Method and system for failure prediction of landing gear for aircraft

Abstract

This invention provides a method and system for predicting landing gear failures in aircraft. The method includes: acquiring landing gear retraction and extension time-series data; correcting the retraction and extension time-series data based on aircraft operating environment parameters; calculating the time trend of the corrected retraction and extension time-series data using a time-series model; and determining the landing gear warning level based on the time trend.

Description

Technical Field

[0001] This invention relates to the field of aircraft fault prediction, and more specifically, to a method and system for predicting the faults of aircraft landing gear. Background Technology

[0002] The landing gear retraction mechanism of a large civil aircraft is a typical semi-open-loop system integrating mechanical, hydraulic, and control systems. Its reliability and safety are directly related to the operational safety and maintenance efficiency of the aircraft. In existing technologies, the functional maintenance of this system mainly relies on two traditional methods: one is periodic inspection based on fixed cycles, and the other is condition monitoring based on fixed thresholds.

[0003] Regular inspections typically require shutdown and manual or basic testing equipment to disassemble and inspect the landing gear system. This approach is not only time-consuming and labor-intensive, increasing maintenance costs, but also makes it difficult to effectively detect potential internal faults (such as partial blockages in actuator oil circuits, corrosion of hydraulic lines) and abnormalities in hard-to-access components (such as wear on upper connection bushings, changes in internal bearing clearance). Because many faults are insidious and progressive, regular inspections often fail to detect early signs of malfunction. Statistics show that more than 200 malfunctions each year are caused by landing gear retraction mechanism jamming. If such jamming is not detected in time, it can lead to flight delays or cancellations, or even cause belly landings and serious accidents resulting in the destruction of the aircraft and loss of life.

[0004] Fixed-threshold-based monitoring methods detect faults by setting predetermined alarm limits (such as pressure, displacement, and time thresholds) in the system. However, this method has significant limitations: if the threshold is set too strictly, the system is susceptible to fluctuations in normal operating conditions or sensor noise interference, leading to a higher false alarm rate and affecting the accuracy of maintenance decisions; if the threshold is set too leniently, potential fault characteristics cannot be identified in a timely manner, and alarms are often triggered only when the fault has significantly affected system performance, thus losing the preventative significance of fault prediction. Furthermore, fixed-threshold methods are difficult to adapt to changes in the landing gear system's state during different operating phases, under different environmental conditions, and due to individual differences, further limiting the accuracy and reliability of fault prediction.

[0005] Therefore, existing technologies lack a method that can identify early fault characteristics of landing gear systems in real time and adaptively and make accurate predictions, resulting in maintenance work still being in a passive response mode, which cannot fully meet the needs of modern civil aircraft for safety, economy and intelligent operation and maintenance. Summary of the Invention

[0006] This summary is provided to introduce, in a simplified form, some concepts that will be further described in the following detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0007] One of the objectives of this invention is to provide a method and system for predicting landing gear failures in aircraft. By monitoring anomalies in the landing gear retraction and extension time, the risk of mechanical jamming during retraction and extension can be predicted, thereby overcoming the limitations of traditional periodic inspections and fixed threshold monitoring, and effectively improving the operational safety and maintenance economy of aircraft.

[0008] According to one aspect of the present invention, a method for predicting landing gear failures for an aircraft is provided, the method comprising: acquiring landing gear retraction and extension time series data; correcting the retraction and extension time series data according to aircraft operating environment parameters; calculating the time trend of the corrected retraction and extension time series data using a time series model; and determining the warning level of the landing gear based on the time trend.

[0009] In one embodiment of the present invention, acquiring the landing gear retraction and extension time sequence data further includes: collecting the upper unlock time, lower lock time, lower unlock time, and upper lock time of the landing gear; and determining the landing gear lowering time based on the upper unlock time and lower lock time, and determining the landing gear retraction time based on the lower unlock time and upper lock time.

[0010] In one embodiment of the present invention, the aircraft's operating environment parameters include: an airspeed correction factor, which is proportional to the aircraft's airspeed; and an ambient temperature correction factor, which is inversely proportional to the aircraft's ambient temperature.

