CN111721480B - Civil aircraft unit oxygen system leakage early warning method based on flight data - Google Patents

Civil aircraft unit oxygen system leakage early warning method based on flight data Download PDF

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CN111721480B
CN111721480B CN202010606697.2A CN202010606697A CN111721480B CN 111721480 B CN111721480 B CN 111721480B CN 202010606697 A CN202010606697 A CN 202010606697A CN 111721480 B CN111721480 B CN 111721480B
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aircraft
unit
leakage
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CN111721480A (en
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孙有朝
彭冲
苏思雨
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Nanjing University of Aeronautics and Astronautics
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Nanjing University of Aeronautics and Astronautics
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Abstract

The embodiment of the invention discloses a civil aircraft oxygen system leakage early warning method based on flight data, which is applied to real-time monitoring and risk early warning of the civil aircraft oxygen system; the method comprises the steps of collecting flight data related to a unit oxygen system of a monitored flight, processing the flight data and extracting characteristics, introducing the extracted characteristic values into a flight unit oxygen system leakage model for calculation, and performing grading early warning processing on the calculation results. The invention can automatically collect the flight data related to the oxygen system of the aircraft unit, analyze the flight data, judge the performance of the oxygen system of the aircraft unit, and finally inform the maintenance personnel of the early warning result, thereby realizing the closed-loop control and the health management of the oxygen system of the aircraft unit, and the maintenance personnel can carry out the maintenance according to the situation and timely eliminate the faults, thereby reducing the maintenance cost of the aircraft.

Description

Civil aircraft unit oxygen system leakage early warning method based on flight data
Technical Field
The invention relates to the technical field of civil aircraft system safety management and risk early warning.
Background
When the civil aircraft loses pressure at high altitude, the oxygen system plays a very important role in ensuring the life safety of passengers on the aircraft, and has very important influence on the safety of the operation of the aircraft. The oxygen system of the unit is a fixed high-pressure oxygen system which is stored by a fixed oxygen cylinder and provides oxygen for breathing for the primary driver, the secondary driver and the observer. The crew oxygen system can provide emergency oxygen for crew members in the cockpit in case of decompression, smoke and fire, and can provide supplementary oxygen for improving night flight eyesight or eliminating fatigue.
At present, an airline company is required to check whether the pressure of an oxygen cylinder of a unit meets a release standard every day for daily inspection and maintenance of the oxygen system of the unit, and if the pressure is lower than the standard, the oxygen cylinder of the unit is replaced; if the pressure meets the requirement, the pressure of the oxygen cylinder of the unit is not recorded and continuously monitored and managed. Therefore, if the abnormal slow leakage of the oxygen system of the aircraft crew is difficult to find in time, the aircraft crew oxygen bottle is frequently replaced by the crew, the workload and the maintenance cost of the maintenance of the air route are increased, but the leakage fault is not really eliminated, and the potential safety hazard always exists.
In addition, the following difficulties often exist in monitoring whether the aircraft crew oxygen system is abnormal or not by analyzing flight data and establishing a crew oxygen system leakage model: the pressure of the oxygen cylinder of the aircraft is influenced by a plurality of factors such as air pressure, temperature and the like, and the temperature of the oxygen cylinder of the aircraft is determined by the temperature of the cockpit and the atmospheric temperature; a temperature sensor is not arranged at the position of the oxygen cylinder of the unit, so that the temperature cannot be directly measured; at the same time, these influencing factors also change from moment to moment during the flight of an aircraft. Therefore, currently, the monitoring threshold of the crew oxygen system is usually set directly according to engineering experience, and when the crew oxygen pressure change of a flight exceeds the set threshold, an alarm is given. The monitoring threshold value set according to engineering experience is often too large, so that the leakage phenomenon of the oxygen system of the unit is frequently missed and failure can not be timely eliminated, and the operation safety of the airplane cannot be guaranteed.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a civil aircraft unit oxygen system leakage early warning method based on flight data, which aims to solve the problems that in the prior art, the maintenance cost is high, faults cannot be cleared in time and the like.
