CN112046761B - Airplane icing on-line detection method based on statistical test and filtering - Google Patents
Airplane icing on-line detection method based on statistical test and filtering Download PDFInfo
- Publication number
- CN112046761B CN112046761B CN202010771628.7A CN202010771628A CN112046761B CN 112046761 B CN112046761 B CN 112046761B CN 202010771628 A CN202010771628 A CN 202010771628A CN 112046761 B CN112046761 B CN 112046761B
- Authority
- CN
- China
- Prior art keywords
- icing
- data
- aircraft
- flight
- overload
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D15/00—De-icing or preventing icing on exterior surfaces of aircraft
- B64D15/20—Means for detecting icing or initiating de-icing
Abstract
The invention discloses an on-line detection method for aircraft icing based on statistical test and filtering, which at least comprises the following steps: s1: acquiring flight state measurement data, engine thrust data and rudder deflection input data in the flight process; s2: detecting the icing starting moment by using a generalized likelihood ratio test method; s3: generating a disturbance signal superimposed on the rudder deflection input data while using H∞The filtering method carries out combined state estimation and icing-influenced pneumatic derivative identification on the flight state measurement data; s4: detecting the icing finishing time by using a generalized likelihood ratio test method; s5: stopping generating and superposing rudder deviation disturbance signals and continuously utilizing H∞The filtering carries out joint state estimation and icing-influenced pneumatic derivative identification on the measured data until the flight is finished. By combining the rapid icing detection method with the parameter estimation method, the aircraft icing detection algorithm with stronger function is obtained.
Description
Technical Field
The invention belongs to the technical field of online detection of aircraft icing, and particularly relates to an online detection method of aircraft icing based on statistical inspection and filtering.
Background
The online detection of the airplane icing means that the icing phenomenon generated in the flying process of the airplane is detected through a detection device or a detection method, and an icing alarm is given to start an anti-icing and deicing device of the airplane, so that the harm of the icing in the flying process to the safety of the airplane is avoided.
The icing on-line detection generally comprises two categories, namely hardware detection and algorithm detection, wherein the hardware detection is to detect icing parts, icing type parameters and the like through hardware devices such as sensors arranged on the surface of the airplane body, and the algorithm detection is to utilize a mathematical method and a physical model to carry out on-line estimation on the aerodynamic characteristic parameters of the airplane affected by icing through flight measurement data so as to obtain the key parameter information of the airplane icing. Compared with algorithm detection, hardware detection needs to arrange a large number of sensing devices on a machine body, so that the development cost of an airplane is generally increased, the problem that the sensor devices are not suitable to be arranged or are not sufficiently arranged exists, and the detection effect of icing hardware is greatly influenced; the online detection of the algorithm can greatly save development cost, the algorithm detection gets rid of the limitation of a sensor, the icing abnormity is found by estimating the characteristic parameters of the airplane body, and the online detection method has better adaptability and flexibility.
Aircraft icing algorithm detection is generally focused on the study of detection algorithms, and is less studied for some specific problems applied to flight procedures. Such as how to ensure the accuracy of the on-line estimation of parameters during flight, how to detect icing, how to add input disturbance signals at appropriate times, etc. Therefore, the icing on-line detection research is needed to be developed based on the actual flight process, the specific problems in algorithm detection are solved, and the airplane icing algorithm detection capability is improved.
Disclosure of Invention
The invention aims to provide a feasible aircraft icing on-line detection method based on statistical test and filtering, aiming at overcoming the technical problem of aircraft icing algorithm detection in actual flight application in the prior art. By combining the rapid icing detection method with the parameter estimation method, a powerful airplane icing detection algorithm is obtained; by adding the input disturbance signal at a proper time, a more accurate aerodynamic characteristic parameter estimation result is obtained under the condition of ensuring that the influence of the input disturbance on an aircraft flight system is small.
The purpose of the invention is realized by the following technical scheme:
an aircraft icing on-line detection method based on statistical test and filtering is characterized by at least comprising the following steps:
s1: acquiring flight state measurement data, engine thrust data and rudder deflection input data in the flight process;
s2: detecting the icing starting moment by using a generalized likelihood ratio test method;
s3: generating a disturbance signal superimposed on the rudder deflection input data while using H∞The filtering method carries out combined state estimation and icing-influenced pneumatic derivative identification on the flight state measurement data;
s4: detecting the icing finishing time by using a generalized likelihood ratio test method;
s5: stopping generating and superposing rudder deviation disturbance signals and continuously utilizing H∞The filtering carries out joint state estimation and icing-influenced pneumatic derivative identification on the measured data until the flight is finished.
