Aero-engine spare part demand determination method and device based on condition-based maintenance mode
By constructing individual and fleet models of aero engines, triggering maintenance tasks, and determining maintenance cycles and costs, the problem of inaccurate spare parts demand prediction when there is insufficient maintenance data for new aero engines is solved, achieving higher accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- AECC HUNAN AVIATION POWERPLANT RES INST
- Filing Date
- 2026-01-12
- Publication Date
- 2026-05-12
AI Technical Summary
Under the condition-based maintenance model, the existing technology is significantly limited in its ability to predict spare parts demand when faced with new models of aero engines or insufficient maintenance data, thus affecting accuracy.
By acquiring the life distribution parameters, maintenance execution strategies, and maintenance constraints of aero-engines, simulation tools are used to construct individual aero-engine models and fleet models, triggering preset maintenance tasks, and performing workshop maintenance in idle maintenance locations to determine maintenance cycles, costs, and time, thereby determining spare parts requirements.
Even in situations involving new aircraft engine models or insufficient maintenance data, the accuracy of spare parts requirements has been improved, ensuring that predictive capabilities are not significantly constrained.
Smart Images
Figure CN122020884A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aircraft engine spare parts prediction technology, and specifically to a method and apparatus for determining spare parts requirements for aircraft engines based on a condition-based maintenance model. Background Technology
[0002] As the core power unit of an aircraft, the aero engine has a complex structure, high reliability requirements, and operates under harsh conditions such as high temperature and high pressure for a long time. Aero engines are bound to experience performance degradation or failure. Modern aero engines generally adopt the Condition-Based Maintenance (CBM) mode. Under this mode, aero engines no longer need to be returned to the factory for regular maintenance. Instead, maintenance decisions are made dynamically based on the actual health status of the aero engine.
[0003] In related technologies, the demand for spare parts for aero engines is generally predicted in a condition-based mode, relying on a large amount of historical operation and maintenance data. However, this method is significantly limited in its predictive ability when faced with new models of aero engines or insufficient operation and maintenance data, which in turn affects the accuracy of the demand for spare parts for aero engines. Summary of the Invention
[0004] This invention provides a method and apparatus for determining spare parts requirements for aero engines based on a condition-based maintenance mode, in order to solve the problem that predictive capabilities are significantly limited when faced with the emergence of new aero engines or insufficient maintenance data, thereby affecting the accuracy of spare parts requirements for aero engines.
[0005] In a first aspect, the present invention provides a method for determining spare parts requirements for an aero-engine based on a condition-based maintenance mode, the method comprising: Obtain the life distribution parameters, maintenance execution strategies, maintenance constraints, and state variable parameters of the aero-engine; Based on the life distribution parameters of aero engines, individual models of aero engines are constructed using simulation tools; Based on the maintenance execution strategy and maintenance constraints, multiple individual aero-engine models are simulated and run using simulation tools to form an aero-engine fleet model; Based on the state variable parameters of the aero-engine and the aero-engine fleet model, simulation tools are used to trigger the aero-engine to perform preset maintenance tasks. When there are available repair slots in the repair shop, the engine to be repaired in the preset repair tasks is repaired in the workshop according to the repair execution strategy and repair constraints, and the repair cycle, repair cost and repair time of the engine to be repaired are determined. Based on the maintenance cycle, maintenance cost, and maintenance time of the engine to be repaired, determine the corresponding spare parts requirements for the engine to be repaired.
[0006] In some optional implementations, the method for determining spare parts requirements for aero-engines based on condition-based maintenance mode in this embodiment of the invention further includes: When the engine to be repaired is completed according to the maintenance schedule, update the spare parts inventory; or, when the simulation time of multiple individual aero-engine models running through simulation tools reaches the spare parts procurement cycle, update the spare parts inventory.
[0007] In some alternative implementations, the life distribution parameters of the aircraft engine are obtained by fitting data on various influencing factors of the aircraft engine and historical flight data under different preset conditions.