The technical scheme is as follows: the invention provides a civil aircraft unit oxygen system leakage early warning method based on flight data, which specifically comprises the following steps:
step 1: collecting flight data of monitored flights of civil aircraft A;
step 2: preprocessing the acquired flight data, wherein the preprocessing comprises missing data supplement;
and step 3: extracting characteristic values of the preprocessed data, wherein the specifically extracted characteristic values comprise: crew oxygen pressure P when aircraft is at takeoff airportdAtmospheric temperature T of take-off airportdOxygen pressure P of unit in approach stageaAnd the lowest atmospheric temperature T in the course of the routemin；
And 4, step 4: obtaining historical flight data of n flights without leakage faults, and executing the model and the civil of the airplane of the flights without leakage faultsThe machine A has the same machine type; preprocessing the historical flight data, extracting characteristic values from the processed data, establishing a unit oxygen system leakage model according to the extracted characteristic values, and extracting P from the step 3d、Td、PaAnd TminLeading the oxygen pressure into the model to obtain the predicted value P of the oxygen pressure of the unit at the approach stage of the monitored flightpre-aThereby obtaining Ppre-aAnd PaResidual error epsilon betweena；
And 5: will epsilonaAnd standard deviation SdComparison is made ifa≤2.5SdIf the flight is normal, the oxygen system of the unit is normal, and the next flight is continuously monitored; if 2.5Sd＜εa＜3.5SdTurning to step 6; if the residual error εa≥3.5SdThen, an alarm is given to the maintenance personnel of the engineering; said SdIntroducing a sample consisting of historical flight data of flights without leakage faults into an oxygen system leakage model to obtain a standard deviation of sample residual errors;
step 6: collecting p pieces of historical flight data of the civil aircraft A adjacent to the monitored flight, and preprocessing the data by using the preprocessing method in the step 2;
and 7: and 6, extracting characteristic values of the data preprocessed in the step 6, wherein the specifically extracted characteristic values comprise: departure time D of each flighttThe crew oxygen pressure P at the takeoff airport of the aircraft on each flightd1Temperature of the cockpit of each flightcocAnd the atmospheric temperature T of the takeoff airport in each flightd1Based on TcocAnd Td1Oxygen pressure P of the aircraft units at the take-off airportd1Conversion to T at standard temperaturestdOxygen pressure value P of the unitstd；
And 8: according to DtAnd PstdEstablishing a model of the variation of the oxygen pressure of the civil aircraft A unit along with the time by utilizing least square normal regression fitting so as to obtain the unit oxygen pressure leakage rate beta of the aircraft5；
And step 9: selecting N continuous histories of h airplanesFlight data, and the unit oxygen systems of the h-frame airplanes are all abnormal, and the h-frame airplanes are all the same as the civil airplane A in type; performing characteristic extraction on flight data of each flight of each airplane to obtain the unit oxygen pressure P when the airplane is positioned at a take-off airport in each flight of the s-th airplaned-sAnd departure time of each flight, will Pd-sConverting into oxygen pressure value P of unit at standard temperaturestdsAccording to P of each flight of the s-th aircraftstdsAnd taking off time, and establishing a time-dependent change equation of the oxygen pressure of the set of the second aircraft by using a least square method so as to obtain the leakage rate beta of the oxygen pressure of the set of the second aircraft5·sAnd(s) is 1,2 and … h, the unit oxygen pressure leakage rate of the h aircrafts is averaged, and the unit oxygen pressure leakage rate beta of the abnormal-free aircrafts in the unit oxygen system is obtainedstd；
Step 10: according to beta5And betastdDetermining a judgment parameter alpha, wherein if alpha is less than or equal to 5 degrees, the oxygen system of the aircraft is normal without slow leakage risk; if alpha is more than 5 degrees, the airplane has the risk of slow leakage of oxygen of the aircrew and gives an alarm to the maintenance personnel of the aircrew.
Further, in the step 1, the flight data of the monitored flight of civil aircraft a is collected in the quick access recorder.
