CN116362142A - Method, device, equipment and storage medium for predicting lubricating oil quantity of aero-engine - Google Patents

Method, device, equipment and storage medium for predicting lubricating oil quantity of aero-engine Download PDF

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CN116362142A
CN116362142A CN202310636973.3A CN202310636973A CN116362142A CN 116362142 A CN116362142 A CN 116362142A CN 202310636973 A CN202310636973 A CN 202310636973A CN 116362142 A CN116362142 A CN 116362142A
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lubricating oil
oil quantity
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wavelet decomposition
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CN116362142B (en
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袭奇
王婧
古书怀
邱佩臻
马驰
徐贵强
朱泊宇
谢承旺
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South China Normal University
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Abstract

The invention relates to the field of aeroengines, in particular to a method for predicting the lubricating oil quantity of an aeroengine, which comprises the steps of constructing a regression mapping model of flight parameters and the lubricating oil quantity through a cyclic neural network algorithm, taking a lubricating oil quantity time sequence and a flying parameter time sequence as a prediction module of the lubricating oil quantity prediction model, carrying out wavelet decomposition, taking a corresponding scale function as training data, effectively reducing the sequence length of the training data, solving the problems of gradient elimination and gradient explosion in the long-time sequence training process, and having stronger fitting capability on the training data with time dependence, so that the prediction module can accurately reflect the correlation between the flying parameters and the lubricating oil quantity, and improving the training accuracy and efficiency, thereby monitoring a lubricating oil system more accurately and effectively.

Description

Method, device, equipment and storage medium for predicting lubricating oil quantity of aero-engine
Technical Field
The invention relates to the field of aero-engines, in particular to a method, a device, equipment and a storage medium for predicting the lubricating oil quantity of an aero-engine.
Background
Among aircraft engine failures, oil system failures account for a significant proportion. Failure of the lubricating oil system can lead to engine shutdown, severely affecting flight safety. At present, research on health monitoring of an lubricating oil system mainly focuses on monitoring characteristic quantities such as lubricating oil temperature, lubricating oil consumption, abrasive particles in lubricating oil, lubricating oil pressure and the like. However, in practical applications, fault monitoring of the oil system is still mostly based on experience and qualitative analysis, which is not accurate and quantitative enough, so how to monitor the oil system and effectively predict the health condition of the oil system is a problem to be studied urgently.
The monitoring means of the traditional lubricating oil system comprises low oil quantity alarming, post-aviation manual monitoring and double-emission difference alarming, however, the monitoring means have the problems of poor real-time performance, time consumption in analysis, difficulty in reflecting different engine characteristic differences and the like, and because the change of the lubricating oil quantity is influenced by various factors, such as the factors of the rotating speed of a high-pressure rotor of an engine, the attitude of an airplane, the flying speed and the like, the lubricating oil system is difficult to accurately and effectively monitor, and the flying safety is influenced.
Disclosure of Invention
Based on the above, the invention aims to provide a method, a device, equipment and a storage medium for predicting the lubricating oil quantity of an aeroengine, which are used for constructing a regression mapping model of flight parameters and the lubricating oil quantity through a cyclic neural network algorithm, taking a time sequence of the lubricating oil quantity and a time sequence of the flight parameters as a prediction module of the lubricating oil quantity prediction model, carrying out wavelet decomposition, taking a corresponding scale function as training data, effectively reducing the sequence length of the training data, solving the problems of gradient elimination and gradient explosion in the long-time sequence training process, and having stronger fitting capability on the training data with time dependence, so that the prediction module can accurately reflect the correlation between the flight parameters and the lubricating oil quantity, improve the training accuracy and efficiency, and monitor the lubricating oil system more accurately and effectively.
In a first aspect, an embodiment of the present application provides a method for predicting an amount of lubricating oil for an aeroengine, including the following steps:
obtaining recording data corresponding to a plurality of flights, and constructing a parameter time sequence set corresponding to the plurality of flights, wherein the recording data comprises lubricating oil quantity recording data and a plurality of flight parameter recording data, and the parameter time sequence set comprises a lubricating oil quantity time sequence and a plurality of flight parameter time sequences;
obtaining a preset lubricating oil mass prediction model, wherein the lubricating oil mass prediction model comprises a wavelet decomposition module and a prediction module to be trained;
inputting the parameter time series set corresponding to the plurality of times of flight into the wavelet decomposition module to perform wavelet decomposition to obtain a scale function value set corresponding to the plurality of times of flight, wherein the scale function value set comprises scale function values corresponding to each flight parameter time series and scale function values corresponding to the lubricating oil quantity time series;
inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained for training to obtain a target lubricating oil quantity prediction model;
and responding to a prediction instruction, obtaining flight parameter record data to be predicted, constructing a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputting the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubricating oil quantity prediction model, and obtaining a lubricating oil quantity prediction result corresponding to the flight parameter record data to be predicted.
In a second aspect, an embodiment of the present application provides an apparatus for predicting an amount of lubricating oil for an aeroengine, including:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring record data corresponding to a plurality of flights and constructing a parameter time sequence set corresponding to the plurality of flights, the record data comprises lubricating oil quantity record data and a plurality of flight parameter record data, and the parameter time sequence set comprises a lubricating oil quantity time sequence and a plurality of flight parameter time sequences;
the model acquisition module is used for acquiring a preset lubricating oil quantity prediction model, wherein the lubricating oil quantity prediction model comprises a wavelet decomposition module and a prediction module to be trained;
the wavelet decomposition module is used for inputting the parameter time series set corresponding to the plurality of times of flight into the wavelet decomposition module to carry out wavelet decomposition, so as to obtain the scale function value set corresponding to the plurality of times of flight, wherein the scale function value set comprises the scale function value corresponding to each flight parameter time series and the scale function value corresponding to the lubricating oil quantity time series;
the model training module is used for inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained for training to obtain a target lubricating oil quantity prediction model;
The lubrication oil quantity prediction module is used for responding to a prediction instruction, obtaining flight parameter record data to be predicted, constructing a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputting the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubrication oil quantity prediction model, and obtaining a lubrication oil quantity prediction result corresponding to the flight parameter record data to be predicted.
In a third aspect, embodiments of the present application provide a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program when executed by the processor implements the steps of the method for predicting the amount of lubricating oil for an aircraft engine according to the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium storing a computer program which, when executed by a processor, implements the steps of the method for predicting an amount of lubricating oil for an aero-engine according to the first aspect.
In the embodiment of the application, the method, the device and the equipment for predicting the lubricating oil quantity of the aeroengine and the storage medium are provided, a regression mapping model of the flight parameter and the lubricating oil quantity is constructed through a cyclic neural network algorithm and is used as a prediction module of the lubricating oil quantity prediction model, the time sequence of the lubricating oil quantity and the time sequence of the flight parameter are subjected to wavelet decomposition, the corresponding scale function is used as training data, the sequence length of the training data is effectively reduced, the problems of gradient elimination and gradient explosion in the long-time sequence training process can be solved, the fitting capacity of the training data with time dependence is higher, the prediction module can accurately reflect the correlation between the flight parameter and the lubricating oil quantity, the training accuracy and the training efficiency are improved, and the lubricating oil system is monitored more accurately and effectively.
For a better understanding and implementation, the present invention is described in detail below with reference to the drawings.
Drawings
Fig. 1 is a schematic diagram of an application scenario of an aero-engine lubricating oil quantity prediction method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for predicting the amount of lubricating oil for an aircraft engine according to one embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of S3 in an aero-engine lubrication oil quantity prediction method according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of S4 in an aero-engine lubrication oil quantity prediction method according to an embodiment of the present application;
FIG. 5 is a schematic flow chart of S5 in an aero-engine lubrication oil quantity prediction method according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of an aircraft engine lubrication oil quantity prediction apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first message may also be referred to as a second message, and similarly, a second message may also be referred to as a first message, without departing from the scope of the present application. The word "if"/"if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.
The data transmitting end can be a computer device or a mobile terminal device, and is used for establishing network connection with the data receiving end, encoding data information transmitted to the data receiving end and analyzing the data information transmitted from the data receiving end.
The data receiving end can be a computer device or a mobile terminal device, and is used for establishing network connection with the data sending end, encoding data information sent to the data sending end, and analyzing the data information sent from the data sending end.
Referring to fig. 1, fig. 1 is a flow chart of a method for predicting an amount of lubricating oil of an aero-engine according to an embodiment of the present application, the method includes the following steps:
s1: and obtaining record data corresponding to the plurality of flights, and constructing a parameter time sequence set corresponding to the plurality of flights.
The main body of execution of the method for predicting the amount of lubricating oil for an aircraft engine of the present application is a prediction device (hereinafter referred to as a prediction device) for the method for predicting the amount of lubricating oil for an aircraft engine. In an alternative embodiment, the prediction device may be a computer device, may be a server, or may be a server cluster formed by combining multiple computer devices.
The recording data is that QAR data recording equipment is adopted to read various data from an airplane bus (such as an ARINC 429 bus or an AFDX bus), the data collected every second is stored in a frame-subframe-subslot mode according to a preset recording configuration table, and binary data streams are converted into engineering values recorded by a sensor by adopting decoding software to serve as the recording data.
The lubricating oil system of the aeroengine comprises a lubricating oil tank, an oil filter, an oil pump, a heat exchanger and other key components, lubricating oil in the lubricating oil tank is supplied to the components needing cooling and lubrication through the oil pump driven by the engine, and lubricating oil from the oil tank and a gear box is returned to the lubricating oil tank through the oil return pump, so that the circulation of the lubricating oil in the engine system is formed. The lubricating oil quantity sensor is positioned in the lubricating oil tank and can be divided into a capacitive oil quantity sensor and a magnetic float type oil level sensor. The capacitive sensor is excited with alternating current and the sensor generates a direct current signal proportional to the liquid level. The magnetic float sensor uses a magnetic reed switch to move the valve rod up and down according to the rising and falling of the oil level, and oil quantity record is obtained and used as the lubricating oil quantity record data.
