CN112906237A - Engine component fault analysis method and system - Google Patents
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Abstract
The invention relates to a method and a system for analyzing engine component faults, and belongs to the technical field of fault detection and risk assessment. The method comprises the following steps: determining shape parameters and dimension parameters of engine components according to historical life data of the components of the fleet engine; constructing a Weibull fault model according to the shape parameters and the scale parameters; carrying out fault Monte Carlo simulation on engine components by using a Weibull fault model, and simulating to obtain the fault occurrence frequency of each component of the engine of the fleet; judging whether the occurrence frequency of the faults is within an acceptable range; if so, continuously monitoring the failure occurrence frequency of each part of the engine of the fleet in real time; if not, measures are taken to correct all parts of the engine of the fleet. When the fault detection is carried out on the engine component, the Monte Carlo simulation is carried out on the fault probability, so that the fault detection and analysis precision of the engine component can be effectively improved, and the safe and stable operation of the engine component is guaranteed.
Description
Technical Field
The invention relates to the technical field of fault detection and risk assessment, in particular to a method and a system for analyzing faults of engine components.
Background
The safety and operational stability of the engine components, which act as a power source for the aircraft, directly determine the various performance and levels of the aircraft in actual flight.
The existing fault analysis method for engine components mainly comprises two methods: monte Carlo simulation method, and method for constructing probability risk assessment mathematical model. Both methods have high requirements on the knowledge level and experience capability of operators, and the existing methods for detecting and analyzing engine component faults are complex to operate. Meanwhile, when the fault detection and analysis are carried out on the engine components, due to technical means, environmental factors and the like, the potential faults of the engine components cannot be detected with a considerable probability.
Accordingly, there is a need to provide a method for detecting and analyzing engine component faults that is relatively simple to operate and has relatively high fault detection and analysis accuracy.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for analyzing the faults of engine components. When the fault detection is carried out on the engine component, the Monte Carlo simulation is carried out on the fault probability on the basis of considering the fault detection probability, so that the fault detection and analysis precision of the engine component can be effectively improved, and the safe and stable operation of the engine component is guaranteed.
In order to achieve the purpose, the invention provides the following scheme:
an engine component failure analysis method comprising the steps of:
determining shape parameters and dimension parameters of engine components according to historical life data of the components of the fleet engine;
constructing a Weibull fault model according to the shape parameters and the scale parameters;
carrying out fault Monte Carlo simulation on the engine components by using the Weibull fault model, and simulating to obtain the fault occurrence times of each component of the engine of the fleet;
judging whether the failure occurrence frequency is within an acceptable range;
if so, continuously monitoring the failure occurrence frequency of each component of the engine of the fleet in real time;
if not, taking measures to correct all parts of the engine of the fleet.
The invention also provides a system for analyzing the fault of the engine component, which comprises:
the parameter acquisition module is used for determining the shape parameters and the scale parameters of the engine components according to historical life data of the components of the fleet engine;
the model building module is used for building a Weibull fault model according to the shape parameters and the scale parameters;
the simulation module is used for carrying out fault Monte Carlo simulation on the engine components by utilizing the Weibull fault model, and simulating to obtain the fault occurrence frequency of each component of the engine of the fleet;
the fault probability judging module is used for judging whether the frequency of the faults is within an acceptable range;
if so, continuously monitoring the failure occurrence frequency of each component of the engine of the fleet in real time;
if not, taking measures to correct all parts of the engine of the fleet.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
when the engine component is subjected to fault detection and analysis, if a potential fault exists in the engine component and the fault cannot be detected, the risk probability is directly increased. In order to solve the problem, Monte Carlo simulation is carried out on the fault probability on the basis of considering the risk probability, and the detection precision of the potential fault of the engine component is effectively improved, so that the fault detection and analysis precision of the engine component is effectively improved, and the safety and stability of the engine component are guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flowchart of an engine component failure analysis method in embodiment 1 of the present invention;
FIG. 2 is a schematic view showing a degree of failure of an engine part with time according to embodiment 2 of the present invention;
FIG. 3 is a schematic diagram showing the degree of failure of an engine component in a potential failure state section at detection points in accordance with the embodiment 2 of the present invention with time;
FIG. 4 is a schematic diagram showing the degree of failure of an engine component in a functional failure state section at a detection point in the embodiment 2 of the present invention with time;
fig. 5 is a flowchart of an engine component failure analysis method according to embodiment 3 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As described in the background, an engine is a common type of power machine system with a complex mechanical structure and a kinematic coupling. Whether any part works normally or not is related to the stability and safety of the whole system, and various problems of physical aging, lack of lubrication, mechanical friction, high-temperature overheating, overload and the like of mechanical parts are easy to cause frequent faults of engine sub-parts and performance degradation of the whole machine, so that the normal work of the engine is influenced. The method has long research and development period and high cost, integrates the crystallization of modern science and technology, and relates to the tips of various fields such as technology, materials, computer science and the like in the research and development process. The engine serves as a power source for the aircraft and also directly determines various performances and levels of the aircraft in actual flight.