[0011] In one embodiment of the present invention, the aircraft's operating environment parameters are determined based on deployment and recovery test data or simulation data.

[0012] In one embodiment of the present invention, calculating the time trend of the corrected landing gear retraction and extension time series data using a time series model further includes: determining the time trend by statistically analyzing the deviation between the actual landing gear retraction and extension time and the theoretical retraction and extension time of multiple aircraft sorties, wherein the theoretical retraction and extension time is derived based on the corrected landing gear retraction and extension time series data.

[0013] In one embodiment of the present invention, determining the warning level of the landing gear based on the time trend further includes: determining that the landing gear is at the corresponding warning level when the time trend reaches a risk threshold.

[0014] In one embodiment of the present invention, the fault prediction method further includes: determining multiple warning levels for multiple landing gears of an aircraft; and determining the warning level of the aircraft based on the highest warning level among the multiple warning levels.

[0015] In one embodiment of the present invention, the fault prediction method further includes providing a corresponding fault prediction indication based on a determined warning level, wherein the warning level includes: a first warning level for instructing the aircraft to immediately stop for landing gear maintenance after flight; a second warning level for instructing the aircraft to stop for landing gear maintenance after the next flight segment; and a third warning level for instructing the aircraft to prioritize landing gear inspection during ground maintenance.

[0016] According to another aspect of the present invention, a fault prediction system for aircraft landing gear is provided, the fault prediction system comprising: a memory; and at least one processor communicatively coupled to the memory, the at least one processor being configured to perform a fault prediction method for aircraft landing gear as described in the present invention.

[0017] In one embodiment of the present invention, the fault prediction system uses multiple proximity sensors to collect the upper unlock time, lower lock time, lower unlock time, and upper lock time of the landing gear.

[0018] In one embodiment of the present invention, the fault prediction system acquires the airspeed of the aircraft through an airspeed sensor and acquires the ambient temperature of the aircraft through a temperature sensor.

[0019] In one embodiment of the invention, the at least one processor is further configured to provide a fault prediction indication to the airborne maintenance health management system, provided that the fault prediction indication is provided based on the determined warning level.

[0020] According to another aspect of the invention, a non-transient computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, cause the processor to perform the fault prediction method for landing gear of an aircraft according to the invention.

[0021] These and other features and advantages will become apparent from the following detailed description and with reference to the accompanying drawings. It should be understood that the foregoing general description and the following detailed description are illustrative only and do not limit the scope of the claims. Attached Figure Description

[0022] To gain a more detailed understanding of the manner in which the above-described features of the present invention are employed, reference can be made to various embodiments to provide a more specific description of the above-briefly summarized aspects, some of which are illustrated in the accompanying drawings. However, it should be noted that the drawings illustrate only certain typical aspects of the invention and should not be considered as limiting its scope, as this description may allow for other equivalent and effective aspects.

[0023] Figure 1 This is a flowchart of a method for predicting landing gear failures for an aircraft according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of a fault prediction system for aircraft landing gear according to an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of a fault prediction process for aircraft landing gear according to an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the retraction and extension process of landing gear for an aircraft according to an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram showing the correspondence between the airspeed and the airspeed correction factor of an aircraft according to an embodiment of the present invention.

[0028] Figure 6 This is a schematic diagram illustrating the relationship between the ambient temperature and the temperature correction factor of an aircraft according to an embodiment of the present invention.

[0029] The accompanying drawings are not drawn to scale. Detailed Implementation

[0030] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0031] In the description of this disclosure, it should be noted that, unless otherwise stated, "a plurality of" means two or more (including two); the terms "upper," "lower," "left," "right," "inner," "outer," etc., indicating orientation or positional relationships are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. "Vertical" is not vertical in the strict sense, but within the allowable tolerance range. "Parallel" is not parallel in the strict sense, but within the allowable tolerance range.

[0032] The directional terms used in the following description refer to the directions shown in the figures and are not intended to limit the specific structure of this disclosure. It should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this disclosure based on the specific circumstances.