According to a preferred embodiment, in step S1, the flight state measurement data at least includes position, speed, overload, angular speed, attitude angle and dynamic pressure data of the aircraft.
According to a preferred embodiment, said step S2 includes: sequentially extracting data segments from flight state measurement data to calculate overload margins, and judging whether the statistical characteristics of each segment of overload margins exceed a given threshold value by using a generalized likelihood ratio test method to judge the margins rzWhether or not an offset occurs, when rzAnd judging that icing has occurred if the deviation except the random noise exists, and taking the end time of the section of data as the icing start time.
According to a preferred embodiment, the calculation of the overload margin in step S2 includes: extracting a data segment of a given detection window length from the data collected in step S1, calculating an aircraft normal overload margin r from the difference between the estimated overload and the measured overloadz，
Wherein N iszmIn order to measure the resulting normal overload,
to estimate the normal overload, and,
m is the aircraft mass, S is the aircraft characteristic area, g0Is the gravity acceleration constant, q, corresponding to sea level∞For measuring dynamic pressure, alpha is for measuring angle of attack, PzIn order to measure the normal thrust force,
and
the coefficient of drag and lift for the aircraft under the ice-free condition is obtained by interpolation of a ground test database or by calculation of a pneumatic derivative in combination with flight state parameters.
According to a preferred embodiment, in step S3, the generated disturbing signal is a "3211" disturbing signal.
According to a preferred embodiment, said step S4 includes: by aerodynamically stable derivative C of pitch directionmαThe identification result of (2) carries out convergence judgment to give the end time of icing.
According to a preferred embodiment, the step S4 specifically includes: first from H∞Sequentially extracting C with given window length from the identification resultmαIdentification result using two adjacent CmαCalculating detection margin r of identification result data segmentCmα，
i is 1,2, …, n, wherein i represents C extracted from the ith segmentmαIdentifying the result, reverse utilizing generalized likelihood ratio test, judging r when the margin statistical characteristic is less than or equal to the given threshold valueCmαIf there is no offset, then CmαThe identification result of (1) is converged, the icing accumulation phase is ended, and the end time of the (i + 1) th data segment is taken as the icing end time.
The main scheme and the further selection schemes can be freely combined to form a plurality of schemes which are all adopted and claimed by the invention; in the invention, the selection (each non-conflict selection) and other selections can be freely combined. The skilled person in the art can understand that there are many combinations, which are all the technical solutions to be protected by the present invention, according to the prior art and the common general knowledge after understanding the scheme of the present invention, and the technical solutions are not exhaustive herein.
The invention has the beneficial effects that:
1. the invention discloses an on-line detection method for aircraft icing, which is a generalized likelihood ratio method andH∞the filtering method is combined, so that the change of the pneumatic derivative influenced by icing accumulation can be obtained while icing early warning is obtained, and compared with a single method, the algorithm can obtain more icing related information and has stronger online airplane icing detection capability;
2. the invention solves the problem of determining the action time of the input disturbance signal in the flight process, and the input disturbance signal is very important for ensuring the identification precision of the pneumatic derivative of the airplane in the icing accumulation process, so the solution provided by the invention can superpose the small disturbance signal of '3211' particularly in the icing accumulation process, can avoid unnecessary or meaningless input disturbance, and can reduce the disturbance of the disturbance signal to the flight system as much as possible.
3. The invention provides a method for judging the end of icing, which can obtain the conclusion whether icing is ended or not by judging whether the identification result of a pneumatic derivative affected by icing is converged or not, wherein the convergence is judged by reversely adopting a generalized likelihood ratio test method, and the method provides a basis for accurately applying a small-disturbance input signal.
Drawings
FIG. 1 is a flow chart of an online detection method for aircraft icing according to the present invention;
FIG. 2 is a diagram of the results of the on-line detection of icing on a flat aircraft wing in an embodiment of the invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that, in order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments.
