CN115071999A - Aircraft landing gear safety diagnostic method and device - Google Patents
Aircraft landing gear safety diagnostic method and device Download PDFInfo
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- CN115071999A CN115071999A CN202210543945.2A CN202210543945A CN115071999A CN 115071999 A CN115071999 A CN 115071999A CN 202210543945 A CN202210543945 A CN 202210543945A CN 115071999 A CN115071999 A CN 115071999A
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Abstract
The invention provides a method for diagnosing the safety of an aircraft landing gear, which comprises the following steps: extracting historical data of the aircraft landing gear system, and obtaining the fault rate of each element in each subsystem according to a preset proportional risk model; calculating probability values and evaluation values of each subsystem under the safe state, the sub-safe state and the dangerous state respectively according to a preset algorithm; acquiring the actual fault state of each subsystem, and revising the evaluation value of each subsystem according to the acquired actual fault state of each subsystem; associating and establishing a Bayesian network according to the aircraft landing gear system subsystem; and determining the system safety degree of the aircraft landing gear system according to the Bayesian network and the revised evaluation value. The method can fuse various information sources, quantize the safety value of the undercarriage system by combining the complexity of the undercarriage system, and accurately and quickly diagnose and evaluate the safety of the undercarriage system.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of safety of an aircraft landing gear, in particular to a method and a device for diagnosing the safety of the aircraft landing gear.
Background
Statistical data shows that the failure frequency of the landing gear accounts for about 20% of the failure rate of the whole machine. The landing gear system is an important component of the airplane and plays a crucial role in the normal operation of the airplane, for example, when the airplane breaks down during taking off or landing, the life safety of flight personnel and passengers is seriously threatened, so that the analysis of the failure generation cause and the safety risk assessment and prediction need to be enhanced.
In the prior art, there are generally three methods for evaluating the safety or failure rate of an aircraft landing gear: firstly, estimating the retirement time by adopting expert experience and combining the design service life of an undercarriage system; secondly, combining some methods of online monitoring, and setting some simple fault thresholds as judgment standards of faults of subsystems or sub-elements of the landing gear system; and thirdly, adopting an intelligent algorithm, such as a neural network and the like, and also introducing the intelligent algorithm into the evaluation of the failure rate of the landing gear of the airplane.
However, the above three evaluation methods have disadvantages, which are: in the first method, the method is susceptible to expert experience, and the design service life of the landing gear of the aircraft is a fixed value for a certain type of equipment and cannot be changed along with the change of the actual use environment of the equipment, so that the early decommissioning of a landing gear system or a component causes huge resource waste, and the reduction of safety may cause a serious disaster; in the second method, the judgment standard of the threshold value in the method has no accuracy, and the identification of some complex online monitoring signals cannot realize the safety evaluation of the multivariate information fusion; in the third method, the method is a black box system, the interpretability is lacked, the aircraft landing gear as a complex system has the structure and characteristics of the aircraft landing gear, different parts have different failure rules, which cannot be considered by a neural network, and therefore the safety of the aircraft landing gear system existing in a plurality of non-redundant subsystems cannot be accurately evaluated.
Therefore, in view of the shortcomings of the above three methods, a diagnosis method for accurately and rapidly evaluating the safety of the landing gear of the airplane is needed.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose aircraft landing gear safety diagnostic methods and apparatus to address one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an aircraft landing gear safety diagnostic method, the method comprising: extracting historical data of the aircraft landing gear system, and obtaining the fault rate of each element in each subsystem according to a preset proportional risk model; calculating the failure rate of each element in each subsystem according to a preset algorithm, wherein the probability values of each subsystem under a plurality of evaluation states respectively are calculated, and the plurality of probability values calculated in each subsystem are combined into corresponding evaluation values respectively; wherein the plurality of evaluation states include a safe state, a less safe state, and a hazardous state; the evaluation value comprises a probability value of a safety state, a probability value of a sub-safety state and a probability value of a dangerous state which are set in sequence; acquiring the actual fault state of each subsystem, and revising the evaluation value of each subsystem according to the acquired actual fault state of each subsystem; wherein the actual fault condition includes a condition in which a defect has occurred and a condition in which a fault has occurred; according to physical subsystem relations of the aircraft landing gear system, which influence system faults, and possible logic principles, the subsystems are correlated and a Bayesian network is established; and obtaining the conditional probability of the aircraft landing gear system corresponding to each evaluation state by adopting a Bayesian formula according to the established Bayesian network and the revised evaluation value of each subsystem, and determining the system safety degree of the aircraft landing gear system according to the conditional probability of each evaluation state obtained by the aircraft landing gear system.
