CN111047916A - Heavy landing risk identification method based on QAR curve area characteristics - Google Patents
Heavy landing risk identification method based on QAR curve area characteristics Download PDFInfo
- Publication number
- CN111047916A CN111047916A CN201911395844.XA CN201911395844A CN111047916A CN 111047916 A CN111047916 A CN 111047916A CN 201911395844 A CN201911395844 A CN 201911395844A CN 111047916 A CN111047916 A CN 111047916A
- Authority
- CN
- China
- Prior art keywords
- curve
- landing
- flight
- curve area
- qar
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0073—Surveillance aids
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/04—Anti-collision systems
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a heavy landing risk identification method based on QAR curve area characteristics, which comprises the following steps: s1: extracting QAR parameters required by risk identification of all flight segments of a single airplane; s2: carrying out data cleaning on the extracted QAR parameters; s3: based on S2, extracting QAR data of landing stages of all the legs; s4: constructing a mean value curve according to the vertical speed mean values of the fixed descending distances of all flight sections in the landing stage, and then extracting the curve area characteristics of each flight section; s5: constructing a loss function, and determining a curve area characteristic threshold value by combining the curve area characteristic of each flight segment; s6: and comparing the curve area characteristic of each flight segment with a curve area characteristic threshold, if the curve area characteristic is larger than the curve area characteristic threshold, determining that the risk of heavy landing exists, and otherwise, determining that the safety exists. According to the method, the calculation mode of the loss value is defined by establishing a vertical speed mean value curve and constructing a loss function, and then the heavy landing risk identification threshold value is obtained.
Description
Technical Field
The invention relates to the field of civil passenger plane heavy landing risk research, in particular to a heavy landing risk identification method based on QAR curve area characteristics.
Background
According to the data of major flight safety accidents of the Boeing company in 1959-2016, the approach and landing stages are the flight stages which are most prone to major safety accidents, and the incidence rate of accidents and unsafe events is obviously higher than that of other flight stages. The landing stage only accounts for 1% of the flight time on average, but the accident rate is as high as 24%.
In the safety events in the landing stage, heavy landing is one type of unsafe events which occur frequently, and the total number of unsafe events in the landing stage is about 20% since the heavy landing unsafe event 125 occurs in China civil aviation in 2006-2011. As a risk event, the heavy landing not only brings bad flight experience to passengers and damages the image of an airline company, but also accelerates the fatigue damage and even breakage of wings, landing gears and engine structures due to frequent heavy landing, increases the occurrence probability of landing safety accidents, brings huge economic loss to the airline company, and causes disastrous accident consequences when the situation is serious, thereby threatening the life safety of passengers.
Disclosure of Invention
In view of this, the present invention provides a heavy landing risk identification method based on the area characteristics of the QAR curve, and the method has high stability and is suitable for being popularized in the field.
The purpose of the invention is realized by the following technical scheme:
a heavy landing risk identification method based on QAR curve area characteristics is specifically as follows: s1: extracting QAR parameters required by risk identification of all flight segments of a single airplane;
s2: carrying out data cleaning on the extracted QAR parameters;
s3: based on S2, extracting QAR data of landing stages of all the legs;
s4: constructing a mean value curve according to the vertical speed mean values of the fixed descending distances of all flight sections in the landing stage, and then extracting the curve area characteristics of each flight section;
s5: constructing a loss function, and determining a curve area characteristic threshold value by combining the curve area characteristic of each flight segment;
s6: and comparing the curve area characteristic of each flight segment with a curve area characteristic threshold, and if the curve area characteristic is greater than the curve area characteristic threshold, determining that the risk of heavy landing exists, otherwise, determining that the safety exists.
Further, the S1 specifically includes:
s11: decoding and analyzing QAR parameters of all flight segments in the civil aircraft to obtain CSV files of all flight segments;
s12: extracting parameter data required by risk identification of CSV files of all the flight segments, wherein the parameter data comprise longitudinal load, radio altitude, engine rotating speed, longitudinal acceleration, airspeed, ground speed, vertical speed, flap state, slat state, undercarriage state, spoiler state, true altitude and pitch angle of the airplane.