The flight parameter recording data comprise recording data corresponding to a plurality of flight parameters, wherein the flight parameters comprise an engine high-pressure rotor rotating speed parameter, a low-pressure rotor rotating speed parameter, an aircraft flight altitude parameter and a flight attitude parameter, and the flight attitude parameter comprises a roll angle, a pitch angle, a roll angle difference value, a pitch angle difference value, a roll angle second-order difference value and a pitch angle second-order difference value.
In this embodiment, the prediction device obtains recording data corresponding to a plurality of flights, and constructs a parameter time series set corresponding to the plurality of flights, where the recording data includes lubricating oil amount recording data and a plurality of flight parameter recording data, and the parameter time series set includes a lubricating oil amount time series and a plurality of flight parameter time series.
In an optional embodiment, the oil quantity recording data includes a plurality of time-corresponding original oil quantities and a reference oil quantity, where the original oil quantities are the sensor valuestThe oil quantity recorded at the beginning of the moment, the reference oil quantity is the oil quantity recorded at the end of the moment t by the sensor, and the reference oil quantity is used for indicating the oil quantity of the aircraft when the landing oil return is completed. The time sequence of the lubricating oil quantity comprises an original lubricating oil quantity and a reference lubricating oil quantity corresponding to a plurality of moments, wherein the reference lubricating oil quantity is the recorded lubricating oil quantity when the plurality of moments are finished.
S2: and obtaining a preset lubricating oil quantity prediction model, wherein the lubricating oil quantity prediction model comprises a wavelet decomposition module and a prediction module to be trained.
In this embodiment, the prediction apparatus obtains a preset lubrication oil amount prediction model, where the lubrication oil amount prediction model includes a wavelet decomposition module and a prediction module to be trained. The wavelet decomposition module comprises a plurality of wavelet decomposition layers, and the wavelet decomposition can focus on any details of the signals for multi-resolution time-frequency domain analysis due to the characteristic of multi-resolution analysis, so that the training accuracy of the prediction module can be improved.
The prediction module to be trained can adopt a long-term memory network (LSTM), wherein the LSTM is a model constructed by adopting a cyclic neural network algorithm, and can store history information to form long-term memory.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for predicting an amount of lubricating oil of an aero-engine according to another embodiment of the present application, and further includes steps S6 to S7, where the steps S6 to S7 are as follows:
s6: and obtaining the lubricating oil temperature record data corresponding to the plurality of flights, and constructing a lubricating oil temperature time sequence corresponding to the plurality of flights.
When the engine is running, the lubricating oil is at different operating temperatures. Before the engine is cold started (typically, the first start in a day), the temperature of the lubricating oil is substantially identical to the outside air temperature. After the engine is started, the temperature of the lubricating oil is generally above 70 ℃, and the value of the lubricating oil can be influenced by the thermal expansion effect.
In order to reduce the influence of the thermal expansion effect on the training of the lubricating oil prediction model, in the embodiment, the prediction equipment obtains lubricating oil temperature record data corresponding to a plurality of flights, and constructs a lubricating oil temperature time sequence corresponding to the plurality of flights, so as to calibrate the original lubricating oil quantity corresponding to each moment in the lubricating oil quantity time sequence, and improve the training accuracy of the prediction module of the lubricating oil quantity prediction model.
S7: and calibrating the original lubricating oil quantity corresponding to each moment in the lubricating oil quantity time sequence according to the lubricating oil temperature time sequence corresponding to the plurality of flights and a preset lubricating oil quantity correction algorithm, so as to obtain the calibrated lubricating oil quantity corresponding to the plurality of moments corresponding to the plurality of flights.
The lubricating oil amount correction algorithm is as follows:
Figure SMS_1
in the method, in the process of the invention,
Figure SMS_2
is the firsttCalibrated amounts of lubricating oil for the respective moments +.>
Figure SMS_3
Is the firsttOil temperature corresponding to each moment +.>
Figure SMS_4
Is the firsttOriginal lubricating oil quantity corresponding to each moment +.>
Figure SMS_5
Is the firsttThe reference amount of lubricating oil corresponding to each moment,
Figure SMS_6
represent the firsttThe end of each moment.
In this embodiment, the prediction device calibrates the original lubrication oil amount corresponding to each moment in the lubrication oil amount time sequence according to the lubrication oil temperature time sequence corresponding to the plurality of flights and a preset lubrication oil amount correction algorithm, so as to obtain the calibrated lubrication oil amount corresponding to the plurality of moments corresponding to the plurality of flights.
S3: and inputting the parameter time sequence set corresponding to the plurality of flights into the wavelet decomposition module to carry out wavelet decomposition to obtain a scale function value set corresponding to the plurality of flights.