[0008] In some alternative implementations, the maintenance execution strategy for the aircraft engine is generated based on the maintenance type and flight time, respectively, according to preventive maintenance and restorative maintenance methods.
[0009] In some optional implementations, maintenance constraints include: field maintenance constraints, return-to-factory maintenance constraints, and spare parts inventory resource constraints. The state variable parameters of the aero-engine include: cumulative flight time, cumulative flight count, and health status parameters. Based on the maintenance execution strategy and maintenance constraints, multiple individual aero-engine models are simulated using simulation tools to form an aero-engine fleet model, including: Based on the constraints of field maintenance, return-to-factory maintenance, and spare parts inventory, multiple individual aero-engine models are simulated using simulation tools. The cumulative flight time and number of flights are iteratively increased, and the health status parameters of the aero-engines are monitored to form an aero-engine fleet model.
[0010] In some optional implementations, the preset maintenance task includes: a planned maintenance task, which is triggered by simulation tools to execute a preset maintenance task based on the state variable parameters of the aero-engine and the aero-engine fleet model, including: Based on the state variable parameters of the aero-engine, the planned maintenance tasks of the aero-engine fleet model are triggered by simulation tools, and the corresponding maintenance actions are performed according to the maintenance execution strategy based on the planned inspection tasks or planned dispatch tasks in the planned maintenance tasks.
[0011] In some optional implementations, the preset maintenance task includes: unplanned maintenance tasks, which are triggered by simulation tools based on the state variable parameters of the aero-engine and the aero-engine fleet model, including: Based on the state variable parameters of the aero-engine and the aero-engine fleet model, simulation tools are used to trigger the aero-engine to perform unplanned maintenance tasks, and corresponding maintenance actions are performed according to the maintenance execution strategy based on the engine failure or line replaceable unit failure in the unplanned maintenance task.
[0012] Secondly, the present invention also provides a spare parts demand prediction device for aero-engines based on a condition-based maintenance mode, the device comprising: The relevant parameter acquisition module is used to acquire the life distribution parameters, maintenance execution strategies, maintenance constraints, and state variable parameters of the aero-engine. The first model building module is used to build individual models of aero engines based on the life distribution parameters of aero engines using simulation tools. The second model building module is used to simulate and run multiple individual aero-engine models using simulation tools based on maintenance execution strategies and maintenance constraints, thereby forming an aero-engine fleet model. The maintenance task triggering module is used to trigger the aero-engine to perform preset maintenance tasks through simulation tools based on the state variable parameters of the aero-engine and the aero-engine fleet model. The maintenance action execution module is used to perform workshop maintenance on the engines to be repaired in the preset maintenance tasks when there are available maintenance positions in the maintenance shop, based on the maintenance execution strategy and maintenance constraints, and to determine the maintenance cycle, maintenance cost and maintenance time of the engines to be repaired. The spare parts requirement determination module is used to determine the spare parts requirements for the engine to be repaired based on the engine's maintenance cycle, maintenance cost, and maintenance time.
[0013] Thirdly, the present invention also provides an electronic device, comprising: The memory and processor are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for determining spare parts requirements of an aircraft engine based on a condition-based maintenance mode, as described in the first aspect or any embodiment of the first aspect.
[0014] Fourthly, the present invention also provides a computer-readable storage medium storing computer instructions, the computer instructions being used to cause a computer to execute the method for determining spare parts requirements of an aero-engine based on a condition-based maintenance mode in the first aspect or any embodiment of the first aspect.
