Fuel flow sensor monitoring method, device, equipment, medium and product

Abstract

The invention discloses a fuel flow sensor monitoring method, device and equipment, a medium and a product, and the method comprises the steps: collecting fuel flow data of fuel flow sensors of two engines, and recording corresponding collection timestamps; performing segmentation according to a preset fixed time interval, performing volatility analysis on the fuel flow data of each segment, and determining all target segments meeting a preset condition according to the fuel flow data of each segment and the corresponding volatility; performing frequency domain analysis on the fuel flow data of each target section to obtain an average value of each target section on a preset frequency band; calculating the frequency domain difference of the fuel flow data in the frequency domain according to all the average values; and according to the frequency domain difference, the flow difference of the fuel flow data is obtained through calculation, and therefore the sensor which breaks down is determined. Sensor parameter analysis is carried out based on a frequency domain analysis and data volatility analysis method, the accuracy of fuel flow anomaly detection can be improved, and the specific source of a sensor fault is judged.

Description

Technical Field

[0001] The present invention relates to the field of aviation technology, and particularly to a method, device, equipment, medium and product for monitoring a fuel flow sensor. Background Art

[0002] The prior art generally adopts the following two methods to monitor and report abnormal fuel flow of an aeroengine: 1. Manual monitoring by flight crew: During flight, crew members observe the engine fuel flow indicator to detect abnormal situations. When abnormal fuel flow is found, the crew members need to immediately report it to the maintenance personnel for handling. However, this method mainly relies on manual observation and is easily affected by factors such as fatigue and lack of experience, resulting in the failure to detect and report abnormal fuel flow in a timely manner. 2. Message monitoring system: This method uses the aircraft's automatic monitoring system to monitor the fuel flow difference of a twin-engine in real time. When there is a large difference in the fuel flow of the two engines, the system will automatically send a message prompt to remind the ground maintenance personnel to check and handle it. However, it is not very sensitive to small abnormal changes, which may lead to the failure to capture potential problems in a timely manner. At the same time, these two methods mainly rely on the difference in twin-engine fuel flow for analysis. When the sensor itself fails, it is difficult to determine which engine's fuel flow sensor has a problem. In this case, it is difficult to effectively distinguish which engine's sensor fails based on the twin-engine difference monitoring method, resulting in the inability to perform accurate fault prediction and judgment. Summary of the Invention

[0003] The present invention provides a method, device, equipment, medium and product for monitoring a fuel flow sensor. By analyzing sensor parameters based on the frequency domain analysis method and the data volatility analysis method, the accuracy of abnormal fuel flow detection can be improved, and the specific source of sensor failure can be accurately determined.

[0004] To achieve the above object, an embodiment of the present invention provides a method for monitoring a fuel flow sensor, including:

[0005] Collecting fuel flow data of fuel flow sensors of two engines of a target aircraft for a certain period of time, and recording the corresponding acquisition timestamps; wherein, the fuel flow data includes first fuel flow data of a first engine and second fuel flow data of a second engine;

[0006] Segmenting the fuel flow data according to a preset fixed time interval based on the acquisition timestamps, and performing volatility analysis on each segment of fuel flow data to obtain the volatility of each segment of fuel flow data;

[0007] Determine all target segments that meet the preset conditions from all fuel flow data segments according to each fuel flow data segment and its corresponding volatility; perform frequency domain analysis on the fuel flow data of each target segment to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in the preset frequency band;

[0008] Calculate the frequency domain difference of the fuel flow data in the frequency domain according to all the average values; calculate the flow difference of the fuel flow data according to the fuel flow data of the target segment corresponding to the frequency domain difference;

[0009] Determine the faulty fuel flow sensor in the target aircraft according to the frequency domain difference and the flow difference.

