GB2637759A

GB2637759A - Aircraft landing gear assembly

Info

Publication number: GB2637759A
Application number: GB2401399.7A
Authority: GB
Inventors: Capener Wayne; Baker Tim
Original assignee: Safran Landing Systems UK Ltd
Current assignee: Safran Landing Systems UK Ltd
Priority date: 2024-02-02
Filing date: 2024-02-02
Publication date: 2025-08-06
Also published as: WO2025163154A1; GB202401399D0

Abstract

An aircraft landing gear system comprising a plurality of sensors 56 to detect a known acoustic signal, and a processor 52, and associated method comprising the detection of a plurality of sound signals, where the processor uses a mapping function to estimate the load experienced by a component from the signal. The sensors may also transmit acoustic signals, which may be the known signal, and the signals may be transmitted continuously or sequentially, and may be transmitted during at least one phase of flight. There may be calibration and training phases, where the landing gear components may be in a known condition during calibration using acoustic devices, where some acoustic devices may be disabled during calibration, and where the training data may be used by a machine learning algorithm in the training phase to derive the mapping function. The calculated load may be stored in a database in storage 54.

Description

AIRCRAFT LANDING GEAR ASSEMBLY

Background to the Invention

Aircraft landing components experience large variations in load during the component lifetime. Therefore, it is important to monitor the components to reduce the likelihood of the component failing due to previous prolonged or excessive loads. A problem with determining health of components is that the testing procedures can be time consuming and expensive. Currently, health monitoring systems include the use of strain gauge bridges within a component to provide data indicative of the surface strain that the component has experienced. However, the harsh operating environment of aircraft landing gear components can result in the need for regular replacement and/or maintenance of the sensors.

As the instalment and calibration of the strain gauge bridges is time consuming, there is a need for a monitoring system with improved maintenance times and long-term reliability. Moreover, as aircraft landing gear assemblies are formed of multiple interconnected components, it can be difficult to determine how measured load on one component will impact load experienced on another component. There is therefore a need for a monitoring system which provides reliable diagnosis of the fatigue of a component whilst providing a prognosis for the assembly in which the component is located.

Summary of Invention

According to a first aspect of the invention, there is provided an aircraft landing gear system comprising: a plurality of sensors arranged to detect a known acoustic input signal within one or more components of an aircraft landing gear structure; and a processor configured to: receive data from the plurality of sensors, the data corresponding to the detected known acoustic input signal; compare the data to a mapping function, wherein the mapping function defines a relationship between a detected acoustic signal and a known load on the one or more components of the aircraft landing gear structure; and estimate the load experienced by the one or more components of the aircraft landing gear structure based on the data and the mapping function.

This system is advantageous as it reduces the time taken to carry out maintenance on the health monitoring system as the plurality of sensors can be acoustic devices which are simpler and can be installed in more accessible locations on the component compared with the traditionally used strain gauges. The use of simpler acoustic sensors in comparison to more complex strain gauges can result in faster installation times and response signals which can be easily characterised. If one acoustic device fails, it can be replaced without requiring all other acoustic devices to be replaced. Instead, an acoustic device can be replaced and the system recalibrated. In some examples, the system may not need to be recalibrated if an acoustic device fails as the system can be implemented to build in redundancy due to the nature of acoustic sensors being low cost and lightweight.

The mapping function can be implemented with machine learning algorithms to provide a prediction of loads experienced by the component, without requiring direct measurement of loads. For example, the detected acoustic signals can be entered into a mapping function which enables accurate prediction of which components have experienced various loads, without needing to directly monitor the stress and/or strain of the components of interest. Therefore, the system can provide predictions on the condition of a component which improves long term reliability of the component by providing an indication that the component may exceed the average life span due to repeated low loads being experienced.

This can therefore reduce unnecessary maintenance. On the other hand, the system can provide an indication that the component has experienced an abnormal load and therefore should be replaced prior to the completion of the average life span of the component.

This is especially advantageous when one type of landing gear is used for different aircraft as different aircraft will impart different loads onto the landing gear. Therefore, it can be difficult for an accurate prediction of component lifetimes across all types of aircraft.