[0015] The technical solution of this invention has the following advantages: This invention discloses a method and apparatus for determining spare parts requirements for aero-engines based on a condition-based maintenance model. The method involves acquiring the aero-engine's lifespan distribution parameters, maintenance execution strategy, maintenance constraints, and state variable parameters. Based on the aero-engine's lifespan distribution parameters, an individual aero-engine model is constructed using simulation tools. Multiple individual aero-engine models are simulated using simulation tools according to the maintenance execution strategy and maintenance constraints to form an aero-engine fleet model. Based on the aero-engine's state variable parameters and the aero-engine fleet model, a preset maintenance task is triggered for the aero-engines using simulation tools. When there are available maintenance positions in the maintenance depot, workshop maintenance is performed on the engines to be maintained in the preset maintenance tasks according to the maintenance execution strategy and maintenance constraints, and the maintenance cycle, maintenance cost, and maintenance time of the engines to be maintained are determined. Based on the maintenance cycle, maintenance cost, and maintenance time of the engines to be maintained, the corresponding spare parts requirements for the engines to be maintained are determined. Therefore, even when facing situations such as the emergence of new aero-engine models or insufficient maintenance data, the predictive capability is not significantly limited, and the accuracy of aero-engine spare parts requirements can be improved. Attached Figure Description
[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0017] Figure 1 This is a flowchart of a method for determining spare parts requirements for aero engines based on a condition-based maintenance mode according to an embodiment of the present invention; Figure 2A This is an experimental schematic diagram illustrating the number of simulation rounds for service time-related indicators according to an embodiment of the present invention; Figure 2B This is an experimental schematic diagram illustrating the number of simulation rounds of fleet-related cost indicators according to an embodiment of the present invention; Figure 3A This is a schematic diagram illustrating the impact of a large manufacturer's maintenance capabilities on average disassembly time and spare parts quantity according to an embodiment of the present invention; Figure 3B This is a schematic diagram illustrating the impact of overhaul facility maintenance capabilities on fleet-related costs according to an embodiment of the present invention; Figure 4A This is a schematic diagram illustrating the impact of the maintenance turnaround cycle on the repair rate and the number of spare parts according to an embodiment of the present invention. Figure 4B This is a schematic diagram illustrating the impact of maintenance turnaround time on fleet-related costs according to an embodiment of the present invention; Figure 5 This is a schematic diagram of three long-cycle simulation samples of an engine fleet according to an embodiment of the present invention.
[0018] Figure 6 This is a structural block diagram of a device for determining spare parts requirements for aero-engines based on a condition-based maintenance mode according to an embodiment of the present invention. Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.
[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0022] According to an embodiment of the present invention, an embodiment of a method for determining spare parts requirements of an aero-engine based on a condition-based maintenance mode is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.
[0023] This embodiment provides a method for determining spare parts requirements for aero engines based on a condition-based maintenance mode. This method can be applied to computer equipment, such as desktop computers, laptops, and servers. Figure 1 As shown, the process includes the following steps: Step S101: Obtain the life distribution parameters, maintenance execution strategy, maintenance constraints, and state variable parameters of the aero-engine.
[0024] In some specific implementations, the life distribution parameters of the aero-engine are obtained by fitting data on various influencing factors of the aero-engine and historical flight data under preset conditions.
[0025] Specifically, the data on various influencing factors includes, but is not limited to, failure modes, operating environment, usage conditions, and maintenance support strategies. For example, failure modes include data on influencing factors such as blade wear, bearing failure, and seal leakage; operating environment includes data on environmental factors such as high temperature, high humidity, and dusty conditions; usage conditions include data on influencing factors such as flight time, number of flight cycles, and number of takeoffs and landings. Maintenance support strategies include data on influencing factors such as preventative maintenance intervals and condition-based maintenance thresholds.
[0026] For example, the life distribution parameters of an aero-engine are obtained by fitting data on various influencing factors and historical flight data under different preset conditions using a fitting tool. This fitting tool can be either MATLAB or Python.
[0027] For example, Table 1 below shows a schematic table of different preset operating environment conditions. Table 1 defines an environmental impact factor, which is used to quantify the accelerating effect of different environmental conditions such as sand concentration, temperature, and humidity on the performance degradation rate of aero-engines.
[0028] Table 1. Schematic diagram of different preset operating environment conditions
[0029] In some specific implementations, the maintenance execution strategy for aircraft engines is generated based on the maintenance type and flight time, respectively, according to preventive maintenance methods and restorative maintenance methods.
