CN112560223A - Aero-engine full life cycle maintenance probability modeling and cost prediction method - Google Patents
Aero-engine full life cycle maintenance probability modeling and cost prediction method Download PDFInfo
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Abstract
The invention discloses a maintenance probability modeling and cost prediction method for the whole life cycle of an aero-engine, relates to the field of maintenance probability modeling and cost prediction of aero-engines, and can more accurately reflect the maintenance cost of the whole life cycle of the engine and the maintenance cost of the unit flight hour. The method adopts a Monte Carlo simulation method to predict the hour repair cost of the whole life cycle of the engine based on the service life distribution of various potential fault reasons of the engine and the maintenance strategy and the maintenance cost of various fault reasons. Accurately reflects the maintenance cost of the whole life cycle of the engine and the maintenance cost of the unit flight hour, provides decision support for manufacturers, contractors and operators, and has important significance for reducing the operation and maintenance cost of the engine.
Description
Technical Field
The invention relates to the field of maintenance probability modeling and cost prediction of an aero-engine, in particular to a method for modeling and predicting the maintenance probability of the aero-engine in the whole life cycle.
Background
Today, each large engine host machine Manufacturer (OEM) takes an hourly packaging model for after-market service of aircraft engines, i.e., the airline packages the service of engine Maintenance to the Manufacturer (OEM) or a third party Maintenance Organization (MRO) at a certain rate. Different operators and different operating environments, the repair rate of the engine is different, for example, the repair rate of a fleet operating in the middle east area is higher than that of a fleet operating in the European area. Therefore, how to determine the appropriate hour contract rate for different operators is a troublesome problem, and the appropriate hour contract rate not only can ensure that the cost pressure of the OEM is reduced, but also can strive for more customers and obtain greater profit. In addition, different maintenance strategies have huge cost difference, and how to select the optimal maintenance strategy leads the maintenance cost to be minimum and the replacement degree to be minimum, which is also a problem to be considered when the OEM carries out maintenance service. Therefore, a reliable model capable of predicting the maintenance cost of the fleet in the whole life cycle and the hourly upbord rate is established, and the method has important significance for determining the upbord rate and reducing the maintenance cost of OEMs or MRO enterprises.
At present, the rate of hourly covered maintenance is determined mainly based on engineering experience, the estimation range is large, and accurate decision cannot be made; the calculation of the maintenance strategy cost is mostly simple calculation based on engineering experience judgment, and multi-strategy maintenance cost comparison is not carried out.
Disclosure of Invention
The invention provides a method for modeling maintenance probability and predicting cost of an aircraft engine in a whole life cycle, which takes the influence factors such as the operating environment and the use characteristics of the engine into consideration, takes the life distribution of potential repair delivery reasons, a maintenance strategy matrix, a maintenance task and a cost matrix as the input of a model, and outputs the repair delivery rate of returned factories, the repair delivery cost and the repair cost per flight hour.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for modeling the maintenance probability and predicting the cost of an aircraft engine in the whole life cycle comprises the following steps:
s1, determining the operating environment of the fleet, performing FMEA (Failure Mode and Effects Analysis) on the aircraft engine according to the operating environment to obtain various potential repair reasons of the engine unit body, and determining the service life distribution of the potential repair reasons;
s2, making a maintenance strategy corresponding to the potential repair reason, and generating a maintenance strategy matrix;
s3, formulating a maintenance task and a corresponding maintenance cost table according to the maintenance strategy matrix, and generating a maintenance task and a maintenance cost matrix;
s4, simulating the first time maintenance delivery time and the first time maintenance delivery reason of the engine, mapping to obtain a maintenance strategy of the first time maintenance delivery according to the first time maintenance delivery reason, and mapping to obtain a maintenance vector M1 aiming at the current maintenance reason according to the maintenance strategy of the first time maintenance delivery;
s5, determining a maintenance vector M2 aiming at the service life of each component according to a maintenance strategy according to the accumulated service time of each component on the basis of the maintenance vector M1;
s6, integrating the maintenance vectors M1 and M2 into a final maintenance vector M3 according to the maintenance operation priority;
s7, according to the final maintenance vector M3, the maintenance task and the cost matrix are combined, the maintenance cost of the first time of repair sending is calculated, and according to S4-S7, the maintenance cost of the second time to the Nth time is calculated;
s8, calculating the maintenance cost of the whole life cycle of the engine and the on-wing time according to each maintenance cost, and then calculating to obtain the unit flight hour package maintenance cost and the sending maintenance rate of the engine;
and S9, repeating S4-S8 for several times to obtain the unit flight hour envelope repair cost and the service rate distribution of the airplane fleet under a certain specific operating environment.