Further, the S3 specifically includes:
s31: dividing flight phases according to the values of the parameter data, and extracting landing phase data;
s32: identifying the landing time point of the airplane through the landing gear state parameters in the landing stage data;
s33: in landing phase data of all legs, a time point t at which the radio altitude is 50ft is extractedstartJudging the grounding time point by the landing gear state, the longitudinal acceleration and the spoiler state parameter, and recording the grounding time point as tend；
S34: extracting [ t ] from all CSV filesstart，tend]Vertical velocity at all time points within the time period.
Further, the S4 specifically includes:
s41: according to the S34, each [ t ] of all the legs is interpolated by B splinesstart，tend]The vertical speed points in the time period are expanded to at least 50, so that the vertical speed points correspond to the flying height, and all flight periods generate respective single flight period vertical speed curves;
s42: calculate each of all legs [ t ]start，tend]Forming a vertical speed mean value curve by the mean value of every 1ft of vertical speed in a time period, wherein the vertical speed mean value curve is a vertical coordinate representing the flight height, and the horizontal coordinate representing the vertical speed mean value;
s43: comparing all the single-flight-section vertical speed curves with the vertical speed mean value curve, wherein the area of the area above the vertical speed mean value curve is a positive-value area, the area of the area below the vertical speed mean value curve is a negative-value area, and calculating the curve area characteristic of each flight section;
wherein the curve area characteristic is positive area-negative area.
Further, the S5 specifically includes:
s51: constructing a loss function, wherein the loss function is defined as:
the loss value is a multiplied by the false negative rate + b multiplied by the false positive rate;
wherein: the false positive rate is the number of the positive examples/the total number of the positive examples;
a and b are coefficients determined according to the severity of the corresponding misjudgment type respectively;
s52: setting different threshold values, judging positive and negative examples according to whether the curve area characteristics exceed the threshold values, if the curve area characteristics exceed the threshold values, judging the positive examples, and if the curve area characteristics do not exceed the threshold values, judging the negative examples.
S53: and calculating loss values under different thresholds according to the loss function, and taking the threshold with the minimum loss value as the threshold for identifying the characteristic re-landing risk.
The invention has the beneficial effects that:
based on QAR data of the airplane, the vertical speed mean curve is established, so that the vertical speed curve of a single flight segment is compared with the vertical speed mean curve to obtain the curve area characteristic, then the loss function is established, the calculation mode of the loss value is defined, and the re-landing risk identification threshold is further obtained.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a plot of the area of a sample curve.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
Example 1
The embodiment provides a heavy landing risk identification method based on QAR curve area characteristics, and the identification method specifically comprises
S1: extracting QAR parameters required by risk identification of all the legs;
s11: carrying out decoding analysis on QAR parameters of all flight segments to obtain CSV files of all flight segments;
s12: extracting parameter data required by risk identification of CSV files of all the flight segments, wherein the parameter data comprise longitudinal load, radio altitude, engine rotating speed, longitudinal acceleration, airspeed, ground speed, vertical speed, flap state, slat state, undercarriage state, spoiler state, true altitude and pitch angle of the airplane.
S2: carrying out data cleaning on the extracted QAR parameters;
the original QAR data has obvious abnormal conditions such as partial data field dislocation or information loss due to factors such as decoding dislocation or acquisition error and the like. And (4) identifying, deleting, deducing and completing the abnormal data by combining all parameter data of the aircraft state in a period of time near the time point of the abnormal data.
Abnormal data identification range: the CSV files are incomplete, and the whole process from take-off to landing is omitted; the CSV file is flight training data with the same departure place and destination; decoding the parameter dislocation of the outputted CSV file, namely displaying the data of the parameter 2 on a certain row in the column of the parameter 1; the parameter value exceeds the theoretical value range; and the parameter value has unrealistic jump and the like.