The component can form part of a more complex aircraft landing gear assembly. The assembly can be formed of a plurality of components, each arranged with a plurality of acoustic devices. This results in the improved system being applied to more complex assemblies so that the performance of an individual component can be determined and/or predicted in order to determine and/or predict the performance of the assembly. In an example, acoustic signals can be monitored within a main fitting of aircraft landing gear during landing. The measured acoustic signals in the main fitting can then be used to infer the load experienced by the wheel assembly using the mapping function. In turn, the loads experienced by the side stay can then be inferred, again via the mapping function. Therefore, in this example, sensors within the main fitting only can be used to monitor loads experienced by several other components of an assembly.

The acoustic signals can be a mechanical wave, such as an acoustic waveform. The acoustic signals can be ultrasound waves. The acoustic signals have wave characteristics such as amplitude and frequency. The acoustic devices can detect the wave characteristics such that the mapping function defines a relationship between a detected acoustic signal characteristic, such as amplitude and/or frequency, and a known load on the aircraft landing gear component.

The plurality of sensors can be further arranged to transmit an acoustic signal.

This is advantageous as it provides means to provide a plurality of acoustic signals to be detected by the plurality of acoustic sensors meaning that larger data sets can be gathered.

The known acoustic input signal may be generated by at least one of the plurality of sensors.

This is advantageous as the input signal can be controlled by a user and therefore activated at any point. It is further advantageous as the input signal can be controlled by a user so that the input signal can have an optimum wave form for the analysis required. Therefore, the system can be more reliable than existing systems.

The mapping function can be determined through a calibration phase and a training phase.

The calibration phase can comprise detecting a plurality of calibration acoustic signals when the one or more components of the aircraft landing gear structure are in a known condition.

The calibration phase therefore provides data which can be compared to data collected when the component is experiencing loads. The data from the calibration phase can provide information on the locations of the acoustic devices with respect to each other and how the acoustic signals travel through the material of the component. The known condition can be when the component is experiencing no load (for example, when secured in the bay of the aircraft) or when it is experiencing known loads, for example during onboarding, push off, taxiing, an initial climb, a cruising altitude, descent, approach, and/or landing.

Therefore, the calibration phase can provide data on the reactions of the material of the one or more components as loads are experienced by the component(s).

The training phase can comprise providing the calibration acoustic signals to a machine learning algorithm to produce the mapping function.

This is advantageous as it can provide a means to compare the acoustic signals received at the sensors with the known input in order to quickly form a function which can be used to estimate unknown loads and how material properties of the one or more components may change.

According to a second aspect of the invention, there is provided a method for monitoring loads on one or more components of an aircraft landing gear structure, the method comprising: detecting a plurality of acoustic signals within one or more components of an aircraft landing gear structure, wherein the acoustic signals are produced when the one or more components experience a load; comparing the detected plurality of acoustic signals to a mapping function, wherein the mapping function defines a relation between a detected acoustic signal and a known load on the aircraft landing gear component; and estimating the load experienced by the one or more components based on the detected plurality of acoustic signals and the mapping function.

The method can further comprise transmitting a plurality of acoustic signals within the one or more components of the aircraft landing gear structure.

The transmitting a plurality of acoustic signals can comprise transmitting the plurality of acoustic signals in sequence or simultaneously.

Often, high loads are experienced for a relatively short period of time. For example, a landing event can take 0.1 seconds to reach peak load. Therefore, it is advantageous to transmit input acoustic signals from a plurality of acoustic devices simultaneously to provide the largest possible window for receiving return signals at the detecting acoustic devices during the period of load. However, it can be difficult to distinguish individual acoustic signals originating from different acoustic devices if the acoustic signals are transmitted simultaneously. Therefore, it can be advantageous to stagger the transmission by transmitting the acoustic signals in a sequence.

The transmitting a plurality of acoustic signals can comprise transmitting the plurality of acoustic signals during at least one phase of flight.