Further, the operating environment includes: natural corrosion environment, artificial pollution environment, and mild and pollution-free environment.
Further, the repair reason includes: hardware damage, performance degradation, life-limiting component to life, and engine hit by foreign objects.
Further, in S4, the simulation method of the repair sending time and the repair sending reason of the first repair sending includes:
randomly sampling a service life from service life distribution obeyed by potential service life reasons to form a service life vector, marking the minimum service life in the service life vector as first service time, and marking the potential service life reason corresponding to the minimum service life as the first service reason.
Further, the operations performed on the component in the maintenance strategy include: maintenance, replacement, detection and continuous use. In S6, the priority of the maintenance operation is:
for the same potential repair reason, if any one of M1 or M2 is marked as a renewal, then M3 is marked as a renewal; if not, any one is marked as maintenance, then M3 is marked as maintenance; if there is no renewal or repair, either one is marked as test, then M3 is marked as test; finally, both M1 and M2 are labeled as continue use, then M3 is labeled as continue use.
The invention has the beneficial effects that:
the method adopts a Monte Carlo simulation method to predict the hour repair cost of the whole life cycle of the engine based on the service life distribution of various potential fault reasons of the engine and the maintenance strategy and the maintenance cost of various fault reasons. The engine full-life-cycle operation and maintenance cost prediction model is a data-driven model, a complex system physical model does not need to be established, the model can be established according to the service life distribution of each part, the maintenance strategy and the single maintenance cost, the service life distribution of each part needs to be counted during modeling, the maintenance strategy is determined according to a maintenance manual, and the single maintenance cost is determined according to the market. The model can more accurately reflect the maintenance cost of the whole life cycle of the engine and the maintenance cost of the unit flight hour, provides decision support for manufacturers, contractors and operators, and has important significance for reducing the operation and maintenance cost of the engine. The method can also be used in the engine design stage, the reliability parameter sensitivity analysis of key parts is developed by means of a model, the reliability design requirement of the key parts is optimized from the perspective of the whole life cycle maintenance cost, and the method is of great help to design research and development units.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic input/output diagram according to the present invention;
FIG. 2 is a schematic of a modeling flow of the present invention;
FIG. 3 is a distribution of service due to performance degradation under different operating environments;
FIG. 4 is a predicted hourly maintenance cost distribution for different operating environments.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention, the present invention will be further described in detail with reference to the following detailed description.
The embodiment of the invention provides a method for modeling the maintenance probability of the whole life cycle of an aircraft engine and predicting the cost, which adopts a Monte Carlo simulation method, takes the service life distribution of various Potential repair reasons (PSVC) of the engine, the maintenance strategy and the maintenance task of various fault reasons and the cost as input, and takes the repair Rate (SVR) of the engine of a fleet, the repair reasons, the repair cost and the maintenance cost per flight hour as output, and a schematic diagram is shown in figure 1.
The aeroengine life cycle maintenance probability model comprises three layers, wherein the first layer is the first time maintenance delivery simulation of an engine, the second layer is the second time maintenance delivery to the last time maintenance delivery simulation of the engine, the first layer and the second layer are used for forming the life cycle simulation of the engine, the third layer is the next time maintenance delivery simulation of the engine, the third layer is repeatedly circulated, the maintenance delivery simulation of the whole fleet can be obtained, and the flow is shown in figure 2.
The invention discloses a method for predicting the hourly covered maintenance rate of a fleet under different operating environments based on PSVC service life distribution under the operating environments, which mainly comprises the following steps:
and S1, judging the operating environment of the fleet according to the operating routes of the fleet, and acquiring the service life distribution of each potential repair reason in the environment.
The operating environments of a fleet of aircraft are generally classified into three categories: A) natural corrosive environment, such as desert and coastal area, B) artificial polluted environment, wherein the area is mainly a place where human life and production activities are gathered, such as big cities, big industrial areas and the like, and the ratio of SO2 to NO2 in the area is far higher than that in other areas; C) is mild and has no environmental pollution. The service life distribution parameters in the three different environments have a certain proportional relation, and if the service life distribution in the environment B is obtained, the distribution in other environments can be obtained according to the proportional relation of the distribution parameters.