Each subsystem comprises a brake subsystem, a front wheel turning subsystem, a wheel subsystem, a power system, a damping subsystem, a retraction subsystem and the like.
In a second aspect, some embodiments of the present disclosure provide an aircraft landing gear safety diagnostic device implemented on a landing gear system comprising a plurality of subsystems and a plurality of elements in each subsystem, the device comprising:
the failure rate acquisition unit is used for extracting historical data of the aircraft landing gear system and obtaining the failure rate of each element in each subsystem according to a preset proportional risk model;
the probability evaluation unit is used for calculating the obtained failure rate of each element in each subsystem according to a preset algorithm to obtain probability values of each subsystem under a plurality of evaluation states respectively, and combining the plurality of probability values calculated in each subsystem into corresponding evaluation values respectively; wherein the plurality of evaluation states include a safe state, a less safe state, and a hazardous state; the evaluation value comprises a probability value of a safety state, a probability value of a sub-safety state and a probability value of a dangerous state which are set in sequence;
the probability evaluation revising unit is used for acquiring the actual fault state of each subsystem and revising the evaluation value of each subsystem according to the acquired actual fault state of each subsystem; wherein the actual fault condition includes a condition that a defect has occurred and a condition that a fault has occurred;
the system comprises a Bayesian network establishing unit, a Bayesian network establishing unit and a data processing unit, wherein the Bayesian network establishing unit is used for associating subsystems and establishing a Bayesian network according to the physical subsystem relationship of an aircraft landing gear system which influences system faults and possible logic principles;
and the system safety degree determining module is used for obtaining the conditional probability of the aircraft landing gear system corresponding to each evaluation state by adopting a Bayesian formula according to the established Bayesian network and the revised evaluation value of each subsystem, and determining the system safety degree of the aircraft landing gear system according to the conditional probability of each evaluation state obtained by the aircraft landing gear system.
Each subsystem comprises a brake subsystem, a front wheel turning subsystem, a wheel subsystem, a power system, a damping subsystem, a retraction subsystem and the like.
The above embodiments of the present disclosure have the following advantages: in the embodiment of the invention, a complex undercarriage system is decomposed into a plurality of subsystems and elements for analysis by adopting a proportional risk model, the probability of each subsystem corresponding to a safe state, a sub-safe state and a dangerous state is evaluated, the probability obtained by revising each subsystem according to an actual fault state is further accurately determined, and therefore, various information sources can be fused, the safety value of the undercarriage system is quantized by combining the complexity of the undercarriage system, and the safety of the undercarriage system is accurately and quickly evaluated.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a flow diagram of some embodiments of an aircraft landing gear safety diagnostic method according to the present disclosure;
FIG. 2 is a schematic structural diagram of some embodiments of the aircraft landing gear safety diagnostic device of the present disclosure;
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a flow 100 of some embodiments of an information transmission method according to the present disclosure is shown. The information sending method comprises the following steps:
step S101, extracting historical data of the aircraft landing gear system, and obtaining the failure rate of each element in each subsystem according to a preset proportional risk model;
in some embodiments, in particular, real-time online detection data and periodic preventive experimental data of each element in each subsystem of the aircraft landing gear are extracted, and the failure rate of each element in each subsystem is obtained according to the following formula:
wherein h (t, Z) is the failure rate of the current element, is not only related to time, but also depends on the value of the variable Z; η is a scale function of the Weibull distribution; β is the shape function of the weibull distribution; z is the covariate vector of the current element, and the object of the covariate vector can be (Z) according to the data which can be actually collected under the working condition of the current landing gear system 1 ，z 2 ...z n ) Composition of each of z i Representing a state variable, wherein the state variable comprises the accumulated switching times, the collecting and releasing times and the like of the current element; and gamma is a vector with the same length as Z, is a regression parameter of the covariate and reflects the influence of the covariate on the fault rate.
