CN107991880A - A kind of fuzzy recognition method of airplane operation state - Google Patents
A kind of fuzzy recognition method of airplane operation state Download PDFInfo
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- CN107991880A CN107991880A CN201711395082.4A CN201711395082A CN107991880A CN 107991880 A CN107991880 A CN 107991880A CN 201711395082 A CN201711395082 A CN 201711395082A CN 107991880 A CN107991880 A CN 107991880A
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- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
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Abstract
The invention discloses a kind of fuzzy recognition method of airplane operation state, the present invention uses Fuzzy Set Theory, the precise volume collected is subjected to Fuzzy processing, change into the fuzzy quantity expressed by degree of membership and fuzzy subset, so as to obtain by the fuzzy relation of conditional statement input language variable fuzzy subset and if output language variable fuzzy subset then expression, and then fuzzy reasoning is carried out to engine and airborne equipment state, realize state fuzzy diagnosis.This method can send data to backstage while aircraft manipulates and be shown, so that the monitoring aircraft state of real-time high-efficiency.
Description
Technical field
The invention belongs to fuzzy diagnosis field, and in particular to a kind of fuzzy recognition method of airplane operation state.
Background technology
The state recognition of the operating process such as the energization of aircraft, test run is whether the operation for judging trainee meets and safeguard hand
The fundamental of volume standard.To ensure the science of auto judge, accurate, reach easy-to-use, efficient Functional Design target, it is necessary to
Accomplish to judge and separated with operation, i.e., operate anyway, auto judge should be able to intelligently differentiate operation and each system of aircraft, hair
Motivation and airborne equipment status, realize full-automatic judge.Therefore, how to accurately identify engine and various set
Standby which kind of stable state or transition state of being in is to judge basis and the core of operating personnel's operational circumstances, and auto judge function is real
The emphasis and difficulties faced during existing.
The content of the invention
, can the purpose of the present invention is to overcome the above shortcomings and to provide a kind of fuzzy recognition method of airplane operation state
The auto judge airplane operation state of quickness and high efficiency.
In order to achieve the above object, the present invention comprises the following steps：
Step 1, with Fuzzy Set Theory, gathers engine speed N and relative speed variation NC, engine speed N and turns
It is real that fast change rate NC combinations electric switch state obtains engine real-time status S, engine speed N, relative speed variation NC and engine
When state S as precise volume；
Step 2, carries out Fuzzy processing by the precise volume collected, changes into and expressed by degree of membership and fuzzy subset
Fuzzy quantity, so as to obtain by conditional statement input language variable fuzzy subset and if output language variable fuzzy subset then
The fuzzy relation γ of expression；
Step 3, fuzzy reasoning is carried out the fuzzy relation γ engine and airborne equipment state with CF models, real
Present condition fuzzy diagnosis, you can obtain engine condition.
In step 2, the domain of input/output variable is first determined, then linguistic variable is quantified having determined that in domain,
Then linguistic variable assignment table is established by degree of membership μ (x)；Similarly, relative speed variation NC and engine real-time status S are determined
Quantizing factor and corresponding domain Y and Z on membership function mui (y) and μ (z).
In step 2, fuzzy relation γ is made up of some fuzzy condition statements, and each fuzzy condition statement determines
One fuzzy relation γi,
γ==[(NxNC)TxS],
In step 3, by fuzzy relation γ, composition rule establishes fuzzy control search table by inference.
In step 3, fuzzy reasoning is by the Bayes formula of fuzzy relation γ combination treatment conditions probability, is obtained
Wherein, E：Engine condition feature；Di：Engine condition, i=1,2 ... n；P(Di) it is in Di" priori " of state
Probability；P(E|Di)：In DiThe probability of E is presented；P(DiE)：E is characterized as state DiProbability.
If certain DiP (DiE) it is more than the threshold value of this feature, and remaining DjP (DjE) it is less than corresponding threshold value, j ≠ i,
In conjunction with the configured information of engine condition feature, you can identification engine condition.
Compared with prior art, the present invention uses Fuzzy Set Theory, and the precise volume collected is carried out Fuzzy processing,
The fuzzy quantity expressed by degree of membership and fuzzy subset is changed into, so as to obtain obscuring son by conditional statement input language variable
Collect the fuzzy relation of and if output language variable fuzzy subset then expression, so to engine and airborne equipment state into
Row fuzzy reasoning, realizes state fuzzy diagnosis.This method can send data to backstage while aircraft manipulates and be shown
Show, so that the monitoring aircraft state of real-time high-efficiency.
Embodiment
During test run, each moment state in which of engine is determined by test run personnel, be for system one not
When definite fuzzy quantity, such as test ground idle speed state performance, do not know, difference starts bicycle and motorcycle rotating speed into slow train
Shake is uncertain, and slow train rotating speed is not known under the conditions of varying environment, and the slow train rotating speed that different personnel are stopped is not known yet, can
See, engine real-time status S, engine real-time status S can be combined by input condition engine speed N and relative speed variation NC
Electric switch state judges；
The present invention comprises the following steps：
Step 1, with Fuzzy Set Theory, gathers engine speed N and relative speed variation NC, engine speed N and turns
Fast change rate NC combinations electric switch state obtains engine real-time status S；
Step 2, carries out Fuzzy processing by the precise volume collected, changes into and expressed by degree of membership and fuzzy subset
Fuzzy quantity, so as to obtain by conditional statement input language variable fuzzy subset and if output language variable fuzzy subset then
The fuzzy relation γ of expression；
First determine the domain of input/output variable, then linguistic variable is quantified having determined that in domain, then pass through
Degree of membership μ (x) establishes linguistic variable assignment table；Similarly, determine the quantization of relative speed variation NC and engine real-time status S because
Son and accordingly membership function mui (y) and μ (z) on domain Y and Z；
Fuzzy relation γ is made up of some fuzzy condition statements, and each fuzzy condition statement determines a fuzzy pass
It is γi, γ=[(N × NC)T× S],
Step 3, fuzzy reasoning is carried out the fuzzy relation γ engine and airborne equipment state with CF models, will
Composition rule establishes fuzzy control search table to fuzzy relation γ by inference, and fuzzy reasoning is by fuzzy relation γ combination treatment conditions
The Bayes formula of probability, obtain
Wherein, E：Engine condition feature；Di：Engine condition, i=1,2 ... n；P(Di) it is in Di" priori " of state
Probability；P(E|Di)：In DiThe probability of E is presented；P(DiE)：E is characterized as state DiProbability；
If certain DiP (DiE) it is more than the threshold value of this feature, and remaining DjP (DjE) it is less than corresponding threshold value, j ≠ i,
In conjunction with the configured information of engine condition feature, you can identification engine condition.