The calibration phase can comprises maintaining the one or more components of the aircraft landing gear structure in a known condition; transmitting a first acoustic signal using a first acoustic device; detecting the first acoustic signal at a second acoustic device; transmitting a second acoustic signal using the second acoustic device; and detecting the second acoustic signal at the first acoustic device.

The calibration phase can further comprise: maintaining the one or more components of the aircraft landing gear structure in the known condition; disabling at least one of the plurality of acoustic devices; transmitting a third acoustic signal using an acoustic device which has not been disabled; and detecting the third acoustic signal at a further acoustic device which has not been disabled.

This is advantageous as it provides a level of redundancy. If, during use, an acoustic sensor fails, the data gathered during the calibration phase will include a sensor not operating.

The training phase can comprise providing the first and second acoustic signals to a machine learning algorithm to produce the mapping function.

The estimated load can be stored on a database.

Brief Description of the Drawing

Embodiments of the invention will now be described, strictly by way of example only, with reference to the accompanying drawings, of which: Figure 1 is a schematic representation of an aircraft; Figure 2 is a schematic representation of an aircraft landing gear assembly; Figures 3A and 3B illustrate a component arranged with an exemplary arrangement of acoustic devices; Figure 3C illustrates a component arranged with an exemplary arrangement of acoustic devices; Figure 4 illustrates a control system; Figure 5 illustrates a method of calibrating and training a mapping function; Figure 6 illustrates a method of using the mapping function.

Detailed Description

Figure 1 is a diagram of an aircraft 10. The aircraft 10 includes subassemblies such as a nose landing gear 12, main landing gear 14 and engines 16. Other aircraft subassemblies will be apparent to the skilled person. A subassembly can be a group of interconnected parts which are arranged to be fitted to the aircraft as a unit.

Referring now to Figure 2, an aircraft subassembly, namely an aircraft landing gear assembly, is shown generally at 14. The landing gear assembly 14 includes aircraft landing gear components such as a foldable stay 18 and a lock link 20. In addition, the landing gear assembly also includes components such as a shock absorber 22, comprising a main fitting and a sliding tube, as well as a wheels and brake assembly. As components are connected to each other, the load on one component can result in various loads experienced on the other components. Therefore, load on one component can result in the strain and stress of said component as well as the strain and stress of other components within the assembly.

Figure 3A shows apparatus of an exemplary system for monitoring the loads experienced by a component 24. Acoustic devices 26A-D are arranged on the surface of the component and/or within an internal structure of the component. Acoustic devices 26A-D can be sensors capable of receiving acoustic signals 28 such as ultrasound waves propagating through and/or along the surface of the component. Acoustic devices 26A-D can be configured to also transmit acoustic signals 30, such as ultrasound waves. Alternatively, the acoustic sensors can be co-located with acoustic transmitters. In the present arrangement, four acoustic devices 26A-D are shown forming a linear transmission pathway. The acoustic devices 26A-D are equidistance however, they can be arranged at various intervals which are not equal. An optimised location for each acoustic device 26A-D can be determined using structural information of the component.

The acoustic devices 26A-D can be piezoelectric devices. Piezoelectric devices arranged within a material can be excited by applying an electrical signal to introduce an acoustic signal into a material it is attached to. Piezoelectric devices can also detect oscillations within the material. These oscillations can result in an output of an electrical signal characteristic of the properties of the oscillation. An input signal can be selected which results in an acoustic signal having discernible waveform properties from waveforms caused by operational conditions of the component. The piezoelectric devices can transmit and receive acoustic signals meaning that they can be used to create the signal input and sensing network for building a multi-variant component characterisation.

The acoustic devices 26A-26D can detect properties of acoustic waves such as the frequency and/or amplitude. When the component 24 is under load and therefore experiencing stress and/or strain, signals transmitted through the component will vary as a function of stress and/or strain. As such, the properties of the output waveform (i.e., properties of an acoustic wave received at an acoustic device) can differ from the properties of the input waveform (i.e., acoustic properties of an acoustic wave transmitted from an acoustic device). Therefore, relating the output signal waveform to the input signal waveform provides information about the transmission medium (i.e., the material of the component 24) and the path on which the signal has travelled. In turn, this provides information regarding the load experienced by the component 24.