The cumulative failure rate is (1):
the relation between the fault rate function and the safety degree function is (2):
wherein the content of the first and second substances,
the security function is:
the likelihood functions of both are (3):
further, substituting the probability density function and the safety function of the two-parameter Weibull proportion fault model into the function (3) can obtain a likelihood function of (4):
n +2 unknown parameters exist in f (t, Z), the distribution is beta, eta and gamma vectors, Bayesian estimation is used, and the prior distribution is beta-N (mu) β ,σ β 2 )、η～Γ(a η ,b η ) And gamma i ～N(μ ri ,σ γi 2 )；
Wherein the shape function beta and the covariate r i Subject to a normal distribution mu β ,σ β 2 ，μ γi ,σ ri 2 Are the parameters in the distribution, respectively; eta obey the gamma distribution, a η ,b η Respectively, are parameters in the distribution.
The posterior distribution of the bayesian estimate is then (5):
according to the posterior distribution, WINBUGS is used to calculate the evaluation values of the unknown parameters beta, eta and gamma.
In some embodiments, each subsystem includes a braking subsystem, a front wheel steering subsystem, a wheels subsystem, a power subsystem, a shock absorption subsystem, a retraction subsystem, and the like.
Step S102, respectively setting the failure rate of each element in each subsystem, calculating probability values of each subsystem under a plurality of evaluation states according to a preset algorithm, and respectively combining the probability values calculated in each subsystem into corresponding evaluation values; wherein the plurality of evaluation states include a safe state, a less safe state, and a hazardous state; the evaluation value comprises a probability value of a safety state, a probability value of a sub-safety state and a probability value of a dangerous state which are set in sequence;
in some embodiments, the method specifically comprises:
substep 1: obtaining a safety value of each element in each subsystem according to the obtained failure rate of each element in each subsystem;
a substep 2, determining the sampling times m by adopting a termination criterion of a Monte Carlo method; wherein m is a positive integer;
substep 3: sampling is performed to generate a signal at [0,1 ]]When N is less than R i When S is present i When N is greater than or equal to R, 1 i When S is present i ＝0；
Comparing the determined safety value of each element in each subsystem with the current random number N, and converting the safety value into corresponding 0 or 1; wherein S is i The state of the current element is represented by i which is a natural number and is less than or equal to the total number of elements in the subsystem to which the current element belongs; 1 represents the working state; 0 represents a fault condition; ri is the current elementA safety value of the piece;
substep 4: respectively counting the number of 0 in each subsystem, determining the current evaluation state corresponding to each subsystem according to a preset evaluation rule, and assigning the current evaluation state of each subsystem to be one;
substep 5: the sampling times m-1 return to step S3;
substep 6: when the sampling times m are equal to 0, respectively counting the number of the evaluation state assignment of each subsystem, which is 1, and dividing the counted number of the evaluation state assignment of each subsystem, which is 1, by the sampling times m to obtain a probability value corresponding to each evaluation state of each subsystem;
substep 7: and setting the evaluation value corresponding to each subsystem according to the probability value corresponding to each evaluation state of each subsystem.
In some embodiments, the method of assembling the components into the subsystem uses a Monte Carlo method to transform the evaluated events into a certain probability model, using a computer to generate random numbers. The combination of the states of all the elements of the subsystem constitutes the state of the entire subsystem, where the state of element i is denoted S i The two states, i.e. the fault state and the normal state, simplified to 0 and 1 can be used in [0,1 ]]The random number N generated in between determines the state of the element, provided that the probability is on average [0, 1%]A number is extracted, the value of the ith extraction is N i ，R i Representing the security value of the current element, then N i Less than R i Then the current element is considered to be normal (1), N in the current sample i Greater than R i The current element is considered to be failed (0) in the current sampling.