[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this disclosure. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0034] In the description of the embodiments of this disclosure, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0035] Figure 1 A method for predicting landing gear failures for an aircraft, according to an embodiment of the present invention, is shown.

[0036] like Figure 1 As shown, in step 102, the landing gear retraction and extension time series data can be obtained.

[0037] In one example, obtaining the landing gear retraction and extension time series data may further include: collecting the upper unlock time, lower lock time, lower unlock time, and upper lock time of the landing gear; and determining the landing gear lowering time based on the upper unlock time and lower lock time, and determining the landing gear retraction time based on the lower unlock time and upper lock time.

[0038] In one example, the upper unlock time, lower lock time, lower unlock time, and upper lock time of the landing gear can be collected, for example, by using multiple proximity sensors located at the landing gear.

[0039] In step 104, the deployment and recovery time series data can be corrected based on the aircraft's operating environment parameters.

[0040] In one example, the aircraft's operating environment parameters may include an airspeed correction factor and an ambient temperature correction factor. The airspeed correction factor is directly proportional to the aircraft's airspeed, while the ambient temperature correction factor is inversely proportional to the aircraft's ambient temperature. Furthermore, the aircraft's airspeed can be acquired, for example, using an airspeed sensor, and the ambient temperature can be acquired using a temperature sensor.

[0041] In one example, the aircraft's operating environment parameters can be determined based on deployment and recovery test data or simulation data.

[0042] In step 106, the time trend of the corrected release and take-off time series data can be calculated using a time series model.

[0043] In one example, calculating the time trend of the corrected landing gear retraction time series data using a time series model can further include: determining the time trend by statistically analyzing the deviations between the actual landing gear retraction time and the theoretical retraction time for multiple aircraft sorties, wherein the theoretical retraction time is derived based on the corrected landing gear retraction time series data.

[0044] In step 108, the warning level of the landing gear can be determined based on the time trend.

[0045] In one example, determining the landing gear warning level based on a time trend can further include: determining that the landing gear is at the corresponding warning level when the time trend reaches a risk threshold. The risk threshold can include multiple different values, corresponding to multiple different warning levels.

[0046] In one example, the fault prediction method may further include: determining multiple warning levels for multiple landing gears of the aircraft; and determining the warning level of the aircraft based on the highest warning level among the multiple warning levels.

[0047] In one example, the fault prediction method may further include providing corresponding fault prediction instructions based on determined warning levels. These warning levels may include: a first warning level instructing the aircraft to immediately ground and inspect the landing gear after flight; a second warning level instructing the aircraft to ground and inspect the landing gear after the next flight segment; and a third warning level instructing the aircraft to prioritize landing gear inspections during ground maintenance. It should be understood that the above three warning levels are merely examples, and more or fewer warning levels can be set according to actual circumstances, and the fault prediction instructions corresponding to each warning level can also be adjusted according to actual conditions.

[0048] It is clear that, depending on the actual situation, the above examples can be implemented individually or in combination.

[0049] Figure 2 A schematic diagram of a fault prediction system for aircraft landing gear according to an embodiment of the present invention is shown. The system illustrates a typical hardware environment in which the invention can be applied according to exemplary embodiments thereof. The system can be any machine configured to perform processing and / or calculations, and can be, but is not limited to, a workstation, server, desktop computer, laptop computer, tablet computer, personal digital assistant (PDA), smartphone, or any combination thereof. The above system can be implemented wholly or at least partially by this device or similar device or system.

[0050] The system may include components that are connected to or communicate with the bus 220. For example, the system may include the bus 220, a processor 205, and one or more memories 210, etc.