In an arrangement of four devices which can each transmit and receive signals, there are six unique transceiver relationships. A first device 26A can transmit a signal which is detected by a second 26B, third 26C, and fourth device 26D. This provides three unique transceiver relationships. The second device 26B can transmit a signal which is detected by the first 26A, third 26C, and fourth device 26D. As the relationship between the first device 26A and the second device 26B has already been established by transmission of a signal from the first device 26A, this provides two unique transceiver relationships. Signals transmitted from the third device 26C provides one unique transceiver relationship between the third device 26C and the fourth device 26D.

An alternative arrangement of acoustic sensors on a component 32 is shown in Figure 3B.

In this arrangement, five acoustic devices 36 are shown. The arrangement of Figure 3B is substantially similar to that of Figure 3A and therefore only the differences will be described.

The acoustic devices 36 are arranged to form a circular transmission pathway. In an arrangement of five devices which can each transmit and receive signals, there are 10 unique transceiver relationships. The number of acoustic devices installed on and/or within a component can be selected based on the complexity of the structure being measured.

Figure 3C shows apparatus of an exemplary system for monitoring the loads experienced by an assembly 44. The arrangement of Figure 3C is substantially similar to that of Figure 3A and therefore only the differences will be described.

The assembly 44 includes components 24 and 34 which are coupled at a joint. Each component 24, 34 comprises a plurality of acoustic devices 26, 46. Each acoustic device 26, 46 can be arranged to transmit signals 28, 38 and receive signals 30, 40. The acoustic devices 26, 46 can provide an array of acoustic devices throughout an aircraft landing gear assembly. The joints between components 24, 34 can be considered as discontinuities in the signal path of the acoustic devices 26, 46.

Figure 4 shows a control system 50 for monitoring the health of a component 24, 32, 34 and/or assembly 44. The control system 50 comprises a processor 52 and a memory device 54. The control system 50 is coupled to an array of acoustic devices 56. The array of acoustic devices can be the acoustic devices 26, 36, 46 described in relation to Figures 3A-C.

The processor 52 can transmit and receive information from an array of acoustic devices 56. For example, the processor 52 can transmit a signal which causes the one or more acoustic devices 56 to produce an acoustic wave. When the acoustic device is a piezoelectric device, the signal from the processor 52 is an electrical signal which causes the piezoelectric device to actuate and produce an acoustic wave. The processor 52 can receive signals from the one or more acoustic devices 56. The received signals provide information regarding any acoustic waves detected by the one or more acoustic devices 56, such as the frequency and/or amplitude of the waveforms. For example, if the acoustic device is a piezoelectric device, acoustic waves across the device will result in an electrical signal which can be transmitted back to the processor 52.

A memory device 54 can store the information received from the array of acoustic devices 56 via the processor 52. The information can be retained in the memory 54 and/or output to an external device 58, such as an information screen or a further processor.

Figure 5 shows an exemplary method of monitoring the health of a component and/or assembly. The method starts by providing a component or assembly arranged with an array of n acoustic devices and in a known condition 62. For example, a component can be arranged with no external load applied. Such a condition can be achieved during storage in the bay. Alternatively, the known condition can be during taxiing, take off, and/or landing. The number of sensors provided can depend on the size of the component and/or the expected load that the component will withstand in use. For example, a component can be arranged with four acoustic devices (n equal to four).

While in the known condition, a signal is transmitted to the first acoustic device to instruct the first acoustic device to transmit an acoustic signal 64 into the material of the component. The first acoustic device can be formed of two discrete components, a transmitter and a detector or can be a single, integral device. Alternatively, in a passive system, an external known source can be transmitted for detection by the acoustic sensors. For example, air flow over a component or a vibration can cause an acoustic signal to propagate through the component. Testing prior to the sensor map being built can highlight any such external sources which can be used. During step 64, the remaining acoustic devices in the array detect the transmitted acoustic signal originating from the first acoustic device or can detect an external known acoustic source. Therefore, in the example of four acoustic devices, the second, third, and fourth acoustic devices would measure the acoustic signal originating from the first acoustic device or external known acoustic source. Therefore, a relationship between sensors can be built. The waveforms detected at these acoustic devices which did not transmit the input acoustic wave are transmitted to the processor. The detected waveforms can be saved in the memory.