If a subsystem has n elements, the state of the subsystem may be combined from the states of the n elements. Can be represented by the following vector: t is i ＝{S 1 ，S 2 ，...S n }; wherein, T i Status i representing subsystem, total 2 n Each element represents the state of an element. Wherein the state combinations T are all sampled times i The number of occurrences is m; t is i Description subsystemStates of all elements in the system, namely, representing a state space; the state space corresponds to an evaluation result set, denoted as H, whose elements have a safe state, a less safe state, and a dangerous state.
State value T i And the evaluation result set H corresponds to a mapping relation, the determination of the mapping relation is determined according to the actual condition and expert experience of the aircraft landing gear system, namely a preset judgment rule, as an example, if no fault occurs in a key element in the subsystem, the key element is marked as a safe state, the faults of less than 2 non-key elements are marked as a secondary safe state, and the faults of more than 2 non-key elements are marked as a dangerous state.
From the knowledge of probability theory, it can be known that after a sufficient number of samples, the average value can be used to approximate the mathematical expectation, so when the number of samples is large enough, denoted as m, the safety state is taken as an example, and all the combined states T corresponding to the safety state as the evaluation result are taken as the example i Q in total, the number of times of occurrence of the q state is m q Then, the probability value K in the safe state is (6):
in one embodiment of the present invention, a damping subsystem is taken as an example, and the damping subsystem includes several elements, such as an outer cylinder, an inner cylinder of a piston, a recoil valve, a damping hole supporting tube, an oil needle, and the like. Assuming that the sampling elements are all normal, i.e. T i Is (1, 1, 1, 1, 1), since all the elements are normal, the subsystem is normal, and the safety state is assigned to be 1; if a certain sampling is the fault of the oil needle (1, 1, 1, 1, 0), the under-health state is assigned as 1 because the system can still work under the condition that the oil needle is in fault, and the state is recorded as a sub-safe state; if a sample is (1, 1, 0,1, 1), the danger state is assigned to 1 because the recoil valve is a key component of the mechanism subsystem; according to the termination criterion of the Monte Carlo method, after sampling for a plurality of times, respectively counting the number of the security state assignment 1, the number of the sub-security state assignment 1 and the danger state assignment in the mechanism subsystemAnd the value is 1, and then the total number m of the samples is divided by the counted values of the safe state, the sub-safe state and the dangerous state respectively to obtain the probability values of the three states of the mechanism subsystem. And finally, sequencing the obtained three probability values according to the probability value of the safety state, the probability value of the secondary safety state and the probability value of the danger state, and combining the three probability values into an evaluation value of the mechanism subsystem.
S103, acquiring the actual fault state of each subsystem, and revising the evaluation value of each subsystem according to the acquired actual fault state of each subsystem; wherein the actual fault condition includes a condition in which a defect has occurred and a condition in which a fault has occurred;
in some embodiments, in order to obtain accurate evaluation values of the subsystems, the evaluation values of the subsystems need to be revised and updated according to the actual failure states of the subsystems. When the actual failure state of the one or more subsystems is acquired as a state in which a defect has occurred, revising the acquired evaluation values of the one or more subsystems to (0, 0.5, 0.5); when the actual failure state of the acquired one or more subsystems is a failure-occurred state, the acquired evaluation values of the one or more subsystems are revised to (0, 0.1, 0.9).
Step S104, associating the subsystems and establishing a Bayesian network according to the physical subsystem relationship of the aircraft landing gear system which influences the system fault and possible logic principles;
in some embodiments, specifically, the aircraft landing gear system is used as a root node, each subsystem comprises a brake subsystem, a nose wheel steering subsystem, an airplane wheel subsystem, a power subsystem, a damping subsystem, a retraction subsystem and the like which are respectively used as primary nodes, the subsystems of each subsystem are secondary nodes, the node is a father node of the secondary nodes, and the like.
And S105, obtaining the conditional probability of the aircraft landing gear system corresponding to each evaluation state by adopting a Bayesian formula according to the established Bayesian network and the revised evaluation values of the subsystems, and determining the system safety of the aircraft landing gear system according to the conditional probability of each evaluation state obtained by the aircraft landing gear system.