When Monte Carlo simulation is carried out on the faults of the aircraft engine, due to the adoption of a Weibull distribution function, the parameters of the operation initial time of the engine component, the maintenance period of the engine component, the shape parameter of the engine component, the dimension parameter of the engine component and the like are required to be used, and most of the parameters come from historical fault data summarized according to the actual experience of an engine institute and the sudden faults or performance decline. The method for evaluating the risk of the engine component region jointly developed by the southwest research institute of America and Rolls-Royce and the like establishes a single-component fault model of the aero-engine, and analyzes the influence of a preventive maintenance mode and a maintenance period on the fault risk on the basis. A large number of experts and scholars establish a probability risk assessment mathematical model of the failure of the life-limiting part of the engine through methods such as regional probability statistics, linear elastic fracture mechanics, stress-intensity interference theory and the like.
However, when the conventional method is used for detecting the aircraft engine component in the maintenance period, due to technical means, environmental factors and the like, the fault cannot be detected at a certain probability, so that the error caused by missed detection or false detection exists in the conventional fault detection of the aircraft engine component. In order to solve the problem, Monte Carlo simulation is carried out on the fault probability on the basis of considering the risk probability, and the detection precision of the potential fault of the engine component is effectively improved, so that the fault detection and analysis precision of the engine component is effectively improved, and the safety and stability of the engine component are guaranteed.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1:
referring to fig. 1, which is a flowchart of a method for analyzing a failure of an engine component according to embodiment 1 of the present invention, steps S1 to S5 are shown.
The engine component fault analysis method comprises the following steps:
determining shape parameters and dimension parameters of engine components according to historical life data of the components of the fleet engine;
constructing a Weibull fault model according to the shape parameters and the scale parameters;
carrying out fault Monte Carlo simulation on engine components by using a Weibull fault model, and simulating to obtain the fault occurrence frequency of each component of the engine of the fleet;
judging whether the occurrence frequency of the faults is within an acceptable range;
if so, continuously monitoring the failure occurrence frequency of each part of the engine of the fleet in real time;
if not, measures are taken to correct all parts of the engine of the fleet.
The above steps are further described in example 2 below.
Example 2:
FIG. 2 is a graphical representation of engine component failure as a function of time. Wherein T represents a maintenance cycle of the engine component, T1-TnRepresenting the 1 st to n th maintenance cycle; t is taThe time that the engine component starts to be damaged is represented, namely the service time of the engine component from the last maintenance to the current time; t is tbIndicating when an engine component begins to develop a latent fault but does not fail; t is tTRepresenting the maintenance inspection time of the engine component in the maintenance period T; t is tfIndicating the time at which a functional failure of an engine component occurs, tcIndicating the time at which the engine component completely failed.
The purpose of engine failure prediction is: the health state of the components or systems of the engine is detected through a simulation method or algorithm, so that the fault occurrence probability of the components or systems of the engine in a future period is predicted. The predicted trend of the engine component is represented by the fault degree, and comprises an initial damage point D of the engine component, a potential initial fault point E of the engine component, a component functional fault point G and a complete component failure point H.