Further, the flight data of the monitored flight obtained in the step 1 is complete flight cycle time sequence data, and the sequence of the flight data is 5 columns, namely time, flight phase, unit oxygen pressure, atmospheric temperature and cockpit temperature parameters; each row has m rows, and the rows correspond to the flight time of the flight; in the step 2, a near supplementation method is adopted for missing data supplementation, and the preprocessed time sequence data is X ═ XijWhere i is 1, 2.., m; j ═ 1, 2., 5, xijThe data of the ith row and the jth column in X.
Further, the step 3 specifically includes:
step 3.1: crew oxygen pressure P when positioning an aircraft at a takeoff airportdAnd atmospheric temperature T of take-off airportdThe data extraction time is set as that the flight enters the engine and is not startedTime t of phasedLet the length of the sampled data be t, then
Figure GDA0003029008150000031
Wherein x isi3Is the data of the ith row and the 3 rd column in the flight cycle time sequence data X, Xi4The data of the 4 th row in the flight cycle time sequence data X;
step 3.2: oxygen pressure P of unit in approaching stageaThe data extraction time of (2) is set as the time t when the flight enters the approach stageaIf the length of the sampled data is t, then
Figure GDA0003029008150000032
Step 3.3: determining a minimum atmospheric temperature T in an airway processminIs the time t of the first occurrence of the minimum value of the atmospheric temperatureminIf the length of the sampled data is t, then
Figure GDA0003029008150000033
Further, the establishing of the unit oxygen system leakage model in the step 4 specifically includes:
step 4.1: combining the eigenvalues extracted in the step 4 into an eigenvalue matrix E1：
Figure GDA0003029008150000041
Wherein P isdn·nCrew oxygen pressure, T, at the takeoff airport of an aircraft on the nth flight in historical flight data representing no leakage faultsdn·nAtmospheric temperature, T, at takeoff airport of nth flight in historical flight data representing no leakage faultminn·nLowest atmospheric temperature, P, during the course of the nth flight in the historical flight data representing no leakage faultsan·nThe crew oxygen pressure of the nth flight in the historical flight data representing no leakage fault in the approach phase;
step 4.2: to E1Characteristic value ofAnd (3) analyzing data: with PanAs a dependent variable, with Pdn、Tdn、TminnIs an independent variable, Pan＝[Pan-1,Pan-2,…Pan-n]，Pdn＝[Pdn-1,Pdn-2,…Pdn-n]，Tdn＝[Tdn-1,Tdn-2,…Tdn-n]，Tminn＝[Tminn-1,Tminn-2,…Tminn-n]And establishing a regression fitting equation by adopting a multiple linear regression method so as to determine the leakage model of the oxygen system of the flight unit: ppre-a＝β0+β1·Pdn+β2·Tdn+β3·TminnWherein beta is0To fit a constant term, beta1、β2、β3Are coefficients.
Further, each historical flight data in the step 6 is a complete flight cycle time sequence data, 5 columns are provided in total, and time, flight phase, unit oxygen pressure, atmospheric temperature and cockpit temperature parameters are sequentially provided; each column has m1 rows, and the preprocessed time sequence data is Xe＝{xIJ1,2, m 1; 1,2, 5, XeIs the e-th time sequence data, e is 1,2, … p, xIJIs XeRow I and column J;
p in said step 7stdComprises the following steps:
Figure GDA0003029008150000042
Figure GDA0003029008150000043
where t1 is the sample length, td1For the moment x when the flight enters the engine non-starting stage in the historical data collected in the step 6I4For the data of row I, column 4 in each time series data.
Further, step 8 specifically adopts a least square method to perform group D on p in step 7tAnd PstdTo carry outLinear regression fitting to establish the time-varying equation P of oxygen pressure in civil aircraft Astd＝β4+β5·DtWherein beta is4Fitting equation constant terms.