[0051] The processor 205 can be any type of processor and may include, but is not limited to, general-purpose processors and / or special-purpose processors (e.g., special processing chips), intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, the processor 205 may be configured to use a memory controller to operate the memory array. In other cases, a memory controller (not shown) may be integrated into the processor 205. The processor 205 may be responsible for managing the bus 220 and general processing, including executing software 215 stored on memory 210. The processor 205 may also be configured to perform various functions described herein related to the failure prediction method for aircraft landing gear. For example, the processor 205 may be configured to: acquire landing gear retraction and extension time-series data; correct the retraction and extension time-series data according to aircraft operating environment parameters; calculate the time trend of the corrected retraction and extension time-series data using a time-series model; and determine the landing gear warning level based on the time trend.

[0052] Memory 210 can be any storage device capable of storing data. Memory 210 may include, but is not limited to, disk drives, optical storage devices, solid-state storage, floppy disks, hard disks, magnetic tapes or any other magnetic media, optical discs or any other optical media, ROM (Read-Only Memory), RAM (Random Access Memory), cache memory and / or any other memory chip or cartridge, and / or any other medium from which a computer can read data, instructions and / or code. Memory 210 may store computer-executable software 215 including computer-readable instructions that, when executed, cause a processor to perform the various functions described herein. Memory 210 may have various data / instructions / code for implementing the various functions described herein related to the design of a fault prediction system for aircraft landing gear.

[0053] Software 215 may be stored in memory 210 and includes, but is not limited to, an operating system, one or more application programs, drivers, and / or other data and code. Instructions for performing the various functions described herein may be included in one or more application programs, and the components of the system may be implemented by processor 205 reading and executing the instructions of one or more application programs. In some cases, software 215 may not be directly executable by the processor, but may (e.g., when compiled and executed) enable the computer to perform the various functions described herein related to the fault prediction method for aircraft landing gear.

[0054] In one example, the fault prediction system can use multiple proximity sensors to collect the upper unlock time, lower lock time, lower unlock time, and upper lock time of the landing gear.

[0055] In one example, the fault prediction system can use an airspeed sensor to collect the aircraft's airspeed and a temperature sensor to collect the aircraft's ambient temperature.

[0056] In one example, processor 205 may be further configured to provide fault prediction indications to the airborne maintenance health management system when providing corresponding fault prediction indications based on the determined warning level.

[0057] It is clear that, depending on the actual situation, the above examples can be implemented individually or in combination.

[0058] As can be seen, the fault prediction method and system for aircraft landing gear of the present invention can predict the risk of mechanical jamming during landing gear retraction and extension by monitoring anomalies in the landing gear retraction and extension time. Specifically, by collecting landing gear retraction and extension time series data in real time, generating dynamic baseline thresholds based on environmental parameters and flight phases, calculating the slope of the retraction and extension time change trend through a time series model, and triggering a graded alarm and providing inspection suggestions when the slope exceeds a preset risk threshold.

[0059] To further understand the above-described method and system for predicting aircraft landing gear failures, a specific embodiment will be used for explanation below. However, it should be understood that this embodiment is merely exemplary and not restrictive.

[0060] Figure 3 A schematic diagram of a fault prediction process for aircraft landing gear according to an embodiment of the present invention is shown.

[0061] Specifically, at point 310, the fault prediction system 304 can receive sensor signals from multiple proximity sensors 302 located on the landing gear, that is, collect landing gear system retraction and extension process parameters through these proximity sensors 302. Because the retraction and extension times of the nose landing gear and main landing gear, as well as the left and right main landing gear, may be asynchronous, they need to be considered separately. (Reference) Figure 4 The specific parameters collected are as follows: (1) The upper unlock timestamp a1 of the left main landing gear is given by the upper lock sensor of the left main landing gear.

[0062] (2) The upper unlock timestamp a2 of the right main landing gear is given by the upper lock sensor of the right main landing gear.

[0063] (3) The upper unlock timestamp a3 of the nose landing gear is given by the upper lock sensor of the nose landing gear.

[0064] (4) The lower locking timestamp b1 of the left main landing gear is given by the lower strut lock locking sensor of the left main landing gear.

[0065] (5) The lower locking timestamp b2 of the right main landing gear is given by the lower strut lock locking sensor of the right main landing gear.

[0066] (6) The lower locking timestamp b3 of the nose landing gear is given by the lower strut lock locking sensor of the nose landing gear.