In some embodiments, specifically, according to the established bayesian network and the revised evaluation values of the subsystems, a bayesian formula is adopted to calculate a conditional probability of each father node in the bayesian network, and the conditional probabilities respectively obtained by the aircraft landing gear system serving as a root node in a plurality of evaluation states are determined according to the calculated conditional probability of each father node;
when the condition probability obtained by the aircraft landing gear system in a healthy state is detected to be maximum, determining that the system safety degree of the aircraft landing gear system is the highest;
when the condition probability obtained by the aircraft landing gear system in the sub-safe state is detected to be the maximum, determining that the system safety degree of the aircraft landing gear system is moderate;
and when the condition probability obtained by the aircraft landing gear system in the dangerous state is detected to be the maximum, determining that the system safety degree of the aircraft landing gear system is the lowest.
In some embodiments of the invention, the conditional probability is the probability that a child node is in a certain state in the parent node in the bayesian network. For example, the child nodes of the shock absorbing subsystem are a shock strut system and a torsion linkage system. When the shock strut system is in the safe state and the power subsystem is in the sub-safe state, the safe condition of the shock strut system is a conditional probability, which can be represented as p (the shock strut system is safe, and the torsion link system is sub-safe). In this example, the probability value of the parent node, i.e., the safety of the suspension strut system, and the probability of the sub-safety of the torsion link system are obtained in step S102 or step S103.
In some embodiments of the present invention, the conditional probability value is calculated according to the following method:
assume that the variable set X ═ X (X) 1 ，X 2 ，...，X n ) May be encoded in a certain network structure S, each variable represents the state of a certain node in the network, such as a damping subsystem, which may be expressed as (7):
wherein in formula (7), θ s Is distribution p (x) i |pa i ，θ s ，S n ) Parameter vector of (1), using θ i Representing a parameter set (theta) 1 ，θ 2 ，...,θ n ) Is a value of n Representing the current network architecture. pa is a i Representing the parent of this node.
Assuming each variable X i Is discrete, having γ i A possible value x i 1 ,x i 2 ,…，x i γi Here γ i ＝3，x i 1 ,x i 2 ,…，x i γi Representing safety, sub-safety and danger, each local function is a set of polynomial distributions, one distribution corresponding to pa i One component of (i.e., (8)
Wherein, theta ij ＝(θ ij1 ,θ ij1 ,...,θ ijn ),(i＝1,2，…，n；j＝1,2，…，q i ；k＝1,2，…，γ i )；
To theta ij The a priori distribution of (2) is Dirichlet distribution (9): dir (theta) ij |a ij1 ,a ij2 ,…,a ijγi ) (ii) a Wherein, a ij1 ,a ij2 ,…,a ijγi Parameters in a Dirichlet distribution;
the posterior distribution obtained was (10):
Using a linear loss function, to obtain θ ij The estimated value of (1) is (11):
with the increase of the number of samples, the proportion of prior parameters in the parameters is reduced, the weight of the samples is increased, when the prior distribution uses an average distribution between 0 and 1 to replace a Dirichlet distribution, the bayesian estimation is degraded into a maximum likelihood estimation, and under the condition of large samples, the conditional probability can be estimated by the following simple formula (12):
wherein count (x) i ^pa i j ) The representation statistics result that the father node is located at pa i j Under the condition that the node is at x i Number of samples under the conditions, count (pa) i j ) Indicates that the parent node is at pa i j Number of samples under conditions. Equation (12) indicates that the parent node is at pa i j Under the condition that the node is at x i Probability value under the condition.
With further reference to fig. 2, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of an aircraft landing gear safety diagnostic device, which correspond to those illustrated in fig. 1, and which may be particularly applicable in various electronic devices.