In this embodiment, the prediction device inputs the parameter time series set corresponding to the plurality of flights to the wavelet decomposition module to perform wavelet decomposition, so as to obtain a scale function value set corresponding to the plurality of flights, where the scale function value set includes a scale function value corresponding to each flight parameter time series and a scale function value corresponding to a lubricating oil amount time series.
Referring to fig. 3, fig. 3 is a schematic flow chart of S3 in the method for predicting the lubrication oil amount of an aero-engine according to an embodiment of the present application, including step S31, specifically as follows:
s31: and taking the parameter time sequence set, each flight parameter time sequence and the lubricating oil quantity time sequence as signals to be decomposed, and obtaining a scale function value set corresponding to a plurality of flights according to a preset wavelet decomposition algorithm.
The wavelet decomposition algorithm is as follows:
Figure SMS_7
in the method, in the process of the invention,
Figure SMS_8
for the signal to be decomposed,kas a function of the amount of the offset, jFor scaling factor, the number of layers of the current wavelet decomposition layer is indicated, < >>
Figure SMS_9
Is the firstj-scale function coefficients of wavelet decomposition layer of layer-1, < ->
Figure SMS_10
Is the firstj-layer 1 wavelet decomposition layer outputtScale function value corresponding to moment, < >>
Figure SMS_11
Is the firstj-layer 1 wavelet decomposition layer outputtWavelet function coefficient corresponding to time, +.>
Figure SMS_12
Is the firstj-layer 1 wavelet decomposition layer outputtThe wavelet function value corresponding to the moment, wherein the scale function value and the wavelet function value are expressed as follows:
Figure SMS_13
in the method, in the process of the invention,
Figure SMS_14
is the firstjWavelet decomposition layer output of layertScale function value corresponding to moment, < >>
Figure SMS_15
Is the firstjLow-pass filter coefficients of wavelet decomposition layer of +1 layer, +.>
Figure SMS_16
Is the firstjWavelet decomposition layer output of layertWavelet function value corresponding to time, < >>
Figure SMS_17
Is the firstjHigh pass filter coefficients of the wavelet decomposition layer of +1 layers.
In this embodiment, the prediction device uses the parameter time sequence set, each time sequence of flight parameters and the time sequence of the amount of lubrication oil as the signal to be decomposed, and obtains a set of scale function values corresponding to a plurality of flights according to a preset wavelet decomposition algorithm, as training data of a prediction module of the lubrication oil prediction model.
The prediction device adopts a biorthogonal wavelet base method, performs wavelet decomposition on a signal to be decomposed, and takes a scale function value after wavelet decomposition as training data, so that the sequence length of input and output parameters of a prediction module for training the lubricating oil quantity prediction model is effectively reduced, and the training accuracy and efficiency are effectively improved.
S4: and inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained for training, and obtaining a target lubricating oil quantity prediction model.
In this embodiment, the prediction device inputs the scale function value set corresponding to the plurality of flights into the prediction module to be trained to perform training, so as to obtain a target lubrication oil quantity prediction model.
Referring to fig. 4, fig. 4 is a schematic flow chart of step S4 in the method for predicting the lubrication oil amount of an aero-engine according to an embodiment of the present application, including steps S41 to S42, specifically as follows:
s41: and taking the scale function values corresponding to the time sequences of the flight parameters in the scale function value set as input data, obtaining output doors corresponding to the time corresponding to the flight times according to the cyclic neural network algorithm, combining the output doors corresponding to the time corresponding to the same flight time, and constructing the scale function values corresponding to the predicted lubricating oil quantity time sequences corresponding to the flight times.
In this embodiment, the prediction device normalizes the scale function values corresponding to the time series of the flight parameters in the set of scale function values, and then uses the normalized scale function values as input data, and obtains output gates corresponding to a plurality of moments corresponding to a plurality of flights according to the cyclic neural network algorithm, where the cyclic neural network algorithm process is as follows:
Figure SMS_18
In the method, in the process of the invention,
Figure SMS_19
is the firsttForgetting door corresponding to each moment +.>
Figure SMS_23
Weight matrix parameters for forgetting gate, +.>
Figure SMS_26
Is the firstt-1 output gate corresponding to moment, < ->
Figure SMS_22
Is the firsttInput data corresponding to the respective time instant +.>
Figure SMS_25
Sigma () is the activation function for the bias parameters of the forgetting gate, +.>
Figure SMS_27
Is the firsttInput gate corresponding to each moment +.>
Figure SMS_30
Weight matrix parameters for input gates, < +.>
Figure SMS_20
For the weight matrix of the input gate, +.>
Figure SMS_24
Is the firsttTime of dayEtching corresponding cell state,/->
Figure SMS_28
Is the firsttThe amount of change of the state of the cell corresponding to the moment, < + >>
Figure SMS_29
Weight matrix parameters for cell states, +.>
Figure SMS_21
Is a bias parameter for the cell state, and tanh is a hyperbolic tangent function.
The output door is:
Figure SMS_31
in the method, in the process of the invention,
Figure SMS_32
is the firstt-1 output gate corresponding to moment, < ->
Figure SMS_33
For the weight matrix parameters of the output gate, < +.>
Figure SMS_34
To output the bias parameters of the gate.
The prediction equipment combines the output doors corresponding to a plurality of moments corresponding to the same flight to construct a scale function value corresponding to a predicted lubrication oil quantity time sequence corresponding to a plurality of flights.
S42: and calculating mean square error data according to the scale function value corresponding to the predicted lubricating oil mass time sequence and the scale function value corresponding to the lubricating oil mass time sequence corresponding to the same flight, training the prediction module to be trained according to the mean square error data to obtain a target prediction module, and combining the target prediction module with a wavelet decomposition module to obtain a target lubricating oil mass prediction model.
In this embodiment, the prediction device calculates the mean square error data according to the scale function value corresponding to the predicted oil mass time sequence and the scale function value corresponding to the oil mass time sequence corresponding to the same flight, trains the prediction module to be trained according to the mean square error data to obtain a target prediction module, and combines the target prediction module with the wavelet decomposition module to obtain a target oil mass prediction model.
The regression mapping model of the flight parameter and the lubricating oil quantity is constructed through a cyclic neural network algorithm and is used as a prediction module of the lubricating oil quantity prediction model, so that the problems of gradient elimination and gradient explosion in the long-time sequence training process can be solved, the fitting capacity of training data with time dependence is stronger, the prediction module can accurately reflect the correlation between the flight parameter and the lubricating oil quantity, and the lubricating oil system can be monitored more accurately and effectively.
S5: and responding to a prediction instruction, obtaining flight parameter record data to be predicted, constructing a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputting the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubricating oil quantity prediction model, and obtaining a lubricating oil quantity prediction result corresponding to the flight parameter record data to be predicted.
The prediction instruction is sent by a user and received by prediction equipment.
The prediction equipment responds to a prediction instruction to obtain flight parameter record data to be predicted, constructs a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputs the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubricating oil quantity prediction model, obtains a lubricating oil quantity prediction result corresponding to the flight parameter record data to be predicted, and displays the lubricating oil quantity prediction result on a preset display interface.
Referring to fig. 5, fig. 5 is a schematic flow chart of step S5 in the method for predicting the lubrication oil amount of an aero-engine according to an embodiment of the present application, including steps S51 to S52, specifically as follows:
s51: and inputting a plurality of flight parameter time sequences corresponding to the flight record data to be predicted into a wavelet decomposition module in the target lubricating oil quantity prediction model to obtain scale function values corresponding to the flight parameter time sequences corresponding to the flight record data to be predicted.
In this embodiment, the prediction device inputs the plurality of flight parameter time sequences corresponding to the flight record data to be predicted to the wavelet decomposition module in the target lubricating oil amount prediction model, and obtains the scale function value corresponding to each flight parameter time sequence corresponding to the flight record data to be predicted, which may refer to step S31, and will not be described herein.
S52: and inputting the scale function values corresponding to the time series of each flight parameter corresponding to the flight record data to be predicted into a prediction module in the target lubricating oil quantity prediction model, obtaining the scale function values corresponding to the time series of the predicted lubricating oil quantity corresponding to the flight record data to be predicted, and performing difference processing on the scale function values corresponding to the time series of the predicted lubricating oil quantity corresponding to the flight record data to be predicted to serve as the lubricating oil quantity prediction result.
In this embodiment, the prediction device inputs the scale function value corresponding to each time series of flight parameters corresponding to the flight record data to be predicted to the prediction module in the target lubricating oil amount prediction model, so as to obtain the scale function value corresponding to the time series of predicted lubricating oil amounts corresponding to the flight record data to be predicted.
And the prediction equipment performs difference processing on the scale function value corresponding to the predicted lubrication oil quantity time sequence corresponding to the flight record data to be predicted, and restores the scale function value to lubrication oil quantity prediction data corresponding to the time sequence corresponding to the flight record data to be predicted, so as to serve as a lubrication oil quantity prediction result, and accurately and effectively monitor the lubrication oil system.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an apparatus for predicting an amount of lubricating oil of an aero-engine according to an embodiment of the present application, where the apparatus may implement all or a part of the apparatus for predicting an amount of lubricating oil of an aero-engine by software, hardware or a combination of both, and the apparatus 6 includes:
the data acquisition module 61 is configured to obtain recording data corresponding to a plurality of flights, and construct a parameter time series set corresponding to the plurality of flights, where the recording data includes lubricating oil volume recording data and a plurality of flight parameter recording data, and the parameter time series set includes a lubricating oil volume time series and a plurality of flight parameter time series;
the model obtaining module 62 is configured to obtain a preset lubrication oil quantity prediction model, where the lubrication oil quantity prediction model includes a wavelet decomposition module and a prediction module to be trained;
the wavelet decomposition module 63 is configured to input the parameter time series set corresponding to the plurality of flights into the wavelet decomposition module to perform wavelet decomposition, so as to obtain a scale function value set corresponding to the plurality of flights, where the scale function value set includes a scale function value corresponding to each flight parameter time series and a scale function value corresponding to a lubricating oil amount time series;
The model training module 64 is configured to input the scale function value set corresponding to the plurality of flights into the prediction module to be trained for training, so as to obtain a target lubrication oil quantity prediction model;
the lubrication oil quantity prediction module 65 is configured to obtain flight parameter record data to be predicted in response to a prediction instruction, construct a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, input the plurality of flight parameter time sequences corresponding to the flight record data to be predicted to the target lubrication oil quantity prediction model, and obtain a lubrication oil quantity prediction result corresponding to the flight parameter record data to be predicted.