Thus, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. 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.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations and positional relationships that are conventionally used in the products of the present invention, and are used merely for convenience in describing the present invention and for simplicity in description, but do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, it should be noted that, in the present invention, if the specific structures, connection relationships, position relationships, power source relationships, and the like are not written in particular, the structures, connection relationships, position relationships, power source relationships, and the like related to the present invention can be known by those skilled in the art without creative work on the basis of the prior art.
Example 1:
referring to fig. 1, the invention discloses an aircraft icing on-line detection method based on statistical test and filtering, which is characterized by at least comprising the following steps:
step S1: and acquiring flight state measurement data, engine thrust data and rudder deflection input data in the flight process.
Preferably, in step S1, the flight state measurement data at least includes position, speed, overload, angular speed, attitude angle and dynamic pressure data of the aircraft.
Step S2: and detecting the icing starting moment by using a generalized likelihood ratio test method.
Preferably, the step S2 includes: sequentially extracting data segments from flight state measurement data to calculate overload margins, and judging whether the statistical characteristics of each segment of overload margins exceed a given threshold value by using a generalized likelihood ratio test method to judge the margins rzWhether or not an offset occurs, when rzAnd judging that icing has occurred if the deviation except the random noise exists, and taking the end time of the section of data as the icing start time.
Further, the calculation process of the overload margin in step S2 includes: extracting a data segment of a given detection window length from the data collected in step S1, calculating an aircraft normal overload margin r from the difference between the estimated overload and the measured overloadz，
Wherein N iszmIn order to measure the resulting normal overload,
to estimate normal overload, and，
m is the aircraft mass, S is the aircraft characteristic area, g0Is the gravity acceleration constant, q, corresponding to sea level∞For measuring dynamic pressure, alpha is for measuring angle of attack, PzIn order to measure the normal thrust force,
and
the coefficient of drag and lift for the aircraft under the ice-free condition is obtained by interpolation of a ground test database or by calculation of a pneumatic derivative in combination with flight state parameters.
Step S3: generating a disturbance signal superimposed on the rudder deflection input data while using H∞The filtering method carries out combined state estimation and icing-influenced pneumatic derivative identification on the flight state measurement data.
Preferably, in the step S3, the generated disturbance signal is a "3211" disturbance signal.
The method solves the problem of determining the action time of the input disturbance signal in the flight process, and the input disturbance signal is very important for ensuring the identification precision of the pneumatic derivative of the airplane in the icing accumulation process.
Step S4: and detecting the icing ending moment by using a generalized likelihood ratio test method.
Preferably, the step S4 includes: by aerodynamically stable derivative C of pitch directionmαThe identification result of (2) carries out convergence judgment to give the end time of icing.
Further, the step S4 specifically includes: first from H∞Sequentially extracting C with given window length from the identification resultmαIdentification result using two adjacent CmαIdentification result data segment calculationDetection margin rCmα，
i is 1,2, …, n, wherein i represents C extracted from the ith segmentmαIdentifying the result, reverse utilizing generalized likelihood ratio test, judging r when the margin statistical characteristic is less than or equal to the given threshold valueCmαIf there is no offset, then CmαThe identification result of (1) is converged, the icing accumulation phase is ended, and the end time of the (i + 1) th data segment is taken as the icing end time.
Namely, the conclusion whether icing is finished or not can be obtained by judging whether the identification result of the pneumatic derivative affected by icing is converged or not, wherein the convergence is judged by reversely adopting a generalized likelihood ratio test method, and the method provides a basis for accurately applying a small-disturbance input signal.
Step S5: stopping generating and superposing rudder deviation disturbance signals and continuously utilizing H∞The filtering carries out joint state estimation and icing-influenced pneumatic derivative identification on the measured data until the flight is finished.
Example 1
The method is used for carrying out icing on-line detection aiming at the longitudinal flight simulation data of a horizontal cruise section of a certain airplane in consideration of the icing accumulation process at the leading edge of the wing, and the specific flow is shown in figure 1.
The cruising altitude of the airplane is 5000m, the cruising speed is 0.3 Mach, the trim elevator deflection angle is 1.06 degrees, the attitude angle and the angular rate are kept to be zero in the cruising process, and if the leading edge of the wing begins to freeze 100s after the simulation begins, the icing accumulation time is Tcld200s, the cumulative velocity is given byice(Tcld) 1.0 and ηice(TcldAnd/2) is described as 0.7.