Further, the method for determining the parameter α in step 10 is as follows:
α＝arctan(β5)-arctan(βstd)。
has the advantages that: the invention designs a civil aircraft unit oxygen system leakage early warning method based on flight data, which comprises the steps of automatically acquiring relevant flight data of an aircraft unit oxygen system, analyzing the data, carrying out grading processing on the calculation result of a leakage model of the flight unit oxygen system, judging the performance of the aircraft unit oxygen system, finally informing a maintenance staff of the aircraft service with the early warning result, guiding the development of maintenance work and timely troubleshooting; by establishing a time-varying equation of the oxygen pressure of the aircraft unit of the monitored aircraft, the oxygen leakage rate of the aircraft unit is obtained, the remaining service time of the aircraft unit oxygen system is predicted, and the crew can carry out maintenance according to the situation, so that the maintenance cost of the aircraft is reduced. The method is automatically operated and executed by platform software, no crew member is required to participate in the operation, the unsupervised monitoring and closed-loop control of the oxygen system of the aircraft unit are realized, the crew member only needs to make a decision according to an output result, and the operation efficiency of an airline company is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a predicted value P of oxygen pressure of a unit in an approach phase obtained by calculating flight data of 100 monitored flights through a flight unit oxygen system leakage model in the inventionpre-aAnd the actual value PaResidual error epsilon betweenaAnd (5) distribution diagram.
FIG. 3 is a plot of crew oxygen pressure versus time plotted from least squares linear regression fit of historical flight data for an aircraft.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.
As shown in fig. 1, the present embodiment provides a leakage early warning method for a civil aircraft oxygen system based on flight data, which specifically includes the following steps:
step 1: a civil aircraft unit oxygen system leakage early warning method based on flight data can monitor a plurality of overhead flights simultaneously. FLIGHT data (M is 100 in this embodiment) related to the CREW oxygen system recorded by M FLIGHTs of the same model, including but not limited to TIME (TIME), FLIGHT PHASE (FLIGHT _ PHASE), CREW oxygen pressure (CREW _ OXY _ PRS), atmospheric temperature (SAT), and COCKPIT temperature (TEMP _ clock).
Step 2: and performing preprocessing work on the acquired flight data, including missing data supplement and the like. The missing data supplement method is near supplement and is suitable for flight parameter data without mutation. The acquired flight data of the monitored flight is complete flight cycle time sequence data, and the sequence of the flight data is 5 columns, namely time, flight stages, unit oxygen pressure, atmospheric temperature and cockpit temperature parameters; each row has m rows, and the rows correspond to the flight time of the flight; the preprocessed time sequence data is X ═ { X ═ XijWhere i is 1, 2.., m; j ═ 1, 2., 5, xijIs ith row and jth column data.
And step 3: and (3) carrying out characteristic value extraction work on the preprocessed data, wherein the specific characteristic values comprise: crew oxygen pressure P when aircraft is at takeoff airportdAtmospheric temperature T of take-off airportdOxygen pressure P of unit in approach stageaAnd the lowest atmospheric temperature T in the course of the routemin. The characteristic value extraction method comprises the following steps:
(1) determining the crew oxygen pressure P at the takeoff airport of the aircraft according to the FLIGHT PHASE (FLIGHT _ PHASE)dAtmospheric temperature T of take-off airportdData extraction point t ofdCorresponds to the moment when the flight has just entered the engine off (eng.stop) phase, i.e. when double-start has just been detected 2N2 < 55%. If the length of the sampled data is t, then
Figure GDA0003029008150000061
xi3For the data in row i, column 3, x in the time series data of the flight cyclei4Is the data of the 4 th column of the ith row in the flight cycle timing data.
(2) Determining the crew oxygen pressure P in the approach PHASE according to the FLIGHT PHASE (FLIGHT _ PHASE)aData extraction point t ofaSetting the length of the sampling data as t corresponding to the moment when the flight just enters the APPROACH (APPROACH) stage
Figure GDA0003029008150000062
(3) Determining a minimum atmospheric temperature T in an airway processminData extraction point t ofminSetting the length of the sampling data as t corresponding to the time of the initial occurrence of the minimum value of the atmospheric temperature
Figure GDA0003029008150000063
(4) In this embodiment, the sample data length t is set to 19s, that is, the characteristic value is an average value within 20 s.