[0030] Preventive maintenance is a proactive maintenance approach, while corrective maintenance is a restorative maintenance approach. Preventive maintenance involves performing inspections, replacements, and overhauls according to planned intervals. Restorative maintenance, on the other hand, involves repairs performed after a failure has occurred. Maintenance types can include planned inspections, planned orders, engine failures, and replaceable spare parts.
[0031] For example, maintenance execution strategies for aircraft engines undergoing field inspections can be generated based on flight time and planned inspection type.
[0032] For example, maintenance execution strategies for replacing engines can be generated based on flight time and plan issuance type.
[0033] For example, a maintenance execution strategy for repairing an engine can be generated based on the type of engine failure.
[0034] For example, maintenance strategies for replacing spare parts can be generated based on the type of fault that allows for the replacement of spare parts.
[0035] In a specific example, maintenance constraints include: field maintenance constraints, return-to-factory maintenance constraints, and spare parts warehouse resource constraints. The state variable parameters of the aero-engine include: cumulative flight time, cumulative number of flights, and health status parameters.
[0036] For example, for field maintenance constraints, the planned downtime is 2 days, and the unplanned downtime is 5 days.
[0037] For example, the return-to-factory repair constraints are: a 30-day turnaround period and a limit of 5 engines to be repaired in the workshop.
[0038] The resource constraint of the spare parts warehouse is the pre-determined inventory capacity of the spare parts warehouse.
[0039] Step S102: Based on the life distribution parameters of the aero-engine, construct an individual model of the aero-engine using simulation tools.
[0040] Specifically, the simulation tool can be MATLAB. Independent models of the aero-engine are randomly generated by loading the life distribution parameters of the aero-engine into MATLAB.
[0041] Step S103: Based on the maintenance execution strategy and maintenance constraints, multiple individual aero-engine models are simulated and run using simulation tools to form an aero-engine fleet model.
[0042] In a specific example, the aircraft engine fleet model includes a single-aircraft module, a waiting-for-maintenance module, a field maintenance module, a workshop maintenance module, and a support resource module.
[0043] The spare parts warehouse module is used to store the engine's own status information: remaining life, field and workshop maintenance decision rules, maintenance activity cost data, etc. The spare parts warehouse waiting for maintenance module updates the engine maintenance sequence based on the different states of the fleet's engines and the triggering conditions of maintenance activities. The spare parts warehouse field maintenance module is based on the waiting maintenance module. According to the decision rules for field maintenance of engines waiting for maintenance, it updates the available support resources and the queue of engines waiting for maintenance.
[0044] The spare parts warehouse workshop maintenance module is based on the waiting maintenance module. According to the maintenance decision rules of the waiting engine workshop, it updates the available support resources and the waiting engine queue. The spare parts inventory module is used to record the number of available spare parts in the fleet and the status information of the spare parts themselves.
[0045] In some specific implementations, step S103 above, which involves simulating multiple individual aero-engine models using simulation tools to form an aero-engine fleet model, includes: Based on the constraints of field maintenance, return-to-factory maintenance, and spare parts inventory, multiple individual aero-engine models are simulated using simulation tools. The cumulative flight time and number of flights are iteratively increased, and the health status parameters of the aero-engines are monitored to form an aero-engine fleet model.
[0046] Step S104: Based on the state variable parameters of the aero-engine and the aero-engine fleet model, trigger the aero-engine to perform a preset maintenance task through simulation tools.
[0047] In some specific implementations, the preset maintenance tasks include: planned maintenance tasks, which are triggered by simulation tools to execute preset maintenance tasks based on the state variable parameters of the aero-engine and the aero-engine fleet model, including: Based on the state variable parameters of the aero-engine, the planned maintenance tasks of the aero-engine fleet model are triggered by simulation tools, and the corresponding maintenance actions are performed according to the maintenance execution strategy based on the planned inspection tasks or planned dispatch tasks in the planned maintenance tasks.