The concrete implementation steps of the calculation example are as follows:
the method comprises the following steps: flight data required to be acquired for online detection are acquired according to simulation of a longitudinal flight dynamics model of the airplane, change of aerodynamic derivatives under icing of wings is considered, and Gaussian white noise is added to a simulation result to serve as measurement noise.
Step two: will simulateThe sampling frequency of the data is 10ms, the length of a detection window is 5s, namely 500 data points are sequentially extracted from the simulation data, and the sampling frequency is utilized
Estimating normal overload N for each sampling pointzIs further compared with NzSubtracting the measured values to obtain the normal overload allowance r corresponding to each sampling pointz。
Step three: calculating mean value of overload margin by using generalized likelihood ratio method
Sum mean square error
To obtain a statistical test quantity Tx，
Where N is the number of data points, the false alarm probability PFAThe small amount is 1e-6, and the judgment threshold gamma is 23.93 which can be calculated by chi-square distribution.
The method of the invention combines a generalized likelihood ratio method with H∞The filtering method is combined, the change of the pneumatic derivative influenced by icing accumulation can be obtained while icing early warning is obtained, and compared with a single method, the algorithm can obtain more icing related information and has stronger airplane icing online detection capability.
Step four: the statistical test quantity T of each extracted data segmentxComparing with a decision threshold gamma if TxIf the value is less than or equal to gamma, returning to the step two, and considering that no icing phenomenon occurs, the pneumatic derivative affected by icing is not identified, and the pneumatic derivative of the airplane is the same as the non-icing state and is kept unchanged; if TxAnd if the data segment is more than gamma, judging that the icing is started, and taking the end time of the data segment as the icing start time.
Step five: when the icing is judged to be started, a small disturbance signal of '3211' with the amplitude of 5 degrees and the period of 5s is generated and is superposed on the matchingWhen the flight simulation result is input to an elevator rudder deflection signal in a flat mode, due to the influence of rudder deflection disturbance, the flight simulation result can generate periodic oscillation so as to be convenient for identifying the change of a pneumatic derivative; by simultaneous use of H∞The filtering method carries out state estimation and pneumatic derivative identification on the flight simulation output data, and obtains the state variable and C of the airplane by reading in the flight simulation output data point by pointDα、CLαAnd CmαEstimation of three aerodynamically stable derivatives that are more severely affected by icing; after judging the onset of icing, it is also necessary to aim at CmαCarrying out convergence judgment detection on the identification result to determine the icing finishing time, setting the length of a detection window to be 5s, and setting 500CmαSequentially extracting identification data, subtracting the front and rear data sections to obtain detection allowance, and obtaining the statistical detection quantity T of each section of detection allowance by using the method in step threex。
Step six: the statistical detection quantity T of the identification result marginxComparing with a decision threshold gamma if TxIf the deviation is larger than gamma, returning to the step five, and if the deviation is still in the icing accumulation stage, continuously superposing the small rudder disturbance signal and identifying the pneumatic derivative; if TxIf gamma is less than or equal to gamma, then C is judgedmαThe data segment is taken as the icing ending time when the icing accumulation process is ended and the ending time of the data segment is taken as the icing ending time, the generation of the small disturbance rudder deflection signal is stopped, and H is continuously utilized∞And (4) filtering and estimating a system state variable and identifying three aerodynamic stability derivatives until the flight process is finished.
Namely, the conclusion whether icing is finished or not can be obtained by judging whether the identification result of the pneumatic derivative affected by icing is converged or not, wherein the convergence is judged by reversely adopting a generalized likelihood ratio test method, and the method provides a basis for accurately applying a small-disturbance input signal.
Aiming at the wing icing calculation example of the level flight cruise section, the steps are adopted, the icing starting time obtained through final calculation is 174.99s, the icing finishing time is 304.99s, and the C is divided by three aerodynamic stability derivatives obtained through identificationDαThe oscillations are more pronounced due to measurement noise, CLαAnd CmαRoot mean square of relative truth of identification resultThe errors are both small, 8.15% and 7.78% of the true value, respectively, and follow the ice accumulation process well, as shown in fig. 2, where: etaiceIs the pneumatic derivative normalization result and represents the icing severity coefficient, t is time, CLαFor stable derivatives of lift, CmαFor steady derivative of pitching moment, tice0For the onset of icing, tice1Rms represents the root mean square error for the end of icing time. The result shows that the method has the capability of detecting the beginning and the end of icing and the capability of tracking the change of the aerodynamic derivative of the icing accumulation process, and can be conveniently popularized to similar online detection of the icing in the flight process of the airplane.