Some embodiments of the present disclosure provide an aircraft landing gear safety diagnostic device implemented on a landing gear system including a plurality of subsystems and a plurality of elements in each subsystem, the device comprising:
the failure rate obtaining unit S110 is used for extracting historical data of the aircraft landing gear system and obtaining the failure rate of each element in each subsystem according to a preset proportional risk model;
a probability evaluation unit S120, configured to calculate probability values of each subsystem corresponding to multiple evaluation states according to a preset algorithm for the obtained failure rate of each element in each subsystem, and combine the multiple probability values calculated in each subsystem into corresponding evaluation values; wherein the plurality of evaluation states include a safe state, a less safe state, and a hazardous state; the evaluation value comprises a probability value of a safety state, a probability value of a sub-safety state and a probability value of a dangerous state which are set in sequence;
a probability evaluation revision unit S130, configured to obtain an actual fault state of each subsystem, and revise the evaluation value of each subsystem according to the obtained actual fault state of each subsystem; wherein the actual fault condition includes a condition in which a defect has occurred and a condition in which a fault has occurred;
a bayesian network establishing unit S140, configured to associate subsystems and establish a bayesian network according to a physical subsystem relationship of the aircraft landing gear system that affects system faults and a possible logical principle;
and the system safety degree determining module S150 is used for obtaining the conditional probability of the aircraft landing gear system corresponding to each evaluation state by adopting a Bayesian formula according to the established Bayesian network and the revised evaluation values of the subsystems, and determining the system safety degree of the aircraft landing gear system according to the conditional probability of each evaluation state obtained by the aircraft landing gear system.
Each subsystem comprises a brake subsystem, a front wheel turning subsystem, a wheel subsystem, a power system, a damping subsystem, a retraction subsystem and the like.
The above embodiments of the present disclosure have the following advantages: in the embodiment of the invention, a complex undercarriage system is decomposed into a plurality of subsystems and elements for analysis by adopting a proportional risk model, the probability of each subsystem corresponding to a safe state, a sub-safe state and a dangerous state is evaluated, the probability obtained by revising each subsystem according to an actual fault state is further accurately determined, and therefore, various information sources can be fused, the safety value of the undercarriage system is quantized by combining the complexity of the undercarriage system, and the safety of the undercarriage system is accurately and quickly evaluated.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
Claims (10)
1. An aircraft landing gear safety diagnostic method, comprising:
s101, extracting historical data of the aircraft landing gear system, and obtaining the failure rate of each element in each subsystem according to a preset proportional risk model;
step S102, calculating the failure rate of each element in each subsystem according to a preset algorithm, wherein the probability values of each subsystem respectively correspond to a plurality of evaluation states, and combining the plurality of probability values calculated in each subsystem into corresponding evaluation values; wherein the plurality of evaluation states include a safe state, a less safe state, and a hazardous state; the evaluation value comprises a probability value of a safety state, a probability value of a sub-safety state and a probability value of a dangerous state which are set in sequence;
step S103, acquiring the actual fault state of each subsystem, and revising the evaluation value of each subsystem according to the acquired actual fault state of each subsystem; wherein the actual fault condition includes a condition in which a defect has occurred and a condition in which a fault has occurred;
step S104, associating the subsystems and establishing a Bayesian network according to the physical subsystem relationship of the aircraft landing gear system which influences the system fault and possible logic principles;
and S105, obtaining the conditional probability of the aircraft landing gear system corresponding to each evaluation state by adopting a Bayesian formula according to the established Bayesian network and the revised evaluation values of the subsystems, and determining the system safety of the aircraft landing gear system according to the conditional probability of each evaluation state obtained by the aircraft landing gear system.
2. The aircraft landing gear safety diagnostic method according to claim 1, wherein the step S101 further comprises:
extracting real-time online detection data and regular preventive experimental data of each element in each subsystem of the aircraft landing gear, and obtaining the failure rate of each element in each subsystem according to the following formula:
wherein h (t, Z) is the failure rate of the current element, is not only related to time, but also depends on the value of the variable Z; eta is a scale function of Weibull distribution; β is the shape function of the weibull distribution; z is the covariate vector of the current element, and the object of the covariate vector can be (Z) according to the data which can be actually collected under the current working condition of the aircraft landing gear system 1 ，z 2 ...z n ) Composition of each of z i Representing a state variable, wherein the state variable comprises the accumulated switching times, the collecting and releasing times and the like of the current element; and gamma is a vector with the same length as Z, is a regression parameter of the covariate and reflects the influence of the covariate on the fault rate.