And (3) deleting operation: for the above-mentioned CSV file format abnormal condition, discarding as invalid data; and for the CSV file, the format is correct, only the data with even abnormal parameter values are used, only the abnormal data in the CSV file are deleted, and then the completion is deduced by combining other parameters.
And (3) a method for deducing completion: generally, taking a front-back average value of continuous numerical parameters such as speed, longitude and latitude, height and the like; for discrete state parameters such as flap state and slat state, the values are typically filled in.
S3: as shown in fig. 1, based on S2, extracting QAR data of landing phases of all legs;
s31: dividing flight phases of all flight phases according to the value of the parameter data, and extracting landing phase data;
s32: in the landing stage data, identifying the landing time point of the aircraft through the landing gear state parameters, namely only adopting the parameter data of the rows corresponding to the landing stage in the CSV file;
s33: in the landing phase data of each leg, a time point t at which the radio altitude is 50ft is extractedstartJudging the grounding time point of the corresponding flight section according to the landing gear state, the longitudinal acceleration and the spoiler state parameters, and recording the grounding time point as tend；
The method for judging the grounding time point of the airplane comprises the following steps:
respectively extracting the highest frequency data of five types of parameters of the radio altitude, the landing gear air-ground electric door state, the spoiler position, the longitudinal acceleration and the radio altitude of the landing stage data;
the other data except the highest frequency data of the five types of parameters are respectively processed into the frequencies which are the same as the corresponding highest frequency data, and the frequencies of the data are different from one time per second to eight times per second, so that the frequency of the low-frequency data needs to be improved to be consistent with the highest frequency data, and the accuracy of the grounding time point is ensured to be higher.
Different frequency boosting methods are adopted for different data, such as: the landing gear air-ground electric door state is filled by adopting a front value; the spoiler position adopts linear interpolation (front and back mean values); the longitudinal acceleration adopts vertical speed to calculate the proportion of each frame of data, and then the data are distributed according to the proportion; the radio altitude adopts a method of combining vertical speed calculation with quadratic spline interpolation.
And judging the grounding time of the airplane based on the decision condition. The method specifically comprises the following steps:
after the start of the landing phase, a first point in time t is found at which the radio altitude is less than 3startAs a time starting point for the start of the loop judgment; from tstartStarting to traverse each time point backwards until a point meeting any one of the decision conditions is met, and marking the point as a grounding point tTDAnd output. The decision condition comprises a first condition, a second condition and a third condition, any condition is met, namely the decision condition is met, wherein the first condition is as follows: from t arbitrarilystartThe spoiler position at the backward traversal time point is changed to be larger than the mutation value I compared with the spoiler position at the last time point, and the mutation value I is 4-6; the second condition is: from t arbitrarilystartThe longitudinal acceleration of the backward traversal time point is changed to be larger than the longitudinal acceleration of the last time point by a mutation value II, and the mutation value II is 0.025-0.035; the third condition is: any slave tstartAnd the state conversion of the landing gear air-ground electric door occurs at the backward traversal time point.
S34: extracting the landing time period [ t ] corresponding to each flight segmentstart，tend]Vertical velocity at all time points in time.
S4: constructing a mean value curve according to the vertical speed mean values of the fixed descending distances of all flight sections in the landing stage, and then extracting the curve area characteristics of each flight section;
s41: according to the S34, applying a B spline interpolation method to carry out [ t ] of each flight segmentstart，tend]The vertical speed points in the time period are expanded to 50, so that the vertical speed points are in phase with the flying heightCorrespondingly, generating respective single-flight-segment vertical speed curves for all flight segments;
the B-spline interpolation method can generate a smooth functional relation curve according to the known vertical speed and the corresponding time point, and expand the vertical speed points in the time period to 50 through the functional relation curve so that the vertical speed corresponds to the flying height of the airplane,
s42: calculate each of all legs [ t ]start，tend]Forming a vertical speed mean value curve by the mean value of every 1ft of vertical speed in the time period, wherein the vertical speed mean value curve is a vertical coordinate representing the flight height, and a horizontal coordinate representing the vertical speed mean value;
s43: comparing all the single-flight-section vertical speed curves with the vertical speed mean value curve, wherein the area of the area above the vertical speed mean value curve is a positive-value area, the area of the area below the vertical speed mean value curve is a negative-value area, and calculating the curve area characteristic of each flight section;
wherein the curve area characteristic is positive area-negative area.