The foregoing basic embodiments of the invention and their various further alternatives can be freely combined to form multiple embodiments, all of which are contemplated and claimed herein. In the scheme of the invention, each selection example can be combined with any other basic example and selection example at will. Numerous combinations will be known to those skilled in the art.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. An aircraft icing on-line detection method based on statistical test and filtering is characterized by at least comprising the following steps:
s1: acquiring flight state measurement data, engine thrust data and rudder deflection input data in the flight process;
in step S1, the flight status measurement data at least includes position, speed, overload, angular speed, attitude angle and dynamic pressure data of the aircraft
S2: detecting the icing starting moment by using a generalized likelihood ratio test method;
the step S2 includes:
sequentially extracting data segments from flight state measurement data to calculate overload margin, and judging by using generalized likelihood ratio test methodJudging whether the statistical characteristic of each overload margin exceeds a given threshold value to judge the margin rzWhether or not a shift has occurred is determined,
when r iszJudging that icing occurs if offset except random noise exists, and taking the ending time of the section of data as the icing starting time;
the calculation process of the overload margin in step S2 includes:
extracting a data segment of a given detection window length from the data collected in step S1, calculating an aircraft normal overload margin r from the difference between the estimated overload and the measured overloadz，
and the number of the first and second electrodes,
m is the aircraft mass, S is the aircraft characteristic area, g0Is the gravity acceleration constant, q, corresponding to sea level∞For measuring dynamic pressure, alpha is for measuring angle of attack, PzIn order to measure the normal thrust force,
and
the drag and lift coefficients of the aircraft under the ice-free condition are obtained by interpolation of a ground test database or calculation by combining pneumatic derivative with flight state parameters;
s3: generating a disturbance signal superimposed on the rudder deflection input data while using H∞The filtering method carries out combined state estimation and icing-influenced pneumatic derivative identification on the flight state measurement data;
s4: detecting the icing finishing time by using a generalized likelihood ratio test method;
s5: stopping generating and superposing rudder deviation disturbance signals and continuously utilizing H∞The filtering carries out joint state estimation and icing-influenced pneumatic derivative identification on the measured data until the flight is finished.
2. The method for detecting icing condition of an aircraft according to claim 1, wherein the disturbance signal generated in step S3 is a "3211" disturbance signal.
3. An aircraft icing on-line detection method based on statistical test and filtering according to claim 1, wherein the step S4 includes: by aerodynamically stable derivative C of pitch directionmαThe identification result of (2) carries out convergence judgment to give the end time of icing.
4. The method for detecting icing on-line of an aircraft based on statistical testing and filtering as claimed in claim 3, wherein said step S4 specifically comprises:
first from H∞Sequentially extracting C with given window length from the identification resultmαIdentification result using two adjacent CmαCalculating detection margin r of identification result data segmentCmα，
Wherein i represents C extracted from the ith segmentmαIdentifying the result, reverse utilizing generalized likelihood ratio test, judging r when the margin statistical characteristic is less than or equal to the given threshold valueCmαIf there is no offset, then CmαThe identification result of (1) is converged, the icing accumulation phase is ended, and the end time of the (i + 1) th data segment is taken as the icing end time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010771628.7A CN112046761B (en) | 2020-08-04 | 2020-08-04 | Airplane icing on-line detection method based on statistical test and filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010771628.7A CN112046761B (en) | 2020-08-04 | 2020-08-04 | Airplane icing on-line detection method based on statistical test and filtering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112046761A CN112046761A (en) | 2020-12-08 |