[0048] For example, based on the state variable parameters of the aero-engine, simulation tools can trigger planned inspection tasks or planned deployment tasks within the planned maintenance tasks of the aero-engine fleet model. A planned inspection task refers to: if a fault occurs, performing corresponding maintenance activities according to the maintenance strategy; a planned deployment refers to: if there is an engine in the spare engine depot, selecting a spare engine for installation and operation, and the faulty engine entering the engine workshop maintenance queue.
[0049] In other specific implementations, the pre-set maintenance tasks include: unplanned maintenance tasks, which are triggered by simulation tools based on the state variable parameters of the aero-engine and the aero-engine fleet model, including: Based on the state variable parameters of the aero-engine and the aero-engine fleet model, simulation tools are used to trigger the aero-engine to perform unplanned maintenance tasks, and corresponding maintenance actions are performed according to the maintenance execution strategy based on the engine failure or line replaceable unit failure in the unplanned maintenance task.
[0050] For example, based on the state variable parameters of the aero-engine and the aero-engine fleet model, simulation tools can be used to trigger unplanned maintenance tasks involving engine failure or line replaceable unit (LRU) failure. For engine failure, if a usable engine is available in the spare parts depot, it is selected for installation and the faulty engine enters the engine workshop's maintenance queue. For line replaceable unit (LRU) failure, if a usable spare part is available in the spare parts depot, the line replaceable unit (LRU) is replaced, and the LRU is either sent for repair or scrapped according to the maintenance execution strategy.
[0051] Step S105: When there is an available repair location in the repair shop, the engine to be repaired in the preset repair task is repaired in the workshop according to the repair execution strategy and repair constraints, and the repair cycle, repair cost and repair time of the engine to be repaired are determined.
[0052] If the maintenance facility has available capacity, the aircraft engines in the workshop maintenance queue will be brought in for maintenance. Based on the maintenance execution strategy and maintenance constraints, the maintenance cycle, maintenance cost, and maintenance time of the engines to be maintained will be determined.
[0053] Step S106: Determine the spare parts requirements for the engine to be repaired based on the repair cycle, repair cost, and repair time.
[0054] In some optional implementations, the method for determining spare parts requirements for aero-engines based on condition-based maintenance mode in this embodiment further includes: When the engine to be repaired is completed according to the maintenance schedule, the spare parts library is updated; or, when the simulation time of multiple individual models of the aero-engine simulated by the simulation tool reaches the spare parts procurement cycle, the spare parts library is updated.
[0055] This invention acquires the lifespan distribution parameters, maintenance execution strategies, maintenance constraints, and state variable parameters of aero-engines. Based on the lifespan distribution parameters, it constructs individual aero-engine models using simulation tools. According to the maintenance execution strategies and constraints, it simulates and runs multiple individual aero-engine models to form an aero-engine fleet model. Based on the state variable parameters and the aero-engine fleet model, it triggers the execution of preset maintenance tasks for the aero-engines using simulation tools. When there are available maintenance positions in the maintenance depot, it performs workshop maintenance on the engines to be repaired in the preset maintenance tasks according to the maintenance execution strategies and constraints, determining the maintenance cycle, cost, and time for each engine. Based on the maintenance cycle, cost, and time, it determines the corresponding spare parts requirements for each engine. Therefore, even when faced with new aero-engine models or insufficient maintenance data, the predictive capability is not significantly limited, improving the accuracy of aero-engine spare parts requirements.
[0056] For example, by repeating the above steps using simulation tools until the end of the simulation cycle, the spare parts demand, cost, and repair rate during the planning period can be obtained. Multiple rounds of simulation experiments are repeated to eliminate randomness and uncertainty in the simulation process, obtaining predicted values for spare parts demand, cost, reliability, and other indicators within the fleet simulation cycle. The basic simulation inputs are shown in Table 2 below.