Predicting time t at which damage should occur from an engine component for a failure of the engine componentaAt the beginning, in advanceTime measurement should distinguish whether an engine component is malfunctioning or potentially malfunctioning.
If the component is a potential fault, the component has a certain probability of missing detection, so that the risk of the fault probability of the component is increased. The time-varying relation of the fault degree of the engine component in the potential fault state interval of the detection point is shown in FIG. 3; wherein, btIndicates maintenance inspection time tTTime interval to maintenance period T.
If the engine component is in the functional fault, the fault can be identified and maintained quickly, the engine fault can be eliminated, and the time-varying relation of the fault degree of the engine component in the functional fault state section of the detection point is shown in FIG. 4.
Based on the above, when an engine component failure is predicted, it should be distinguished whether the engine component failure can be detected relatively clearly. If the engine component is potential failure, maintaining and checking time t of the engine componentTThe condition that the engine part has a certain probability of generating missing detection or undetected detection is checked, and the fault risk is correspondingly increased; if the function is failure, the maintenance time t of the engine part is determinedTWhen the engine component is inspected, the engine component can be immediately repaired or replaced, and the fault risk is low.
Furthermore, if a potential fault can be detected, the condition of missing detection or undetected detection is avoided, the fault detection precision of the engine component is effectively improved, and the fault risk can be reduced.
The engine component failure analysis method provided by the present invention will be described in detail with reference to fig. 5.
Firstly, determining the shape parameters and the dimension parameters of all the components of the engine according to historical life data of all the components of the engine of the fleet. The method comprises the following specific steps:
obtaining service life data T of components i of the fleet engine within a preset number of maintenance cycles TsiAnd summarizing; where i 1, 2.. m, m represents the number of components in the fleet engine;
performing least square processing on the collected service life data of the component i to obtain the shape parameter beta of the component i in the engine of the fleetiAnd a scale parameter ηi。
And then constructing a Weibull fault model according to the shape parameters and the scale parameters. The method comprises the following specific steps:
constructing a Weibull fault model:
wherein, Fi(ti) Represents a random number within 0-1, Fi(ti)＝random(0,1)，βiRepresenting the shape parameter, η, of a component i in a fleet engineiRepresenting a dimensional parameter, t, of a component i in a fleet engineiIndicating the initial time to failure of component i in the fleet engine.
And then carrying out fault Monte Carlo simulation on the engine components by using the constructed Weibull fault model, and simulating to obtain the fault occurrence times of each component of the engine of the fleet. The method specifically comprises the following steps:
and (3) carrying out form transformation on the Weibull fault model to obtain:
wherein, Fi(ti) Represents a random number within 0-1, Fi(ti)＝random(0,1)，βiRepresenting the shape parameter, η, of a component i in a fleet engineiRepresenting a dimensional parameter, t, of a component i in a fleet engineiRepresenting the initial failure time of a component i in the fleet engine;
will tiWith the initial time of use t of component i in the fleet engineaiAnd comparing, and calculating the occurrence frequency of the faults of the fleet engine components i according to the first comparison result of the comparison.
The "calculating the number of times of occurrence of the failure of the fleet engine component i according to the first comparison result of the comparison between the two" specifically includes:
if ti＜taiLet Zei＝Zei+1, recalculating ti(ii) a Wherein Z iseiRepresents the number of additional simulations for component i;
if ti≥taiWill tiMaintenance period T with fleet Engine component iiAnd comparing, and judging whether the component i of the engine is about to generate faults according to a second comparison result of the comparison.
The "determining whether the component i of the engine will fail according to the second comparison result of the comparison of the two" specifically includes:
if ti＜TiThen it is assumed that component i of the engine will fail, according to tiCalculating the month M in which the predicted component i will failiAnd according to tiAnd MiCalculate that component i of the engine will be at MthiSimulation times of month fault generation;
if ti≥TiThen the component i of the engine is considered to be in the maintenance period TiNo fault occurs in the system, or a potential fault has occurred with a certain probability, but the fault is not detected due to missing detection or other reasons;
calculating the component i of the engine to be at the Mth according to the set probability that the component i of the engine is normaliNumber of simulations of the month generated failures.