[0010] As an improvement to the above solution, the step of segmenting the fuel flow data at a preset fixed time interval according to the acquisition timestamp and performing volatility analysis on each segment of fuel flow data to obtain the volatility of each segment of fuel flow data includes:

[0011] Segment the first fuel flow data and the second fuel flow data at a preset fixed time interval according to the acquisition timestamp;

[0012] Calculate the difference between the maximum value and the minimum value of each segment of the first fuel flow data and the second fuel flow data, and use the difference as the volatility of each segment of the first fuel flow data and the second fuel flow data.

[0013] As an improvement to the above solution, the preset conditions include:

[0014] Both the first fuel flow data and the second fuel flow data are greater than the preset flow value, and the volatility of the first fuel flow data or the volatility of the second fuel flow data is less than or equal to the preset volatility value.

[0015] As an improvement to the above solution, the step of performing frequency domain analysis on the fuel flow data of each target segment to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in the preset frequency band includes:

[0016] Preprocess the fuel flow data of each target segment by applying a Hann window function;

[0017] Perform frequency domain conversion on the preprocessed fuel flow data by using the fast Fourier transform to obtain the frequency domain spectrum of the fuel flow data;

[0018] Calculate the average value of the frequency domain spectrum in the preset frequency band to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in the preset frequency band.

[0019] As an improvement to the above solution, calculating the frequency domain difference of the fuel flow data in the frequency domain based on all the averages includes:

[0020] Calculating the percentage difference of the averages of the first fuel flow data and the second fuel flow data based on all the averages to obtain the percentage difference of the averages of all target segments;

[0021] Based on the absolute value of the percentage difference, taking the percentage difference with the largest absolute value as the frequency domain difference of the fuel flow data in the frequency domain.

[0022] As an improvement to the above solution, calculating the flow difference of the fuel flow data based on the fuel flow data of the target segment corresponding to the frequency domain difference includes:

[0023] Calculating all the flow differences between the first fuel flow data and the second fuel flow data of the target segment corresponding to the frequency domain difference based on the fuel flow data of the target segment corresponding to the frequency domain difference;

[0024] Based on the absolute value of the flow difference, taking the flow difference with the largest absolute value as the flow difference of the fuel flow data.

[0025] To achieve the above object, an embodiment of the present invention provides a fuel flow sensor monitoring device, including:

[0026] A flow data acquisition module, configured to acquire the fuel flow data of the fuel flow sensors of two engines of a target aircraft within a certain period of time and record the corresponding acquisition timestamps; wherein, the fuel flow data includes the first fuel flow data of the first engine and the second fuel flow data of the second engine;

[0027] A flow data segmentation module, configured to segment the fuel flow data at a preset fixed time interval according to the acquisition timestamps, and perform volatility analysis on each segment of fuel flow data to obtain the volatility of each segment of fuel flow data;

[0028] A flow data analysis module, configured to determine all target segments that meet the preset conditions from all segments of fuel flow data according to each segment of fuel flow data and the corresponding volatility; perform frequency domain analysis on the fuel flow data of each target segment to obtain the averages of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band;

[0029] A data difference calculation module, configured to calculate the frequency domain difference of the fuel flow data in the frequency domain based on all the averages; calculate the flow difference of the fuel flow data based on the fuel flow data of the target segment corresponding to the frequency domain difference;

[0030] A target fault location module, configured to determine a fuel flow sensor with a fault in the target aircraft according to the frequency domain difference and the flow rate difference.

[0031] To achieve the above object, an embodiment of the present invention correspondingly provides a fuel flow sensor monitoring device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above fuel flow sensor monitoring method is implemented.

[0032] To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above fuel flow sensor monitoring method.

[0033] To achieve the above object, an embodiment of the present invention further provides a computer program product. The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the above fuel flow sensor monitoring method.

