is the quantity of carry-on aircraft parts that needs to be predicted. The replenishment of carry-on aircraft parts quantity by the repaired aircraft parts quantity is delayed due to the repair cycle, the unit of repair cycle is: weeks;

[0070] The quantity of aircraft parts under repair = INTEG(material consumption - quantity of repaired aircraft parts - quantity of unrepaired aircraft parts, 0), with the unit being: pieces; the quantity of aircraft parts under repair on the aircraft carrier is set to zero before the mission is executed;

[0071] Quantity of repaired aircraft parts = INTEGER (Quantity of aircraft parts under repair * Repair rate), unit: pieces;

[0072] The repair rate depends on the repair level. The relationship between the repair rate and the repair level is represented by a table function. The value between 0 and 1 represents multiple repair level levels, and different repair level levels correspond one-to-one with different repair rates.

[0073] The cumulative number of scrapped aircraft parts is expressed as: Cumulative number of scrapped aircraft parts = INTEG(number of scrapped aircraft parts, 0), with the unit being: pieces; The number of scrapped aircraft parts is the number of unrepaired aircraft parts, which is the difference between the number of aircraft parts under repair and the number of repaired aircraft parts;

[0074] Test1 data, which follows a Poisson distribution, is generated based on the number of faulty aircraft parts, and Test2 data is generated based on the number of aircraft parts that have reached the end of their service life. The sum of the two is used as the aircraft part consumption pattern. Test1 data uses the RANDOM POISSON function to simulate the fault situation, and the mean is the number of faulty aircraft parts. Test2 data uses the PULSE TRAIN function to simulate the aircraft parts reaching the end of their service life, and it is assumed that the end of service life has been determined.

[0075] Number of faulty aircraft parts = Planned flight time * Number of aircraft parts installed * Failure rate * Repair cycle * Environmental correction factor, unit: piece; Where, Number of aircraft parts installed = Number installed per aircraft * Aircraft capacity;

[0076] Aircraft material support good rate A Represented as:

[0077] (1)

[0078] in, E i For the first i The cumulative shortage of a particular aircraft material represents the expected shortage of that aircraft material when the carried inventory is equal to the demand. The cumulative shortage is calculated as follows: Cumulative shortage = INTEG(shortage, 0) and Shortage = IF THEN ELSE(Carried Aircraft Material Quantity < 0, ABS(Carried Aircraft Material Quantity) / 26, 0). N For aircraft strength, b i For the first i The number of aircraft materials installed.

[0079] Specifically, the symbol is an arrow. It should be noted that the symbol is not limited to arrows; it can also be other symbols that indicate relationships, and can be set according to actual needs.

[0080] Specifically, in this embodiment, the values ​​between 0 and 1 are set to represent four levels of repair quality: poor, medium, good, and excellent.

[0081] When the aircraft carrier maintenance department is rated as "Good", then:

[0082] The maintenance level is set to 0.75, and the maintenance level is calculated as: WITH LOOKUP(0.75,[(0,0)-(10,10)],(0.25,0.7),(0.5,0.85),(0.75,0.9),(1,0.95)).

[0083] It should be noted that the repair rate depends on the level of repair. Different repair levels will have different values, but these values ​​are only numerical representations based on the table function and do not represent the impact on the repair rate.

[0084] The method described in this invention, when predicting the carrying capacity of a certain aircraft material in the constructed model, can calculate the number of faulty aircraft materials given the number of installations per aircraft and the failure rate of that material. Test 1 simulates the consumption of aircraft materials following a Poisson distribution, and Test 2 simulates the lifespan of a certain type of aircraft material to obtain the total consumption. Through continuous adjustments, the carrying capacity requirement of the aircraft material is obtained. Furthermore, simulations can be performed using typical aircraft materials to simulate their respective demand data, and with a final aircraft material support rate of 95% as the expected target, the optimal carrying capacity of each aircraft material is analyzed and determined.

[0085] To verify the reliability and stability of the model constructed by the method of this invention, the model is tested under extreme conditions and a sensitivity test is performed on the model.

[0086] 1. Extreme condition test

[0087] Extreme condition testing is used to verify whether the model's behavior is reasonable. Although such extreme assumptions may not occur, it can improve the model's reliability. Testing under extreme conditions allows us to verify whether the model can exhibit a dynamic response consistent with the real world under extreme conditions. Therefore, the extreme condition test was conducted on the SD model of long-range, long-life, repairable aircraft parts carrying demand constructed by the method described above in this invention. When testing the model under extreme conditions, the following assumptions were made: demand quantity = 10, number of units installed per aircraft = 4, aircraft strength = 30, planned flight time = 10, repair cycle = 1, failure rate = 0.0008, environmental correction factor = 1.1, number of aircraft parts nearing the end of their service life = 4, simulation time set to 26 weeks, simulation step size = 1 week. The repair rate was set to 1, and the model was tested.

