Aircraft hydraulic oil leakage fault diagnosis and prediction method based on two-step correction strategy

Abstract

The embodiment of the invention provides an aircraft hydraulic oil leakage fault diagnosis and prediction method based on a two-step correction strategy, and the method comprises the steps: obtaining at least two monitoring parameters of a hydraulic system of target equipment in a historical time interval, the at least two monitoring parameters comprise a hydraulic oil quantity parameter; correcting the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; under the condition that it is determined that the characteristic value belongs to a pre-generated parameter value interval corresponding to the hydraulic oil quantity parameter, the parameter value interval is updated to generate a target parameter value interval; inputting the characteristic value into a pre-trained fault detection model for processing, and generating a hydraulic oil quantity predicted value of the hydraulic system at the target moment; and determining a fault detection result of the hydraulic system at the target moment based on the hydraulic oil quantity prediction value and the target parameter value interval.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of hydraulic systems, and more particularly to a method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy. Background Art

[0002] A hydraulic system refers to a set of devices that uses hydraulic oil as the working medium, transmits pressure through hydraulic oil, and drives the actuator to complete specific operating actions. The basic components of a hydraulic system include hydraulic oil pumps, control valves, actuators, etc. Hydraulic systems have many excellent properties, such as stable operation, high reliability, high power-to-weight ratio, and convenient automatic control. Therefore, since the 1940s, hydraulic systems have been widely used in various equipment. In the 1960s, electrical and electronic technology and automatic control theory developed rapidly, driving hydraulic technology to become one of the hot research directions in the field of mechanical electronics, which in turn promoted the rapid development of hydraulic system technology. Hydraulic systems have gradually become an indispensable and important part of many modern equipment.

[0003] In order to improve the reliability of hydraulic systems, the diagnosis and prediction of hydraulic system leakage faults is an important method. However, at present, the diagnosis and prediction methods of hydraulic system leakage faults for many devices are still at the stage of manual judgment. For example, observe whether there is oil leakage in the hydraulic system through visual inspection, regular inspection, etc.; or directly observe the change trend of the hydraulic system oil volume data within a certain period of time to manually judge whether there is oil leakage in the hydraulic system. This manual judgment method relies on experience, and the diagnosis frequency is low, and intelligent diagnosis and active prevention cannot be achieved. Therefore, how to achieve accurate intelligent diagnosis and prediction of hydraulic oil leakage faults is an urgent problem to be solved in the current field of hydraulic system fault diagnosis. Summary of the invention

[0004] In view of this, an embodiment of this specification provides an aircraft hydraulic oil leakage fault diagnosis prediction method based on a two-step correction strategy. One or more embodiments of this specification also relate to an aircraft hydraulic oil leakage fault diagnosis prediction device based on a two-step correction strategy, a computing device, a computer-readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0005] According to a first aspect of an embodiment of this specification, a method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy is provided, comprising: Acquire at least two monitoring parameters of the hydraulic system of the target equipment within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil volume parameter; Correcting the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; When it is determined that the characteristic value belongs to the pre-generated parameter value interval corresponding to the hydraulic oil quantity parameter, the parameter value interval is updated to generate a target parameter value interval; Inputting the characteristic value into a pre-trained fault detection model for processing to generate a predicted value of the hydraulic oil volume of the hydraulic system at a target time; Based on the predicted value of the hydraulic oil quantity and the target parameter value interval, a fault detection result of the hydraulic system at the target time is determined.

[0006] Optionally, the modifying the parameter value of the hydraulic oil quantity parameter includes: Determine a first parameter value of a hydraulic oil volume parameter of the hydraulic system at a current start-up time, and determine a target downtime duration of the hydraulic system according to the current start-up time and a downtime time before the current start-up time; Based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, the first parameter value is corrected to generate a second parameter value.

[0007] Optionally, the at least two detection parameters further include a historical downtime duration and an increase in hydraulic oil corresponding to the historical downtime duration; Accordingly, the first parameter value is corrected based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, including: Determining a reference downtime duration of the hydraulic system according to a plurality of historical downtime durations within the historical time interval; Establishing an empirical function between the downtime and the amount of hydraulic oil increase according to the correspondence between the multiple historical downtimes and the multiple amounts of hydraulic oil increase; The first parameter value is corrected based on the target downtime duration, the reference downtime duration and the empirical function.

[0008] Optionally, the at least two detection parameters further include a historical hydraulic oil surge increment of the hydraulic system when the target device releases the parking brake; Correspondingly, the correction of the parameter value of the hydraulic oil quantity parameter includes: determining whether the target device releases a parking brake before the engine of the target device is started; If not, determining a target hydraulic oil surge increment of the hydraulic system when the brake is released according to a plurality of historical hydraulic oil surge increments within the historical time interval; The second parameter value is corrected based on the target hydraulic oil surge increase.

[0009] Optionally, the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy further includes: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Calculate the mean and standard deviation corresponding to the multiple historical characteristic values; Based on the mean value and the standard deviation, a parameter value interval corresponding to the hydraulic oil quantity parameter is constructed using the three sigma principle.

[0010] Optionally, updating the parameter value interval to generate a target parameter value interval includes: The upper and lower limits of the parameter value interval are used as prior information, the characteristic value and at least two of the multiple historical characteristic values ​​are used as posterior information, and the upper and lower limits of the parameter value interval are updated using Bayesian estimation to generate a target parameter value interval.