Embodiment：
With Fuzzy Set Theory, engine speed N and relative speed variation NC, engine speed N and rotation speed change are gathered
Rate NC combination electric switch states obtain engine real-time status S, first determine the domain of input/output variable, then exist to linguistic variable
Have determined that in domain and quantified, linguistic variable assignment table is then established by degree of membership μ (x)；Similarly, rotation speed change is determined
Membership function mui (y) and μ (z) on the quantizing factor and corresponding domain Y and Z of rate NC and engine real-time status S；
Table 1NSlow trainAssignment table
| Quantification gradation | -2 | -1 | 0 | 1 | 2 |
| PB (higher) | 0.1 | 0.5 | 0.7 | 1 | |
| PS (slightly higher) | 0.1 | 0.5 | 0.7 | 1 | 0.7 |
| 0 (normal) | 0.5 | 0.7 | 1 | 0.7 | 0.5 |
| NS (slightly lower) | 0.7 | 1 | 0.7 | 0.5 | 0.1 |
| NB (relatively low) | 1 | 0.7 | 0.5 | 0.1 |
Table 1 establishes linguistic variable assignment table for the degree of membership μ (x) by taking slow train as an example.
Based on operating personnel's experience, with reference to engine condition electric switch signal, one group is drawn by 12 fuzzy condition statement structures
Into judgment rule：
If N slow trains=NB and NC slow trains=NB and electric switches=... then S=PB
Or if N slow trains=NS and NC slow trains=NB and electric switches=... then S=PS
…
Each sentence all determines a fuzzy relation γ i.
γ=[(N × NC)T×S]
Judge in conjunction with other states, fuzzy condition statement shares 62, is respectively γ1, γ2..., γ62, total is fuzzy
Relation is：
After calculating fuzzy relation γ, based on push-pull picklingline, so that it may fuzzy control search table is established, further according to treatment conditions
The Bayes formula of probability, with threshold comparison, you can identification engine condition.
Claims (6)
1. a kind of fuzzy recognition method of airplane operation state, it is characterised in that comprise the following steps：
Step 1, with Fuzzy Set Theory, gathers engine speed N and relative speed variation NC, engine speed N and rotating speed become
Rate NC combination electric switch states obtain engine real-time status S, engine speed N, relative speed variation NC and the real-time shape of engine
State S is as precise volume；
Step 2, Fuzzy processing is carried out by the precise volume collected, changes into the mould expressed by degree of membership and fuzzy subset
Paste amount, so as to obtain by conditional statement input language variable fuzzy subset and if output language variable fuzzy subset then expression
Fuzzy relation γ；
Step 3, carries out fuzzy reasoning with CF models to the fuzzy relation γ engine and airborne equipment state, realizes shape
State fuzzy diagnosis, you can obtain engine condition, realize the fuzzy recognition method of airplane operation state.
A kind of 2. fuzzy recognition method of airplane operation state according to claim 1, it is characterised in that in step 2,
First determine the domain of input/output variable, then linguistic variable is quantified having determined that in domain, then pass through degree of membership μ
(x) linguistic variable assignment table is established；Similarly, the quantizing factor of relative speed variation NC and engine real-time status S and corresponding is determined
Membership function mui (y) and μ (z) on domain Y and Z.
A kind of 3. fuzzy recognition method of airplane operation state according to claim 1, it is characterised in that in step 2,
Fuzzy γ relations are made up of some fuzzy condition statements, and each fuzzy condition statement determines a fuzzy relation γi,
γ=[(N × NC)T× S],
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A kind of 4. fuzzy recognition method of airplane operation state according to claim 1, it is characterised in that in step 3,
By fuzzy relation γ, composition rule establishes fuzzy control search table by inference.
A kind of 5. fuzzy recognition method of airplane operation state according to claim 1, it is characterised in that in step 3,
Fuzzy reasoning is by the Bayes formula of fuzzy relation γ combination treatment conditions probability, is obtained
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Wherein, E：Engine condition feature；Di：Engine condition, i=1,2 ... n；P(Di) it is in Di" priori " probability of state；
P(E|Di)：In DiThe probability of E is presented；P(DiE)：E is characterized as state DiProbability.
6. the fuzzy recognition method of a kind of airplane operation state according to claim 5, it is characterised in that if certain DiP
(DiE) it is more than the threshold value of this feature, and remaining DjP (DjE) it is less than corresponding threshold value, j ≠ i is special in conjunction with engine condition
The configured information of sign, you can identification engine condition.
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2017
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| CN104238374A (en) * | 2014-09-18 | 2014-12-24 | 东南大学 | Fuzzy control method of low-temperature low-pressure environment test device of engine |
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Application publication date: 20180504 |