[0088] When the repair rate is 1, all aircraft parts under repair after a malfunction are successfully repaired, and the number of aircraft parts under repair equals the number of repaired aircraft parts (see [reference]). Figure 4 ).

[0089] Since all aircraft parts under repair have been repaired, the number of unrepaired aircraft parts is zero, therefore the cumulative number of scrapped aircraft parts is also zero (see...). Figure 5 ).

[0090] Repaired aircraft materials replenish the quantity of carried aircraft materials. Due to delays in the repair cycle, the quantity of carried aircraft materials shows a fluctuating trend (see...). Figure 6 ).

[0091] Since the demand assigned in the assumptions satisfies the demand for carry-on materials under these conditions, the quantity of carry-on materials is always greater than zero. Therefore, the cumulative shortage is zero (see...). Figure 7 At this point, the spare parts availability rate for this type of aircraft remained at 1 (see...). Figure 8 ).

[0092] In summary, when the repair rate is 1, the extreme condition test of the SD model for the carrying requirements of long-range, repairable aircraft materials for offshore operations constructed by the above method of the present invention meets the expected situation.

[0093] 2. Sensitivity test

[0094] Sensitivity tests are used to analyze and verify the structural stability of the model. To more realistically reflect the impact of uncertainties in actual support processes, sensitivity tests were conducted on the established SD model of long-range, serviceable, repairable aircraft parts carrying demand. In the model sensitivity test, the following assumptions were made: demand quantity = 20, number of units per aircraft = 2, aircraft strength = 20, planned flight time = 15, repair cycle = 1, failure rate = 0.001, environmental correction factor = 1.2, serviceable parts quantity = 6, maintenance level = good, simulation time set to 26 weeks, simulation step size = 1 week, designated as Sensitivity Test 1. Sensitivity Test 2, based on Sensitivity Test 1, incorporated a pulse function PULSE(10,1)*4 as interference to the parts consumption, meaning that in the tenth week, 4 parts suddenly failed (see...). Figure 9 ).

[0095] The model was simulated according to the set conditions. Because four pieces of aircraft parts failed in week ten, the number of parts under repair also increased by four (see...). Figure 10 ).

[0096] After the malfunctioning aircraft parts are repaired and stored, the quantity of carried aircraft parts is replenished to maintain the original quantity (see [reference]). Figure 11 ).

[0097] In summary, when the model input changes, the simulation results will change slightly due to disturbances, but the overall behavior trend of the model will not change. The above sensitivity test proves the stability of the simulation model, indicating that the SD model for the carrying demand of repairable aircraft materials for long-range maritime voyages should be relatively insensitive to parameter changes. The model structure is good, and it is feasible to use this model to predict the consumption of carried aircraft materials.

[0098] The following simulation prediction is performed on the SD model of the carrying requirements of long-range, repairable aircraft materials for distant seas constructed by the above method of the present invention, based on specific implementation cases.

[0099] Example: When simulating and predicting the carrying demand of repairable aircraft parts for long-range maritime operations using the SD model, the mission background conditions are first set. Assume that during a certain long-range training mission by an aircraft carrier, the aircraft strength is 25, the planned flight time is 20, and the environmental correction factor is 1.2. To facilitate observation and comparison of operational results, the demand is uniformly set to 50, the simulation time is set to 26 weeks, and the simulation step size is 1 week. Based on the characteristics of aircraft parts support work, five important repairable aircraft parts are listed as simulation objects, and their main data are shown in Table 1.

[0100] Table 1

[0101]

[0102] Model system simulations were performed on the aforementioned aircraft materials, and the required quantities were then adjusted based on the operational results of the carried aircraft materials. The simulation results for each aircraft material's carrying capacity are available in [link to simulation results]. Figure 12 .

[0103] The simulation results show that the quantity of carried aircraft materials was not fully consumed during the mission period. Therefore, the required quantities of these materials were adjusted to ensure that all carried aircraft materials were used up within the mission period. The revised simulation results for the carried materials are available in [reference needed]. Figure 13 .

[0104] The specific demand data for various aviation materials are shown in Table 2.

[0105] Table 2

[0106]

[0107] The good maintenance rate of the aircraft materials for this model after simulation is shown in Table 3.

[0108] Table 3

[0109]

[0110] According to formula (1), the final good maintenance rate A of the aircraft materials is 99.0803%, which meets the expected target of the good maintenance rate of the aircraft materials reaching more than 95%. Therefore, the demand of various aircraft materials at this time is the amount of materials required for long-range maritime missions.