And 4, step 4: importing the characteristic value extracted in the step 3 into an oxygen system leakage model of the flight unit for calculation to obtain a predicted value P of the oxygen pressure of the flight unit at the approach stage of the monitored flightpre-aAnd the actual value PaResidual error epsilon betweena. The model of the leakage of the oxygen system of the flight set is established as follows:
(1) and (4) selecting historical flight data without leakage faults of 200 flights, wherein the historical flight data is from the same model as the model in the step 1. Data processing and characteristic value extraction are carried out through the steps 1-3, and a characteristic value matrix formed by the extracted characteristic values is as follows:
Figure GDA0003029008150000071
wherein P isdn·nCrew oxygen pressure, T, at the takeoff airport of an aircraft on the nth flight in historical flight data representing no leakage faultsdn·nHistory number of flights indicating no leakage faultAtmospheric temperature, T, at the takeoff airport of the nth flightminn·nLowest atmospheric temperature, P, during the course of the nth flight in the historical flight data representing no leakage faultsan·nThe crew oxygen pressure of the nth flight in the historical flight data representing no leakage fault in the approach phase;
(2) to E1The characteristic value data in the interior is analyzed by Pdn、Tdn、TminnIs an independent variable, Pan＝[Pan-1,Pan-2,…Pan-n]，Pdn＝[Pdn-1,Pdn-2,…Pdn-n]，Tdn＝[Tdn-1,Tdn-2,…Tdn-n]，Tminn＝[Tminn-1,Tminn-2,…Tminn-n]Establishing a regression fitting equation by adopting a multiple linear regression method, and determining the leakage model of the oxygen system of the flight unit as follows:
Ppre-a＝β0+β1·Pd+β2·Td+β3·Tmin
in the formula beta0≈107.97，β1≈0.97，β2≈-1.44，β3≈1.88。
(3) P of oxygen pressure of unit in approach phasea＝Ppre-a+εa，εa～N(μ,σ2) Unbiased estimation of (normal distribution of the path), sigma
Figure GDA0003029008150000072
In the formula
Figure GDA0003029008150000073
Is the residual mean of the sample, where k is 1, 2.
(4) Leading the extracted characteristic value of the monitored flight into a model for calculation to obtain a predicted value P of the oxygen pressure of the unit at the approach stage of the monitored flightpre-aResidual epsilon between actual valuesa。
And 5: carrying out flight operation on sample data consisting of residual errors calculated by the model in the step 4 and abnormal flights of the unit oxygen systemResidual standard deviation S calculated by oxygen system leakage modeldAnd comparing and carrying out grading early warning treatment. If the residual error epsilon of the flight is monitoreda≤2.5SdJudging that the oxygen system of the unit is normal without leakage risk, and monitoring the next flight; if the residual is 2.5Sd＜εa＜3.5SdIf the possibility of slow leakage of the oxygen system of the unit exists, turning to step 6; if the residual error εa≥3.5SdAnd then the alarm is given to the maintenance personnel of the engine and the leakage of the oxygen system of the unit is checked.
FIG. 2 is a residual epsilon calculated by a flight crew oxygen system leakage model of flight data of 100 monitored flights in an embodimentaAnd (5) distribution diagram. It can be seen that the calculated residual error epsilon of most flight dataaAre all less than 2.5SdWithin the normal range; residual 2.5S after small part (circled) flight data calculationd＜εa＜3.5SdIf the unit oxygen system has a slow leakage tendency, turning to step 6; does not exceed 3.5SdOf flights.
Step 6: residual 2.5Sd＜εa＜3.5SdWhen the airplane is monitored within the range, the airplane has the tendency of slow leakage of a crew oxygen system, and needs to be further judged. 30 flights of historical flight record data are collected that the aircraft was next to before performing the monitoring flight (the flight is circled in fig. 2). And (4) carrying out pretreatment work according to the method of the step 2.
And 7: extracting characteristic values of the historical flight record data preprocessed in the step 6, wherein each piece of historical flight data in the step 6 is complete flight cycle time sequence data, the complete flight cycle time sequence data comprises 5 columns, each column comprises m1 rows, and the specific characteristic values comprise: departure time D of each flighttThe crew oxygen pressure P at the takeoff airport of the aircraft on each flightd1Temperature of the cockpit of each flightcocAnd the atmospheric temperature T of the takeoff airport in each flightd1Based on TcocAnd Td1Oxygen pressure P of the aircraft units at the take-off airportd1Conversion to standard temperatureLower TstdOxygen pressure value P of the unitstd；
(1)Pd1，Td1In a manner consistent with that described in step 3.