S5: and constructing a loss function, and determining a curve area characteristic threshold value by combining the curve area characteristics of each flight segment.
S51: constructing a loss function, wherein the loss function is defined as:
the loss value is a multiplied by the false negative rate + b multiplied by the false positive rate;
wherein: the false positive rate is the total number of the positive examples/the negative examples which are preliminarily determined to be the actual positive examples;
the method for preliminarily judging the positive and negative examples comprises the following steps:
extracting the landing load in the parameter data, wherein:
and if the landing load is greater than 1.5, determining that the landing risk exists, namely, the landing is a positive case, and otherwise, determining that the landing risk exists as a negative case.
a and b are coefficients determined according to the severity of the corresponding misjudgment type respectively;
s52: setting different thresholds, judging positive and negative examples according to whether the curve area characteristics exceed the thresholds, and considering the positive examples and not considering the negative examples if the curve area characteristics exceed the thresholds;
s53: and calculating loss values under different thresholds according to the loss function, and taking the threshold with the minimum loss value as the threshold for identifying the characteristic re-landing risk.
This model may then be applied to other leg data (non-sample data) as a feature to identify risk of re-landing. Specifically, the curve area characteristic value of the new sample is calculated, the relation between the curve area characteristic value and the threshold value is compared, if the curve area characteristic value is larger than the threshold value, the risk of heavy landing is considered to exist, and otherwise, the curve area characteristic value is considered to be safe.
Example 2
In the actual application process, the model uses more than 20000 pieces of data for fitting, and takes one piece of sample data as an example, and the vertical speed data of the landing stage is extracted and is shown in table 1:
TABLE 1 sample data
Grounding point in time determination method according to embodiment 1, tstartSpecifically, 21.75 seconds, tend30.5 seconds, and gray is the time period made up of the last two integer time points that encompass this time point.
For tstartTo tendB-spline interpolation is carried out on the data in the interval to obtain vertical speed data serving as a characteristic vector, and the data are as follows:
[-651.33,-654.81,-656.86,-657.13,-655.71,-652.71,-648.23,-642.37,-635.25,-626.86,-617.15,-606.07,-593.54,-579.52,-564.0,-547.37,-530.22,-513.16,-496.77,-481.67,-468.2,-455.46,-442.05,-426.59,-407.71,-384.0,-354.75,-321.89，-287.99，-255.64，-227.41，-205.9，-192.65，-186.3，-184.98，-186.81，-189.92，-192.45，-193.15，-192.06，-189.38，-185.29，-179.99，-173.67，-166.88，-160.69，-156.22，-154.56，-156.83，-164.09]
after repeating the above operations and calculating all sample feature vectors, a mean vector is calculated, and the data of the samples and the mean are shown in table 2:
table 2 sample and mean data
| SAMPLE | MEAN | |
| 1 | -651.325 | -745.227 |
| 2 | -654.812 | -744.545 |
| 3 | -656.864 | -744.042 |
| 4 | -657.134 | -743.565 |
| 5 | -655.713 | -743.229 |
| 6 | -652.708 | -743.225 |
| 7 | -648.226 | -742.053 |
| 8 | -642.373 | -740.15 |
| 9 | -635.249 | -737.825 |
| 10 | -626.862 | -735.542 |
| 11 | -617.153 | -733.083 |
| 12 | -606.066 | -730.075 |
| 13 | -593.541 | -727.254 |
| 14 | -579.522 | -725.014 |
| 15 | -563.999 | -723.034 |
| 16 | -547.367 | -721.284 |
| 17 | -530.222 | -719.316 |
| 18 | -513.159 | -716.87 |