| CN112046761B true CN112046761B (en) | 2021-10-15 |
Family
ID=73602413
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010771628.7A Active CN112046761B (en) | 2020-08-04 | 2020-08-04 | Airplane icing on-line detection method based on statistical test and filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112046761B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113777931B (en) * | 2021-11-09 | 2022-02-11 | 中国空气动力研究与发展中心计算空气动力研究所 | Icing wing type pneumatic model construction method, device, equipment and medium |
| CN114625159B (en) * | 2022-01-21 | 2023-07-28 | 中国空气动力研究与发展中心计算空气动力研究所 | Icing aircraft control method based on controlled variable |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5028929A (en) * | 1990-04-30 | 1991-07-02 | University Corporation For Atmospheric Research | Icing hazard detection for aircraft |
| US6490510B1 (en) * | 1999-04-30 | 2002-12-03 | Thales Avionics S.A. | Fixed multifunction probe for aircraft |
| US6757668B1 (en) * | 1999-11-05 | 2004-06-29 | General Electric Company | Information fusion of classifiers in systems with partial redundant information |
| EP2829473A1 (en) * | 2013-07-24 | 2015-01-28 | Air China Limited | Method and apparatus for monitoring a wing anti-icing valve |
| CN109911216A (en) * | 2017-12-12 | 2019-06-21 | 波音公司 | The system and method for hitting the position of the limit for correcting the water in aerofoil profile |
Family Cites Families (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5195046A (en) * | 1989-01-10 | 1993-03-16 | Gerardi Joseph J | Method and apparatus for structural integrity monitoring |
| FR2958622B1 (en) * | 2010-04-09 | 2013-05-03 | Airbus Operations Sas | METHOD AND SYSTEM FOR CONTROLLING ICE FORMATION ON A FLIGHT AIRCRAFT. |
| CN102313512B (en) * | 2010-07-02 | 2013-08-07 | 中国商用飞机有限责任公司 | Image icing detection method |
| GB201014244D0 (en) * | 2010-08-26 | 2010-10-13 | Airbus Uk Ltd | Laminar flow monitor |
| CN102139766B (en) * | 2010-10-28 | 2013-01-23 | 中国科学院力学研究所 | Method for obtaining design attack angle for reentry flight of suborbital vehicle |
| US8831884B2 (en) * | 2011-10-25 | 2014-09-09 | Rosemount Aerospace Inc. | Methods of determining water droplet size distributions of clouds |
| CN102890734A (en) * | 2012-09-18 | 2013-01-23 | 天津空中代码工程应用软件开发有限公司 | Method for building flight icing reduced-order model in flight simulator |
| US9612163B2 (en) * | 2013-10-10 | 2017-04-04 | The Boeing Company | Methods and apparatus for detecting ice formation on aircraft |
| US9297755B2 (en) * | 2013-10-24 | 2016-03-29 | The Regents Of The University Of Michigan | Ice and supercooled water detection system |
| CN104166348B (en) * | 2014-08-07 | 2016-08-17 | 北京理工大学 | A kind of dynamic stability decision method of single sliding block Moving mass control aircraft |
| CN105629725B (en) * | 2014-10-31 | 2019-01-08 | 北京临近空间飞行器***工程研究所 | A kind of elastic movement modeling method of rear rudder glide vehicle |
| EP3314120B1 (en) * | 2015-06-24 | 2023-06-07 | Vestas Wind Systems A/S | Blade load sensing system for a wind turbine |
| CN105389438A (en) * | 2015-11-25 | 2016-03-09 | 中国航空工业集团公司沈阳飞机设计研究所 | Gravity-center normal overload calculation method for ski-jump takeoff of aircraft |
| GB2554062A (en) * | 2016-08-22 | 2018-03-28 | Norwegian Univ Of Science And Technology | Icing control system |
| CN107127190B (en) * | 2017-05-31 | 2023-04-07 | 中国空气动力研究与发展中心低速空气动力研究所 | Active and passive anti-icing device and method with coupled super-hydrophobic material and vibration deicing |
| US20190039742A1 (en) * | 2017-08-01 | 2019-02-07 | Honeywell International Inc. | Managing response to icing threat |
| CN107702879B (en) * | 2017-09-20 | 2019-06-18 | 中国空气动力研究与发展中心计算空气动力研究所 | A kind of aircraft dynamic ice ice type microstructure features prediction technique |
| EP3704022A1 (en) * | 2017-10-30 | 2020-09-09 | Battelle Memorial Institute | Ice protection system and controller |
| CN107976296B (en) * | 2017-11-13 | 2019-10-22 | 北京临近空间飞行器***工程研究所 | A kind of aerodynamic characteristics of vehicle on-line identification method based on backtracking adaptive algorithm |