[0057] Table 2. Basic Input Table for Simulation
[0058] In a single simulation, because the model uses Monte Carlo simulation to sample the lifespan of each engine in the fleet, the service life of the engine fleet can change, thus affecting the output indicators of the fleet model. To ensure the convergence and accuracy of the aero-engine fleet model results, and also to improve the efficiency of simulation experiments and avoid unnecessary time waste, it is necessary to conduct simulation trials to determine the number of simulations required for indicator convergence. The experimental results are as follows: Figure 2A , Figure 2B As shown, from Figure 2A , Figure 2B It can be seen that when the number of simulations reaches 150, the output indicators of the aero-engine fleet model basically converge.
[0059] To verify the model's rationality, a model sensitivity analysis was conducted on two factors that affect the quantity of spare parts issued: maintenance turnaround time and overhaul plant capacity.
[0060] The capacity of an overhaul facility is defined here as the maximum number of engines that can be repaired simultaneously in the workshop. Figure 3A , Figure 3BThe diagram illustrates the impact of different overhaul facility capacities (e.g., 12, 15, 18, 23, 26) on spare parts and cost indicators. Figure 3A , Figure 3B As shown, the required spare engines and total fleet cost hourly rates decrease with increasing overhaul capacity. This is presumably because higher maintenance capacity leads to significantly reduced waiting times for maintenance resources, thereby reducing turnaround time and lowering spare engine demand. However, it can also be observed that the decline in spare engine and fleet maintenance costs is relatively gradual when the overhaul capacity exceeds 18 engines, indicating that the benefits of increasing overhaul capacity beyond 18 engines gradually diminish.
[0061] Engine maintenance turnaround time is defined as the total time spent on transportation from the airport to the maintenance workshop, maintenance at the workshop, and transportation back to the airport, excluding time spent in the maintenance queue due to limited overhaul capacity, as this time can vary significantly depending on the number of engines in the maintenance queue. In this embodiment, engine maintenance time is assumed to depend randomly on the various workshop maintenance workloads and is modeled using a gamma distribution, with maintenance turnaround times of 480EFH (60 days), 520EFH (65 days), 560EFH (70 days), 600EFH (75 days), and 640EFH (80 days). Figure 4A , Figure 4B The simulation results assessing the impact of engine maintenance time on fleet-level performance indicators are shown. Figure 4 shows that the fleet repair rate and maintenance hour rate do not vary significantly, while... Figure 4B Both the number of spare engines in the fleet and the total fleet cost-per-hour rate increased as the average repair time (TAT) increased from 480 EFH (60 days) to 640 EFH (80 days). These results were expected, as the increased engine maintenance time would require more time to be spent at overhaul depots, leading to an increase in the number of spare engines and the cost of spare engines for the fleet.
[0062] Simulation results are as follows Figure 5 As shown, three long-cycle simulation samples of the engine fleet indicate that after a certain Monte Carlo stabilization period, the difference between the initial reserve engine quantity and the 98th percentile support level (dashed line) is the fleet's reserve engine demand, i.e., 120 - 98.8 ≈ 22 units (rounded down).
[0063] This embodiment also provides a device for determining spare parts requirements for aero-engines based on a condition-based maintenance mode. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0064] This invention also provides a spare parts demand prediction device for aero-engines based on a condition-based maintenance mode, such as... Figure 6 As shown, the device includes: The relevant parameter acquisition module 601 is used to acquire the life distribution parameters, maintenance execution strategies, maintenance constraints and state variable parameters of the aero-engine; The first model building module 602 is used to build an individual model of the aero-engine based on the life distribution parameters of the aero-engine using simulation tools. The second model building module 603 is used to simulate and run multiple individual aero-engine models through simulation tools according to the maintenance execution strategy and maintenance constraints to form an aero-engine fleet model; The maintenance task triggering module 604 is used to trigger the aero-engine to perform preset maintenance tasks through simulation tools based on the state variable parameters of the aero-engine and the aero-engine fleet model. The maintenance action execution module 605 is used to perform workshop maintenance on the engine to be repaired in the preset maintenance task when there is an available maintenance position in the maintenance shop, according to the maintenance execution strategy and maintenance constraints, and to determine the maintenance cycle, maintenance cost and maintenance time of the engine to be repaired. The spare parts requirement determination module 606 is used to determine the spare parts requirements for the engine to be repaired based on the engine's maintenance cycle, maintenance cost, and maintenance time.