[0034] Compared with the prior art, a fuel flow sensor monitoring method, device, equipment, medium, and product disclosed in an embodiment of the present invention collect fuel flow data of fuel flow sensors of two engines of a target aircraft within a certain period of time and record the corresponding acquisition timestamps. The fuel flow data includes first fuel flow data of a first engine and second fuel flow data of a second engine. The fuel flow data is segmented at a preset fixed time interval according to the acquisition timestamps, and volatility analysis is performed on each segment of fuel flow data to obtain the volatility of each segment of fuel flow data. According to each segment of fuel flow data and the corresponding volatility, all target segments that meet the preset conditions are determined from all segments of fuel flow data. Frequency domain analysis is performed on the fuel flow data of each target segment to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band. The frequency domain difference of the fuel flow data in the frequency domain is calculated according to all the average values. The flow rate difference of the fuel flow data is calculated according to the fuel flow data of the target segment corresponding to the frequency domain difference. A fuel flow sensor with a fault in the target aircraft is determined according to the frequency domain difference and the flow rate difference. By combining frequency domain analysis and data volatility analysis methods for sensor parameter analysis and determining the faulty fuel flow sensor according to the analysis results, the accuracy of fuel flow anomaly detection can be improved, and the specific source of the sensor fault can be accurately judged. Description of the Drawings

[0035] Figure 1It is a schematic flow chart of a fuel flow sensor monitoring method provided by an embodiment of the present invention;

[0036] Figure 2 It is a schematic structural diagram of a fuel flow sensor monitoring device provided by an embodiment of the present invention;

[0037] Figure 3 It is a structural block diagram of a fuel flow sensor monitoring device provided by an embodiment of the present invention. Detailed implementation manners

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] It should be noted that the terms "including" and "specific" in the present invention and any of their deformations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0040] Please refer to Figure 1 , Figure 1 It is a schematic flow chart of a fuel flow sensor monitoring method provided by an embodiment of the present invention. The fuel flow sensor monitoring method includes:

[0041] S1. Collect fuel flow data of the fuel flow sensors of two engines of a target aircraft within a certain period of time and record the corresponding acquisition timestamps; wherein, the fuel flow data includes first fuel flow data of a first engine and second fuel flow data of a second engine;

[0042] S2. Segment the fuel flow data according to the preset fixed time interval based on the acquisition timestamps, and perform volatility analysis on each segment of fuel flow data to obtain the volatility of each segment of fuel flow data;

[0043] S3. Determine all target segments that meet the preset conditions from all segments of fuel flow data according to each segment of fuel flow data and the corresponding volatility; perform frequency domain analysis on the fuel flow data of each target segment to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band;

[0044] S4. Calculate the frequency-domain difference of the fuel flow data in the frequency domain based on all the averages; calculate the flow difference of the fuel flow data corresponding to the target segment according to the frequency-domain difference.

[0045] S5. Determine the faulty fuel flow sensor in the target aircraft according to the frequency-domain difference and the flow difference.

[0046] Exemplarily, data is collected from the fuel flow sensors of two engines and the corresponding timestamps are recorded; the collected data is segmented at a preset fixed time interval (e.g., 600 seconds), the maximum-minimum difference of each segment of data is calculated, and it is judged whether all the data is higher than a preset flow value (e.g., 4000 units) and the data range of one engine is within a preset fluctuation value (e.g., 2000). If the conditions are met, frequency-domain analysis is performed. After applying the Hanning window function to each segment of data, fast Fourier transform (FFT) is performed, the amplitude of the spectrum is calculated, and the average value in a preset frequency band (e.g., between 0.25 HZ and 0.375 HZ) is extracted; calculate the difference in the frequency domain (frequency-domain difference) between the fuel flow sensors of the two engines, and conduct a comprehensive evaluation in combination with the original flow difference. According to the magnitudes of the frequency-domain difference and the flow difference, the results are classified into different fault event levels (e.g., CL0, CL1, CL2); judge which engine's fuel flow sensor is faulty based on the positive and negative values of the frequency-domain difference.