In this embodiment, recording data corresponding to a plurality of flights is obtained through a data obtaining module, and a parameter time sequence set corresponding to the plurality of flights is constructed, wherein the recording data includes lubricating oil quantity recording data and a plurality of flight parameter recording data, and the parameter time sequence set includes a lubricating oil quantity time sequence and a plurality of flight parameter time sequences; a preset lubricating oil quantity prediction model is obtained through a model obtaining module, wherein the lubricating oil quantity prediction model comprises a wavelet decomposition module and a prediction module to be trained; inputting the parameter time series set corresponding to the plurality of times of flight into the wavelet decomposition module through the wavelet decomposition module to carry out wavelet decomposition, and obtaining a scale function value set corresponding to the plurality of times of flight, wherein the scale function value set comprises scale function values corresponding to each flight parameter time series and scale function values corresponding to the lubricating oil quantity time series; inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained for training through a model training module to obtain a target lubricating oil quantity prediction model; and responding to a prediction instruction through a lubricating oil quantity prediction module, obtaining flight parameter record data to be predicted, constructing a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputting the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubricating oil quantity prediction model, and obtaining a lubricating oil quantity prediction result corresponding to the flight parameter record data to be predicted. The regression mapping model of the flight parameter and the lubricating oil quantity is constructed through a cyclic neural network algorithm and is used as a prediction module of the lubricating oil quantity prediction model, the time sequence of the lubricating oil quantity and the time sequence of the flight parameter are subjected to wavelet decomposition, the corresponding scale function is used as training data, the sequence length of the training data is effectively reduced, the problems of gradient elimination and gradient explosion in the long-time sequence training process can be solved, the fitting capacity of the training data with time dependence is stronger, the prediction module can accurately reflect the correlation between the flight parameter and the lubricating oil quantity, the training accuracy and efficiency are improved, and the lubricating oil system is monitored more accurately and effectively.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present application, where the computer device 7 includes: a processor 71, a memory 72, and a computer program 73 stored on the memory 72 and executable on the processor 71; the computer device may store a plurality of instructions adapted to be loaded by the processor 71 and to perform the method steps of fig. 1 to 5, and the specific implementation procedure may be referred to in the specific description of fig. 1 to 5, which is not repeated here.
Wherein processor 71 may include one or more processing cores. The processor 71 performs various functions of the aircraft engine oil amount prediction device 6 and processes the data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 72, and invoking data in the memory 72, using various interfaces and various parts within the wired server, alternatively the processor 71 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field-programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programble Logic Array, PLA). The processor 71 may integrate one or a combination of several of a central processing unit 71 (Central Processing Unit, CPU), an image processor 71 (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the touch display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 71 and may be implemented by a single chip.
The Memory 72 may include a random access Memory 72 (Random Access Memory, RAM) or a Read-Only Memory 72 (Read-Only Memory). Optionally, the memory 72 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 72 may be used to store instructions, programs, code sets, or instruction sets. The memory 72 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 72 may optionally be at least one memory device located remotely from the aforementioned processor 71.
The embodiment of the present application further provides a storage medium, where the storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executed by the processor to perform the method steps of fig. 1 to 5, and a specific execution process may refer to specific descriptions of fig. 1 to 5, which are not repeated herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc.
The present invention is not limited to the above-described embodiments, but, if various modifications or variations of the present invention are not departing from the spirit and scope of the present invention, the present invention is intended to include such modifications and variations as fall within the scope of the claims and the equivalents thereof.

Claims (10)

1. The method for predicting the lubricating oil quantity of the aero-engine is characterized by comprising the following steps of:
obtaining recording data corresponding to a plurality of flights, and constructing a parameter time sequence set corresponding to the plurality of flights, wherein the recording data comprises lubricating oil quantity recording data and a plurality of flight parameter recording data, and the parameter time sequence set comprises a lubricating oil quantity time sequence and a plurality of flight parameter time sequences;
obtaining a preset lubricating oil mass prediction model, wherein the lubricating oil mass prediction model comprises a wavelet decomposition module and a prediction module to be trained;
inputting the parameter time series set corresponding to the plurality of times of flight into the wavelet decomposition module to perform wavelet decomposition to obtain a scale function value set corresponding to the plurality of times of flight, wherein the scale function value set comprises scale function values corresponding to each flight parameter time series and scale function values corresponding to the lubricating oil quantity time series;
Inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained for training to obtain a target lubricating oil quantity prediction model;
and responding to a prediction instruction, obtaining flight parameter record data to be predicted, constructing a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputting the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubricating oil quantity prediction model, and obtaining a lubricating oil quantity prediction result corresponding to the flight parameter record data to be predicted.
2. The method for predicting the amount of lubricating oil for an aircraft engine according to claim 1, wherein: the oil quantity recording data comprise original oil quantity and reference oil quantity corresponding to a plurality of moments, the oil quantity time sequence comprises the original oil quantity and the reference oil quantity corresponding to the plurality of moments, and the reference oil quantity is the recorded oil quantity when the plurality of moments are finished.
3. The method for predicting the lubricating oil quantity of an aeroengine according to claim 2, wherein before inputting the parameter time series set corresponding to the plurality of flights into the wavelet decomposition module to perform wavelet decomposition to obtain the scale function value set corresponding to the plurality of flights, the method further comprises the steps of:
Obtaining lubricating oil temperature record data corresponding to a plurality of flights, and constructing a lubricating oil temperature time sequence corresponding to the plurality of flights, wherein the lubricating oil temperature time sequence comprises lubricating oil temperatures corresponding to a plurality of moments;
according to the time series of the temperature of the lubricating oil corresponding to the plurality of flights and a preset lubricating oil quantity correction algorithm, the parameter time series corresponding to the plurality of flights are concentrated, the original lubricating oil quantity corresponding to each moment in the lubricating oil quantity time series is calibrated, and the calibrated lubricating oil quantity corresponding to the plurality of moments corresponding to the plurality of flights is obtained, wherein the lubricating oil quantity correction algorithm is as follows:
Figure QLYQS_1
in the method, in the process of the invention,
Figure QLYQS_2
is the firsttCalibrated amounts of lubricating oil for the respective moments +.>
Figure QLYQS_3
Is the firsttThe temperature of the lubricating oil corresponding to each moment,
Figure QLYQS_4
is the firsttOriginal lubricating oil quantity corresponding to each moment +.>
Figure QLYQS_5
Is the firsttReference lubricating oil quantity corresponding to each moment +.>
Figure QLYQS_6
Represent the firsttThe end of each moment.
4. The method for predicting the amount of lubricating oil for an aircraft engine according to claim 3, wherein: the wavelet decomposition module comprises a plurality of wavelet decomposition layers;
inputting the parameter time sequence set corresponding to the plurality of flights into the wavelet decomposition module to perform wavelet decomposition to obtain a scale function value set corresponding to the plurality of flights, wherein the method comprises the following steps of:
Taking the parameter time sequence set, each flight parameter time sequence and the lubricating oil quantity time sequence as signals to be decomposed, and obtaining a scale function value set corresponding to a plurality of flights according to a preset wavelet decomposition algorithm, wherein the wavelet decomposition algorithm is as follows:
Figure QLYQS_7
in the method, in the process of the invention,
Figure QLYQS_8
for the signal to be decomposed,kas a function of the amount of the offset,jfor scaling factor, the number of layers of the current wavelet decomposition layer is indicated, < >>
Figure QLYQS_9
Is the firstj-scale function coefficients of wavelet decomposition layer of layer-1, < ->
Figure QLYQS_10
Is the firstj-layer 1 wavelet decomposition layer outputtScale function value corresponding to moment, < >>
Figure QLYQS_11
Is the firstj-layer 1 wavelet decomposition layer outputtWavelet function coefficient corresponding to time, +.>
Figure QLYQS_12
Is the firstj-layer 1 wavelet decomposition layer outputtThe wavelet function value corresponding to the moment, wherein the scale function value and the wavelet function value are expressed as follows:
Figure QLYQS_13
in the method, in the process of the invention,
Figure QLYQS_14
is the firstjWavelet decomposition layer output of layertScale function value corresponding to moment, < >>
Figure QLYQS_15
Is the firstjLow-pass filter coefficients of wavelet decomposition layer of +1 layer, +.>
Figure QLYQS_16
Is the firstjWavelet decomposition layer output of layertWavelet function value corresponding to time, < >>
Figure QLYQS_17
Is the firstjHigh pass filter coefficients of the wavelet decomposition layer of +1 layers.
5. The method for predicting the amount of lubricating oil for an aircraft engine according to claim 4, wherein: the prediction module to be trained is a model constructed by adopting a cyclic neural network algorithm, wherein the cyclic neural network algorithm process is as follows:
Figure QLYQS_18
In the method, in the process of the invention,
Figure QLYQS_20
is the firsttForgetting door corresponding to each moment +.>
Figure QLYQS_23
Weight matrix parameters for forgetting gate, +.>
Figure QLYQS_27
Is the firstt-1 output gate corresponding to moment, < ->
Figure QLYQS_19
Is the firsttInput data corresponding to the respective time instant +.>
Figure QLYQS_22
Sigma () is the activation function for the bias parameters of the forgetting gate, +.>
Figure QLYQS_24
Is the firsttInput gate corresponding to each moment +.>
Figure QLYQS_25
Weight matrix parameters for input gates, < +.>
Figure QLYQS_26
As a weight matrix of the input gates,
Figure QLYQS_28
is the firsttThe cell states corresponding to the respective moments +.>
Figure QLYQS_29
Is the firsttThe amount of change of the state of the cell corresponding to the moment, < + >>
Figure QLYQS_30
Weight matrix parameters for cell states, +.>
Figure QLYQS_21
Is a bias parameter for the cell state, and tanh is a hyperbolic tangent function.
6. The method for predicting the amount of lubricating oil of an aeroengine according to claim 5, wherein the step of inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained to perform training to obtain a target prediction model of the amount of lubricating oil comprises the steps of:
the scale function values corresponding to the time series of the flight parameters are collected to be used as input data, output doors corresponding to the time corresponding to the flight times are obtained according to the cyclic neural network algorithm, the output doors corresponding to the time corresponding to the same flight time are combined, and the scale function values corresponding to the time series of the predicted lubricating oil quantity corresponding to the flight times are constructed, wherein the output doors are:
Figure QLYQS_31
In the method, in the process of the invention,
Figure QLYQS_32
is the firstt-1 output gate corresponding to moment, < ->
Figure QLYQS_33
For the weight matrix parameters of the output gate, < +.>
Figure QLYQS_34
The bias parameters of the output gate;
and calculating mean square error data according to the scale function value corresponding to the predicted lubricating oil mass time sequence and the scale function value corresponding to the lubricating oil mass time sequence corresponding to the same flight, training the prediction module to be trained according to the mean square error data to obtain a target prediction module, and combining the target prediction module with a wavelet decomposition module to obtain a target lubricating oil mass prediction model.
7. The method for predicting the amount of lubricating oil of an aeroengine according to claim 6, wherein the step of inputting the plurality of time series of flight parameters corresponding to the flight record data to be predicted to the target lubricating oil amount prediction model to obtain the predicted result of the amount of lubricating oil corresponding to the flight record data to be predicted includes the steps of:
inputting a plurality of flight parameter time sequences corresponding to the flight record data to be predicted into a wavelet decomposition module in the target lubricating oil quantity prediction model to obtain scale function values corresponding to the flight parameter time sequences corresponding to the flight record data to be predicted;
And inputting the scale function values corresponding to the time series of each flight parameter corresponding to the flight record data to be predicted into a prediction module in the target lubricating oil quantity prediction model, obtaining the scale function values corresponding to the time series of the predicted lubricating oil quantity corresponding to the flight record data to be predicted, and performing difference processing on the scale function values corresponding to the time series of the predicted lubricating oil quantity corresponding to the flight record data to be predicted to serve as the lubricating oil quantity prediction result.
8. An aeroengine lubrication oil quantity prediction device, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring record data corresponding to a plurality of flights and constructing a parameter time sequence set corresponding to the plurality of flights, the record data comprises lubricating oil quantity record data and a plurality of flight parameter record data, and the parameter time sequence set comprises a lubricating oil quantity time sequence and a plurality of flight parameter time sequences;
the model acquisition module is used for acquiring a preset lubricating oil quantity prediction model, wherein the lubricating oil quantity prediction model comprises a wavelet decomposition module and a prediction module to be trained;
the wavelet decomposition module is used for inputting the parameter time series set corresponding to the plurality of times of flight into the wavelet decomposition module to carry out wavelet decomposition, so as to obtain the scale function value set corresponding to the plurality of times of flight, wherein the scale function value set comprises the scale function value corresponding to each flight parameter time series and the scale function value corresponding to the lubricating oil quantity time series;
The model training module is used for inputting the scale function value sets corresponding to the plurality of flights into the prediction module to be trained for training to obtain a target lubricating oil quantity prediction model;
the lubrication oil quantity prediction module is used for responding to a prediction instruction, obtaining flight parameter record data to be predicted, constructing a plurality of flight parameter time sequences corresponding to the flight record data to be predicted, inputting the plurality of flight parameter time sequences corresponding to the flight record data to be predicted into the target lubrication oil quantity prediction model, and obtaining a lubrication oil quantity prediction result corresponding to the flight parameter record data to be predicted.
9. A computer device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for predicting the amount of lubricating oil of an aeroengine as claimed in any one of claims 1 to 7 when the computer program is executed by the processor.
10. A storage medium storing a computer program which, when executed by a processor, implements the steps of the aeroengine lubrication oil quantity prediction method of any of claims 1 to 7.
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