[0067] (7) The lower unlocking timestamp c1 of the left main landing gear is given by the lower strut lock locking sensor of the left main landing gear.

[0068] (8) The lower unlocking timestamp c2 of the right main landing gear is given by the lower strut lock locking sensor of the right main landing gear.

[0069] (9) The lower unlock timestamp c3 of the nose landing gear is given by the lower strut lock locking sensor of the nose landing gear.

[0070] (10) The upper lock timestamp d1 of the left main landing gear is given by the upper lock sensor of the left main landing gear.

[0071] (11) The upper lock timestamp d2 of the right main landing gear is given by the upper lock sensor of the right main landing gear.

[0072] (12) The upper lock timestamp d3 of the nose landing gear is given by the upper lock sensor of the nose landing gear.

[0073] Additionally, at point 320, the fault prediction system 304 can also receive ambient temperature and airspeed signals, thus obtaining the aircraft's ambient temperature and airspeed. (1) The ambient temperature t is given by the onboard temperature sensor.

[0074] (2) Aircraft airspeed s is given by the onboard airspeed sensor.

[0075] Next, the fault prediction system 304 can process the collected parameters as follows: (1) Establish a database of release and takeoff times. For example, by calculating the collected release and takeoff process parameters, the following time series data can be obtained: Left main landing gear deployment time: T L-EXT = b1-a1 Right main landing gear deployment time: T R-EXT = b2-a2 Front landing gear deployment time: T N-EXT = b3-a3 Left main landing gear retraction time: T L-RET= d1-c1 Right main landing gear retraction time: T RRET = d2-c2 Nose landing gear retraction time: T N-RET = d3-c3 Accordingly, the above data can be used to construct a database of release and takeoff times.

[0076] (2) Generate correction data based on aircraft operating environment parameters. In this embodiment, two parameters related to the aircraft's airspeed and ambient temperature are mainly considered to generate correction baseline data.

[0077] Regarding the airspeed correction factor Ca, generally speaking, the higher the airspeed, the greater the external aerodynamic load on the landing gear retraction and extension, and the slower the retraction and extension time under the same conditions. Therefore, the airspeed correction factor generally needs to increase accordingly with the increase of airspeed, such as... Figure 5 As shown. Furthermore, the airspeed correction factor, depending on the aircraft, can be defined with specific values ​​based on measured or simulated data from deployment and recovery tests.

[0078] Regarding the temperature correction factor Ct, generally speaking, the higher the temperature, the smaller the pressure drop in the hydraulic system, and the stronger the overall control capability. Therefore, the temperature correction factor generally needs to decrease as the temperature increases. Figure 6 As shown, the temperature correction factor can be defined with specific values ​​based on measured or simulated data from deployment and recovery tests, depending on the aircraft.

[0079] The corrected time parameter can be: Left main landing gear deployment time: T L-EXT-corrected = Ca Ct T L-EXT

[0080] Right main landing gear deployment time: T R-EXT-corrected = Ca Ct T R-EXT

[0081] Front landing gear deployment time: T N-EXT-corrected = Ca Ct T N-EXT

[0082] Left main landing gear retraction time: T L-RET-corrected = Ca Ct T L-RET

[0083] Right main landing gear retraction time: T R-RET-corrected= Ca Ct T R-RET

[0084] Nose landing gear retraction time: T N-RET-corrected = Ca Ct T N-RET

[0085] Based on steps (1) and (2), a theoretical database of landing gear retraction and extension time can be obtained, which can be calibrated using flight test data.

[0086] (3) Calculate the changing trend of release and withdrawal time and the early warning model through time series model.

[0087] For example, by introducing time series trend analysis algorithms, a small increasing trend in the release and take-off times of multiple consecutive times can be identified (such as from 7.2 seconds → 7.5 seconds → 7.8 seconds). Multiple risk thresholds can be set, and an early warning can be issued when one or more risk thresholds are reached, so as to eliminate potential failures at the hidden stage.