(2) Recorded departure time D for each flighttThe aircraft departure time for the corresponding flight; aircraft cockpit temperature T at takeoff airportcocCorresponding to the time t when the flight just enters the engine off (ENGd1If the sample data length is t1, the sample data length is set to
Figure GDA0003029008150000081
xI4For the data of row I, column 4 in each time series data.
(3) The sample data length t is set to 19s, i.e., the characteristic value is the average value within 20 s.
(4) The oxygen pressure P of the unit when the airplane is positioned at the take-off airportd1Conversion to standard temperature TstdOxygen pressure value P of lower unitstdIs calculated by the formula
Figure GDA0003029008150000082
In this example Tstd＝20℃，Dt,PstdThe eigenvalue matrix of the composition is as follows:
Figure GDA0003029008150000091
wherein Dt·pFor the departure time of the aircraft of the P flight, Pstd·pP for the P-th flight pointstdIn (1).
And 8: for eigenvalue matrix E2Establishing an equation P of the change of the oxygen pressure of the aircraft unit along with the time by using least square normal linear regression fittingstd＝β4+β5·DtObtaining the oxygen pressure leakage rate beta of the aircraft unit5. In the formula beta4≈1856.75，β5≈-2.42。
And step 9: the oxygen pressure leakage rate beta of the aircraft unit5Crew oxygen pressure leakage rate beta of healthy state aircraft (aircraft without abnormal state of crew oxygen system)stdComparing, and calculating a judgment parameter alpha; if alpha is less than or equal to 5 degrees, judging that the oxygen system of the unit is normal without slow leakage risk; if alpha is more than 5 degrees, the risk of slow leakage of oxygen of the unit exists, the alarm is given to maintenance personnel of the crew, and the leakage of the oxygen system of the unit is checked. Oxygen pressure leakage rate beta of unit of healthy aircraftstdThe calculation method of the judgment parameter alpha comprises the following steps:
(1) oxygen pressure leakage rate beta of unit of healthy aircraftstdSelecting continuous M (M is 50 in the embodiment) flight data of 30 airplanes in the same type of health state, and sequentially establishing an equation of change of oxygen pressure of airplane unit with time according to steps 7 and 8 to obtain the unit oxygen pressure leakage rate beta5·s(s ═ 1,2, …, h) according to the formula
Figure GDA0003029008150000092
And (6) obtaining. Beta is astdThe aircraft has universality for the same type of aircraft. Oxygen pressure leakage rate beta of unit obtained by embodimentstd＝-0.4543。
(2) The calculation formula of the judgment parameter alpha is as follows: α ═ arctan (β)std)-arctan(β5). The judgment parameter α obtained in this embodiment is 43.15 °, and is much larger than 5 °, so that the risk of slow leakage of oxygen of the unit exists, and an alarm should be given to the crew maintenance personnel to check the leakage of the oxygen system of the unit.