| 19 | -496.775 | -714.035 |
| 20 | -481.666 | -709.201 |
| 21 | -468.202 | -699.505 |
| 22 | -455.459 | -679.604 |
| 23 | -442.051 | -642.977 |
| 24 | -426.595 | -584.347 |
| 25 | -407.706 | -505.942 |
| 26 | -384 | -417.118 |
| 27 | -354.754 | -332.954 |
| 28 | -321.889 | -265.253 |
| 29 | -287.988 | -219.815 |
| 30 | -255.635 | -195.643 |
| 31 | -227.412 | -153.816 |
| 32 | -205.901 | -120.611 |
| 33 | -192.651 | -127.079 |
| 34 | -186.301 | -78.3901 |
| 35 | -184.977 | -53.0421 |
| 36 | -186.808 | -40.3578 |
| 37 | -189.92 | -41.8736 |
| 38 | -192.445 | -44.5733 |
| 39 | -193.147 | -43.1673 |
| 40 | -192.061 | -43.3547 |
| 41 | -189.379 | -42.1593 |
| 42 | -185.292 | -41.3004 |
| 43 | -179.989 | -37.6459 |
| 44 | -173.669 | -34.7692 |
| 45 | -166.881 | -31.4725 |
| 46 | -160.694 | -27.5653 |
| 47 | -156.216 | -23.0006 |
| 48 | -154.558 | -17.6923 |
| 49 | -156.829 | -11.5501 |
| 50 | -164.087 | -4.32967 |
A graph as shown in FIG. 2 is established, and the area of SAMPLE-MEAN is integrated, so that the curve area characteristic value of the SAMPLE is calculated to be 853.2365075. And repeating the above operations to calculate the curve area characteristic values of all the samples. Selecting a certain threshold, judging the sample to be a positive case if the area characteristic of the sample curve is larger than the threshold, otherwise, judging the sample to be a negative case, judging all samples according to the method, comparing the actual number of the positive cases and the actual number of the negative cases of the sample, calculating a false negative case rate and a false positive case rate, and calculating a loss value corresponding to the threshold on the basis of the false negative case rate and the false positive case rate, wherein the loss value is a multiplied by the false negative case rate + b multiplied by the false positive case rate. In this example, the loss values corresponding to the plurality of thresholds are calculated by repeating the operation with a being 0.75 to 1.25 and b being 1.75 to 2.25 while changing the thresholds. And taking a threshold value corresponding to the minimum loss value as a characteristic threshold value for identifying the risk of the heavy landing. In this example, the minimum loss value corresponds to a threshold value of 10287.72. The loss values corresponding to 5 thresholds are as follows:
TABLE 3
The sample curve area characteristic value is 853.2365075, which is much smaller than the threshold value, the landing load is 1.148, which is much smaller than the heavy landing risk value of 1.5.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (5)
1. A heavy landing risk identification method based on QAR curve area characteristics is characterized in that: the identification method specifically comprises the following steps: s1: extracting QAR parameters required by risk identification of all flight segments of a single airplane;
s2: carrying out data cleaning on the extracted QAR parameters;
s3: based on S2, extracting QAR data of landing stages of all the legs;
s4: constructing a mean value curve according to the vertical speed mean values of the fixed descending distances of all flight sections in the landing stage, and then extracting the curve area characteristics of each flight section;
s5: constructing a loss function, and determining a curve area characteristic threshold value by combining the curve area characteristic of each flight segment;
s6: and comparing the curve area characteristic of each flight segment with a curve area characteristic threshold, and if the curve area characteristic is greater than the curve area characteristic threshold, determining that the risk of heavy landing exists, otherwise, determining that the safety exists.