3. The aircraft landing gear safety diagnostic method according to claim 1, wherein the step S102 further comprises:
substep 1: obtaining a safety value of each element in each subsystem according to the obtained failure rate of each element in each subsystem;
a substep 2, determining the sampling times m by adopting a termination criterion of a Monte Carlo method; wherein m is a positive integer;
substep 3: sampling is performed to generate a signal at [0,1 ]]When N is less than R i When S is present i When N is greater than or equal to R, 1 i When S is present i ＝0；
Comparing the determined safety value of each element in each subsystem with the current random number N, and converting the safety value into corresponding 0 or 1; wherein S is i The state of the current element is represented by i, which is a natural number and is less than or equal to the total number of elements in the subsystem to which the current element belongs; 1 represents the working state; 0 represents a fault condition; ri is the security value of the current element.
4. The aircraft landing gear safety diagnostic method according to claim 1, wherein the step S103 further comprises:
when the actual failure state of the one or more subsystems is acquired as a state in which a defect has occurred, revising the acquired evaluation values of the one or more subsystems to (0, 0.5, 0.5); when the actual failure state of the one or more subsystems is acquired as a state in which a failure has occurred, the acquired evaluation values of the one or more subsystems are revised to (0, 0.1, 0.9).
5. The aircraft landing gear safety diagnostic method according to claim 1, wherein the step S104 further comprises:
the logic principle comprises the structural dependency of internal subsystems of the aircraft landing gear system, and comprises the steps of constructing a Bayesian network by taking the aircraft landing gear system as a root node and each subsystem as a child node.
6. The aircraft landing gear safety diagnostic method according to claim 1, wherein the step S105 further comprises:
according to the established Bayesian network and the revised evaluation values of the subsystems, calculating the conditional probability of each father node in the Bayesian network by adopting a Bayesian formula, and determining the conditional probability respectively obtained when the aircraft landing gear system is used as a root node under a plurality of evaluation states according to the calculated conditional probability of each father node;
when the condition probability obtained by the aircraft landing gear system in a healthy state is detected to be maximum, determining that the system safety degree of the aircraft landing gear system is the highest;
when the condition probability obtained by the aircraft landing gear system in the sub-safe state is detected to be the maximum, determining that the system safety degree of the aircraft landing gear system is moderate;
and when the condition probability obtained by the aircraft landing gear system in the dangerous state is detected to be the maximum, determining that the system safety degree of the aircraft landing gear system is the lowest.
7. An aircraft landing gear safety diagnostic method according to claim 1 or claim 6, wherein the conditional probability is a probability that a child node in a Bayesian network is in a certain state at a parent node, and is calculated by:
assume that the variable set X ═ X (X) 1 ，X 2 ，...，X n ) May be encoded in a certain network structure S, each variable represents the state of a certain node in the network, such as a damping subsystem, which may be expressed as (7):
wherein in formula (7), θ s Is the distribution p (x) i |pa i ，θ s ，S n ) Parameter vector of (1), using θ i Representing a parameter set (theta) 1 ，θ 2 ，...,θ n ) Is a value of n Representing the current network architecture. pa is a i Representing the parent of this node.
Assuming each variable X i Is discrete, having γ i A possible value x i 1 ,x i 2 ,…，x i γi Here γ i ＝3，x i 1 ,x i 2 ,…，x i γi Representing safety, sub-safety and danger, each local function is a set of polynomial distributions, one distribution corresponding to pa i One component of (i.e., (8)
Wherein, theta ij ＝(θ ij1 ,θ ij1 ,...,θ ijn ),(i＝1,2，…，n；j＝1,2，…，q i ；k＝1,2，…，γ i )；
To theta ij The a priori distribution of (2) is Dirichlet distribution (9): dir (theta) ij |a ij1 ,a ij2 ,…,a ijγi ) (ii) a Wherein, a ij1 ,a ij2 ,…,a ijγi Parameters in a Dirichlet distribution;
the posterior distribution obtained was (10):
Using linear lossesFunction of to obtain theta ij The estimated value of (1) is (11):
with the increase of the number of samples, the proportion of prior parameters in the parameters is reduced, the weight of the samples is increased, when the prior distribution uses an average distribution between 0 and 1 to replace a Dirichlet distribution, the bayesian estimation is degraded into a maximum likelihood estimation, and under the condition of large samples, the conditional probability can be estimated by the following simple formula (12):
wherein count (x) i ^pa i j ) The representation statistics result that the father node is located at pa i j Under the condition that the node is at x i Number of samples under the conditions, count (pa) i j ) Indicates that the parent node is at pa i j Number of samples under conditions. Equation (12) indicates that the parent node is at pa i j Under the condition that the node is at x i Probability value under the condition.