[0047] It should be noted that when the aircraft starts, takes off, climbs, bumps, or changes altitude levels, it will bring large-amplitude frequency changes and amplitude changes in the fuel flow in a short time. This frequency change will mask the abnormal frequency of the fuel flow sensor failure. Therefore, a detection method for finding the stable working time of the engine needs to be introduced. If the data fluctuation line analysis is too sensitive, the time interval left for fault diagnosis is too small; if the data volatility analysis is too insensitive, the fault diagnosis effect is not good. By analyzing a large amount of flight data, the flow change interval during stable flight is finally determined. The data volatility analysis is used to find the stable working interval of the engine. In this working interval, the fuel flow of the engine does not change too much. It can better highlight the frequency change brought by the fuel flow sensor failure. The frequency analysis is used to extract the short-period frequency anomaly brought by the fuel flow sensor failure. By comparing the short-period frequency values and amplitudes of the fuel flow sensors of the left and right engines. Furthermore, confirm the location of the faulty sensor and the severity of the fault.

[0048] Specifically, in step S2, it includes:

[0049] S21. Segment the first fuel flow data and the second fuel flow data at a preset fixed time interval according to the acquisition timestamps.

[0050] S22. Calculate the difference between the maximum and minimum values of the first fuel flow rate data and the second fuel flow rate data for each segment, and use the difference as the volatility of the first fuel flow rate data and the second fuel flow rate data for each segment.

[0051] Exemplarily, extract the fuel flow rate data (Value) and time (Time) of two fuel flow sensors (FF1 and FF2) from the input context data, create a data frame containing the FF1 values (the first fuel flow rate data), the FF2 values (the second fuel flow rate data), and the time. Assume that the time data is aligned with the fuel flow rate data, and segment the data at a preset fixed time interval (such as 600 rows) to form multiple data intervals. For the data in each interval, calculate the differences between the maximum and minimum values of FF1 and FF2 (max_min_diff_FF1 and max_min_diff_FF2), and use max_min_diff_FF1 and max_min_diff_FF2 as the volatility of the first fuel flow rate data and the second fuel flow rate data for each segment.

[0052] Specifically, the preset conditions include:

[0053] Both the first fuel flow rate data and the second fuel flow rate data are greater than a preset flow rate value, and the volatility of the first fuel flow rate data or the volatility of the second fuel flow rate data is less than or equal to a preset volatility value.

[0054] Exemplarily, the preset conditions include: whether the values of FF1 and FF2 in the interval are both greater than a preset flow rate value (such as 4000 units), and whether the volatility (i.e., max_min_diff_FF1 or max_min_diff_FF2) is less than or equal to a preset volatility value (such as 2000).

[0055] Specifically, in step S3, perform frequency domain analysis on the fuel flow rate data of each target segment to obtain the average values of the first fuel flow rate data and the second fuel flow rate data of each target segment in a preset frequency band, including:

[0056] S31. Apply a Hann window function to preprocess the fuel flow rate data of each target segment;

[0057] S32. Use the fast Fourier transform to perform frequency domain conversion on the preprocessed fuel flow rate data to obtain the frequency domain spectrum of the fuel flow rate data;

[0058] S33. Calculate the average value of the frequency domain spectrum in the preset frequency band to obtain the average values of the first fuel flow rate data and the second fuel flow rate data of each target segment in the preset frequency band.

[0059] Exemplarily, set the sampling rate to 1, and apply a Hanning Window to the values of each interval FF1 and FF2 that meet the preset conditions for preprocessing; use the Fast Fourier Transform (FFT) to perform a frequency-domain conversion on the preprocessed data to obtain a frequency-domain spectrum; calculate the average value of the frequency-domain spectrum in a specific frequency band (e.g., between 0.25 HZ and 0.375 HZ).

[0060] Specifically, in step S4, calculating the frequency-domain difference of the fuel flow data in the frequency domain based on all the average values includes:

[0061] S41, calculating the percentage difference of the average values of the first fuel flow data and the second fuel flow data based on all the average values to obtain the percentage difference of the average values of all target segments;

[0062] S42, taking the percentage difference with the largest absolute value as the frequency-domain difference of the fuel flow data in the frequency domain according to the absolute value of the percentage difference.

[0063] Exemplarily, calculate the percentage difference (result) of the frequency-domain average values of FF1 and FF2, and find the frequency-domain difference percentage (freq_diff) with the largest absolute value as the frequency-domain difference of the fuel flow data in the frequency domain based on the analysis results of all intervals.

[0064] Specifically, in step S4, calculating the flow difference of the fuel flow data based on the fuel flow data of the target segment corresponding to the frequency-domain difference includes:

[0065] S43, calculating all the flow differences between the first fuel flow data and the second fuel flow data of the target segment corresponding to the frequency-domain difference based on the fuel flow data of the target segment corresponding to the frequency-domain difference;

[0066] S44, taking the flow difference with the largest absolute value as the flow difference of the fuel flow data according to the absolute value of the flow difference.

[0067] Exemplarily, for the fuel flow data of the target segment corresponding to the frequency-domain difference, calculate the difference between FF1 and FF2, find the maximum and minimum values of the difference, and select the difference with the larger absolute value as the flow difference (flow_diff) of the fuel flow data.

[0068] Specifically, step S5 includes:

[0069] S51, determining the failure event level of the fuel flow sensor according to the absolute values of the frequency-domain difference and the flow difference;

[0070] S52. Determine the fuel flow sensor with a fault in the target aircraft according to the absolute value of the frequency domain difference and the fault event level.

[0071] Exemplarily, determine the fault event level (eventRank) according to the absolute values of freq_diff and flow_diff: when the absolute value of the percentage of frequency domain difference is greater than or equal to 100 and the absolute value of the flow difference is greater than or equal to 400, the fault event level is CL2; when the absolute value of the percentage of frequency domain difference is greater than or equal to 100 but the absolute value of the flow difference is less than 400, the fault event level is CL1; when the absolute value of the percentage of frequency domain difference is less than 100, the fault event level is CL0; if there is such a fault event level, determine the sensor fault according to freq_diff: when the percentage of frequency domain difference is greater than or equal to 80, it is determined that the left engine fuel flow sensor has a fault; when the percentage of frequency domain difference is less than or equal to -80, it is determined that the right engine fuel flow sensor has a fault; when the percentage of frequency domain difference is between -80 and 80, it is determined that there is no fault. If all intervals do not meet the conditions, generate an event object with a time of 0 and an event level of CL0, and note "no stable interval" in the analysis report.

[0072] A fuel flow sensor monitoring method disclosed in an embodiment of the present invention collects fuel flow data of fuel flow sensors of two engines of a target aircraft for a certain period of time and records the corresponding acquisition timestamps; wherein, the fuel flow data includes first fuel flow data of a first engine and second fuel flow data of a second engine; segment the fuel flow data according to a preset fixed time interval according to the acquisition timestamps, perform volatility analysis on each segment of fuel flow data to obtain the volatility of each segment of fuel flow data; determine all target segments that meet the preset conditions from all segments of fuel flow data according to each segment of fuel flow data and the corresponding volatility; perform frequency domain analysis on the fuel flow data of each target segment to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band; calculate the frequency domain difference of the fuel flow data in the frequency domain according to all the average values; calculate the flow difference of the fuel flow data according to the fuel flow data of the target segment corresponding to the frequency domain difference; determine the fuel flow sensor with a fault in the target aircraft according to the frequency domain difference and the flow difference. By combining the frequency domain analysis and the data volatility analysis method to analyze the sensor parameters and determining the fuel flow sensor with a fault according to the analysis results, the accuracy of fuel flow anomaly detection can be improved, and the specific source of the sensor fault can be accurately judged.

[0073] See Figure 2 , Figure 2 is a schematic structural diagram of a fuel flow sensor monitoring device 10 provided by an embodiment of the present invention. The fuel flow sensor monitoring device 10 includes:

[0074] A fuel flow data acquisition module 11 is configured to acquire fuel flow data of fuel flow sensors of two engines of a target aircraft within a certain period of time and record corresponding acquisition timestamps. Among them, the fuel flow data includes first fuel flow data of a first engine and second fuel flow data of a second engine.

[0075] A fuel flow data segmentation module 12 is configured to segment the fuel flow data at a preset fixed time interval according to the acquisition timestamps, perform volatility analysis on each segment of fuel flow data, and obtain the volatility of each segment of fuel flow data.

[0076] A fuel flow data analysis module 13 is configured to determine all target segments that meet preset conditions from all segments of fuel flow data according to each segment of fuel flow data and the corresponding volatility; perform frequency domain analysis on the fuel flow data of each target segment to obtain the average values of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band.

[0077] A data difference calculation module 14 is configured to calculate the frequency domain difference of the fuel flow data in the frequency domain according to all the average values; calculate the flow difference of the fuel flow data according to the fuel flow data of the target segment corresponding to the frequency domain difference.

[0078] A target fault location module 15 is configured to determine the fuel flow sensor that fails in the target aircraft according to the frequency domain difference and the flow difference.

[0079] A fuel flow sensor monitoring device 10 provided by an embodiment of the present invention can implement all processes of the fuel flow sensor monitoring method in the above embodiment. The functions of each module in the device and the achieved technical effects respectively correspond to the functions and the achieved technical effects of the fuel flow sensor monitoring method in the above embodiment, and will not be elaborated here.

[0080] See Figure 3 , Figure 3 FIG. is a schematic structural diagram of a fuel flow sensor monitoring device 20 provided by an embodiment of the present invention. The fuel flow sensor monitoring device 20 in this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above embodiment of the fuel flow sensor monitoring method are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module in the above embodiment of the fuel flow sensor monitoring device are implemented.

[0081] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the fuel flow sensor monitoring device 20.

[0082] The fuel flow sensor monitoring device 20 may be a computing device such as a desktop computer, a notebook, a palm computer, or a cloud server. The fuel flow sensor monitoring device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the fuel flow sensor monitoring device 20 and does not constitute a limitation on the fuel flow sensor monitoring device 20. It may include more or fewer components than shown in the figure, or combine certain components, or have different components. For example, the fuel flow sensor monitoring device 20 may further include input / output devices, network access devices, a bus, etc.

[0083] The so-called processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the fuel flow sensor monitoring device 20, and connects various parts of the entire fuel flow sensor monitoring device 20 through various interfaces and lines.

[0084] The memory 22 can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory 22, and invoking the data stored in the memory 22, the processor 21 realizes various functions of the fuel flow sensor monitoring device 20. The memory 22 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 22 can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0085] Among them, if the modules integrated in the fuel flow sensor monitoring device 20 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can 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, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0086] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0087] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the fuel flow sensor monitoring method as described in the above embodiment.

[0088] In addition, an embodiment of the present invention also provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the steps of the fuel flow sensor monitoring method as described in the above embodiment.

[0089] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A fuel flow sensor monitoring method, characterized in that: include: Collecting fuel flow data from fuel flow sensors of two engines of the target aircraft in a certain period of time, and recording corresponding collection timestamps; wherein the fuel flow data includes first fuel flow data of the first engine and second fuel flow data of the second engine; The fuel flow data is segmented at preset fixed time intervals according to the acquisition timestamp, and volatility analysis is performed on each segment of the fuel flow data to obtain volatility of each segment of the fuel flow data; According to each segment of fuel flow data and the corresponding volatility, all target segments that meet the preset conditions are determined from all segments of fuel flow data; the fuel flow data of each target segment is subjected to frequency domain analysis to obtain the average value of the first fuel flow data and the second fuel flow data of each target segment in the preset frequency band; Calculate the frequency domain difference of the fuel flow data in the frequency domain according to all the average values; calculate the flow difference of the fuel flow data according to the fuel flow data of the target section corresponding to the frequency domain difference; A fuel flow sensor having a fault in the target aircraft is determined according to the frequency domain difference and the flow difference.