FIG. 3 shows an embodiment of performing residual 2.5Sd＜εa＜3.5SdThe time-varying curve of the oxygen pressure of the crew is drawn by least square normal linear regression fitting of 30 adjacent historical flight record data of the airplane before the flight, the time-varying curve of the oxygen pressure of the crew comprising the airplane and the standard crew oxygen pressure leakage rate beta of the airplane according to the health statestdAnd (5) drawing a time-varying curve of the oxygen pressure of the unit. It can be clearly seen that the flight crew oxygen decay rate of the aircraft is far greater than that of the healthy aircraft, and the existence of flight crew oxygen in the aircraft can be determinedA system leak failure.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (8)

1. A civil aircraft unit oxygen system leakage early warning method based on flight data is characterized by specifically comprising the following steps:
step 1: collecting flight data of monitored flights of civil aircraft A;
step 2: preprocessing the acquired flight data, wherein the preprocessing comprises missing data supplement;
and step 3: extracting characteristic values of the preprocessed data, wherein the specifically extracted characteristic values comprise: crew oxygen pressure P when aircraft is at takeoff airportdAtmospheric temperature T of take-off airportdOxygen pressure P of unit in approach stageaAnd the lowest atmospheric temperature T in the course of the routemin；
And 4, step 4: obtaining historical flight data of n flights without leakage faults, wherein the model of an airplane executing the flights without leakage faults is the same as that of a civil airplane A; preprocessing the historical flight data, extracting characteristic values from the processed data, establishing a unit oxygen system leakage model according to the extracted characteristic values, and extracting P from the step 3d、Td、PaAnd TminLeading the oxygen pressure into the model to obtain the predicted value P of the oxygen pressure of the unit at the approach stage of the monitored flightpre-aThereby obtaining Ppre-aAnd PaResidual error epsilon betweena；
And 5: will epsilonaAnd standard deviation SdComparison is made ifa≤2.5SdIf the flight is normal, the oxygen system of the unit is normal, and the next flight is continuously monitored; if 2.5Sd＜εa＜3.5SdTurning to step 6; if the residual error εa≥3.5SdThen, an alarm is given to the maintenance personnel of the engineering; the above-mentionedSdIntroducing a sample consisting of historical flight data of flights without leakage faults into an oxygen system leakage model to obtain a standard deviation of sample residual errors;
step 6: collecting p pieces of historical flight data of the civil aircraft A adjacent to the monitored flight, and preprocessing the data by using the preprocessing method in the step 2;
and 7: and 6, extracting characteristic values of the data preprocessed in the step 6, wherein the specifically extracted characteristic values comprise: departure time D of each flighttThe crew oxygen pressure P at the takeoff airport of the aircraft on each flightd1Temperature of the cockpit of each flightcocAnd the atmospheric temperature T of the takeoff airport in each flightd1Based on TcocAnd Td1Oxygen pressure P of the aircraft units at the take-off airportd1Conversion to T at standard temperaturestdOxygen pressure value P of the unitstd；
And 8: according to DtAnd PstdEstablishing a model of the variation of the oxygen pressure of the civil aircraft A unit along with the time by utilizing least square normal regression fitting so as to obtain the unit oxygen pressure leakage rate beta of the aircraft5；
And step 9: selecting continuous N pieces of historical flight data of h airplanes, wherein the unit oxygen systems of the h airplanes are not abnormal, and the h airplanes are the same as the civil airplane A in type; performing characteristic extraction on flight data of each flight of each airplane to obtain the unit oxygen pressure P when the airplane is positioned at a take-off airport in each flight of the s-th airplaned-sAnd departure time of each flight, will Pd-sConverting into oxygen pressure value P of unit at standard temperaturestdsAccording to P of each flight of the s-th aircraftstdsAnd taking off time, and establishing a time-dependent change equation of the oxygen pressure of the set of the second aircraft by using a least square method so as to obtain the leakage rate beta of the oxygen pressure of the set of the second aircraft5·sAnd(s) is 1,2 and … h, the unit oxygen pressure leakage rate of the h aircrafts is averaged, and the unit oxygen pressure leakage rate beta of the abnormal-free aircrafts in the unit oxygen system is obtainedstd；
Step 10: according to beta5And betastdDetermining a judgment parameter alpha, wherein if alpha is less than or equal to 5 degrees, the oxygen system of the aircraft is normal without slow leakage risk; if alpha is more than 5 degrees, the airplane has the risk of slow leakage of oxygen of the aircrew and gives an alarm to the maintenance personnel of the aircrew.
2. The aircraft crew oxygen system leakage early warning method based on flight data as claimed in claim 1, wherein in step 1, the flight data of the monitored flight of civil aircraft a is collected in a fast access recorder.
3. The civil aircraft crew oxygen system leakage early warning method based on flight data as claimed in claim 1, wherein the flight data of the monitored flight obtained in step 1 is a complete flight cycle time sequence data, which has 5 columns in total, and sequentially comprises time, flight phase, crew oxygen pressure, atmospheric temperature and cockpit temperature parameters; each row has m rows, and the rows correspond to the flight time of the flight; in the step 2, a near supplementation method is adopted for missing data supplementation, and the preprocessed time sequence data is X ═ XijWhere i is 1, 2.., m; j ═ 1, 2., 5, xijThe data of the ith row and the jth column in X.
4. The civil aircraft crew oxygen system leakage early warning method based on flight data as claimed in claim 3, wherein the step 3 is specifically:
step 3.1: crew oxygen pressure P when positioning an aircraft at a takeoff airportdAnd atmospheric temperature T of take-off airportdThe data extraction time of (a) is set as the time t when the flight enters the engine non-starting stagedLet the length of the sampled data be t, then
Figure FDA0003029008140000021
Wherein x isi3Is the data of the ith row and the 3 rd column in the flight cycle time sequence data X, Xi4The data of the 4 th row in the flight cycle time sequence data X;
step 3.2: oxygen pressure P of unit in approaching stageaThe data extraction time of (2) is set as the time t when the flight enters the approach stageaThen, then
Figure FDA0003029008140000031
Step 3.3: determining a minimum atmospheric temperature T in an airway processminIs the time t of the first occurrence of the minimum value of the atmospheric temperatureminThen, then
Figure FDA0003029008140000032
5. The civil aircraft crew oxygen system leakage early warning method based on flight data according to claim 1, wherein the establishing of the crew oxygen system leakage model in the step 4 specifically comprises:
step 4.1: combining the eigenvalues extracted in the step 4 into an eigenvalue matrix E1：
Figure FDA0003029008140000033
Wherein P isdn·nCrew oxygen pressure, T, at the takeoff airport of an aircraft on the nth flight in historical flight data representing no leakage faultsdn·nAtmospheric temperature, T, at takeoff airport of nth flight in historical flight data representing no leakage faultminn·nLowest atmospheric temperature, P, during the course of the nth flight in the historical flight data representing no leakage faultsan·nThe crew oxygen pressure of the nth flight in the historical flight data representing no leakage fault in the approach phase;
step 4.2: to E1And analyzing characteristic value data: with PanAs a dependent variable, with Pdn、Tdn、TminnIs an independent variable, Pan＝[Pan-1,Pan-2,…Pan-n]，Pdn＝[Pdn-1,Pdn-2,…Pdn-n]，Tdn＝[Tdn-1,Tdn-2,…Tdn-n]，Tminn＝[Tminn-1,Tminn-2,…Tminn-n]And establishing a regression fitting equation by adopting a multiple linear regression method so as to determine the leakage model of the oxygen system of the flight unit: ppre-a＝β0+β1·Pdn+β2·Tdn+β3·TminnWherein beta is0To fit a constant term, beta1、β2、β3Are coefficients.
6. The civil aircraft crew oxygen system leakage early warning method based on flight data as claimed in claim 1, wherein each historical flight data in step 6 is a complete flight cycle time sequence data, there are 5 columns in total, and time, flight phase, crew oxygen pressure, atmospheric temperature and cockpit temperature parameters are sequentially set; each column has m1 rows, and the preprocessed time sequence data is Xe＝{xIJ1,2, m 1; 1,2, 5, XeIs the e-th time sequence data, e is 1,2, … p, xIJIs XeRow I and column J;
p in said step 7stdComprises the following steps:
Figure FDA0003029008140000041
Figure FDA0003029008140000042
where t1 is the sample length, td1For the moment x when the flight enters the engine non-starting stage in the historical data collected in the step 6I4For the data of row I, column 4 in each time series data.
7. The aircraft crew oxygen system leakage early warning method based on flight data as claimed in claim 1,characterized in that step 8 is to adopt a least square method to carry out the P groups D in step 7tAnd PstdLinear regression fitting is carried out to establish a time-varying equation P of the oxygen pressure of the civil aircraft Astd＝β4+β5·DtWherein beta is4Fitting equation constant terms.
8. The aircraft crew oxygen system leakage early warning method based on flight data according to claim 1, wherein the parameter α in the step 10 is determined by a method comprising:
α＝arctan(β5)-arctan(βstd)。
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