[0065] In some optional embodiments, the spare parts demand determination device for aero-engines based on condition-based maintenance mode in this invention embodiment further includes: The spare parts quantity update module is used to update the spare parts library when the engine to be repaired is completed according to the maintenance time, or when the simulation time of multiple individual models of aero engines running through simulation tools reaches the spare parts procurement cycle.
[0066] In some alternative implementations, the life distribution parameters of the aircraft engine are obtained by fitting data on various influencing factors of the aircraft engine and historical flight data under different preset conditions.
[0067] In some alternative implementations, the maintenance execution strategy for the aircraft engine is generated based on the maintenance type and flight time, respectively, according to preventive maintenance and restorative maintenance methods.
[0068] In some optional implementations, maintenance constraints include: field maintenance constraints, return-to-factory maintenance constraints, and spare parts inventory resource constraints. The state variable parameters of the aero-engine include: cumulative flight time, cumulative flight count, and health status parameters. The second model construction module 603 is specifically used for: Based on the constraints of field maintenance, return-to-factory maintenance, and spare parts inventory, multiple individual aero-engine models are simulated using simulation tools. The cumulative flight time and number of flights are iteratively increased, and the health status parameters of the aero-engines are monitored to form an aero-engine fleet model.
[0069] In some optional implementations, the preset maintenance task includes: a planned maintenance task, and a maintenance task triggering module 604, specifically used for: Based on the state variable parameters of the aero-engine, the planned maintenance tasks of the aero-engine fleet model are triggered by simulation tools, and the corresponding maintenance actions are performed according to the maintenance execution strategy based on the planned inspection tasks or planned dispatch tasks in the planned maintenance tasks.
[0070] In some optional implementations, the preset maintenance tasks include: unplanned maintenance tasks, and the maintenance task triggering module 604 is specifically used for: Based on the state variable parameters of the aero-engine and the aero-engine fleet model, simulation tools are used to trigger the aero-engine to perform unplanned maintenance tasks, and corresponding maintenance actions are performed according to the maintenance execution strategy based on the engine failure or line replaceable unit failure in the unplanned maintenance task.
[0071] The spare parts demand prediction device for aero-engines based on condition-based maintenance mode provided in this embodiment of the invention can execute the spare parts demand prediction method for aero-engines based on condition-based maintenance mode provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.
[0072] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.
[0073] The following is a detailed reference. Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.
[0074] The following is a detailed reference. Figure 7The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 702 or a program loaded from memory 708 into random access random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.
[0075] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.
[0076] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the methods of the embodiments of the present invention.
[0077] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
[0078] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the spare parts demand prediction method for aero-engines based on condition-based maintenance mode shown in the above embodiments is implemented.
[0079] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.
[0080] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.
Claims
1. A method for determining spare parts requirements for aero-engines based on a condition-based maintenance model, characterized in that, The method includes: Obtain the life distribution parameters, maintenance execution strategies, maintenance constraints, and state variable parameters of the aero-engine; Based on the life distribution parameters of the aero-engine, an individual model of the aero-engine is constructed using simulation tools; Based on the maintenance execution strategy and the maintenance constraints, multiple individual models of the aero-engines are simulated and run using the simulation tool to form an aero-engine fleet model; Based on the state variable parameters of the aero-engine and the aero-engine fleet model, the simulation tool triggers the aero-engine to perform a preset maintenance task. When there are available repair positions in the repair shop, the engine to be repaired in the preset repair task is repaired in the workshop according to the repair execution strategy and repair constraints, and the repair cycle, repair cost and repair time of the engine to be repaired are determined. Based on the maintenance cycle, maintenance cost, and maintenance time of the engine to be repaired, determine the corresponding spare parts requirements for the engine to be repaired.