[0088] Specifically, the deployment and recovery time during the actual operation of the aircraft is defined as T. real The theoretical value corresponding to this environmental parameter is T. corrected Accordingly, corresponding to multiple landing gears, T real This can include the actual measured T L-EXT-real T R-EXT-real T N-EXT-real T L-RET-real T R-RET-real and T N-RET-real And T corrected This can include T obtained from steps (1) and (2) above. L-EXT-corrected T R-EXT-corrected T N-EXT-corrected T L-RET-corrected T R-RET-corrected and T N-RET-corrected .

[0089] For ease of explanation, taking one of the landing gear units as an example, the deviation between its actual retraction and extension time and the theoretical retraction and extension time can be calculated as follows, with the unit being, for example, seconds: K i = T real - T corrected Taking the same landing gear as an example, let the time trend be T. n , representing the average deviation between the actual and theoretical landing gear retraction times over the most recent n sorties. Correspondingly, the time trend of this landing gear can be calculated as follows:

[0090] Similarly, we can obtain the time trend efficiency (or time trend) of left and right main starting points, front starting point closing point, and left and right main starting points, front starting point closing point, respectively, as T. L-EXT-AVE T R-EXT-AVE T N-EXT-AVE T L-RET-AVE T R-RET-AVE and T N-RET-AVE .

[0091] Based on the above, and considering the risk levels, a multi-window periodic early warning function is established. The early warning level, AlertLevel, can be divided into three levels: Level 1 Emergency, Level 2 Medium, and Level 3 General, with corresponding risk thresholds set for each. Specifically, Level 1 Emergency may recommend immediate shutdown for maintenance after flight; Level 2 Medium may recommend shutdown for maintenance after the next flight segment; and Level 3 General may recommend priority inspection during ground maintenance.

[0092] In a non-limiting embodiment, the warning levels can be classified as follows:

[0093] If all of the above criteria are met, the one with the higher severity level shall be applied.

[0094] As you can understand, the specific values ​​of the risk thresholds in the above categories (such as 1, 2, 20) are only examples. In actual applications, different risk thresholds can be set according to different aircraft models or security strategies.

[0095] Furthermore, the above steps allow for the determination of a corresponding warning level for each landing gear. For the aircraft as a whole, given its multiple landing gears, multiple warning levels can be obtained corresponding to each landing gear. The overall warning level for the aircraft can then be determined based on the highest warning level among all landing gear warning levels.

[0096] Furthermore, the fault prediction system 304 can provide a corresponding fault prediction indication based on the determined warning level. At 330, the fault prediction system 304 can provide this fault prediction indication to the airborne maintenance and health management system 306.

[0097] It is understood that the process in this embodiment is merely exemplary, and each step can be adjusted, modified, added, and / or deleted according to actual needs, as long as the reference can be achieved. Figure 1 The aforementioned method for predicting landing gear failures in aircraft is sufficient.

[0098] The above describes the method and system for predicting landing gear failures in aircraft according to the present invention. It is evident that, compared to the prior art, the present invention has at least the following advantages: (1) High cost-effectiveness and convenient deployment: It can be based on the aircraft's existing sensors and airborne data without the need to install any additional hardware, and correspondingly, it does not increase the system weight or procurement costs. In other words, the present invention can achieve full functionality through software upgrades or algorithm integration, which greatly reduces the cost of technical transformation and deployment difficulty, and is especially conducive to rapid promotion in the existing fleet.

[0099] (2) Accurate early warning and high reliability: Through dynamic baseline calibration technology, the interference of environmental fluctuations on key parameters is effectively eliminated, thereby improving the accuracy of state judgment and the reliability of early warning, and fundamentally reducing the false alarm rate.

[0100] (3) Foresight and intelligent operation and maintenance: By adopting multi-cycle trend analysis, performance degradation trends can be identified in the early stage, realizing the transformation from "post-event alarm" to "pre-event prediction". Combined with the hierarchical early warning mechanism, it can provide scientific decision support for maintenance support, optimize maintenance plans, effectively reduce unplanned downtime, and improve aircraft operation efficiency.