2. The fuel flow sensor monitoring method according to claim 1, characterized in that: The step of segmenting the fuel flow data at preset fixed time intervals according to the acquisition timestamp, performing volatility analysis on each segment of the fuel flow data, and obtaining the volatility of each segment of the fuel flow data includes: segmenting the first fuel flow data and the second fuel flow data at preset fixed time intervals according to the acquisition timestamp; The difference between the maximum value and the minimum value of each section of the first fuel flow data and the second fuel flow data is calculated, and the difference is used as the volatility of each section of the first fuel flow data and the second fuel flow data.

3. The fuel flow sensor monitoring method according to claim 1, characterized in that: The preset conditions include: The first fuel flow data and the second fuel flow data are both greater than a preset flow value, and the volatility of the first fuel flow data or the volatility of the second fuel flow data is less than or equal to a preset volatility value.

4. The fuel flow sensor monitoring method according to claim 1, characterized in that: The frequency domain analysis is performed on the fuel flow data of each target segment to obtain the average value of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band, including: The fuel flow data of each target section is preprocessed by applying the Hanning window function; Performing frequency domain conversion on the preprocessed fuel flow data using fast Fourier transform to obtain a frequency domain spectrum of the fuel flow data; The average value of the frequency domain spectrum in the preset frequency band is calculated to obtain the average value of the first fuel flow data and the second fuel flow data in each target segment in the preset frequency band.

5. The fuel flow sensor monitoring method according to claim 1, characterized in that: The step of calculating the frequency domain difference of the fuel flow data in the frequency domain according to all the average values ​​includes: Calculate the difference percentage between the average values ​​of the first fuel flow data and the second fuel flow data based on all the average values ​​to obtain the difference percentage between the average values ​​of all the target sections; According to the absolute values ​​of the difference percentages, the difference percentage with the largest absolute value is used as the frequency domain difference of the fuel flow data in the frequency domain.

6. The fuel flow sensor monitoring method according to claim 1, characterized in that: The step of calculating the flow difference of the fuel flow data according to the fuel flow data of the target segment corresponding to the frequency domain difference includes: According to the fuel flow data of the target segment corresponding to the frequency domain difference, all flow difference values ​​between the first fuel flow data and the second fuel flow data of the target segment corresponding to the frequency domain difference are calculated; According to the absolute values ​​of the flow rate differences, the flow rate difference with the largest absolute value is used as the flow rate difference of the fuel flow rate data.

7. A fuel flow sensor monitoring device, characterized in that: include: A flow data acquisition module, used to collect fuel flow data of fuel flow sensors of two engines of the target aircraft in a certain period of time, and record corresponding acquisition timestamps; wherein the fuel flow data includes first fuel flow data of the first engine and second fuel flow data of the second engine; A flow data segmentation module, used to segment the fuel flow data at preset fixed time intervals according to the acquisition timestamp, perform volatility analysis on each segment of fuel flow data, and obtain the volatility of each segment of fuel flow data; The flow data analysis module is used to determine all target segments that meet preset conditions from all segments of fuel flow data according to each segment of fuel flow data and the corresponding volatility; perform frequency domain analysis on the fuel flow data of each target segment to obtain the average value of the first fuel flow data and the second fuel flow data of each target segment in a preset frequency band; A data difference calculation module is used to calculate the frequency domain difference of the fuel flow data in the frequency domain according to all average values; and calculate the flow difference of the fuel flow data according to the fuel flow data of the target section corresponding to the frequency domain difference; The target fault locating module is used to determine the fuel flow sensor having a fault in the target aircraft according to the frequency domain difference and the flow difference.

8. A fuel flow sensor monitoring device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the fuel flow sensor monitoring method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the fuel flow sensor monitoring method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the fuel flow sensor monitoring method according to any one of claims 1 to 6.

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