2. The QAR curve area feature-based heavy landing risk identification method according to claim 1, wherein: the S1 specifically includes:
s11: decoding and analyzing QAR parameters of all flight segments in the civil aircraft to obtain CSV files of all flight segments;
s12: extracting parameter data required by risk identification of CSV files of all the flight segments, wherein the parameter data comprise longitudinal load, radio altitude, engine rotating speed, longitudinal acceleration, airspeed, ground speed, vertical speed, flap state, slat state, undercarriage state, spoiler state, true altitude and pitch angle of the airplane.
3. The QAR curve area feature-based heavy landing risk identification method according to claim 2, wherein: the S3 specifically includes:
s31: dividing flight phases according to the values of the parameter data, and extracting landing phase data;
s32: identifying the landing time point of the airplane through the landing gear state parameters in the landing stage data;
s33: in landing phase data of all legs, a time point t at which the radio altitude is 50ft is extractedstarJudging the grounding time point by the landing gear state, the longitudinal acceleration and the spoiler state parameter, and recording the grounding time point as tend；
S34: extracting [ t ] from all CSV filesstart，tend]Vertical velocity at all time points within the time period.
4. The QAR curve area feature-based heavy landing risk identification method according to claim 3, wherein: the S4 specifically includes:
s41: according to the S34, each [ t ] of all the legs is interpolated by B splinesstart，tend]The number of the vertical speed points in the time period is expanded to at least 50, so that the vertical speed points correspond to the flying height, and all the flight sections generate respective single flight section vertical speed curves;
s42: calculate each of all legs [ t ]start，tend]Forming a vertical speed mean value curve by the mean value of every 1ft of vertical speed in a time period, wherein the vertical speed mean value curve is a vertical coordinate representing the flight height, and the horizontal coordinate representing the vertical speed mean value;
s43: comparing all the single-flight-section vertical speed curves with the vertical speed mean value curve, enabling the area of the area above the vertical speed mean value curve to be a positive-value area, enabling the area of the area below the vertical speed mean value curve to be a negative-value area, and calculating the curve area characteristic of each flight section;
wherein the curve area characteristic is positive area-negative area.
5. The QAR curve area feature-based heavy landing risk identification method according to claim 4, wherein: the S5 specifically includes:
s51: constructing a loss function, wherein the loss function is defined as:
the loss value is a multiplied by the false negative rate + b multiplied by the false positive rate;
wherein: the false positive rate is the number of the positive examples/the total number of the positive examples;
a and b are coefficients determined according to the severity of the corresponding misjudgment type respectively;
s52: setting different thresholds, judging positive and negative examples according to whether the curve area characteristics exceed the thresholds, and considering the positive examples and not considering the negative examples if the curve area characteristics exceed the thresholds;
s53: and calculating loss values under different thresholds according to the loss function, and taking the threshold with the minimum loss value as the threshold for identifying the characteristic re-landing risk.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911395844.XA CN111047916B (en) | 2019-12-30 | 2019-12-30 | Heavy landing risk identification method based on QAR curve area characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911395844.XA CN111047916B (en) | 2019-12-30 | 2019-12-30 | Heavy landing risk identification method based on QAR curve area characteristics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111047916A true CN111047916A (en) | 2020-04-21 |
| CN111047916B CN111047916B (en) | 2022-04-12 |
Family
ID=70242033
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911395844.XA Active CN111047916B (en) | 2019-12-30 | 2019-12-30 | Heavy landing risk identification method based on QAR curve area characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111047916B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113850931A (en) * | 2021-11-29 | 2021-12-28 | 武汉大学 | Flight feature extraction method for flight abnormity |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090050746A1 (en) * | 2007-08-20 | 2009-02-26 | Airbus France | Method and device for automatically protecting an aircraft against a hard landing |