| FR3074145B1 (en) * | 2017-11-28 | 2022-04-29 | Zodiac Aerotechnics | METHOD FOR DETECTING THE PRESENCE OF ICE CRYSTALS IN AN ENVIRONMENT |
| US10160550B1 (en) * | 2017-12-21 | 2018-12-25 | United Technologies Corporation | Ice detection and real time ice estimation using innovative sensor data learning approaches |
| FR3077059B1 (en) * | 2018-01-24 | 2020-01-31 | Airbus Operations | METHOD FOR DETECTING ICING CONDITIONS FOR AN AIRCRAFT BY SUPERVISED AUTOMATIC LEARNING |
| KR101982071B1 (en) * | 2018-08-24 | 2019-05-24 | 주식회사 에그 (Egg) | method for autonomous icing protection apparatus |
| CN109878740B (en) * | 2019-03-25 | 2024-02-27 | 上海工程技术大学 | Anti-icing and deicing device and method for wings and tail wings of turboprop aircraft |
-
2020
- 2020-08-04 CN CN202010771628.7A patent/CN112046761B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5028929A (en) * | 1990-04-30 | 1991-07-02 | University Corporation For Atmospheric Research | Icing hazard detection for aircraft |
| US6490510B1 (en) * | 1999-04-30 | 2002-12-03 | Thales Avionics S.A. | Fixed multifunction probe for aircraft |
| US6757668B1 (en) * | 1999-11-05 | 2004-06-29 | General Electric Company | Information fusion of classifiers in systems with partial redundant information |
| EP2829473A1 (en) * | 2013-07-24 | 2015-01-28 | Air China Limited | Method and apparatus for monitoring a wing anti-icing valve |
| CN109911216A (en) * | 2017-12-12 | 2019-06-21 | 波音公司 | The system and method for hitting the position of the limit for correcting the water in aerofoil profile |
Non-Patent Citations (1)
| Title |
|---|
| 大飞机机翼结冰对飞行动力学特性影响研究;邵元培等;《飞行力学》;20180228;第36卷(第1期);12-19 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112046761A (en) | 2020-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2434296B1 (en) | Airspeed sensing system for an aircraft | |
| US6273370B1 (en) | Method and system for estimation and correction of angle-of-attack and sideslip angle from acceleration measurements | |
| CN103323624B (en) | Method and device for verifying the consistency of the measurements of the angle-of-attack probe of an aircraft | |
| CN112046761B (en) | Airplane icing on-line detection method based on statistical test and filtering | |
| US20070130096A1 (en) | Fault detection in artificial intelligence based air data systems | |
| US7257470B2 (en) | Fault isolation method and apparatus in artificial intelligence based air data systems | |
| CN108170127B (en) | A kind of fault detection method of UAV Flight Control System | |
| US20190193866A1 (en) | Method and assistance system for detecting a degradation of light performance | |
| CN106840093A (en) | A kind of detection method of unmanned plane during flying height, device and unmanned plane | |
| CN109885854A (en) | The real-time forecasting system of Flutter Boundaries and prediction technique based on arma modeling | |
| US20060212181A1 (en) | Method and apparatus for extending useful range of air data parameter calculation in flush air data systems | |
| US20110264308A1 (en) | Method And Device For Automatically Estimating An Air Speed Of An Aircraft | |
| CN111290428A (en) | Flight control system for an aircraft and method of controlling the same | |
| CN116127842A (en) | Post-fault flight envelope online prediction method based on radial basis-counter propagation neural network | |
| JPH08211082A (en) | Method and system for determining anemobaroclinometric parameter of flying aircraft | |
| EP3819641A2 (en) | Airflow angle, static and total pressure probe | |
| EP0629166A1 (en) | Aerodynamic pressure sensor systems | |
| RU2364846C1 (en) | Method for automated assessment of aircraft engines total thrust in process of flight | |
| JP2022032051A5 (en) | ||
| Siu et al. | Flight test results of an angle of attack and angle of sideslip calibration method using Output-Error optimization | |
| CN112528492A (en) | Fault detection method and device under wing damage condition | |
| RU2214582C1 (en) | Method establishing aerodynamic corrections to readings of pitot-static tubes | |
| WO2020141316A1 (en) | Improvements in or relating to angle of attack sensing | |
| Korsun et al. | Detection of dynamic errors in aircraft flight data | |
| Majeed et al. | Multi sensor data fusion based approach for the calibration of airdata systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |