CN115982942B - Method, device, equipment and storage medium for predicting residual life of aviation precooler - Google Patents
Method, device, equipment and storage medium for predicting residual life of aviation precooler Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for predicting the residual life of an aviation precooler. The method comprises the following steps: acquiring current key parameter data of a precooler; determining whether the precooler currently belongs to a life cycle normal stage according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler; if the key parameter data does not belong to the key parameter data, fitting the key parameter data to obtain a fitting curve equation; and updating a correction model by utilizing multidimensional data to process the fitting curve equation and the MLBF equation so as to obtain the residual life predicted value of the precooler. According to the method for predicting the residual life of the aviation precooler, provided by the application, the fitting curve equation and the MLBF equation are processed by utilizing the multidimensional data update correction model, so that the residual life predicted value of the precooler is obtained, the accuracy of predicting the residual life of the precooler is improved, and the actual application requirement can be well met.
Description
Technical Field
The application relates to the technical field of precoolers, in particular to a method, a device, equipment and a storage medium for predicting the residual life of an aviation precooler.
Background
The aircraft bleed air system has the function of providing pressurized air with stable flow, pressure and temperature for user systems such as an air conditioning system, a pressurizing system, an anti-icing system, an aircraft engine starting system, a water tank pressurizing system, a hydraulic oil tank pressurizing system and the like. Precoolers are an important component of bleed air systems whose performance decays, directly affecting flight safety. Therefore, it is necessary to study the prediction of the remaining life of the precooler. The service life prediction result of the precooler by the related technology is not accurate enough, and the actual application requirement cannot be met.
Disclosure of Invention
The application aims to provide a method, a device, equipment and a storage medium for predicting the residual life of an aviation precooler, so as to improve the accuracy of predicting the residual life of the precooler. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to one aspect of an embodiment of the present application, there is provided a method of aviation precooler remaining life prediction, comprising:
Acquiring current key parameter data of a precooler;
determining whether the precooler currently belongs to a life cycle normal stage according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler;
if the key parameter data does not belong to the key parameter data, fitting the key parameter data to obtain a fitting curve equation;
and processing the fitting curve equation and the MLBF equation by using the multidimensional data updating correction model to obtain the residual life predicted value of the precooler.
In some embodiments of the present application, the method further comprises:
and if the precooler currently belongs to the life cycle normal stage, acquiring the residual life value of the precooler by adopting an MLBF algorithm.
In some embodiments of the present application, the method for acquiring the corresponding condition of the life cycle stage of the precooler includes:
acquiring preprocessing historical data of key parameters of a precooler;
determining a life cycle stage parameter threshold of the precooler according to the preprocessing history data;
and determining the corresponding condition according to the life cycle stage parameter threshold.
In some embodiments of the present application, the obtaining preprocessing history data of critical parameters of the precooler includes:
Acquiring historical data of key parameters of the precooler;
cleaning the historical data to obtain cleaned data;
and correcting the cleaned data to obtain preprocessing historical data.
In some embodiments of the present application, the cleaning the historical data includes: and eliminating false data recorded by the anomaly of the aircraft sensor in the historical data.
In some embodiments of the present application, the key parameters include precooler outlet temperature and precooler inlet pressure; the precooler life cycle stage comprises a normal stage and an abnormal stage; corresponding conditions for the normal phase include an MLBF value less than or equal to a first threshold, an outlet temperature less than or equal to a second threshold, and an inlet pressure less than or equal to a third threshold; the corresponding conditions for the anomaly phase include an MLBF value greater than a first threshold and an outlet temperature greater than a second threshold or an inlet pressure greater than a third threshold.
In some embodiments of the present application, the processing the fitted curve equation and the MLBF equation by using the multi-dimensional data update correction model to obtain a predicted value of the remaining lifetime of the precooler includes:
and taking the fitting curve equation as a state equation of the multi-dimensional data updating correction algorithm, taking the MLBF equation as an observation equation of the multi-dimensional data updating correction algorithm, and predicting by utilizing the multi-dimensional data updating correction model to obtain the optimal estimated residual life.
According to another aspect of an embodiment of the present application, there is provided an apparatus for predicting remaining life of an aviation precooler, comprising:
the key parameter data acquisition module is used for acquiring current key parameter data of the precooler;
the normal stage determining module is used for determining whether the precooler currently belongs to a life cycle normal stage according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler;
the fitting module is used for fitting the key parameter data if the key parameter data does not belong to the key parameter data to obtain a fitting curve equation;
and the residual life prediction module is used for processing the fitting curve equation and the MLBF equation by utilizing the multidimensional data updating correction model to obtain a residual life prediction value of the precooler.
According to another aspect of an embodiment of the present application, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of aviation precooler remaining life prediction of any one of the above.
According to another aspect of embodiments of the present application, there is provided a computer readable storage medium having stored thereon a computer program for execution by a processor to implement the method of aviation precooler remaining life prediction of any one of the above.
One of the technical solutions provided in one aspect of the embodiments of the present application may include the following beneficial effects:
according to the method for predicting the remaining life of the aircraft precooler based on the multi-dimensional data updating correction, whether the precooler belongs to the life cycle normal stage currently or not is determined according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler, if not, the key parameter data is fitted to obtain a fitted curve equation, the fitted curve equation and the MLBF equation are processed by using the multi-dimensional data updating correction model, the remaining life predicted value of the precooler is obtained, the accuracy of predicting the remaining life of the precooler is improved, and the requirements of practical application can be well met.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a flow chart of a method of aviation precooler remaining life prediction in accordance with an embodiment of the present application.
Fig. 2 shows a flow chart of a method of aviation precooler remaining life prediction in accordance with another embodiment of the present application.
Fig. 3 illustrates a precooler performance lifecycle diagram in one embodiment of the present application.
FIG. 4 illustrates precooler pressure decay trend along with fitted trend lines for precooler pressure data in one embodiment of the present application.
FIG. 5 shows a fleet aircraft precooler pressure history QAR in one embodiment of the present application
English: quick Access Recorder, chinese: a fast access recorder) data analysis map.
FIG. 6 shows a fitted plot of precooler outlet temperature maximum data in one embodiment of the present application.
FIG. 7 illustrates a precooler remaining life prediction result graph corrected based on multi-dimensional data updates in one embodiment of the present application.
Fig. 8 shows a block diagram of an apparatus for aviation precooler remaining life prediction according to an embodiment of the present application.
Fig. 9 shows a block diagram of an electronic device according to an embodiment of the present application.
FIG. 10 illustrates a computer-readable storage medium schematic of one embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Precooler refers to a heat exchanger or an evaporative cooler for reducing the temperature of the working fluid before it begins to be compressed. The precooler may be applied to an air conditioning system or to a bleed air system of an aircraft. Taking the bleed air system of an aircraft as an example, the precooler is the most important component for the bleed air system, and the service life of the precooler directly influences the service life of the bleed air system, so that for the bleed air system of the aircraft, the prediction of the remaining life of the precooler is important for maintenance and service of the bleed air system.
The inventors of the present application have found that the aircraft bleed air system precooler decays from early performance to failure, with significant decay in performance over time in the wing. By analyzing the historical performance degradation trend, the residual time from the current moment to the final failure is predicted. Accurate life predictions may improve reliability of aircraft components or systems and reduce maintenance costs. One embodiment of the present application thus proposes a precooler remaining life prediction method based on multidimensional data update correction.
As shown in fig. 1, a method for predicting the remaining life of an aviation precooler according to an embodiment of the present application includes the following steps:
acquiring key QAR data and operation data of a precooler; performing data acquisition and data correction processing; calculating residual MLBF (English: mean Life Before Failure average use time before failure, which is called MLBF for short), and defining characteristics of precoolers in a normal period, a decay period and a failure period aiming at a precooler failure mode; judging whether the precooler is in a decay period and a failure period by integrating the precooler health index threshold and the calculated residual MLBF threshold, if not, continuously monitoring performance, and if so, performing fitting analysis on key characteristic data such as temperature, pressure and the like on the precooler in the decay period and the failure period to obtain a state equation; obtaining a measurement equation by using the calculated residual MLBF; the multidimensional data update correction algorithm is performed using the state equation and the measurement equation.
In one possible implementation, the calculated estimated remaining life PV1 is based on a key data fitting function of the aircraft component; in addition, based on the average use time before failure MLBF algorithm, a corrected residual life value PV2 is calculated, a data fitting function, PV1 and PV2 are input into a multi-dimensional data correction updated residual life prediction calculation model, correction is carried out by using PV2, iterative updating is carried out in the model, and finally the more accurate residual life of the precooler is obtained.
In one possible implementation, the precooler remaining life prediction method of the present embodiment may include two parts, performance state evaluation and remaining life prediction. In the performance evaluation part, firstly, a model reflecting the health state of the precooler is established by utilizing full life cycle data analysis of the precooler, data fitting is carried out on historical data of the performance attenuation of the precooler, and the current performance of the precooler is evaluated; at the same time, the MLBF is introduced to obtain the average flight hours in the normal stage, and the state of the precooler is comprehensively judged: normal phase, decay phase, failure phase. In a data-driven residual life prediction part, a discrete data fitting formula is obtained by utilizing a least square method, a residual life data function of a predicted precooler, a residual life value PV1 and fitting reliability are obtained through historical QAR data analysis, and meanwhile, a predicted life PV2 is calculated through an MLBF algorithm; at this time, a multidimensional data updating correction algorithm is introduced, and finally, a residual life predicted value is obtained.
In one possible implementation, the transfer relationship of the state equation at time k and time k-1 can be obtained by fitting pre-cooler key data such as temperature and the like. The method comprises the steps of obtaining a time sequence function for predicting parameters such as the subsequent temperature of the precooler through data fitting, setting a complete failure threshold value based on the time sequence function and combining the precooler technical principle, and predicting the residual life PV1 so as to obtain the estimated residual life.
In addition, another estimated value PV2 of the remaining life is obtained as corrected remaining life data of the remaining life based on average service time before MLBF failure of the aircraft component manufacturer and the daily, weekly, monthly and annual components of the organic team and historical unplanned replacement of the components.
The time updating process can be regarded as a residual life estimating process and is mainly used for estimating the state variable value and the estimated error of the current moment so as to determine the priori estimated value of the state variable at the next moment; the metrology update process of the actual usage data of a large number of aircraft components may be considered a calibration process for comparing and correcting new observations to a priori estimates to obtain improved posterior estimates.
And taking the time sequence function and the PV1 and PV2 as input calculation models, continuously updating by using the time sequence function, correcting and iterating, and finally converging to calculate the optimal residual life.
As shown in fig. 2, another embodiment of the present application provides a method of aviation precooler remaining life prediction, the method comprising steps S10 to S40.
S10, acquiring current key parameter data of the precooler.
The pre-cooler current key parameter data may include, for example, pre-cooler outlet temperature and pre-cooler inlet pressure.
S20, determining whether the precooler currently belongs to a life cycle normal stage according to key parameter data and pre-acquired corresponding conditions of the life cycle stage of the precooler.
The precooler lifecycle includes a normal phase and an abnormal phase. The normal phase is a normal phase, and the abnormal phase includes a decay phase and a failure phase, and is specifically shown in fig. 3.
In some embodiments, the method for acquiring the corresponding condition of the life cycle stage of the precooler comprises the following steps: acquiring preprocessing historical data of key parameters of a precooler; determining a life cycle stage parameter threshold of the precooler according to the preprocessing history data; and determining the corresponding condition according to the life cycle stage parameter threshold.
Specifically, the obtaining preprocessing history data of key parameters of the precooler may include: acquiring historical data of key parameters of the precooler; cleaning the historical data to obtain cleaned data; and correcting the cleaned data to obtain history correction data, wherein the history correction data is preprocessing history data. Cleaning the historical data may include: and eliminating false data in the historical data recorded by the sensor, so as to avoid influencing subsequent processing steps.
Illustratively, the key parameters may include precooler outlet temperature and precooler inlet pressure; the precooler life cycle stage comprises a normal stage and an abnormal stage; corresponding conditions for the normal phase include an MLBF value less than or equal to a first threshold, an outlet temperature less than or equal to a second threshold, and an inlet pressure less than or equal to a third threshold; the corresponding conditions for the anomaly phase include an MLBF value greater than a first threshold and an outlet temperature greater than a second threshold or an inlet pressure greater than a third threshold. For example, in one specific example, the first threshold is 90%, the second threshold is 235 ℃, and the third threshold is 40PSI. The first threshold, the second threshold and the third threshold can be set according to actual needs.
Exemplary, by means of two data acquisition modes, namely WQAR (wireless rapid memory recorder) and ACARS (aircraft communication addressing and reporting system), locking representative key performance parameters, acquiring parameter data such as precooler temperature, pressure, PRV (Pressure Regulating Valve) upstream pressure, PRV actuation duration, external altitude and temperature (TAT) of the bleed air system in the whole life cycle of the wing.
As the precooler of the aircraft bleed air system has different working temperatures and altitudes when the precooler runs in the air and on the ground, the parameters such as the outlet temperature and the like can be influenced by the external temperature and the altitude air pressure. In order to evaluate each running condition of the precooler, the influence of temperature and altitude on key parameters of the precooler is eliminated by a mathematical method, the key parameters of the precooler are converted into uniform external temperature and altitude environments (such as standard air pressure sea level), and corrected parameter values of the key parameters are obtained.
For example, the precooler corrects the outlet temperature PRE-T cor The formula:
wherein Precool outlet temp is the outlet temperature of the precooler, θ is a correction factor, K is an external interference factor, TAT is the total temperature, T 0 Is the absolute temperature of the atmosphere in the standard state at sea level, T 0 =288.15 degrees kelvin, K is the ambient temperature interference factor.
In some embodiments, the life cycle of the precooler is divided into a plurality of stages according to the history correction data. And analyzing the history correction data of the precooler, and dividing the life cycle of the precooler in stages.
Illustratively, from analysis of the bleed air system technology principle, it is known that the fan bleed air flap always maintains the engine bleed air temperature at 200 ℃. The engine bleed valve regulates the outlet air pressure at 8-36 PSI; when the engine is at a high rotating speed, the engine bleed valve regulates the outlet air pressure to be 44PSI; a temperature controller that controls a solenoid valve (CTS) by means of a bleed air pressure regulating flap limits the bleed air temperature downstream of the precooler. When the temperature rises to 235 ℃, the bleed air pressure regulating flap (PRV) starts to control its downstream bleed air pressure to drop rapidly.
Analyzing historical QAR data of the precooler of the bleed air system to obtain performance life cycle data of the precooler. The following describes the lifecycle division concept in detail, taking in-wing data from 2016 to 2022 of a B-8XXX aircraft as an example.
As shown in fig. 3, the 10 month installation in 2015 eliminates the break-in period data. During the period from 4 months in 2016 to 6 months in 2019, the pressure and the temperature data of the precooler tend to be stable, the temperature and the pressure normally fluctuate within the manual range, and in addition, the period is set as a normal period for the initial period (such as the first 30% of the MLBF time period) of the component installation in the process of comprehensive judgment. During the period from 7 months in 2019 to 2 months in 2021, the outlet temperature data of the precooler of the bleed air system shows an ascending trend, the pressure shows a descending trend, and the section (namely the period from 7 months in 2019 to 2 months in 2021) is comprehensively judged and set as a declining period.
During the period from 3 months to 4 months of 2021, the outlet temperature data of the right bleed air precooler shows an ascending trend, and the occurrence temperature of a plurality of continuous flights exceeds 235 ℃ and faults are frequent. If the post-voyage inspection of the first city finds that the base at 7170HM2 and 10HA2 is inspected for air leakage at the time of 26 days of 4 months, the sealing ring is replaced, and the subsequent monitoring and data results show performance recovery. And 4, 7 days of 2021, checking that the left HPV valve of the ECAM page is not in the closed position by passing the station, and executing replacement of the left HPV valve. And 3, 2021, 03 and 31 days after voyage of the second city, replacing the left PRV. Subsequent monitoring and data results indicate performance recovery. The data shows that the left-hand precooler pressure shows a significant decrease. The section is comprehensively judged and set as a failure period.
And in the failure stage, the occurrence temperature of a plurality of continuous flights exceeds 235 ℃, the maximum value of the pressure of the precooler is lower than 42PSI, and the precooler deviates from a normal interval, so that obvious performance attenuation characteristics of the precooler are shown, and the obvious performance attenuation characteristics correspond to theoretical basis in an aircraft maintenance manual.
The whole service life of the precooler can be divided into 3 stages of a normal stage, a decay stage and a failure stage. When the precooler is in a failure period, the precooler cannot reliably work and must be maintained or replaced in time. Thus, the first 2 phases of operating state monitoring are of practical significance, while the remaining life prediction is estimated from the precooler entering the decay period.
After historical data analysis, the output results are as follows:
1) Related parameters of a normal period, a decay period and a failure period of the precooler; the normal phase is a normal phase, and the abnormal phase comprises a decay phase and a failure phase;
2) Meanwhile, a degradation model (relationship between observable data and hidden state) of the precooler performance attenuation is determined through historical QAR data analysis, and a general precooler attenuation fitting curve formula is obtained, wherein the pressure attenuation is a linear trend and the temperature attenuation is a curve trend;
3) And obtaining the threshold value of the performance attenuation of the precooler through technical principle analysis and historical fault data verification.
And judging the life cycle stage of the precooler according to the currently acquired key parameter data. And judging the stage of the precooler according to the generated temperature and other data. According to the data such as the temperature generated by the precooler, the output result of the historical data analysis is used as a judgment standard to judge the stage of the life cycle of the precooler at present, and the output result is:
case 1: a normal stage precooler; case 2: a decay stage precooler; case 3: a failure period precooler.
When a temperature trigger threshold occurs, and the existing temperature data fitting curve is similar to the failure period, and the usage time of the precooler reaches more than 90% of the MLBF (average usage time before failure) (e.g., the MLBF reaches 95%). For example, if the temperature of the outlet of a precooler exceeds a threshold value of 235 degrees celsius and the data fitting formula approaches the precooler formula of the failure period, the precooler can be determined to be in the failure period.
And S30, if the key parameter data do not belong to the normal stage, fitting the key parameter data to obtain a fitting curve equation.
And if the precooler is in the decay phase or the failure phase, fitting the key parameter discrete data of the corresponding period phase to obtain a fitted curve equation.
For the precooler in the degradation period of fig. 4 below, a discrete data fit is performed, y=f (x) to reflect the dependency between the quantities x and y. Curve fitting such as linear type attenuation, waterfall type attenuation, parabolic type attenuation curves and the like can be realized, and pressure data fitting examples are as follows: and preliminarily judging that the precooler pressure is in a linear relation with time, and setting y=bx+a. By using least square method (also called least squares method), the best function matching of data is found by minimizing the sum of squares of errors, and the formula is as follows
According to the formula, the data fitting is performed to solve the best estimate of the linear functions a and b. As shown in fig. 4, a linear fit function y= -0.001x+45.199 is obtained, where x is the number of flights after performance decay (alsoCan be converted into flight hours by number of flights time of flight), the R square value R of trend line 2 ＝0.7395，R 2 The closer to 1 indicates a better fitness, and the confidence level is set to 0.74.
Predicted remaining life with pressure data fit examples: according to the analysis of historical QAR precooler pressure data, as shown in FIG. 5, setting the precooler performance decay early warning threshold value to 41PSI, it can be predicted that the precooler will trigger performance decay warning when executing the 4199 th flight, obtain the number of flight hours of each flight, and can calculate the remaining life and also estimate the remaining number of flight hours by estimating according to 6 flights every day assuming the machine daily utilization rate of 9.5 FH. A remaining life value PV1 based on the precooler data fit is obtained.
Similarly, a precooler temperature curve can be fitted, and the fleet data analysis operation is repeatedly performed, and the result is shown in fig. 6.
And S40, utilizing the multidimensional data to update a correction model to process the fitting curve equation and the MLBF equation, and obtaining the residual life predicted value of the precooler.
In one embodiment, step S40 may include steps S401 to S404:
s401, calculating estimated residual life based on a key data fitting function of the aircraft component.
S402, calculating a corrected residual life value based on a time-to-average-before-failure MLBF algorithm.
S403, the data fitting function, the estimated residual life and the corrected residual life value are input into the multidimensional data to update the correction model.
S404, correcting by using the corrected residual life value, and performing iterative updating in a multidimensional data updating correction model to obtain the residual life predicted value of the precooler.
The MLBF is calculated by accumulating a large amount of usage data of the aircraft component in the actual use process, and the obtained average usage time before the fault of the aircraft component reflects the reliability state of the component in a certain time period. An MLBF (mean time to failure) calculation method is adopted, and a threshold value of key parameters such as temperature and pressure of an airplane precooler is combined as a judgment condition. If the precooler currently belongs to the life cycle normal stage, MLBF is less than 60% or MLBF is less than 80%, and a proper threshold value is set according to different principles of different aircraft components; if the precooler currently belongs to the decay or failure phase of the life cycle, MLBF >80% or MLBF >90%, the threshold value being dependent on the situation.
Specifically, the remaining life value PV2 of the precooler may be obtained by using an MLBF algorithm, which includes the following steps:
the aircraft component calculation method is set as follows:
in the case of different (average time of use before MLBF failure) aircraft components, the following detailed calculation method is set as division mlbf=10000 FH (flight hours):
1. the MLBF of an aircraft part of a certain model or part number is calculated as follows, less than 10000 FH:
A. first calculation
B. Subsequent calculation mode
Mlbf= (the cumulative number of flight hours of a component since the last calculation by a certain fleet reaches 3 times MLBF, and is not less than 3 calendar months)/(the number of failed removals during the period);
the minimum time interval for calculating the MLBF cannot be smaller than the part, the accumulated flight hours of the part from the last calculation are more than 3 times of the MLBF, and the time interval is not smaller than 3 calendar months, otherwise, the calculated MLBF value is too large because the time interval is too small, so that the MLBF calculation loses practical significance.
2. When the MLBF of an aircraft component with a certain model or part number is greater than or equal to 10000FH, the calculation mode is as follows:
A. first calculation
B. Subsequent calculation mode
The minimum time interval for calculating the MLBF cannot be smaller than the part, the accumulated flight hours of the part from the last calculation are more than 1 time of the MLBF, and are not smaller than 6 calendar months, otherwise, the calculated MLBF value is too large because the time interval is too small, so that the MLBF calculation is not practical.
Based on the MLBF calculation method, the system automatically acquires data such as the installation date of the left-handed precooler and the right-handed precooler (for example, the installation date of the B-8XXX aircraft precooler is 2015, 10 months), TSI (Time Since Installation) self-installation time: 19741.099FH, fault replacement number and the like, automatic operation is realized according to the MLBF calculation logic, the average use time before the MLBF faults is obtained, the used time is subtracted, so that a residual life value PV2 is obtained, meanwhile, the data of the actual temperature and the time of the fault precooler can be recorded, and when the residual 46 flight cycles (residual 115 FH) exist, the outlet temperature of the fault precooler is 230 degrees, and the actual data points can be used for the correction of the follow-up predicted value.
Taking prediction of the precooler temperature as an example, a prediction algorithm for updating and correcting multidimensional data is introduced. When the fitted data curve of the precooler temperature is obtained, the time required for the future temperature to reach the threshold temperature can be predicted, the time is the residual life, and meanwhile, the temperature data point of the real fault precooler is utilized for correction, so that the more accurate predicted residual life PV1 is obtained.
The outlet temperature of the precooler of an aircraft is 216 ℃, and an estimated temperature equation based on a time sequence can be obtained according to the temperature data fitting of the precooler:
T(t)＝0.5*0.004*t 2 +200
Setting the predicted temperature as X (k), Z (k) is a temperature value calculated by a data fitting function,
i.e. Z (k) =0.5×0.004×k 2 +200
Then Z (k+1) =Z (k) +0.004k+0.002
To implement the update correction function, the following formula is proposed:
X(k)＝X(k-1)+(Z(k)-X(k-1))*K(k)+(V(k)-X(k-1))(1-K(k))
P k ＝(1-K k-1 )P k-1
K k ＝P k /(P k +W)
P k in order to estimate the error of the temperature, X (K) is the predicted temperature, K (K) is the compensation coefficient, W is set to be in accordance with the Gaussian distribution noise, and V (K) represents the temperature value of the precooler with the real fault.
The above formula contains two information update procedures: a time update process and a correction update process. The time updating process is a pre-estimating process of the temperature value, and is used for calculating the errors of the temperature variable value and the pre-estimated value at the current moment so as to determine the state variable priori juice estimating value at the next moment. The other is a correction procedure for comparing the predicted value calculated by the new time series fitting function and the actual value at the time of the actual faulty aircraft component with the a priori estimate to be corrected to obtain an improved a priori estimate. Updating iteration is carried out through the equation, the correction is carried out continuously, and a predicted temperature value is finally obtained, and the method comprises the following detailed steps:
in the first step, an initial state of a failed precooler is set, that is, when k=0:
T(0)＝200℃
W＝0.2
from the temperature data of the historical real fault precooler, a data set, V (0) =201 ℃, V (53), can be obtained
＝206℃、V(10)＝210℃…V(87)＝216℃…
P 1 ＝1
K 1 ＝P 1 /(P 1 +0.2)
Then K is 1 ＝0.833
Substitution into the formula yields:
P 2 ＝(1-K 1 )P 1 ＝(1-0.833)*1＝0.167
K 2 ＝P 2 /(P 2 +0.2)
K 2 ＝0.166/(0.166+0.2)＝0.454
X(2)＝200+(200.008-200)*0.454+(201.008-200)*0.546＝200.554
step 3, when k=3:
3 ＝(1-K 2 )P 2
K 3 ＝P 3 /(P 3 +0.2)＝0.313
X(3)＝200.554+(200.018-200.554)*0.313+(201.019-200.554)*0.687＝200.706
…
step 87, repeating the above steps when k=87:
X(87)＝X(86)+(Z(87)-X(86))*K(87)+(V(87)-X(86))*(1-K(87))X(87)＝216.509+(215.138-216.509)*0.013+(216.895-216.509)*0.987＝216.872
…
step 143, when k=143, repeating the above steps:
X(143)＝X(142)+(Z(143)-X(142))*K(143)+(V(143)-X(142))*(1-K(143))
X(143)＝243.321+(240.898-243.321)*0.008+(243.943-243.321)*0.992＝243.919
at this time, the precooler temperature is 216 ℃, when the failure threshold value is set to 243 ℃, the estimated remaining life of the precooler is 56 flight cycles, and calculated according to 2.5FH of each flight on average of the fleet, the estimated remaining life is 140FH.
…
X(154)＝X(153)+(Z(154)-X(153))*K(153)+(V(153)-X(152))*(1-K(153))
X(154)＝250.779
at this time, when the precooler temperature is 216 ℃, and the failure threshold value is set to 250 ℃, the estimated remaining life of the precooler is 70 flight cycles, and the estimated remaining life PV1 is 175FH according to the number of flight hours of the average flight of the fleet, for example, 2.5 FH.
Substituting the data of PV1, PV2, corresponding temperature and the like into the following formula to perform correction and update calculation so as to obtain more accurate estimated residual life:
let Z (k) be the remaining life PV1 obtained based on temperature data fitting, V (k) be the remaining life PV2 based on MLBF, and the initial remaining life X (0) be the predicted value based on MLBF, calculated according to the following formula:
PV(k+1)＝PV(k)+(PV1(k+1)-PV(k))*K(k)+(PV2(k+1)-PV(k))(1-K(k))
K k ＝P k /(P k +W)
P k ＝(1-K k )P k-1
i.e. the predicted optimal remaining life is equal to the last predicted value, plus a deviation correction of the remaining life based on the MLBF and based on the data fit, resulting in a more accurate predicted value of remaining life. And in the residual life estimation process, the state variable prior juice estimation value at the next moment is estimated by using the state variable value at the current moment and the estimated error. In addition, the remaining life value PV1 is used to correct the actual remaining life value PV2 of the actual faulty aircraft component at that time. And the residual life prediction can be realized by utilizing automatic calculation of the system. The calculation result is shown in fig. 7.
The following are examples of applications in aircraft air conditioning systems:
and obtaining a time state equation of the outlet temperature of the air compressor through fitting key data in the air conditioning system, such as the outlet temperature of the air compressor, and obtaining a transfer relation between the k moment and the k-1 moment state equation.
For example, performance monitoring of an aircraft shows an ascending trend of the maximum value of the outlet temperature of the air compressor, the system gives an early warning of abnormal outlet temperature of the air compressor, and the data fitting function of the maximum value of the outlet temperature of the air compressor and the failure period is as follows:
T(t)＝0.002*t 2 +200
wherein when entering the failure period, the initial temperature T (0) =200 ℃, T being time; the relationship between the temperatures T (T) and T (T-1) is as follows:
T(t)＝T(t-1)+0.004t+0.002
the outlet temperature T (T) >200 ℃ of the compressor entering the failure period can be set to 250 ℃ or 230 ℃ as a threshold value, for example, the threshold value for complete failure of the compressor is set to 250 ℃, and the estimated residual life equation is as follows:
the derivation process of the estimated remaining life equation based on the time sequence is as follows:
and T (T) =0.5×0.004×t 2 +200
T(t+1)＝0.002*(t+1) 2 +200
＝0.002*(t 2 +2t+1)+200
＝(0.002*t 2 +200)+(0.002*(2t+1))
T(t+1)＝T(t)+0.004t+0.002
Deducing:
obtaining:
while
Finally, the method comprises the following steps:
and obtaining the transfer relation between the time t and the time t+1, and predicting the residual life equation of the PV 1. In addition, the residual service life PV2 of the air conditioner system air compressor is obtained through an MLBF algorithm, and meanwhile, the data of the outlet temperature of the real fault air compressor is obtained and is used as correction data.
The following formula is repeatedly calculated:
P k ＝(1-K k )P k-1
K k ＝P k /(P k +W)
PV(k+1)＝PV(k)+(PV1(k+1)-PV1(k))*K(k)+(Z(k+1)-PV(k))*(1-K(k))
and PV (k) is the residual life PV1 obtained based on temperature data fitting, Z (k) is the residual life PV2 based on MLBF, corresponding failure temperature thresholds are set according to different types of compressors with different piece numbers, and the calculation process is the same as the residual life prediction thought of the precooler, so that the optimal residual life estimated value can be obtained.
According to the method for predicting the residual life of the aviation precooler, whether the precooler belongs to the normal life cycle stage currently or not is determined according to key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler, if not, the key parameter data are fitted to obtain a fitted curve equation, the fitted curve equation and the MLBF equation are processed by using the multi-dimensional data updating correction model to obtain the residual life predicted value of the precooler, key parameter data of different stages of the life cycle are comprehensively adopted, the advantages of the residual life prediction based on the MLBF and the residual life prediction based on data fitting are comprehensively adopted, and the theory of multi-dimensional data updating correction is adopted, so that the accuracy of the prediction of the residual life of the precooler is improved, and the requirements of practical application can be well met.
As shown in fig. 8, another embodiment of the present application provides an apparatus for predicting remaining life of an aviation precooler, comprising:
the key parameter data acquisition module is used for acquiring current key parameter data of the precooler;
the normal stage determining module is used for determining whether the precooler currently belongs to a life cycle normal stage according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler;
the fitting module is used for fitting the key parameter data if the key parameter data does not belong to the key parameter data to obtain a fitting curve equation;
and the residual life prediction module is used for processing the fitting curve equation and the MLBF equation by utilizing the multidimensional data updating correction model to obtain a residual life prediction value of the precooler.
In some implementations, the remaining life prediction module is further to:
and if the precooler currently belongs to the life cycle normal stage, acquiring the residual life value of the precooler by adopting an MLBF algorithm.
In some embodiments, the method for acquiring the corresponding condition of the life cycle stage of the precooler includes:
acquiring preprocessing historical data of key parameters of a precooler;
determining a life cycle stage parameter threshold of the precooler according to the preprocessing history data;
And determining the corresponding condition according to the life cycle stage parameter threshold.
In some embodiments, the obtaining pre-processing history data of critical parameters of the precooler includes:
acquiring historical data of key parameters of the precooler;
cleaning the historical data to obtain cleaned data;
and correcting the cleaned data to obtain preprocessing historical data.
In some embodiments, the cleaning the historical data comprises: and rejecting false data in the historical data.
In some embodiments, the key parameters include precooler outlet temperature and precooler inlet pressure; the precooler life cycle stage comprises a normal stage and an abnormal stage; corresponding conditions for the normal phase include an MLBF value less than or equal to a first threshold, an outlet temperature less than or equal to a second threshold, and an inlet pressure less than or equal to a third threshold; the corresponding conditions for the anomaly phase include an MLBF value greater than a first threshold and an outlet temperature greater than a second threshold or an inlet pressure greater than a third threshold.
In some embodiments, the processing the fitted curve equation and the MLBF equation by using the multi-dimensional data update correction model to obtain the remaining lifetime prediction value of the precooler includes: and taking the fitting curve equation as a state equation of the multi-dimensional data updating correction algorithm, taking the MLBF equation as an observation equation of the multi-dimensional data updating correction algorithm, and predicting by utilizing the multi-dimensional data updating correction model to obtain the optimal estimated residual life.
According to the device for predicting the residual life of the aviation precooler, whether the precooler belongs to the life cycle normal stage currently or not is determined according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler, if not, the key parameter data are fitted to obtain a fitted curve equation, the fitted curve equation and the MLBF equation are processed by using the multidimensional data updating correction model, the residual life predicted value of the precooler is obtained, the key parameter data of different stages of the life cycle are comprehensively adopted, the prediction accuracy of the residual life of the precooler is improved, and the requirements of practical application can be well met.
Another embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement a method for predicting remaining life of an aviation precooler according to any one of the above embodiments.
As shown in fig. 9, the electronic device 10 may include: processor 100, memory 101, bus 102 and communication interface 103, processor 100, communication interface 103 and memory 101 being connected by bus 102; the memory 101 has stored therein a computer program executable on the processor 100, which when executed by the processor 100 performs the method provided by any of the embodiments described herein.
The memory 101 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and the at least one other network element is implemented via at least one communication interface 103 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
The processor 100 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 100 or by instructions in the form of software. The processor 100 may be a general-purpose processor, and may include a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), and the like; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 101, and the processor 100 reads the information in the memory 101 and, in combination with its hardware, performs the steps of the method described above.