| CN108725417A (en) * | 2017-04-18 | 2018-11-02 | 波音公司 | Use the brake health indicator system for outputting and inputting energy |
| CN109977517A (en) * | 2019-03-19 | 2019-07-05 | 北京瑞斯克企业管理咨询有限公司 | A kind of personal landing again and group's offline mode comparative analysis method based on QAR parameter curve |
-
2019
- 2019-12-30 CN CN201911395844.XA patent/CN111047916B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090050746A1 (en) * | 2007-08-20 | 2009-02-26 | Airbus France | Method and device for automatically protecting an aircraft against a hard landing |
| CN108725417A (en) * | 2017-04-18 | 2018-11-02 | 波音公司 | Use the brake health indicator system for outputting and inputting energy |
| CN109977517A (en) * | 2019-03-19 | 2019-07-05 | 北京瑞斯克企业管理咨询有限公司 | A kind of personal landing again and group's offline mode comparative analysis method based on QAR parameter curve |
Non-Patent Citations (2)
| Title |
|---|
| 王天明: "基于QAR数据的飞行安全模型研究", 《中国优秀硕士学位论文全文数据库》 * |
| 赵剑: "基于QAR的数据挖掘应用基础研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113850931A (en) * | 2021-11-29 | 2021-12-28 | 武汉大学 | Flight feature extraction method for flight abnormity |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111047916B (en) | 2022-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104246637B (en) | Analyze the flying quality of aircraft record with the method being truncated to mission phase | |
| EP1884852B1 (en) | Tuning procedure for aircraft fault detection | |
| CN109240327B (en) | Method for identifying flight phase of fixed-wing aircraft | |
| CN104156594B (en) | Dynamic flight station-crossing time estimation method based on Bayes network | |
| CN104166787A (en) | Aero-engine remaining life prediction method based on multi-stage information fusion | |
| EP3479181B1 (en) | Method and assistance system for detecting a degradation of flight performance | |
| CN111047916B (en) | Heavy landing risk identification method based on QAR curve area characteristics | |
| CN113589847B (en) | Method for determining flight radius of flexible aircraft | |
| CN111159817A (en) | Design method of mixed scaling wing airfoil for icing wind tunnel test | |
| CN111210668A (en) | Landing stage flight trajectory offset correction method based on time sequence QAR parameter | |
| CN114065407B (en) | Dynamic wake interval reduction method based on multi-element perception of airport wind speed | |
| CN110866707A (en) | Method for quantitatively analyzing landing operation quality of fixed-wing aircraft by using QAR (quality enhancement Rate) data | |
| Qian et al. | An improved aircraft hard landing prediction model based on panel data clustering | |
| Vitale et al. | An innovative angle of attack virtual sensor for physical-analytical redundant measurement system applicable to commercial aircraft | |
| CN102521901A (en) | Method for automatically estimating and realizing flight quality of pilots | |
| Deiler et al. | Facing the challenges of supercooled large droplet icing: Results of a flight test based joint DLR-embraer research project | |
| CN115293225B (en) | Method and device for analyzing causes of pilot flat-floating ejector rod | |
| Jiang et al. | Research on the flight anomaly detection during take-off phase based on FOQA data | |
| Elmore et al. | A high resolution spatial and temporal multiple Doppler analysis of a microburst and its application to aircraft flight simulation | |
| Zheng et al. | Risk prediction method of aircraft hard landing based on flight data | |
| CN111125924B (en) | Airplane landing automatic deceleration gear identification method based on QAR parameter feature extraction | |
| CN110414038B (en) | Airplane service life monitoring method based on historical flight data | |
| CN109871562B (en) | Method for calculating floating characteristic of airplane forced landing on water through hydrostatic surface based on Catia secondary development | |
| CN114692308B (en) | Aircraft wiping tail flight segment screening method and system based on geometric constraint model | |
| Brown | Negative speed stability, a factor in transport airplane icing upsets |
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 | ||
| CB03 | Change of inventor or designer information |
Inventor after: Qi Lin Inventor after: Zheng Linjiang Inventor after: Liu Liu Inventor after: Liao Ziwen Inventor before: Qi Lin Inventor before: Liu Liu Inventor before: Liao Ziwen |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |