CN112560223B - Method for modeling maintenance probability and predicting cost of whole life cycle of aeroengine - Google Patents
Method for modeling maintenance probability and predicting cost of whole life cycle of aeroengine Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 156
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Abstract
The invention discloses a maintenance probability modeling and cost prediction method for an aero-engine whole life cycle, 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 per unit flight hour. The invention predicts the hour maintenance cost of the whole life cycle of the engine by adopting a Monte Carlo simulation method based on the life distribution of various potential fault reasons of the engine, the maintenance strategy and the maintenance cost of various fault reasons. The maintenance cost of the whole life cycle of the engine and the maintenance cost of the unit flight hour are accurately reflected, decision support is provided for manufacturers, contractors and operators, and the method has important significance in 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 aeroengines, in particular to a method for modeling and cost prediction of maintenance probability of an aeroengine in a whole life cycle.
Background
Today, each large engine hosting vendor (OEM, original Equipment Manufacturer) takes an hour of after-market repair mode for aircraft engines, i.e. airlines package launched repair services to manufacturers (OEMs) or third party repair organizations (MROs, maintenance and Repair Organization) at a certain rate. Different operators and different operating environments, so that the repair rates of the engines are different, for example, the repair rates of the fleet operating in the middle east are higher than those of the fleet operating in the European region. Therefore, how to provide different operators with proper hour maintenance rates is a troublesome problem, and the proper hour maintenance rates can ensure to reduce the cost pressure of OEMs and also can fight more clients to obtain larger profits. In addition, different maintenance strategies have huge cost difference, and how to select the optimal maintenance strategy, so that the minimum maintenance cost and the minimum replacement degree are also problems that the OEM needs to consider for maintenance service. Therefore, the establishment of a reliable model capable of predicting the maintenance cost of the whole life cycle of the fleet and the maintenance rate of the hour package has great significance for the OEM or MRO enterprises to determine the package rate and reduce the maintenance cost.
At present, the determination of the hour maintenance rate is mainly based on engineering experience, the estimated range is large, and accurate decision cannot be made; the maintenance strategy cost is calculated based on engineering experience judgment and is simply calculated, and multi-strategy maintenance cost comparison is not carried out.
Disclosure of Invention
The invention provides a method for modeling and predicting the maintenance probability and cost of an aeroengine in a whole life cycle, which takes the influence factors such as the engine operating environment, the use characteristics and the like into consideration, takes a life distribution and maintenance strategy matrix, a maintenance task and a cost matrix of potential maintenance reasons as the input of a model, and outputs the maintenance rate, maintenance cost and maintenance cost per flight hour of a factory.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an aeroengine full life cycle maintenance probability modeling and cost prediction method, comprising:
s1, determining an operation environment of a fleet, performing FMEA (Failure Mode and Effects Analysis failure mode and result analysis) on an aeroengine according to the operation environment to obtain various potential repair reasons of an engine unit body, and determining service life distribution of the potential repair reasons;
s2, formulating a maintenance strategy corresponding to the potential repair reasons, and generating a maintenance strategy matrix;
s3, a maintenance task and a corresponding maintenance cost table are formulated according to the maintenance strategy matrix, and a maintenance task and maintenance cost matrix is generated;
s4, simulating the first repair time and the first repair reason of the engine, mapping according to the first repair reason to obtain a repair strategy of the first repair, and mapping according to the repair strategy of the first repair to obtain a repair vector M1 aiming at the repair reason;
s5, on the basis of the maintenance vector M1, determining a maintenance vector M2 aiming at the service life of each part according to a maintenance strategy according to accumulated service time of each part;
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, combining the maintenance task and the cost matrix, calculating to obtain the maintenance cost of the first maintenance, and according to S4-S7, calculating to obtain the maintenance cost of the second to Nth maintenance;
s8, calculating the maintenance cost and the wing time of the whole life cycle of the engine according to the maintenance cost of each time, and then calculating to obtain the maintenance cost and the repair rate of the engine per unit flight hour;
and S9, repeatedly executing the steps S4 to S8 for a plurality of times to obtain the unit flight hour package repair cost and the repair rate distribution of the fleet under a specific operation environment.
Further, the operating environment includes: natural corrosion environment, artificial pollution environment, mildness and no pollution.
Further, the repair reasons include: hardware damage, performance degradation, life limiting element to life, and engine subject to foreign object impact.
Further, in S4, the simulation method of the repair time and the repair cause of the first repair is:
and randomly sampling a service life from service life distribution obeyed by the potential service 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 reason corresponding to the minimum service life as first service reason.
Further, operations performed on the component in the repair strategy include: maintaining, replacing, detecting and continuing to use. In S6, the priority of the maintenance operation is:
for the same potential repair reasons, in M1 or M2, any one of the two is marked as a new replacement, and then M3 is marked as a new replacement; if no replacement is made, any one is marked as maintenance, and M3 is marked as maintenance; if no replacement or maintenance is performed, any one is marked as detection, and M3 is marked as detection; finally, M1 and M2 are both marked as continued use, and M3 is marked as continued use.
The beneficial effects of the invention are as follows:
the invention predicts the hour maintenance cost of the whole life cycle of the engine by adopting a Monte Carlo simulation method based on the life distribution of various potential fault reasons of the engine, the maintenance strategy and the maintenance cost of various fault reasons. The prediction model of the whole life cycle operation and maintenance cost of the engine is a data-driven model, a complex system physical model is not required to be established, the model can be established according to life distribution, maintenance strategy and single maintenance cost of each part, statistics is required to be carried out on the life distribution of each part in modeling, the maintenance strategy is determined according to a maintenance manual, and the single maintenance cost is determined according to a market. The model can more accurately reflect the maintenance cost of the whole life cycle of the engine and the maintenance cost of unit flight hour, provides decision support for manufacturers, maintenance suppliers and operators, and has important significance for reducing the operation and maintenance cost of the engine. The invention can also be used in the engine design stage, and the reliability parameter sensitivity analysis of the key parts is carried out by means of a model, so that the reliability design requirement of the key parts is optimized from the perspective of the maintenance cost of the whole life cycle, and the invention is of great help to the design research and development unit.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed 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 other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the input/output of the present invention;
FIG. 2 is a schematic diagram of a modeling flow of the present invention;
FIG. 3 is a distribution of repair due to performance degradation under different operating environments;
FIG. 4 is a graph of predicted maintenance cost per hour for various operating environments.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments for better understanding of the technical solution of the present invention by those skilled in the art.
The embodiment of the invention provides a full life cycle maintenance probability modeling and cost prediction method of an aeroengine, which adopts a Monte Carlo simulation method, takes the life distribution of various potential maintenance reasons (PSVC, potential Shop Visit Cause) of the engine, the maintenance strategy and maintenance tasks and cost of various fault reasons as input, and takes the maintenance Rate (SVR) of the engine of a fleet, the maintenance reasons, the maintenance cost and the maintenance cost per flight hour as output, wherein a schematic diagram is shown in figure 1.
The aeroengine full life cycle maintenance probability model comprises three layers, wherein the first layer is a first repair simulation of an engine, the second layer is a second repair to last repair simulation of the engine, the first layer and the second layer form a full life cycle simulation of the engine, the third layer is a next repair simulation of the engine, and the third layer simulation is repeatedly circulated, so that repair simulation of the whole fleet can be obtained, and the flow is shown in fig. 2.
The invention relates to a method for predicting an hourly package repair rate of a fleet in different operating environments based on PSVC service life distribution in the operating environments, which mainly comprises the following steps:
s1, determining the operation environment of the fleet according to the operation route of the fleet, and acquiring the service life distribution of each potential repair reason under the environment.
The operating environments of a fleet are generally divided into three categories: a) Natural corrosion environments, such as deserts and coastal areas, B) artificial pollution environments, wherein the areas are mainly places where human life and production activities gather, such as large cities, large industrial areas and the like, and the ratio of SO2 to NO2 is far higher than that of other areas; c) Is mild and has no environmental pollution. The life distribution parameters of the three different environments have a certain proportion relation, for example, the life distribution in the environment B can be obtained, and the distribution in other environments can be obtained according to the proportion relation of the distribution parameters.
S2, obtaining life distribution of various potential repair reasons according to life data of failures 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 fault mode and the influence analysis, potential engine return repair reasons include: 1) Hardware damage classes, such as failure modes of fans, turbine disks, turbine blades, compressor blade failures, etc., failure times can be assumed to obey the weibull distribution, etc.; 2) Performance degradation is measured mainly according to engine exhaust temperature margin parameters, when the margin is lower than a certain value, the engine needs to be repaired, and the repair time is assumed to obey a certain distribution, such as lognormal distribution; 3) The life limiting piece reaches the service life, when the life limiting piece reaches the preset service life, the engine needs to be repaired, and the life limiting piece is detached and replaced; 4) The engine is subject to accidental injury such as a foreign object strike, for example, a bird strike, and is not generally assumed to be subject to an exponential distribution.
In summary, the potential failure causes and the corresponding distributions are shown in table 1.
TABLE 1 potential repair cause and life distribution type
S3, determining a maintenance strategy corresponding to each repair cause according to a maintenance manual and maintenance experience, wherein I represents detection only, N represents replacement, and R represents maintenance as shown in table 2.
Table 2 engine maintenance strategy matrix
S4, determining maintenance tasks and corresponding maintenance costs, wherein the maintenance tasks and the corresponding maintenance costs are shown in Table 3.
TABLE 3 engine maintenance tasks and corresponding cost matrices
S5, simulating repair reasons and repair time of the first repair of the engine, wherein the specific operation is as follows: and randomly sampling a group of data from the life distribution of each potential repair cause of S2, and taking the data as a life vector L= (L1, L2 … Ln), wherein the minimum value in the life vector L is the repair time of the factory, and the fault cause corresponding to the minimum life value is the repair fault cause of the factory.
S6, according to the fault reasons in S5, corresponding to the maintenance strategies in S3, determining maintenance vectors M1.
In table 2, the transverse header is the potential repair cause and the longitudinal header is the actual repair cause. The actual repair cause is determined in S5, and the repair vector M1 is obtained from table 2. For example, the repair cause in S5 is a failure of the turbine blade, requiring replacement, denoted by 1; the rear bearing and the turbine disk need to be detected, indicated by 3; the other components continue to be used, indicated by 0. The maintenance vector m1= (compressor blade, compressor disk, front bearing, combustor, rear bearing, turbine blade, turbine disk, external striker, compressor life limiter, turbine life limiter, performance degradation) = (0,0,0,0,3,1,3,0,0,0,0,0).
S7, determining a maintenance vector M2 according to the accumulated use time of each component and referring to the maintenance strategy rule.
According to the cumulative service life of the component, if the cumulative service life of a component reaches a certain proportion of the service life, the component needs to be correspondingly operated (maintained, replaced, detected and used continuously) according to the maintenance strategy in S3, so that another maintenance vector M2 is obtained. If the turbine blade and the compressor blade accumulate a certain percentage of their service life over the life, the operation performed is a refurbishment, m2= (1,0,0,0,0,1,0,0,0,0,0,0).
S8, integrating the maintenance vectors M1 and M2 into a final maintenance vector M3 according to the maintenance operation priority. And (3) integrating the maintenance vectors M1 and M2 according to the priority of a certain operation to obtain a final maintenance vector M3.
If M1 or M2 contains a renew, marking the corresponding part in M3 as a renew; if the M1 or M2 contains maintenance, the corresponding part in the M3 is marked as maintenance, if the M1 or M2 contains detection, the corresponding part in the M3 is marked as detection, otherwise, the M3 is marked as continuous use.
S9, calculating the repair cost of the first repair of the engine according to the repair tasks and the cost table in the final repair vectors M3 and S4.
S10, calculating the repair cost of the engine from the second time to the Nth time according to the method of S5-S9. Assuming that the whole life cycle of the engine is repaired N times, the cycle is executed S5-S9N-1 times.
S11, calculating the total maintenance cost and the wing time of the whole service life cycle of the engine.
The total maintenance cost of the whole life cycle of the engine is equal to the sum of N times of factory repair cost, the wing time is the sum of N times of repair time, in addition, the time of operation after the Nth repair is added, and the time of operation after the Nth repair is recorded as half of the time of operation after the Nth-1 th repair.
S12, calculating the unit flight hour maintenance cost and SVR of the first engine.
The flight hour maintenance cost is equal to the total life cycle maintenance cost divided by the cumulative wing-in-wing time. The repair rate per thousand hours is the number of SVR repair divided by the wing time, multiplied by 1000.
S13, according to the method provided in the S12, the unit flight hour maintenance cost and SVR of all engines of the fleet are obtained, and then the unit flight hour maintenance cost and SVR distribution of the fleet are calculated.
And each engine is simulated to obtain the unit flight hour maintenance cost and SVR, if the fleet has M engines, the M unit flight hour maintenance costs and SVR are obtained, the distribution of the two units flight hour maintenance costs and SVR can be fitted, and the probability distribution and expected value of the fleet unit flight hour maintenance costs and SVR can be evaluated to guide OEM or MRO to make decisions.
The beneficial effects of the invention are as follows:
the invention provides a model which takes a life distribution and maintenance strategy matrix, maintenance tasks and cost matrix of potential repair reasons as input and takes a repair rate, repair cost and maintenance cost per flight hour of a mould return factory as output. By means of the model, the influence of different running environments, different reliability designs and different maintenance strategies on the multi-model output, such as repair rate, repair reasons, repair cost and maintenance cost per unit hour, can be analyzed, so that the product reliability design and the maintenance strategy optimization are guided, and the contract pricing of the operator contract for different running environments and use characteristics is guided.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The method for modeling the maintenance probability and predicting the cost of the whole life cycle of the aeroengine is characterized by comprising the following steps of:
s1, determining an operation environment of a fleet, performing FMEA on an aeroengine according to the operation environment to obtain various potential repair reasons of an engine unit body, and determining service life distribution of the potential repair reasons;
s2, formulating a maintenance strategy corresponding to the potential repair cause, and generating a maintenance strategy matrix;
s3, making a maintenance task and a corresponding maintenance cost table according to the maintenance strategy matrix, and generating a maintenance task and maintenance cost matrix;
s4, simulating the first repair time and the first repair reason of the engine, mapping according to the first repair reason to obtain a repair strategy of the first repair, and mapping according to the repair strategy of the first repair to obtain a repair vector M1 aiming at the repair reason;
s5, on the basis of the maintenance vector M1, determining a maintenance vector M2 for the service life of each component according to the maintenance strategy according to the accumulated service time of each component;
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, combining the maintenance task and the cost matrix, calculating to obtain the maintenance cost of the first maintenance, and according to S4-S7, calculating to obtain the maintenance cost of the second to Nth maintenance;
s8, calculating the maintenance cost and the wing time of the whole life cycle of the engine according to the maintenance cost of each time, and then calculating to obtain the maintenance cost and the repair rate of the engine per unit flight hour;
and S9, repeatedly executing the steps S4 to S8 for a plurality of times to obtain the unit flight hour package repair cost and the repair rate distribution of the fleet under a specific operation environment.
2. The method for modeling and predicting the maintenance probability of an aircraft engine during a life cycle of the aircraft engine according to claim 1, wherein the operating environment comprises: natural corrosion environment, artificial pollution environment, mildness and no pollution.
3. The method for modeling and predicting the total life cycle maintenance probability of an aeroengine according to claim 1, wherein the repair cause comprises: hardware damage, performance degradation, life limiting element to life, and engine subject to foreign object impact.
4. The method for modeling and predicting the total life cycle maintenance probability of an aeroengine according to claim 1, wherein in S4, the simulation method for the repair time and the repair cause of the first repair is as follows:
and randomly sampling a service life from the service life distribution obeyed by the potential service 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 reason corresponding to the minimum service life as the first service reason.
5. The method for modeling and predicting the probability of a full life cycle repair of an aircraft engine of claim 1, wherein the operations performed on the component in the repair strategy comprise: maintaining, replacing, detecting and continuing to use;
in the step S6, the maintenance operation has the following priority:
for the same potential repair reasons, in M1 or M2, either one is marked as a new replacement, and then M3 is marked as a new replacement; if no replacement is made, any one is marked as maintenance, and M3 is marked as maintenance; if no replacement or maintenance is performed, any one is marked as detection, and M3 is marked as detection; finally, M1 and M2 are both marked as continued use, and M3 is marked as continued use.
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| CN109918707A (en) * | 2019-01-21 | 2019-06-21 | 南京航空航天大学 | A kind of aero-engine method for predicting residual useful life based on L é vy process |
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| CN106529715A (en) * | 2016-10-25 | 2017-03-22 | 哈尔滨工业大学 | Aeroengine maintenance strategy optimization method based on Markoff decision process |
| CN109918707A (en) * | 2019-01-21 | 2019-06-21 | 南京航空航天大学 | A kind of aero-engine method for predicting residual useful life based on L é vy process |
| CN111652461A (en) * | 2020-04-13 | 2020-09-11 | 北京航空航天大学 | Aero-engine continuous health state evaluation method based on SAE-HMM |
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| Cost Effectiveness Evaluation Model for Civil Aircraft Maintenance Based on Prognostics and Health Management;Yan Hongsheng等;2017 International Conference on Sensing, Diagnostics, Prognostics, and Control;第161-167页 * |
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