And S2, obtaining the service life distribution of various potential repair reasons according to the service life data of the failure caused by the potential repair reasons of the engine, wherein the data are from design data, experimental data, outfield data and the like.
According to the engine failure mode and the influence analysis, potential engine return to the factory for repair reasons comprise: 1) hardware damage, such as failure modes of fans, turbine discs, turbine blades, compressor blades and the like, wherein failure time can be assumed to obey Weibull distribution and the like; 2) performance degradation, which is mainly measured according to an engine exhaust temperature margin parameter, when the margin is lower than a certain value, the engine needs to be delivered for maintenance, and the delivery time is assumed to be subjected to certain distribution, such as lognormal distribution and the like; 3) when the service life of the service life limiting piece reaches a preset service life, the engine needs to be repaired, and the service life limiting piece is disassembled; 4) it is not generally assumed that an engine suffering accidental damage such as a collision with a foreign object, for example a bird strike, follows an exponential distribution.
In summary, the underlying fault causes and the corresponding distributions are shown in table 1.
TABLE 1 potential service reasons and types of life distributions
S3, according to the maintenance manual and the maintenance experience, the maintenance strategy corresponding to each repair delivery reason is determined, wherein I represents detection only, N represents renewal, and R represents maintenance, as shown in Table 2.
TABLE 2 Engine maintenance strategy matrix
S4, determining the maintenance task and the corresponding maintenance cost, see Table 3.
TABLE 3 Engine repair tasks and corresponding cost matrix
S5, simulating the service reason and service time of the first service of the engine, and specifically operating as follows: and randomly sampling a group of data from the life distribution of each potential service reason in the step S2, and regarding the data as a life vector L (L1, L2 … Ln), wherein the minimum value in the life vector L is the time for returning to the factory for service, and the fault reason corresponding to the minimum life value is the fault reason for returning to the factory for service.
And S6, according to the fault reason in the S5, corresponding to the maintenance strategy in the S3, and determining a maintenance vector M1.
In table 2, the horizontal header is a potential repair cause and the vertical header is an actual repair cause. In S5, the actual reason for repair is determined, and a repair vector M1 can be obtained according to Table 2. For example, the reason for repair in S5 is that the turbine blade failed, needs to be replaced, indicated by 1; the rear bearing and the turbine disk need to be inspected, indicated with 3; the other components continue to be used, indicated by 0. Therefore, the maintenance vector M1 is (0,0,0,0,3,1,3,0,0, 0) (compressor blade, compressor disk, front bearing, combustor, rear bearing, turbine blade, turbine disk, external impact, compressor life limiter, turbine life limiter, performance degradation).
And S7, determining a maintenance vector M2 by referring to the maintenance strategy rule according to the accumulated use time of each component.
According to the accumulated service life of the component, if the accumulated service life of a certain component reaches a certain proportion of the service life, corresponding operations (maintenance, replacement, detection and continuous use) are required to be carried out on the component according to the maintenance strategy in S3, so that another maintenance vector M2 is obtained. If the cumulative operating life of the turbine blades and compressor blades exceeds a certain percentage of the life and the operation performed is a refresh operation, then M2 is (1,0,0,0,0,1,0,0,0,0, 0).
And S8, integrating the maintenance vectors M1 and M2 into a final maintenance vector M3 according to the maintenance operation priority. And (5) synthesizing the M1 and M2 maintenance vectors according to certain operation priority to obtain a final maintenance vector M3.
If M1 or M2 contains the renewal, the corresponding part in M3 is marked as the renewal; if M1 or M2 contains maintenance, the corresponding part in M3 is marked as maintenance, if M1 or M2 contains detection, the corresponding part in M3 is marked as detection, otherwise, M3 is marked as continuous use.
And S9, calculating the repair sending cost of the first repair sending of the engine according to the repair tasks and the cost table in the final repair vectors M3 and S4.
S10, calculating the second to Nth repair costs of the engine according to the method of S5-S9. Assuming that the engine is sent for repair N times in the whole life cycle, S5-S9N-1 times are executed circularly.
And S11, calculating the total maintenance cost and wing time of the engine in the whole life cycle.
The total maintenance cost of the engine in the whole life cycle is equal to the sum of N times of factory returning and maintenance sending cost, the wing time is the sum of N times of maintenance sending time, in addition, the time of operation after the Nth time of repair is added, the time of operation after the Nth time of repair is recorded as half of the time of operation after the Nth-1 th time of repair.
And S12, calculating the unit flight hour repair cost and SVR of the first engine.