2. The method according to claim 1, characterized in that, Also includes: When the engine to be repaired is completed according to the maintenance schedule, the spare parts library is updated; or, when the simulation time of multiple individual models of the aero-engine simulated by the simulation tool reaches the spare parts procurement cycle, the spare parts library is updated.
3. The method according to claim 1, characterized in that, The life distribution parameters of the aero-engine are obtained by fitting data on various influencing factors of the aero-engine and historical flight data under different preset conditions.
4. The method according to claim 1, characterized in that, The maintenance execution strategy for the aircraft engine is generated based on the maintenance type and flight time, according to preventive maintenance and restorative maintenance methods respectively.
5. The method according to claim 1, characterized in that, The maintenance constraints include: field maintenance constraints, return-to-factory maintenance constraints, and spare parts warehouse resource constraints. The state variable parameters of the aero-engine include: cumulative flight time, cumulative flight count, and health status parameters. Based on the maintenance execution strategy and the maintenance constraints, multiple individual aero-engine models are simulated and run using the simulation tool to form an aero-engine fleet model, including: Based on the field maintenance constraints, the return-to-factory maintenance constraints, and the spare parts warehouse resource constraints, multiple individual models of the aero-engines are simulated and run using simulation tools. The cumulative flight time and the cumulative number of flights are iteratively increased, and the health status parameters of the aero-engines are monitored to form the aero-engine fleet model.
6. The method according to claim 1, characterized in that, The preset maintenance tasks include: planned maintenance tasks, which are triggered by the simulation tool to execute preset maintenance tasks based on the state variable parameters of the aero-engine and the aero-engine fleet model, including: Based on the state variable parameters of the aero-engine, the simulation tool triggers the planned maintenance task of the aero-engine fleet model, and performs corresponding maintenance actions according to the maintenance execution strategy based on the planned inspection task or planned dispatch task in the planned maintenance task.
7. The method according to claim 1, characterized in that, The preset maintenance tasks include: unplanned maintenance tasks, which are triggered by the simulation tool based on the state variable parameters of the aero-engine and the aero-engine fleet model, including: Based on the state variable parameters of the aero-engine and the aero-engine fleet model, the simulation tool triggers the aero-engine to perform unplanned maintenance tasks, and performs corresponding maintenance actions according to the maintenance execution strategy based on the engine failure or line replaceable unit failure in the unplanned maintenance task.
8. A spare parts demand prediction device for aero-engines based on condition-based maintenance mode, characterized in that, The device includes: The relevant parameter acquisition module is used to acquire the life distribution parameters, maintenance execution strategies, maintenance constraints, and state variable parameters of the aero-engine. The first model building module is used to build an individual model of the aero-engine based on the life distribution parameters of the aero-engine using simulation tools. The second model building module is used to simulate and run multiple individual models of the aero-engines using the simulation tool according to the maintenance execution strategy and the maintenance constraints, thereby forming an aero-engine fleet model; The maintenance task triggering module is used to trigger the aero-engine to perform a preset maintenance task through the simulation tool based on the state variable parameters of the aero-engine and the aero-engine fleet model. The maintenance action execution module is used to perform workshop maintenance on the engine to be repaired in the preset maintenance task when there is an available maintenance position in the maintenance shop, according to the maintenance execution strategy and maintenance constraints, and to determine the maintenance cycle, maintenance cost and maintenance time of the engine to be repaired. The spare parts requirement determination module is used to determine the spare parts requirements for the engine to be repaired based on the engine's maintenance cycle, maintenance cost, and maintenance time.
9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for determining spare parts requirements of an aircraft engine based on a condition-based maintenance mode as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for determining spare parts requirements of an aircraft engine based on a condition-based maintenance mode, as described in any one of claims 1 to 7.