More specifically, the present invention is to provide a novel,
if ti＜TiBuilding months M in which component i of the engine will failiThe calculation formula of (2):
wherein, tiRepresenting the initial time of failure, t, of component i in the fleet's engineaiRepresenting the initial time of use, t, of a component i in a fleet enginegRepresents a usage parameter of the engine in the unit of h/month]Represents the rounding of the values in brackets;
month M in which failure will occur depending on component i of the engineiDetermines the failure occurrence month M of the component i in the enginei；
Component i, which will then build the fleet engines, will be at MthiThe calculation formula of the simulation times of month failure is as follows:
wherein M isiMonth of failure of engine component i, ei1Indicating that the 1 st engine component i in the fleet will be at MthiNumber of months of simulation of failures, ei2Indicating that the 2 nd engine component i in the fleet will be at MthiNumber of months of simulation of failures, einIndicating that the nth engine component i in the fleet will be at MthiNumber of simulated monthly failures, j represents the jth engine in the fleet, eijIndicating that the jth engine component i in the fleet will be at MthiThe number of simulations of a month's failure, n representing the total number of engines in the fleet;
if ti≥TiSetting up PrRandom (0,1), i.e. the i-th component has not failed or the failure has been in a previous service period TiThe probability of finishing internal trimming is Pr；
Then the jth engine component is at MthiThe simulation frequency of month fault is eik，
eik＝1-PrN, n represents the total number of engines in the fleet.
The initial failure time t of each component i in n engines of the fleet is calculated through the processiAnd the month of failure occurrence MiAnd in month MiNumber of internal failures eikThen, the simulation times of each engine are set to be Z times, and the Mth component i of each engine in the fleet is obtainediThe probability of monthly fault is PMi,i：
Wherein, Engine [ M ]i][i]Component i representing the fleet Engine will be at MthiTotal number of simulations of monthly failures, n represents total number of engines in the fleet, ZeiRepresenting the total number of simulations of component i;
further according to PMi,iIt can be calculated that all the components i of the n engines in the fleet are at MthiMonthly failure occurrence frequency EngineFlet [ M ]i][i]：
EngineFleet[Mi][i]＝n·PMi,i。
Then judging that the component i is at the M-thiWhether the number of monthly failures is within an acceptable range.
Before judgment, a risk factor calculation formula of an engine component i in the fleet is constructed:
Ri＝fi·Ci；
wherein R isiA risk factor, f, indicating the failure of component i in the engineiIndicating the number of occurrences of failure of component i in the engine, CiRepresenting the hazard coefficient of component i in the engine;
constructing a calculation formula of the risk of each flight of an engine component i in the fleet:
wherein HiRepresents the total number of flight hours in h;
comparing the value of the risk of each flight of an engine component i in the fleet with a preset range;
if the number of the faults of the engine component i of the fleet is within the preset range, the number of the faults of the engine component i of the fleet is considered to be within the acceptable range, and real-time monitoring is continuously carried out on the number of the faults of the engine component i of the fleet;
if not, measures are taken to correct the fleet engine component i.
In summary, the present invention considers that when a fault of an engine component is detected and analyzed, if a potential fault exists in the engine component and the fault cannot be detected, the risk probability is directly increased. Therefore, Monte Carlo simulation is carried out on the fault probability on the basis of considering the risk probability, the detection precision of the potential fault of the engine component is effectively improved, the fault detection and analysis precision of the engine component is effectively improved, and the safety and stability of the engine component are guaranteed.
The method in example 2 is described in detail by actual data next.
Example 3:
in this example, three failure modes, i.e., turbine disk cracking, blade damage, and combustor cracking, were analyzed.
TABLE 1 shape and Scale parameters for three failure modes
As shown in table 1, the shape parameters and dimensional parameters are for three failure modes, turbine disk cracking, blade damage and combustor cracking. The shape parameters and the scale parameters are calculated by utilizing a least square method based on historical life data of all components of the engine of the fleet.