The flight hour envelope cost is equal to the total life cycle repair cost divided by the cumulative wing time. The repair rate per thousand hours is the number of times of SVR repair divided by the time of on wing, and then multiplied by 1000.
And S13, obtaining the unit flight hour overhaul cost and the SVR of all the engines of the fleet according to the method provided by S12, and then calculating the unit flight hour overhaul cost and the SVR distribution of the fleet.
Each engine obtains one unit flight hour envelope repair cost and SVR through simulation, if a fleet has M engines, M unit flight hour envelope repair costs and SVR can be obtained, the distribution of the M unit flight hour envelope repair costs and the SVR can be fitted, and the probability distribution and the expected value of the unit flight hour envelope repair cost and the SVR of the fleet can be evaluated to guide OEM or MRO to make decisions.
The invention has the beneficial effects that:
the invention provides a model which takes the service life distribution of potential repair reasons, a maintenance strategy matrix, a maintenance task and a cost matrix as input and takes the repair rate of a return-to-the-factory, the repair cost and the repair cost per flight hour as output. By means of the model, the influence of multi-model output of different operating environments, different reliability designs and different maintenance strategies, such as the repair sending rate, the repair sending reason, the repair sending cost and the unit hour maintenance cost, can be analyzed so as to guide the product reliability design and the maintenance strategy optimization and guide the contract pricing of the service contractor for the service contractors with different operating environments and use characteristics.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A method for modeling maintenance probability and predicting cost of an aircraft engine in a whole life cycle is characterized by comprising the following steps:
s1, determining the operating environment of the fleet, performing FMEA (Failure Mode and Effects Analysis) on the aircraft engine according to the operating environment to obtain various potential repair reasons of the engine unit body, and determining the service life distribution of the potential repair reasons;
s2, making a maintenance strategy corresponding to the potential repair reason, and generating a maintenance strategy matrix;
s3, formulating a maintenance task and a corresponding maintenance cost table according to the maintenance strategy matrix, and generating a maintenance task and a maintenance cost matrix;
s4, simulating the first time maintenance delivery time and the first time maintenance delivery reason of the engine, mapping to obtain a maintenance strategy of the first time maintenance delivery according to the first time maintenance delivery reason, and mapping to obtain a maintenance vector M1 aiming at the current maintenance reason according to the maintenance strategy of the first time maintenance delivery;
s5, determining a maintenance vector M2 aiming at the service life of each component according to the maintenance strategy and the accumulated service time of each component on the basis of the maintenance vector M1;
s6, integrating the maintenance vectors M1 and M2 into a final maintenance vector M3 according to the maintenance operation priority;
s7, according to the final maintenance vector M3, the maintenance task and the cost matrix are combined, the maintenance cost of the first time of repair sending is calculated, and according to S4-S7, the second time to the Nth time of maintenance cost is calculated;
s8, calculating the maintenance cost of the whole life cycle of the engine and the on-wing time according to each maintenance cost, and then calculating to obtain the unit flight hour package maintenance cost and the sending maintenance rate of the engine;
and S9, repeating S4-S8 for several times to obtain the unit flight hour envelope repair cost and the service rate distribution of the airplane fleet under a certain specific operating environment.
2. The method of claim 1, wherein the operating environment comprises: natural corrosion environment, artificial pollution environment, and mild and pollution-free environment.
3. The method of claim 1 for modeling probability of repair and cost prediction for an aircraft engine over a life cycle, wherein the reason for repair comprises: hardware damage, performance degradation, life-limiting component to life, and engine hit by foreign objects.
4. The method for modeling probability of repair and predicting cost for aircraft engine life cycle according to claim 1, wherein in S4, the simulation method of repair delivery time and repair delivery reason of the first repair delivery comprises:
randomly sampling a service life from the service life distribution obeyed by the potential service life reasons to form a service life vector, marking the minimum service life in the service life vector as the first service time, and marking the potential service life reason corresponding to the minimum service life as the first service reason.
5. The method of claim 1, wherein the operation performed on the component in the repair strategy comprises: maintenance, replacement, detection and continuous use;
in S6, the priority of the maintenance operation is:
for the same potential service reason, if any one of M1 or M2 is marked as a renewal, then M3 is marked as a renewal; if not, any one is marked as maintenance, then M3 is marked as maintenance; if there is no renewal or repair, either one is marked as test, then M3 is marked as test; finally, both M1 and M2 are labeled as continue use, then M3 is labeled as continue use.
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