[0101] (4) Efficient maintenance and clear guidance: The electronic checklist generated by the system can accurately locate suspicious parts, greatly shorten the troubleshooting time and manpower cost, and improve the pertinence of maintenance work and the one-time repair rate.

[0102] The foregoing description includes examples of various aspects of the claimed subject matter. It is certainly impossible to describe every conceivable combination of components or methods for the purpose of depicting the claimed subject matter, but those skilled in the art will recognize that many further combinations and arrangements of the claimed subject matter are possible. Thus, the disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A method for predicting landing gear failures in aircraft, characterized in that, The fault prediction method includes: Obtain the landing gear retraction and extension time sequence data; The deployment and recovery time series data are corrected based on the aircraft's operating environment parameters; The time trend of the corrected release and withdrawal time series data is calculated using a time series model; and The warning level of the landing gear is determined based on the time trend.

2. The fault prediction method as described in claim 1, characterized in that, Obtaining the landing gear retraction and extension time series data further includes: The system collects the upper-position unlocking time, lower-position locking time, lower-position unlocking time, and upper-position locking time of the landing gear; and The landing gear lowering time is determined based on the upper unlocking time and the lower locking time, and the landing gear retraction time is determined based on the lower unlocking time and the upper locking time.

3. The fault prediction method as described in claim 1, characterized in that, The aircraft's operating environment parameters include: Aircraft airspeed correction factor, which is proportional to the airspeed of the aircraft; and An ambient temperature correction factor, which is inversely proportional to the ambient temperature of the aircraft.

4. The fault prediction method as described in claim 1, characterized in that, The operating environment parameters of the aircraft are determined based on launch and recovery test data or simulation data.

5. The fault prediction method as described in claim 1, characterized in that, Calculating the time trend of the corrected release and withdrawal time series data using time series models further includes: The time trend is determined by statistically analyzing the deviation between the actual and theoretical landing gear retraction times for multiple sorties of the aircraft, wherein the theoretical retraction times are derived based on corrected retraction time series data.

6. The fault prediction method as described in claim 1, characterized in that, Determining the warning level of the landing gear based on the aforementioned time trend further includes: If the time trend reaches the risk threshold, the landing gear is determined to be at the corresponding warning level.

7. The fault prediction method as described in any one of claims 1-6, characterized in that, The fault prediction method further includes: Determine multiple warning levels for multiple landing gears of the aircraft; and The warning level of the aircraft is determined based on the highest warning level among the multiple warning levels.

8. The fault prediction method as described in claim 7, characterized in that, The fault prediction method further includes providing a corresponding fault prediction indication based on a determined warning level, wherein the warning level includes: The first warning level is used to instruct the aircraft to immediately stop at the airfield after flight to inspect and repair the landing gear; The second warning level is used to instruct the aircraft to stop for maintenance of the landing gear after the next flight segment; and The third warning level is used to instruct the aircraft to prioritize inspection of the landing gear during ground maintenance.

9. A fault prediction system for aircraft landing gear, characterized in that, The fault prediction system includes: Memory; and At least one processor communicatively coupled to the memory, the at least one processor being configured to perform a fault prediction method for landing gear of an aircraft as claimed in any one of claims 1 to 8.

10. The fault prediction system as described in claim 9, characterized in that, The fault prediction system uses multiple proximity sensors to collect the upper-level unlocking time, lower-level locking time, lower-level unlocking time, and upper-level locking time of the landing gear.

11. The fault prediction system as described in claim 9, characterized in that, The fault prediction system uses an airspeed sensor to collect the aircraft's airspeed and a temperature sensor to collect the aircraft's ambient temperature.

12. The fault prediction system as described in claim 9, characterized in that, The at least one processor is further configured to: When providing corresponding fault prediction indications based on the determined warning level, the fault prediction indications are provided to the airborne maintenance and health management system.

13. A non-transient computer-readable medium storing computer-executable instructions that, when executed by a processor, perform the landing gear failure prediction method for an aircraft as described in any one of claims 1 to 8.

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