As can be seen from table 1, the turbine disk crack failure mode is set as the first failure mode, the blade damage failure mode is set as the second failure mode, and the combustor crack failure mode is set as the third failure mode.
Then beta is1＝2.09，η1＝10193，
β2＝1.89，η2＝12050，
β3＝2.03，η3＝10100，
Based on Fi(ti) Initial failure time t corresponding to three failure modes of turbine disc crack, damage crack and combustion chamber crack can be calculated according to random (0,1)1、t2And t3。
Then calculating the month M of the failure mode according to the initial failure time corresponding to the three calculated failure modes1、M2And M3。
From this, the M < th > failure modes corresponding to the three failure modes are calculatediThe number of occurrences of failure in each month, i.e., EngineFlet [ M1][1]、EngineFleet[M2][2]And EngineFlet [ M ]3][3]Then, the calculated values need to be compared with a preset range to determine whether the failure occurrence times of the three failure modes are within an acceptable range.
Specifically, EngineFlet [ M ] is introduced1][1]、EngineFleet[M2][2]And EngineFlet [ M ]3][3]The values of the three failure times are respectively used as the parameter f1、f2And f3And expressing and constructing a risk factor calculation formula of the engine component:
Ri＝fi·Ci；
wherein R isiA risk factor, f, indicating the failure of component i in the engineiIndicating the number of occurrences of failure of component i in the engine, CiRepresenting the hazard coefficient of component i in the engine;
in the embodiment, three failure modes of turbine disk crack, blade damage and combustion chamber crack with a three-level risk coefficient C can be obtained from history data of the engine uncontained failure event1、C2And C3. Wherein, C1＝0.14、C2＝0.13、C3＝0.08。
By combining the shape parameters and the scale parameters of the three fault modes shown in the table 1 and the fault analysis method for the engine component provided by the invention, one million times of simulation is carried out, the fault times of the three faults 30 months before the fault time are recorded, and the parameters f corresponding to the three fault modes can be obtained1、f2And f3The value of (c):
f1＝12.388602、f2＝12.6742268、f3＝12.1966335。
then, R corresponding to the three fault modes can be calculated according to the numerical values1 R2And R3The value of (c). Wherein:
R1＝1.73、R2＝1.65、R3＝0.98。
and then constructing a calculation formula of the risk of each flight of the engine component i in the fleet:
wherein HiRepresents the total number of flight hours in h.
In the present embodiment, the total number of flight hours of the engine is 109h, the calculated risk factors for the three failure modes and the risk of each flight are shown in table 2.
TABLE 2 Risk factors and Risk of flight for three failure modes
Assuming that the acceptable range of the risk factor in this embodiment is as small as 1, the acceptable range of the risk factor per flight is as small as 1 × 109. As can be seen from Table 2, the risk factors of the two failure modes of the crack of the turbine disk and the damage of the blade exceed 1, and the risk of each flight exceeds 1 multiplied by 109(ii) a Risk factor of combustion chamber crack failure mode although smallAt 1, but very close to 1. All three failure modes are considered to be out of acceptable limits, i.e. it is necessary to take manufacturer-provided solution measures or other necessary measures to reduce the flight risk, for example to modify the time intervals of maintenance cycles or to increase the strength of the engine component material itself.
Example 4:
the present invention also provides, in embodiment 4, an engine component failure analysis system capable of implementing the engine component failure analysis method of the present invention as described in embodiments 1 to 3. The system comprises a parameter acquisition module, a model construction module, an analog simulation module and a fault probability discrimination module.
The parameter acquisition module is used for determining the shape parameters and the scale parameters of engine components according to historical life data of the components of the fleet engine;
the model building module is used for building a Weibull fault model according to the shape parameters and the scale parameters;
the simulation module is used for carrying out fault Monte Carlo simulation on the engine components by utilizing a Weibull fault model, and simulating to obtain the fault occurrence times of each component of the engine of the fleet;
the fault probability judging module is used for judging whether the frequency of the faults is within an acceptable range;
if so, continuously monitoring the failure occurrence frequency of each part of the engine of the fleet in real time;
if not, measures are taken to correct all parts of the engine of the fleet.