[0111] To improve the reliability of various aviation materials, we will conduct simulation and comparative analysis by changing different parameters.

[0112] 1. Taking aircraft material 5 as an example, assuming all other parameters remain unchanged, only the maintenance level value is altered. After simulation, the output results are compared and analyzed, and the results are as follows: Figure 14 As shown in Table 4.

[0113] Depend on Figure 14 It is evident that the level of maintenance significantly impacts the quantity of spare parts carried in the later stages. The lower the maintenance level, the fewer spare parts can be repaired and replenished, resulting in a reduction in the quantity of spare parts carried. If a shortage of spare parts occurs, it is necessary to stockpile spare parts in advance, thus consuming inventory resources.

[0114] Table 4

[0115]

[0116] As shown in Table 4, considering the actual situation that maintenance funds and other resources will also increase with the improvement of maintenance level, it is considered that the maintenance level is good.

[0117] 2. Taking aircraft part 5 as an example, assuming all other parameters remain unchanged, only the repair cycle value is altered. After simulation, the output results are compared and analyzed, and the results are as follows: Figure 15 As shown in Table 5.

[0118] Depend on Figure 15 It is evident that maintenance capability significantly impacts the quantity of carried aircraft parts, persisting throughout the entire mission cycle. A longer repair cycle results in fewer parts being repaired or replenished per unit of time, leading to a reduction in carried parts and potential shortages. Conversely, a shorter repair cycle allows for more parts to be repaired or replenished per unit of time, ensuring a substantial amount of carried aircraft parts remain unused at the end of the mission.

[0119] Table 5

[0120]

[0121] As shown in Table 5, considering that as the repair cycle shortens, maintenance manpower and resources are also over-occupied, and there will be surplus inventory, it is considered that a repair cycle of 1 is the best.

[0122] 3. Taking aircraft material 5 as an example, assuming all other parameters remain unchanged, only the value of the environmental correction factor is altered. The environmental correction factor represents the combined impact of the marine environment (high temperature, high humidity, high salt spray) and the shipboard environment (vibration, electromagnetic fields) on aircraft material consumption. After simulation, the output results are compared and analyzed, and the results are as follows: Figure 16 As shown in Table 6.

[0123] Table 6

[0124]

[0125] Depend on Figure 16 As can be seen from Table 6, the higher the value of the environmental correction factor, the greater the consumption of aircraft materials, which leads to a reduction in the amount of inventory in the ship's compartments, and even a shortage of parts. Therefore, an increase in the environmental correction factor will also lead to a decrease in the good condition of aircraft materials.

[0126] To further improve the maintenance of repairable spare parts for carrier-based aircraft during long-range, long-duration sea operations, the following measures can also be taken:

[0127] 1. Given that maintenance level and repair cycle have a significant impact on the amount of aircraft parts carried, in order to improve these two main maintenance factors—namely, improving maintenance level and reducing repair cycle—the overall maintenance capability of aircraft carrier maintenance personnel should be enhanced.

[0128] 2. Long-duration, long-range maritime operations present challenges in replenishing spare parts and components, coupled with limited ship-based maintenance capabilities. Therefore, the maintainability and reliability of aircraft materials are paramount. Improving manufacturing processes and materials can enhance these aspects, thereby increasing maintenance capabilities, reducing failure rates, and ultimately improving overall maintenance and operational support.

[0129] 3. Because the good condition of aircraft materials decreases as the environmental severity increases, it is crucial to properly store the aircraft materials carried on board the aircraft carrier. To reasonably reduce the impact of environmental factors on the maintenance of carried aircraft materials, high-standard storage of carried aircraft materials can be achieved by improving the packaging standards for carried aircraft materials on board and taking shockproof measures.

[0130] The above embodiments are used to explain the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A method for constructing a SD model of the carrying requirements for repairable aircraft parts with long service life and long-range maritime operations, characterized in that, Includes the following steps: S1, Assumption: (1) Only repairable aircraft parts that have experienced failures or have potential failures are considered; other aircraft parts can meet the field requirements. (2) Only the lifespan of the aircraft materials installed during the mission is considered, and the lifespan of the aircraft materials stored in the cabins of the aircraft carrier is not considered; (3) During the mission, each piece of aircraft material is subject to occasional failures, and the failure rate is a fixed constant. (4) The failures of aircraft materials are random failures, and the failures are independent, with the number of failures following a Poisson distribution; (5) Without considering the repair of multiple parts, multiple parts can be repaired at the same time, with a repair rate of 0.7~0.