8. An aircraft landing gear safety diagnostic method according to claim 1, wherein each subsystem comprises a brake subsystem, a nose wheel steering subsystem, a wheel subsystem, a power system, a shock absorption subsystem, a retraction subsystem and the like, and the subsystems of the shock absorption subsystem comprise a shock absorption strut system and a torsion linkage system.
9. An aircraft landing gear safety diagnostic device, comprising:
the failure rate acquisition unit is used for extracting historical data of the aircraft landing gear system and obtaining the failure rate of each element in each subsystem according to a preset proportional risk model;
the probability evaluation unit is used for calculating the obtained failure rate of each element in each subsystem according to a preset algorithm to obtain probability values of each subsystem under a plurality of evaluation states respectively, and combining the plurality of probability values calculated in each subsystem into corresponding evaluation values respectively; wherein the plurality of evaluation states include a safe state, a less safe state, and a hazardous state; the evaluation value comprises a probability value of a safety state, a probability value of a sub-safety state and a probability value of a dangerous state which are set in sequence;
the probability evaluation revising unit is used for acquiring the actual fault state of each subsystem and revising the evaluation value of each subsystem according to the acquired actual fault state of each subsystem; wherein the actual fault condition includes a condition in which a defect has occurred and a condition in which a fault has occurred;
the system comprises a Bayesian network establishing unit, a Bayesian network establishing unit and a data processing unit, wherein the Bayesian network establishing unit is used for associating subsystems and establishing a Bayesian network according to the physical subsystem relationship of an aircraft landing gear system which influences system faults and possible logic principles;
and the system safety degree determining module is used for obtaining the conditional probability of the aircraft landing gear system corresponding to each evaluation state by adopting a Bayesian formula according to the established Bayesian network and the revised evaluation value of each subsystem, and determining the system safety degree of the aircraft landing gear system according to the conditional probability of each evaluation state obtained by the aircraft landing gear system.
10. An aircraft landing gear safety diagnostic device according to claim 9, wherein the subsystems include a brake subsystem, a nose wheel steering subsystem, a wheel subsystem, a power system, a shock absorption subsystem, a retraction subsystem, and the like, and the shock absorption subsystem includes a shock absorption strut system and a torsion linkage system.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210543945.2A CN115071999A (en) | 2022-05-18 | 2022-05-18 | Aircraft landing gear safety diagnostic method and device |
| JP2023061907A JP7351050B1 (en) | 2022-05-18 | 2023-04-06 | Safety diagnosis method and device for airplane landing gear |
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| CN202210543945.2A CN115071999A (en) | 2022-05-18 | 2022-05-18 | Aircraft landing gear safety diagnostic method and device |
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| CA2487704A1 (en) | 2004-11-18 | 2006-05-18 | R. Kyle Schmidt | Method and system for health monitoring of aircraft landing gear |
| GB0517351D0 (en) | 2005-08-24 | 2005-10-05 | Airbus Uk Ltd | Landing load monitor for aircraft landing gear |
| GB2496386A (en) | 2011-11-08 | 2013-05-15 | Ge Aviat Systems Ltd | Method for integrating models of a vehicle health management system |
| CN114329782A (en) | 2021-12-30 | 2022-04-12 | 中国商用飞机有限责任公司 | Method for verifying aircraft landing gear recovery interval |
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