In summary, according to the method and the system for analyzing the fault of the engine component provided by the invention, on the basis of considering the risk probability, monte carlo simulation is performed on the fault probability, so that the detection precision of the potential fault of the engine component can be effectively improved, the fault detection and analysis precision of the engine component can be effectively improved, and the guarantee is provided for the safe and stable operation of the engine component.
The principle and the implementation of the present invention are explained in the present text by applying specific examples, and the above description of the examples is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. An engine component failure analysis method, comprising the steps of:
determining shape parameters and dimension parameters of engine components according to historical life data of the components of the fleet engine;
constructing a Weibull fault model according to the shape parameters and the scale parameters;
carrying out fault Monte Carlo simulation on the engine components by using the Weibull fault model, and simulating to obtain the fault occurrence times of each component of the engine of the fleet;
judging whether the failure occurrence frequency is within an acceptable range;
if so, continuously monitoring the failure occurrence frequency of each component of the engine of the fleet in real time;
if not, taking measures to correct all parts of the engine of the fleet.
2. The engine component failure analysis method of claim 1, wherein determining shape parameters and dimensional parameters of components of the engine based on historical life data of the components of the fleet engine specifically comprises:
acquiring service life data T of the components i of the fleet engines within a preset number of maintenance cycles TsiAnd summarizing; wherein i 1, 2.. m, m represents the number of components in the fleet engine;
performing least square processing on the collected service life data of the component i to obtain the shape parameter beta of the component i in the engine of the fleetiAnd a scale parameter ηi。
3. The engine component fault analysis method of claim 1, wherein constructing the weibull fault model from the shape parameters and the scale parameters specifically comprises:
constructing the Weibull fault model:
wherein, Fi(ti) Represents a random number within 0-1, Fi(ti)＝random(0,1)，βiRepresenting a shape parameter, η, of a component i in said fleet engineiRepresenting a dimensional parameter, t, of a component i in said fleet engineiIndicating an initial time to failure of component i in the fleet engine.
4. The engine component fault analysis method according to claim 3, wherein the step of performing fault monte carlo simulation on the engine component by using the weibull fault model, and the step of simulating the number of times of occurrence of the fault of each component of the fleet engine specifically comprises the steps of:
and carrying out form transformation on the Weibull fault model to obtain:
wherein, Fi(ti) Represents a random number within 0-1, Fi(ti)＝random(0,1)，βiRepresenting a shape parameter, η, of a component i in said fleet engineiRepresenting a dimensional parameter, t, of a component i in said fleet engineiRepresenting an initial time to failure of component i in the fleet engine;
will tiWith the initial time of use t of component i in the fleet engineaiAnd comparing, and calculating the occurrence frequency of the faults of the fleet engine components i according to the first comparison result of the comparison.
5. The engine of claim 4A method for analyzing a failure of a machine component, characterized in that t is calculatediWith the initial time of use t of component i in the fleet engineaiComparing, and calculating the failure occurrence frequency of the fleet engine component i according to the comparison result specifically comprises the following steps:
if ti＜taiLet Zei＝Zei+1, recalculating ti(ii) a Wherein Z iseiRepresents the number of additional simulations for component i;
if ti≥taiWill tiMaintenance period T with said fleet Engine component iiAnd comparing, and judging whether the component i of the engine is about to generate faults according to a second comparison result of the comparison.
6. The engine component failure analysis method according to claim 5, wherein the judging whether or not the component i of the engine will fail based on the second comparison result of the comparison of the two specifically includes:
if ti＜TiThen it is assumed that component i of the engine will fail, according to tiCalculating the month M in which the predicted component i will failiAnd according to tiAnd MiCalculating that component i of the engine will be at MthiSimulation times of month fault generation;
if ti≥TiThen the component i of the engine is considered to be in the maintenance period TiNo fault occurs in the system, or a potential fault has occurred with a certain probability, but the fault is not detected due to missing detection or other reasons;
calculating that the component i of the engine is to be at the Mth according to the set probability that the component i of the engine is normaliNumber of simulations of the month generated failures.