95. If they cannot be repaired, they are scrapped. (6) The duration of long-range maritime missions is 26 weeks ± 1 week; (7) The impact of economic factors on the support of aviation materials for long-range maritime voyages is not considered; Six factors affecting the consumption of the long-range, long-duration, repairable aircraft parts supply system were selected as dynamic variables of the long-range, long-duration, long-duration, long-duration, long-duration, long-life ... S2. Analyze the operation mechanism of aircraft material supply guarantee during the flight, and establish a causal relationship diagram of the long-range, repairable aircraft material supply system based on the relationship between the number of aircraft materials carried, the number of aircraft materials under repair, the amount of aircraft materials consumed, the number of faulty aircraft materials, the number of aircraft materials that have reached the end of their service life, and the number of aircraft materials to be repaired, as well as the relationship between each factor and other factors related to each factor. S3. Based on the cause-and-effect diagram and combined with the feedback mechanism for aircraft material supply during operation, the dynamic variables and symbols of the long-range, repairable aircraft material supply system are organically combined to establish a flow graph for the long-range, repairable aircraft material supply system, namely, the SD model of the long-range, repairable aircraft material carrying demand. The dynamic equations of the SD model of the long-range, repairable aircraft material carrying demand are as follows: The quantity of carry-on aircraft parts = INTEG(DELAY1I(repaired aircraft parts quantity, repair cycle, 0) - aircraft parts consumption, demand), the unit is: pieces; the demand is the quantity of carry-on aircraft parts that needs to be predicted. The replenishment of carry-on aircraft parts quantity by the repaired aircraft parts quantity is delayed due to the repair cycle, the unit of repair cycle is: weeks; The quantity of aircraft parts under repair = INTEG(material consumption - quantity of repaired aircraft parts - quantity of unrepaired aircraft parts, 0), with the unit being: pieces; the quantity of aircraft parts under repair on the aircraft carrier is set to zero before the mission is executed; Quantity of repaired aircraft parts = INTEGER (Quantity of aircraft parts under repair * Repair rate), unit: pieces; The repair rate depends on the repair level. The relationship between the repair rate and the repair level is represented by a table function. The value between 0 and 1 represents multiple repair level levels, and different repair level levels correspond one-to-one with different repair rates. The cumulative number of scrapped aircraft parts is expressed as: Cumulative number of scrapped aircraft parts = INTEG(number of scrapped aircraft parts, 0), with the unit being: pieces; The number of scrapped aircraft parts is the number of unrepaired aircraft parts, which is the difference between the number of aircraft parts under repair and the number of repaired aircraft parts; Test1 data, which follows a Poisson distribution, is generated based on the number of faulty aircraft parts, and Test2 data is generated based on the number of aircraft parts that have reached the end of their service life. The sum of the two is used as the aircraft part consumption pattern. Test1 data uses the RANDOM POISSON function to simulate the fault situation, and the mean is the number of faulty aircraft parts. Test2 data uses the PULSE TRAIN function to simulate the aircraft parts reaching the end of their service life, and it is assumed that the end of service life has been determined. Number of faulty aircraft parts = Planned flight time * Number of aircraft parts installed * Failure rate * Repair cycle * Environmental correction factor, unit: piece; Where, Number of aircraft parts installed = Number installed per aircraft * Aircraft capacity; Aircraft material support good rate A Represented as: （1） in, E i For the first i The cumulative shortage of a particular aircraft material represents the expected shortage of that aircraft material when the carried inventory is equal to the demand. The cumulative shortage is calculated as follows: Cumulative shortage = INTEG(shortage, 0) and Shortage = IF THEN ELSE(Carried Aircraft Material Quantity < 0, ABS(Carried Aircraft Material Quantity) / 26, 0). N For aircraft strength, b i For the first i The number of aircraft materials installed.

2. The method for constructing the SD model of the carrying requirements for long-range, repairable aircraft parts with a service life as described in claim 1, characterized in that, In step S2, when constructing the causal relationship diagram, other factors related to the number of faulty aircraft parts include the number of installed aircraft parts, planned flight time, and failure rate. Other factors related to both aircraft parts consumption and the number of repaired aircraft parts are the repair rate.

3. The method for constructing the SD model of the carrying requirements for long-range, repairable aircraft parts as described in claim 1, characterized in that, In step S3, the symbol is an arrow.

4. The method for constructing the SD model of the carrying requirements for long-range, repairable aircraft parts with a service life as described in claim 1, characterized in that, In step S3, values ​​between 0 and 1 represent four levels of maintenance skill: poor, medium, good, and excellent. If the aircraft carrier maintenance department is set to good, then the maintenance skill value is 0.75, and the maintenance skill level is calculated as: WITH LOOKUP(0.75,[(0,0)-(10,10)],(0.25,0.7),(0.5,0.85),(0.75,0.9),(1,0.95)).

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