[0011] Optionally, the fault detection model comprises an encoder, a decoder and a prediction network, the encoder and the decoder are respectively composed of two layers of long short-term memory networks, and the prediction network is composed of four layers of fully connected layers; Accordingly, the inputting the characteristic value into a pre-trained fault detection model for processing includes: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Inputting the feature value and at least two of the multiple historical feature values ​​as input data into the encoder, and encoding the input data by the encoder to generate a corresponding embedding vector; The embedding vector is input into the prediction network for processing.

[0012] Optionally, the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy further includes: Slidingly segmenting the multiple historical feature values ​​to generate sample data and sample labels; Inputting the sample data into the encoder of the fault detection model to be trained for encoding processing to generate a first embedding vector; Inputting the first embedding vector into the prediction network of the fault detection model to be trained for processing to generate a corresponding prediction result; Calculate a first loss value of the fault detection model to be trained based on the sample label and the prediction result; Calculate a first gradient value corresponding to a network parameter of the prediction network according to the chain derivative rule and in combination with the first loss value; Updating the network parameters according to a gradient descent method in combination with the first gradient value; When it is determined according to the first loss value that the loss function of the fault detection model to be trained converges, the training is stopped to obtain the fault detection model.

[0013] Optionally, the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy further includes: Inputting the sample data into the encoder of the initial fault detection model for encoding processing to generate a second embedding vector; Inputting the second embedding vector into the decoder of the initial fault detection model for decoding processing to generate reconstructed sample data corresponding to the sample data; Calculate a second loss value of the initial fault detection model based on the sample data and the reconstructed sample data; Calculate second gradient values ​​corresponding to network parameters of the encoder and the decoder according to the chain derivative rule and in combination with the second loss value; Update the network parameters of the encoder and the decoder according to the gradient descent method and in combination with the second gradient value; When it is determined according to the second loss value that the loss function of the initial fault detection model converges, the training is stopped to obtain the fault detection model to be trained.

[0014] According to a second aspect of an embodiment of this specification, there is provided an aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy, comprising: An acquisition module is configured to acquire at least two monitoring parameters of a hydraulic system of a target device within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil volume parameter; a correction module, configured to correct the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; An updating module is configured to update the parameter value interval to generate a target parameter value interval when it is determined that the characteristic value belongs to the pre-generated parameter value interval corresponding to the hydraulic oil quantity parameter; a processing module configured to input the characteristic value into a pre-trained fault detection model for processing, and generate a predicted value of the hydraulic oil volume of the hydraulic system at a target time; The determination module is configured to determine a fault detection result of the hydraulic system at the target time based on the predicted value of the hydraulic oil quantity and the target parameter value interval.

[0015] According to a third aspect of an embodiment of this specification, a computing device is provided, including: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement any step of the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0016] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of any one of the aircraft hydraulic oil leakage fault diagnosis and prediction methods based on the two-step correction strategy are implemented.

[0017] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0018] The aircraft hydraulic oil leakage fault diagnosis and prediction method based on a two-step correction strategy provided in the embodiments of the present specification obtains at least two monitoring parameters of the hydraulic system of the target equipment within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil quantity parameter; corrects the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; when it is determined that the characteristic value belongs to a parameter value interval corresponding to the pre-generated hydraulic oil quantity parameter, updates the parameter value interval to generate a target parameter value interval; inputs the characteristic value into a pre-trained fault detection model for processing to generate a predicted value of the hydraulic oil quantity of the hydraulic system at the target time; and determines the fault detection result of the hydraulic system at the target time based on the predicted value of the hydraulic oil quantity and the target parameter value interval. By correcting the parameter value of the hydraulic oil quantity parameter, not only the oil quantity in the hydraulic oil tank but also the hydraulic oil quantity in the hydraulic pipeline is taken into account, so that the corrected hydraulic oil quantity is closer to the actual oil quantity and has better authenticity and predictability; in addition, by updating the parameter value interval, it is beneficial to ensure the long-term effectiveness of the upper and lower limit thresholds of the interval, which can effectively reduce the false alarm rate; in addition, the hydraulic oil quantity is predicted based on the fault detection model, so as to realize the fault prediction of the hydraulic system, which is beneficial to improve the accuracy and stability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy provided by an embodiment of this specification; Figure 2 is a structural schematic diagram of a fault detection model provided by an embodiment of this specification; Figure 3 It is a processing flow chart of a method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy provided by an embodiment of the present specification; Figure 4 It is a flow chart of a parameter value correction process of a hydraulic oil quantity parameter provided by an embodiment of this specification; Figure 5 is a flow chart of a parameter value interval update process provided by an embodiment of this specification; Figure 6 is a flow chart of a fault detection process provided by an embodiment of this specification; Figure 7 It is a structural schematic diagram of an aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy provided by an embodiment of this specification; Figure 8 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0020] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0021] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0022] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0023] First, the terms involved in one or more embodiments of this specification are explained.

[0024] At present, most aircraft use electro-hydraulic servo systems as the aircraft's actuation system. The aircraft's hydraulic actuation system is responsible for the opening and closing of the movable parts on the aircraft and other complex control functions. For example, the retraction and control of important parts such as aircraft wheels, rudders, speed brakes, flaps, and engine reverse thrust are all undertaken by the hydraulic system. In order to ensure the reliability of the hydraulic system, the aircraft is generally equipped with two or more independent hydraulic systems. One of the hydraulic systems assumes the main control responsibility, which is called the public hydraulic system; the remaining hydraulic systems assume secondary control responsibilities or are used as backups, which are called power-assisted hydraulic systems (or control hydraulic systems). The public hydraulic system is generally used for the retraction and extension of landing gear, speed brakes, flaps and other parts, front wheel turning control, and driving the fuel pump motor. The power-assisted hydraulic system is only used to drive the booster and damping servo of the above-mentioned flight control system.

[0025] The health status of the aircraft hydraulic system directly affects the flight safety of the aircraft. According to statistics, hydraulic system failures account for 30% of all mechanical failures in aircraft. Once the aircraft hydraulic system fails, on the one hand, it will bring great instability to the flight process and cause the failure of the flight mission; on the other hand, if the emergency equipment cannot effectively respond to the environmental conditions when the hydraulic system fails, it may cause serious consequences such as aircraft destruction and death. Therefore, it is crucial to diagnose and predict aircraft hydraulic system failures in a timely and accurate manner.

[0026] Among the failure modes of hydraulic systems, hydraulic oil leakage, including running, bubbling, dripping, and leaking, accounts for the vast majority. Therefore, the diagnosis and prediction of hydraulic system leakage failures is an important method to improve the reliability of hydraulic systems.

[0027] In this specification, a method for diagnosing and predicting an aircraft hydraulic oil leakage fault based on a two-step correction strategy is provided. This specification also relates to an aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy, a computing device, a computer-readable storage medium, and a computer program, which are described in detail one by one in the following embodiments.

[0028] Figure 1 A flowchart of a method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0029] Step 102: Obtain at least two monitoring parameters of the hydraulic system of the target device within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil volume parameter.

[0030] In the embodiments of this specification, the target equipment includes but is not limited to machine tools and metal processing equipment, construction machinery and engineering equipment, metallurgical and mining equipment, aerospace and military equipment, automobiles and vehicles, light industrial equipment and heavy industrial equipment, etc. However, the embodiments of this specification only take the aircraft as the target equipment for illustration, and the aircraft hydraulic oil leakage fault diagnosis prediction process based on the two-step correction strategy for other types of target equipment is consistent with the fault detection process of the aircraft hydraulic system, which will not be repeated here.

[0031] Step 104: Correct the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter.

[0032] In an optional implementation manner, the modifying the parameter value of the hydraulic oil quantity parameter includes: Determine a first parameter value of a hydraulic oil volume parameter of the hydraulic system at a current start-up time, and determine a target downtime duration of the hydraulic system according to the current start-up time and a downtime time before the current start-up time; Based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, the first parameter value is corrected to generate a second parameter value.

[0033] Furthermore, the at least two detection parameters also include a historical downtime duration and an increase in hydraulic oil corresponding to the historical downtime duration; Accordingly, the first parameter value is corrected based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, including: Determining a reference downtime duration of the hydraulic system according to a plurality of historical downtime durations within the historical time interval; Establishing an empirical function between the downtime and the amount of hydraulic oil increase according to the correspondence between the multiple historical downtimes and the multiple amounts of hydraulic oil increase; The first parameter value is corrected based on the target downtime duration, the reference downtime duration and the empirical function.

[0034] Furthermore, the at least two detection parameters also include a historical hydraulic oil surge increment of the hydraulic system when the target device releases the parking brake; Correspondingly, the correction of the parameter value of the hydraulic oil quantity parameter includes: determining whether the target device releases a parking brake before the engine of the target device is started; If not, determining a target hydraulic oil surge increment of the hydraulic system when the brake is released according to a plurality of historical hydraulic oil surge increments within the historical time interval; The second parameter value is corrected based on the target hydraulic oil surge increase.

[0035] Specifically, the parameter value of the hydraulic oil volume parameter is corrected, that is, the hydraulic oil tank oil volume of the hydraulic system at the time of engine start is corrected, and before the correction is made, the parameters of the extrapolation correction and brake release correction algorithms need to be determined according to historical data. In the embodiment of this specification, the at least two monitoring parameters obtained within the historical time interval include the hydraulic oil tank oil volume of the hydraulic system at the time of engine start and other historical data.

[0036] The parameters of the extrapolation correction algorithm include the baseline downtime , the empirical function of the hydraulic oil increase as the downtime changes. The downtime here refers to the difference between the engine start time and the last engine downtime. Calculate the historical downtime in the historical data , taking the mode of historical downtime duration as the benchmark downtime duration The hydraulic oil increase during the downtime refers to the difference between the hydraulic oil tank oil volume corresponding to the hydraulic system at the time of engine start and the hydraulic oil tank oil volume corresponding to the hydraulic system at the last engine downtime. Corresponding hydraulic oil increase As the hydraulic oil in the hydraulic pipeline continues to flow into the oil tank, the rate of increase of the oil volume in the oil tank follows the law from fast to slow, so the relationship between the increase of hydraulic oil and the historical downtime approximately follows a logarithmic function . Transform this formula into , based on historical downtime The corresponding increase in hydraulic oil , the empirical function of the hydraulic oil increase amount changing with the historical downtime can be obtained by using the least squares method as follows:

[0037] The parameters of the brake release correction algorithm include the target hydraulic oil surge of the hydraulic system when the brake is released. . Since some aircraft may release the parking brake before the engine is started, the release of the parking brake will cause the hydraulic oil sealed in the brake to flow back to the hydraulic oil tank, causing a slight increase in the amount of hydraulic oil. For aircraft that do not release the parking brake before the engine is started, in order to make the extracted hydraulic oil amount closer to the actual hydraulic oil amount, the impact of this sudden increase needs to be considered. Count the sizes of multiple historical hydraulic oil sudden increases in historical data, and use the average of multiple historical hydraulic oil sudden increases as the target hydraulic oil sudden increase of the hydraulic system when the brake is released. .

[0038] When correcting the parameter value of the hydraulic oil volume parameter, firstly, the first parameter value of the hydraulic oil volume parameter of the hydraulic system at the current startup time and the target downtime are adaptively extracted. Then, the first parameter value is used as the hydraulic oil volume reference value , and based on the target downtime Compared with the baseline downtime Hydraulic oil volume reference value To make an extrapolation correction:

[0039] The corrected second parameter value is .

[0040] Then, it is determined whether the acquired data contains relevant data of "the aircraft has brake release action before engine start". If so, no brake release correction is performed. As the corrected characteristic value; if it does not exist, perform brake release correction:

[0041] The corrected eigenvalue is .

[0042] The embodiment of this specification performs two-step correction on the input hydraulic oil quantity data, namely, extrapolation correction and brake release correction, to form a hydraulic oil quantity characteristic value. Specifically, firstly, the hydraulic oil tank oil quantity data just before the engine is started is extracted as a reference value. Then, the aircraft parking time is extracted, and the hydraulic oil quantity extrapolation correction value is determined according to the actual parking time, the reference parking time and the empirical formula, and the reference hydraulic oil quantity is added with the extrapolation correction value to realize the extrapolation correction. Finally, it is determined whether the aircraft releases the parking brake before the engine is started. If the parking brake has been released, the brake release correction is not performed; if the parking brake has not been released, the hydraulic oil quantity after extrapolation correction is added with the correction value to realize the brake release correction. Finally, the hydraulic oil quantity after two corrections is used as the hydraulic oil quantity characteristic value. By performing extrapolation correction and brake release correction in this way, not only the hydraulic oil tank oil quantity is considered, but also the hydraulic oil quantity in the hydraulic pipeline is considered, so that the corrected hydraulic oil quantity is closer to the actual oil quantity, with better authenticity and predictability.

[0043] Step 106: When it is determined that the characteristic value belongs to the parameter value interval corresponding to the pre-generated hydraulic oil quantity parameter, the parameter value interval is updated to generate a target parameter value interval.

[0044] In an optional implementation, the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy further includes: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Calculate the mean and standard deviation corresponding to the multiple historical characteristic values; Based on the mean value and the standard deviation, a parameter value interval corresponding to the hydraulic oil quantity parameter is constructed using the three sigma principle.

[0045] Further, the updating of the parameter value interval to generate a target parameter value interval includes: The upper and lower limits of the parameter value interval are used as prior information, the characteristic value and at least two of the multiple historical characteristic values ​​are used as posterior information, and the upper and lower limits of the parameter value interval are updated using Bayesian estimation to generate a target parameter value interval.

[0046] Specifically, before updating the parameter value interval, the multiple historical characteristic value data of the hydraulic oil volume parameter in the historical time interval can be used to update the parameter value interval. Determine the upper and lower thresholds of the characteristic value of the hydraulic oil quantity parameter Assuming that the characteristic value data of the hydraulic oil volume parameter obeys the normal distribution, the upper and lower thresholds are constructed using the three sigma principle:

[0047] The parameter value interval formed by the upper and lower thresholds is [ ].

[0048] When correcting and generating the second parameter value, it is possible to first determine whether the second parameter value is within the parameter value range; if not, it is determined that the second parameter value is abnormal, that is, a hydraulic oil leakage fault has occurred. In this case, a fault warning needs to be issued; if so, it is determined that the second parameter value is normal, and no hydraulic oil leakage fault has occurred. Therefore, the second parameter value can be used to update the aforementioned upper and lower limit thresholds.

[0049] In the threshold update process, in order to simplify the calculation, the latest m-1 historical feature values ​​are selected With the second parameter value Used to update the threshold. Using the existing threshold as prior information and the above m samples as posterior information, the upper and lower thresholds are updated using Bayesian estimation:

[0050] in, is the probability distribution, They are the updated mean, standard deviation, upper threshold, and lower threshold respectively.

[0051] The target parameter value interval generated after the update is [ ].

[0052] In the embodiments of this specification, threshold construction and threshold update are performed based on the characteristic value of the hydraulic oil quantity. First, based on the characteristic value extracted from the historical data, the upper and lower limit thresholds of the characteristic value are constructed using the Bayesian estimation method and the three sigma principle. Then, it is determined whether the newly input oil quantity characteristic value is within the upper and lower limit thresholds. If it is within the threshold, the data is judged to be normal, and the threshold is updated based on the data; if it is not within the threshold, the data is judged to be abnormal, and the threshold is not updated. The adaptive threshold constructed through historical data can overcome the uncertainty and subjectivity of traditional empirical diagnosis. The long-term effectiveness of the adaptive threshold guaranteed by continuous threshold updates can effectively reduce the false alarm rate.

[0053] Step 108: Input the characteristic value into a pre-trained fault detection model for processing to generate a predicted value of the hydraulic oil volume of the hydraulic system at the target time.

[0054] In an optional implementation, the fault detection model includes an encoder, a decoder, and a prediction network, the encoder and the decoder are respectively composed of two layers of long short-term memory networks, and the prediction network is composed of four layers of fully connected layers; Accordingly, the inputting the characteristic value into a pre-trained fault detection model for processing includes: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Inputting the feature value and at least two of the multiple historical feature values ​​as input data into the encoder, and encoding the input data by the encoder to generate a corresponding embedding vector; The embedding vector is input into the prediction network for processing.

[0055] In another optional implementation, the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy further includes: Inputting the sample data into the encoder of the initial fault detection model for encoding processing to generate a second embedding vector; Inputting the second embedding vector into the decoder of the initial fault detection model for decoding processing to generate reconstructed sample data corresponding to the sample data; Calculate a second loss value of the initial fault detection model based on the sample data and the reconstructed sample data; Calculate second gradient values ​​corresponding to network parameters of the encoder and the decoder according to the chain derivative rule and in combination with the second loss value; Update the network parameters of the encoder and the decoder according to the gradient descent method and in combination with the second gradient value; When it is determined according to the second loss value that the loss function of the initial fault detection model converges, the training is stopped to obtain the fault detection model to be trained.

[0056] Furthermore, the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy also includes: Slidingly segmenting the multiple historical feature values ​​to generate sample data and sample labels; Inputting the sample data into the encoder of the fault detection model to be trained for encoding processing to generate a first embedding vector; Inputting the first embedding vector into the prediction network of the fault detection model to be trained for processing to generate a corresponding prediction result; Calculate a first loss value of the fault detection model to be trained based on the sample label and the prediction result; Calculate a first gradient value corresponding to a network parameter of the prediction network according to the chain derivative rule and in combination with the first loss value; Updating the network parameters according to a gradient descent method in combination with the first gradient value; When it is determined according to the first loss value that the loss function of the fault detection model to be trained converges, the training is stopped to obtain the fault detection model.

[0057] A structural diagram of a fault detection model provided in the embodiments of this specification is as follows Figure 2 As shown in Figure 2, the fault detection model consists of three parts: encoder, decoder and prediction network. The encoder and decoder are composed of two layers of long short-term memory networks respectively, and the prediction network is composed of four fully connected layers.

[0058] The embodiment of this specification trains and generates a fault detection model through the following steps: 1) Determine multiple historical characteristic values ​​of hydraulic oil volume parameters within the historical time interval , slidingly split it into sample data With sample labels . Where i is the sample number, tf is the input vector dimension, and tp is the output vector dimension. The physical meaning of tf and tp is to use tf historical eigenvalues ​​to predict the future tp eigenvalues.

[0059] 2) First, pre-train the encoder and decoder. During the pre-training process, the sample data First input into the encoder to get the embedding vector :

[0060] in, Represents an encoder module.

[0061] Then embed the vector Input to the decoder, the sample data Restore to reconstruct sample data :

[0062] in, Represents a decoder module.

[0063] Then the sample data and reconstructing sample data The mean absolute error between :

[0064] Calculate the gradient of model parameters according to the chain rule :

[0065] In the formula, are the parameters of the encoder and decoder, including weight W, bias B and other parameters.

[0066] According to the gradient descent method, the parameters of the encoder and decoder are updated as :

[0067] in, represents the learning rate, Express gradient.

[0068] Determine whether the loss function has converged. If the loss function has converged, end the training; otherwise, use the updated parameters Replace the current parameters , and repeat the above training process.

[0069] 3) Train the prediction network. During the training process, the encoder parameters are first frozen, and then the sample data is Input into the encoder to get the embedding vector :

[0070] In the formula, Represents an encoder module.

[0071] Then embed the vector Input into the prediction network to get the prediction result :

[0072] In the formula, Represents the prediction network.

[0073] Then the sample labels And the prediction results The mean absolute error between :

[0074] Calculate the gradient corresponding to the network parameters of the prediction network according to the chain rule :

[0075] In the formula, To predict the network parameters of the network, including weights , Bias and other parameters.

[0076] According to the gradient descent method, the network parameters of the prediction network are updated as :

[0077] in, represents the learning rate, Express gradient.

[0078] Determine whether the loss function has converged. If the loss function has converged, end the training; otherwise, use the updated parameters Replace the current parameters , and repeat the above training process.

[0079] 4) Carry out the hydraulic oil leakage fault prediction process. In this process, the input of the fault detection model is the corrected eigenvalue First, select the latest tf-1 historical characteristic values ​​from multiple historical characteristic values ​​of hydraulic oil volume parameters in the historical time interval and compare it with Combined as input data for fault detection models Then enter the data Input into the encoder to get the embedding vector :

[0080] In the formula, Represents an encoder module.

[0081] Embedding vector Input into the prediction network to obtain the prediction result, i.e. the predicted value of hydraulic oil volume :

[0082] In the formula, Represents the prediction network.

[0083] The embodiment of this specification uses a fault detection model to predict the input hydraulic oil volume. First, based on the feature values ​​extracted from the historical data, the parameters of the fault detection model are optimized using the gradient descent method to achieve a smaller training loss. During use, the trained fault detection model is used to predict the hydraulic oil volume. Compared with traditional prediction methods, it has better accuracy and stability and can accurately warn of hydraulic oil leakage faults.

[0084] Step 110: Determine a fault detection result of the hydraulic system at the target time based on the predicted value of the hydraulic oil volume and the target parameter value interval.

[0085] After obtaining the prediction result, that is, the predicted value of the hydraulic oil volume, the fault detection result of the hydraulic system at the target time can be determined based on the predicted value of the hydraulic oil volume and the target parameter value range.

[0086] Specifically, it can be determined whether the predicted value of the hydraulic oil volume is within the target parameter value range. If so, it is considered that the probability of a hydraulic oil leakage failure occurring within the future tp time is low; if not, it is considered that the probability of a hydraulic oil leakage failure occurring within the future tp time is high, and a fault warning needs to be issued to provide a reference for maintenance work.

[0087] The embodiment of this specification utilizes a two-step correction strategy to obtain corrected hydraulic oil quantity data from the original hydraulic oil tank oil quantity data. The corrected data has more realistic representation and better predictability. The threshold interval of the corrected hydraulic oil quantity is constructed through the Bayesian method. The fault diagnosis based on the threshold interval overcomes the reliance of the manual judgment method on professional experience. The efficiency and accuracy of hydraulic oil fault prediction can be significantly improved.

[0088] The aircraft hydraulic oil leakage fault diagnosis and prediction method based on a two-step correction strategy provided in the embodiments of the present specification obtains at least two monitoring parameters of the hydraulic system of the target equipment within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil quantity parameter; corrects the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; when it is determined that the characteristic value belongs to a parameter value interval corresponding to the pre-generated hydraulic oil quantity parameter, updates the parameter value interval to generate a target parameter value interval; inputs the characteristic value into a pre-trained fault detection model for processing to generate a predicted value of the hydraulic oil quantity of the hydraulic system at the target time; and determines the fault detection result of the hydraulic system at the target time based on the predicted value of the hydraulic oil quantity and the target parameter value interval. By correcting the parameter value of the hydraulic oil quantity parameter, not only the oil quantity in the hydraulic oil tank but also the hydraulic oil quantity in the hydraulic pipeline is taken into account, so that the corrected hydraulic oil quantity is closer to the actual oil quantity and has better authenticity and predictability; in addition, by updating the parameter value interval, it is beneficial to ensure the long-term effectiveness of the upper and lower limit thresholds of the interval, which can effectively reduce the false alarm rate; in addition, the hydraulic oil quantity is predicted based on the fault detection model, so as to realize the fault prediction of the hydraulic system, which is beneficial to improve the accuracy and stability of the prediction results.

[0089] Figure 3 A processing flow chart of a method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy provided by an embodiment of the present specification is shown, which specifically includes the following steps.

[0090] Step 302: Obtain historical data of the aircraft hydraulic system within a historical time interval.

[0091] Step 304: extracting the hydraulic oil volume data segment before the engine is started.

[0092] Step 306: Perform extrapolation correction and brake release correction on the parameter value of the hydraulic oil quantity parameter.

[0093] Step 308: Constructing a parameter value interval corresponding to the hydraulic oil volume parameter based on the three sigma principle.

[0094] Step 310: For the parameter value of the new hydraulic oil volume parameter, based on Bayesian estimation and in combination with the parameter value, the upper and lower limits of the parameter value interval are updated.

[0095] Step 312: Fault detection model training.

[0096] Step 314: Predict the hydraulic oil volume using the fault detection model.

[0097] Figure 4A flow chart of a parameter value correction process of a hydraulic oil quantity parameter provided in an embodiment of the present specification is shown, which specifically includes the following steps.

[0098] Step 402: Obtain historical data of the aircraft hydraulic system within a historical time interval.

[0099] Step 404: Determine the parameters of the extrapolation correction algorithm, including the reference downtime duration and the empirical function of the hydraulic oil increase amount varying with the downtime duration.

[0100] Step 406: Determine the parameters of the brake release correction algorithm, including the target hydraulic oil surge increment when the brake is released.

[0101] Step 408: For the parameter value of the new hydraulic oil quantity parameter, perform extrapolation correction and brake release correction on the parameter value.

[0102] Step 410: Obtain the corrected hydraulic oil volume.

[0103] Specifically, the corrected hydraulic oil quantity is the characteristic value of the corrected hydraulic oil quantity parameter.

[0104] Figure 5 A flowchart of a parameter value interval update process provided by an embodiment of the present specification is shown, which specifically includes the following steps.

[0105] Step 502: Obtain historical data of the aircraft hydraulic system within a historical time interval.

[0106] Specifically, the historical data includes multiple historical characteristic values ​​of the hydraulic oil volume parameter.

[0107] Step 504: Calculate the mean and standard deviation of multiple historical feature values.

[0108] Step 506: construct a parameter value interval corresponding to the hydraulic oil volume parameter using the Three Sigma principle.

[0109] Step 508: for the parameter value of the new hydraulic oil volume parameter, determine whether the parameter value is within the parameter value range; if so, execute step 510; if not, end.

[0110] Step 510: taking the upper and lower limits of the parameter value interval as prior information, taking the feature value and at least two of the multiple historical feature values ​​as posterior information, and using Bayesian estimation to update the upper and lower limits of the parameter value interval.

[0111] Figure 6 A flowchart of a fault detection process provided by an embodiment of the present specification is shown, which specifically includes the following steps.

[0112] Step 602: Obtain historical data of the aircraft hydraulic system within a historical time interval.

[0113] Step 604: Slidingly split the historical data into sample data and sample labels.

[0114] Step 606: Input the sample data into the encoder and decoder for pre-training.

[0115] Step 608: Freeze the parameters of the encoder, input sample data into the encoder and prediction network for training, and generate a fault detection model.

[0116] Step 610: Take at least two historical feature values ​​and merge them with the new feature value as an input sample.

[0117] Step 612: Input the input sample into the fault detection model for prediction processing to generate a predicted value of the hydraulic oil volume at the target time.

[0118] Step 614: Based on the predicted value of the hydraulic oil volume and the target parameter value interval, determine the fault detection result of the hydraulic system at the target time.

[0119] Corresponding to the above method embodiment, this specification also provides an embodiment of an aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy, Figure 7 FIG. 1 is a schematic diagram showing a structure of an aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy provided by an embodiment of the present specification. Figure 7 As shown, the device comprises: An acquisition module 702 is configured to acquire at least two monitoring parameters of a hydraulic system of a target device within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil volume parameter; A correction module 704 is configured to correct the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; An updating module 706 is configured to update the parameter value interval to generate a target parameter value interval when it is determined that the characteristic value belongs to the pre-generated parameter value interval corresponding to the hydraulic oil quantity parameter; The processing module 708 is configured to input the characteristic value into a pre-trained fault detection model for processing, and generate a predicted value of the hydraulic oil volume of the hydraulic system at a target time; The determination module 710 is configured to determine a fault detection result of the hydraulic system at the target time based on the predicted value of the hydraulic oil quantity and the target parameter value interval.

[0120] Optionally, the correction module 704 is further configured to: Determine a first parameter value of a hydraulic oil volume parameter of the hydraulic system at a current start-up time, and determine a target downtime duration of the hydraulic system according to the current start-up time and a downtime time before the current start-up time; Based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, the first parameter value is corrected to generate a second parameter value.

[0121] Optionally, the at least two detection parameters further include a historical downtime duration and an increase in hydraulic oil corresponding to the historical downtime duration; Accordingly, the correction module 704 is further configured to: Determining a reference downtime duration of the hydraulic system according to a plurality of historical downtime durations within the historical time interval; Establishing an empirical function between the downtime and the amount of hydraulic oil increase according to the correspondence between the multiple historical downtimes and the multiple amounts of hydraulic oil increase; The first parameter value is corrected based on the target downtime duration, the reference downtime duration and the empirical function.

[0122] Optionally, the at least two detection parameters further include a historical hydraulic oil surge increment of the hydraulic system when the target device releases the parking brake; Accordingly, the correction module 704 is further configured to: determining whether the target device releases a parking brake before the engine of the target device is started; If not, determining a target hydraulic oil surge increment of the hydraulic system when the brake is released according to a plurality of historical hydraulic oil surge increments within the historical time interval; The second parameter value is corrected based on the target hydraulic oil surge increase.

[0123] Optionally, the aircraft hydraulic oil leakage fault diagnosis and prediction device based on the two-step correction strategy further includes a building module configured to: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Calculate the mean and standard deviation corresponding to the multiple historical characteristic values; Based on the mean value and the standard deviation, a parameter value interval corresponding to the hydraulic oil quantity parameter is constructed using the three sigma principle.

[0124] Optionally, the updating module 706 is further configured to: The upper and lower limits of the parameter value interval are used as prior information, the characteristic value and at least two of the multiple historical characteristic values ​​are used as posterior information, and the upper and lower limits of the parameter value interval are updated using Bayesian estimation to generate a target parameter value interval.

[0125] Optionally, the fault detection model comprises an encoder, a decoder and a prediction network, the encoder and the decoder are respectively composed of two layers of long short-term memory networks, and the prediction network is composed of four layers of fully connected layers; Accordingly, the processing module 708 is further configured to: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Inputting the feature value and at least two of the multiple historical feature values ​​as input data into the encoder, and encoding the input data by the encoder to generate a corresponding embedding vector; The embedding vector is input into the prediction network for processing.

[0126] Optionally, the aircraft hydraulic oil leakage fault diagnosis and prediction device based on the two-step correction strategy further includes a training module configured to: Slidingly segmenting the multiple historical feature values ​​to generate sample data and sample labels; Inputting the sample data into the encoder of the fault detection model to be trained for encoding processing to generate a first embedding vector; Inputting the first embedding vector into the prediction network of the fault detection model to be trained for processing to generate a corresponding prediction result; Calculate a first loss value of the fault detection model to be trained based on the sample label and the prediction result; Calculate a first gradient value corresponding to a network parameter of the prediction network according to the chain derivative rule and in combination with the first loss value; Updating the network parameters according to a gradient descent method in combination with the first gradient value; When it is determined according to the first loss value that the loss function of the fault detection model to be trained converges, the training is stopped to obtain the fault detection model.

[0127] Optionally, the training module is further configured to: Inputting the sample data into the encoder of the initial fault detection model for encoding processing to generate a second embedding vector; Inputting the second embedding vector into the decoder of the initial fault detection model for decoding processing to generate reconstructed sample data corresponding to the sample data; Calculate a second loss value of the initial fault detection model based on the sample data and the reconstructed sample data; Calculate second gradient values ​​corresponding to network parameters of the encoder and the decoder according to the chain derivative rule and in combination with the second loss value; Update the network parameters of the encoder and the decoder according to the gradient descent method and in combination with the second gradient value; When it is determined according to the second loss value that the loss function of the initial fault detection model converges, the training is stopped to obtain the fault detection model to be trained.

[0128] The above is a schematic scheme of an aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy of this embodiment. It should be noted that the technical scheme of the aircraft hydraulic oil leakage fault diagnosis and prediction device based on the two-step correction strategy and the technical scheme of the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy belong to the same concept. The details of the technical scheme of the aircraft hydraulic oil leakage fault diagnosis and prediction device based on the two-step correction strategy that are not described in detail can all be referred to the description of the technical scheme of the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0129] Figure 8 The block diagram of a computing device 800 according to an embodiment of the present specification is shown. The components of the computing device 800 include but are not limited to a memory 810 and a processor 820. The processor 820 is connected to the memory 810 via a bus 830, and a database 850 is used to store data.

[0130] The computing device 800 also includes an access device 840 that enables the computing device 800 to communicate via one or more networks 860. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 840 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a World Wide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0131] In one embodiment of the present specification, the above components of the computing device 800 and Figure 8 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 8The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0132] The computing device 800 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or PC. The computing device 800 may also be a mobile or stationary server.

[0133] The processor 820 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0134] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy belong to the same concept, and the details not described in detail in the technical scheme of the computing device can be referred to the description of the technical scheme of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0135] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0136] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0137] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to execute the steps of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0138] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the above-mentioned aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy.

[0139] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0141] It should be noted that, for the convenience of description, the aforementioned method embodiments are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0142] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for diagnosing and predicting aircraft hydraulic oil leakage faults based on a two-step correction strategy, comprising: Acquire at least two monitoring parameters of the hydraulic system of the target equipment within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil volume parameter; Correcting the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; When it is determined that the characteristic value belongs to the pre-generated parameter value interval corresponding to the hydraulic oil quantity parameter, the parameter value interval is updated to generate a target parameter value interval; Inputting the characteristic value into a pre-trained fault detection model for processing to generate a predicted value of the hydraulic oil volume of the hydraulic system at a target time; Based on the predicted value of the hydraulic oil quantity and the target parameter value interval, a fault detection result of the hydraulic system at the target time is determined.

2. According to the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy of claim 1, the correction of the parameter value of the hydraulic oil quantity parameter comprises: Determine a first parameter value of a hydraulic oil volume parameter of the hydraulic system at a current start-up time, and determine a target downtime duration of the hydraulic system according to the current start-up time and a downtime time before the current start-up time; Based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, the first parameter value is corrected to generate a second parameter value.

3. According to the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy of claim 2, the at least two detection parameters also include the historical downtime and the hydraulic oil increase amount corresponding to the historical downtime; Accordingly, the first parameter value is corrected based on the target downtime duration and the correlation between the downtime duration and the increase in hydraulic oil, including: Determining a reference downtime duration of the hydraulic system according to a plurality of historical downtime durations within the historical time interval; Establishing an empirical function between the downtime and the amount of hydraulic oil increase according to the correspondence between the multiple historical downtimes and the multiple amounts of hydraulic oil increase; The first parameter value is corrected based on the target downtime duration, the reference downtime duration and the empirical function.

4. The aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy according to claim 2 or 3, wherein the at least two detection parameters further include a historical hydraulic oil surge increment of the hydraulic system when the target device releases the parking brake; Correspondingly, the correction of the parameter value of the hydraulic oil quantity parameter includes: determining whether the target device releases a parking brake before the engine of the target device is started; If not, determining a target hydraulic oil surge increment of the hydraulic system when the brake is released according to a plurality of historical hydraulic oil surge increments within the historical time interval; The second parameter value is corrected based on the target hydraulic oil surge increase.

5. The aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy according to claim 1 further comprises: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Calculate the mean and standard deviation corresponding to the multiple historical characteristic values; Based on the mean value and the standard deviation, a parameter value interval corresponding to the hydraulic oil quantity parameter is constructed using the three sigma principle.

6. The aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy according to claim 5, wherein the updating of the parameter value interval to generate the target parameter value interval comprises: The upper and lower limits of the parameter value interval are used as prior information, the characteristic value and at least two of the multiple historical characteristic values ​​are used as posterior information, and the upper and lower limits of the parameter value interval are updated using Bayesian estimation to generate a target parameter value interval.

7. According to the aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy of claim 1, the fault detection model comprises an encoder, a decoder and a prediction network, the encoder and the decoder are respectively composed of two layers of long short-term memory networks, and the prediction network is composed of four layers of fully connected layers; Accordingly, the inputting the characteristic value into a pre-trained fault detection model for processing includes: Determining a plurality of historical characteristic values ​​of the hydraulic oil volume parameter within the historical time interval; Inputting the feature value and at least two of the multiple historical feature values ​​as input data into the encoder, and encoding the input data by the encoder to generate a corresponding embedding vector; The embedding vector is input into the prediction network for processing.

8. The aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy according to claim 7 further comprises: Slidingly segmenting the multiple historical feature values ​​to generate sample data and sample labels; Inputting the sample data into the encoder of the fault detection model to be trained for encoding processing to generate a first embedding vector; Inputting the first embedding vector into the prediction network of the fault detection model to be trained for processing to generate a corresponding prediction result; Calculate a first loss value of the fault detection model to be trained based on the sample label and the prediction result; Calculate a first gradient value corresponding to a network parameter of the prediction network according to the chain derivative rule and in combination with the first loss value; Updating the network parameters according to a gradient descent method in combination with the first gradient value; When it is determined according to the first loss value that the loss function of the fault detection model to be trained converges, the training is stopped to obtain the fault detection model.

9. The aircraft hydraulic oil leakage fault diagnosis and prediction method based on the two-step correction strategy according to claim 8 further comprises: Inputting the sample data into the encoder of the initial fault detection model for encoding processing to generate a second embedding vector; Inputting the second embedding vector into the decoder of the initial fault detection model for decoding processing to generate reconstructed sample data corresponding to the sample data; Calculate a second loss value of the initial fault detection model based on the sample data and the reconstructed sample data; Calculate second gradient values ​​corresponding to network parameters of the encoder and the decoder according to the chain derivative rule and in combination with the second loss value; Update the network parameters of the encoder and the decoder according to the gradient descent method and in combination with the second gradient value; When it is determined according to the second loss value that the loss function of the initial fault detection model converges, the training is stopped to obtain the fault detection model to be trained.

10. An aircraft hydraulic oil leakage fault diagnosis and prediction device based on a two-step correction strategy, comprising: An acquisition module is configured to acquire at least two monitoring parameters of a hydraulic system of a target device within a historical time interval, wherein the at least two monitoring parameters include a hydraulic oil volume parameter; a correction module, configured to correct the parameter value of the hydraulic oil quantity parameter to generate a characteristic value of the hydraulic oil quantity parameter; An updating module is configured to update the parameter value interval to generate a target parameter value interval when it is determined that the characteristic value belongs to the pre-generated parameter value interval corresponding to the hydraulic oil quantity parameter; a processing module configured to input the characteristic value into a pre-trained fault detection model for processing, and generate a predicted value of the hydraulic oil volume of the hydraulic system at a target time; The determination module is configured to determine a fault detection result of the hydraulic system at the target time based on the predicted value of the hydraulic oil quantity and the target parameter value interval.

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