7. The engine component failure analysis method according to claim 6,
if ti＜TiBuilding up the month M in which component i of the engine will failiThe calculation formula of (2):
wherein, tiRepresenting the initial time of failure, t, of a component i in the fleet engineaiRepresenting the initial time of use, t, of a component i in the fleet enginegRepresents a usage parameter of the engine in the unit of h/month, [ 2 ]]Represents the rounding of the values in brackets;
month M in which a failure will occur depending on component i of the engineiDetermining the failure occurrence month M of the component i in the enginei；
Component i constructing the fleet Engine will be at MthiThe calculation formula of the simulation times of month failure is as follows:
wherein M isiMonth, e, indicating failure of said engine component ii1Indicating that the 1 st engine component i in the fleet will be at MthiNumber of months of simulation of failures, ei2Indicating that the 2 nd engine component i in the fleet will be at MthiNumber of months of simulation of failures, einIndicating that the nth engine component i in the fleet will be at MthiNumber of simulated monthly failures, j represents the jth engine in the fleet, eijIndicating that the jth engine component i in the fleet will be at MthiThe number of simulations of a month's failure, n representing the total number of engines in the fleet;
if ti≥TiSetting up PrRandom (0,1), i.e. the i-th component has not failed or the failure has been in a previous service period TiThe probability of finishing internal trimming is Pr；
Then the jth engine component is at MthiNumber of months of failureNumber eik，
eik＝1-PrN, n represents the total number of engines in the fleet.
8. The engine component failure analysis method according to claim 7, wherein an initial failure time t of a component i of each of n engines of the fleet is calculatediAnd the month of failure occurrence MiAnd in month MiNumber of internal failures eik；
Setting the simulation times of each engine to be Z times to obtain the condition that the component i of each engine in the fleet is at the MthiThe probability of monthly fault is PMi,i：
Wherein, Engine [ M ]i][i]Component i representing the fleet Engine will be at MthiTotal number of simulations of a month's failure, n representing the total number of said engines in the fleet, ZeiRepresenting the total number of simulations of component i;
further according to PMi,iCalculating to obtain the Mth component of all components i of the n engines in the fleetiMonthly failure occurrence frequency EngineFlet [ M ]i][i]：
EngineFleet[Mi][i]＝n·PMi,i。
9. The engine component failure analysis method according to claim 1, wherein determining whether the number of occurrences of the failure is within an acceptable range specifically includes:
constructing a risk factor calculation formula of an engine component i in the fleet:
Ri＝fi·Ci；
wherein R isiA risk factor, f, indicating the failure of a component i in said engineiRepresenting components in said enginei number of occurrences of failure, CiRepresenting a hazard coefficient of component i in the engine;
constructing a calculation formula of the risk of each flight of the engine component i in the fleet:
wherein HiRepresents the total number of flight hours in h;
comparing the value of the risk per flight of an engine component i in the fleet with a preset range;
if the number of the faults of the fleet engine component i is within the preset range, the number of the faults of the fleet engine component i is considered to be within the acceptable range, and real-time monitoring is continuously carried out on the number of the faults of the fleet engine component i;
if not, taking measures to correct the fleet engine component i.
10. An engine component failure analysis system, comprising:
the parameter acquisition module is used for determining the shape parameters and the scale parameters of the engine components according to historical life data of the components of the fleet engine;
the model building module is used for building a Weibull fault model according to the shape parameters and the scale parameters;
the simulation module is used for carrying out fault Monte Carlo simulation on the engine components by utilizing the Weibull fault model, and simulating to obtain the fault occurrence frequency of each component of the engine of the fleet;
the fault probability judging module is used for judging whether the frequency of the faults is within an acceptable range;
if so, continuously monitoring the failure occurrence frequency of each component of the engine of the fleet in real time;
if not, taking measures to correct all parts of the engine of the fleet.
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