The electronic device provided by the embodiment of the application and the method provided by the embodiment of the application are the same in the invention conception, and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Another embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program for execution by a processor to implement a method of aviation precooler residual life prediction in accordance with any one of the above embodiments. Referring to fig. 10, a computer readable storage medium is shown as an optical disc 20 having a computer program (i.e., a program product) stored thereon, which, when executed by a processor, performs the method provided by any of the embodiments described above.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above-described embodiments of the present application has the same advantageous effects as the method adopted, operated or implemented by the application program stored therein, for the same inventive concept as the method provided by the embodiments of the present application.
It should be noted that:
the term "module" is not intended to be limited to a particular physical form. Depending on the particular application, modules may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same components. There may or may not be clear boundaries between different modules.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may also be used with the examples herein. The required structure for the construction of such devices is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The foregoing examples merely represent embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (9)
1. A method of aviation precooler remaining life prediction comprising:
acquiring current key parameter data of a precooler;
determining whether the precooler currently belongs to a life cycle normal stage according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler;
if the key parameter data does not belong to the key parameter data, fitting the key parameter data to obtain a fitting curve equation;
updating a correction model by utilizing multidimensional data to process the fitting curve equation and the MLBF equation so as to obtain a residual life predicted value of the precooler;
the key parameters comprise precooler outlet temperature and precooler inlet pressure; the precooler life cycle stage comprises a normal stage and an abnormal stage; corresponding conditions for the normal phase include an MLBF value less than or equal to a first threshold, an outlet temperature less than or equal to a second threshold, and an inlet pressure less than or equal to a third threshold; the corresponding conditions of the anomaly phase include an MLBF value greater than a first threshold value and an outlet temperature greater than a second threshold value or an inlet pressure greater than a third threshold value;
The MLBF equation includes
Subtracting the used time of the precooler by using MLBF to obtain the residual life prediction value of the precooler.
2. The method according to claim 1, wherein the method further comprises:
if the precooler currently belongs to the life cycle normal stage, acquiring the residual life value of the precooler by adopting an MLBF algorithm;
the obtaining the remaining life value of the precooler by adopting the MLBF algorithm comprises the following steps: subtracting the used time of the precooler by using MLBF to obtain the residual life prediction value of the precooler.
3. The method of claim 1, wherein the method for obtaining the pre-cooler lifecycle stage corresponding condition comprises:
acquiring preprocessing historical data of key parameters of a precooler;
determining a life cycle stage parameter threshold of the precooler according to the preprocessing history data;
and determining the corresponding condition according to the life cycle stage parameter threshold.
4. The method of claim 3, wherein the obtaining pre-processing history data for critical parameters of the precooler comprises:
acquiring historical data of key parameters of the precooler;
cleaning the historical data to obtain cleaned data;
And correcting the cleaned data to obtain preprocessing historical data.
5. The method of claim 4, wherein the cleaning the historical data comprises: and rejecting false data in the historical data.
6. The method of claim 1, wherein the processing the fitted curve equation and the MLBF equation using the multi-dimensional data update correction model to obtain the remaining life prediction value of the precooler comprises:
calculating estimated residual life based on a key data fitting function of the aircraft component;
calculating a corrected remaining lifetime value based on a pre-fault average usage time MLBF algorithm;
the data fitting function, the estimated residual life and the corrected residual life value are input into the multidimensional data to update the correction model;
and correcting by utilizing the corrected residual life value, and carrying out iterative updating in the multidimensional data updating correction model to obtain the residual life predicted value of the precooler.
7. An apparatus for predicting remaining life of an aircraft precooler, comprising:
the key parameter data acquisition module is used for acquiring current key parameter data of the precooler;
the normal stage determining module is used for determining whether the precooler currently belongs to a life cycle normal stage according to the key parameter data and the pre-acquired corresponding conditions of the life cycle stage of the precooler;
The fitting module is used for fitting the key parameter data if the key parameter data does not belong to the key parameter data to obtain a fitting curve equation;
the residual life prediction module is used for updating a correction model by utilizing multidimensional data to process the fitting curve equation and the MLBF equation so as to obtain a residual life prediction value of the precooler;
the key parameters comprise precooler outlet temperature and precooler inlet pressure; the precooler life cycle stage comprises a normal stage and an abnormal stage; corresponding conditions for the normal phase include an MLBF value less than or equal to a first threshold, an outlet temperature less than or equal to a second threshold, and an inlet pressure less than or equal to a third threshold; the corresponding conditions of the anomaly phase include an MLBF value greater than a first threshold value and an outlet temperature greater than a second threshold value or an inlet pressure greater than a third threshold value;
the MLBF equation includes
Subtracting the used time of the precooler by using MLBF to obtain the residual life prediction value of the precooler.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of aviation precooler residual life prediction of any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, wherein the program is executed by a processor to implement the method of aviation precooler residual life prediction of any one of claims 1-6.
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