Again, while in the known condition, a further signal is transmitted to the second acoustic device to instruct the second acoustic device to transmit an acoustic signal 66 into the material of the component. During this step 66, the remaining acoustic devices in the array detect the transmitted acoustic signal originating from the second acoustic device.

Therefore, in the example of four acoustic devices, the first, third, and fourth acoustic devices would measure the acoustic signal originating from the second acoustic device. The waveforms detected at these acoustic devices which did not transmit the input acoustic wave are transmitted to the processor. The detected waveforms can be saved in the memory.

The method described at steps 64 and 66 continues for each acoustic device until all n acoustic devices have transmitted an acoustic signal which is detected by the remaining, non-transmitting devices in the array 68. The control system memory can store the data corresponding to the output waveforms, such as the frequency and/or amplitude of the output waveforms.

Steps 64, 66, and 68 of the method can occur in sequence, as described, or simultaneously. If carried out simultaneously, the characteristics of the input waveforms can be adjusted so that the input acoustic wave of each acoustic device is unique. This enables the various input waveforms to be distinguished.

At step 70, further loads and/or different loads can be applied to the component and steps 64-68 repeated 72. Therefore, a data set of acoustic signals can be built using different load conditions.

The processor is provided with the data corresponding to the input and output waveforms from all acoustic devices. The processor can create a mapping function 74 using the data to provide relationships between the n acoustic devices during different known conditions. The mapping function is a function which details how each acoustic device relates to the other acoustic devices under different loads, for example no load and abnormal load. For example, a known vertical load, drag, and/or torsional load is applied. Other input conditions can include variations in temperature and/or pressure. The mapping function is therefore considered to be a sensor map. The relationships between the acoustic devices can include the time and/or attenuation in signal strength between transmission and detection of acoustic signals.

Steps 62 to 70 of the method therefore provides a calibration of the system. After the system is calibrated, the mapping function is trained 74 using the calibration data gathered at steps 62 to 70. To train the mapping function 74, the calibration data is provided to a machine learning algorithm The processor is provided with the data corresponding to the input and output waveforms from all acoustic devices during the known input load conditions (the calibration phase). The mapping function can be updated with data of the relationships between the n acoustic devices 74 during further known input loads. The known input load condition relationships between the acoustic devices can include the time and/or attenuation in signal strength between transmission and detection of acoustic signals. Differences between first condition relationships and second condition relationships can be due to an increase or decrease in the time taken for the acoustic signals to be detected at some or all acoustic devices as the transmission pathway of the acoustic devices changes with a different amount of load applied in the two conditions. Other differences can be due to signal distortion or attenuation.

The training of the mapping function 74 can therefore be carried out with a plurality of input conditions. To increase the number of data points provided to the processor when creating the mapping function, test data can be combined with data from analysis models.

The mapping function can be produced using machine learning techniques, such as fuzzy logic algorithms.

After the mapping function has been trained with a suitable number of known loads (for example X), the mapping function can be used in combination with unknown loads 76.

In any of the arrangements of sensors shown in Figures 3A-C, additional sensors can be incorporated such that during the calibration phase, the additional sensors are calibrated using the same loads as the other acoustic sensors. This provides a redundancy to the system in the case of failure of an acoustic sensor. Due to the nature of the calibration phase described above, the data gathered can include the system responding to nonfunctioning devices. Therefore, if an acoustic sensor were to be non-operational, the calibration phase would provide the data to enable the mapping function to operate under that arrangement.

Figure 6 shows an exemplary system 80 for monitoring the load experienced by a component and/or assembly formed of multiple components. Unknown conditions 82, for example unknown vertical and/or tortional loads and/or drag, are applied to a component 84 arranged with a plurality of acoustic devices. Other unknow conditions 82 can include variations in temperature and/or pressure. As the unknown conditions 82 are experienced by the component, the acoustic devices transmit input acoustic waves and detect output acoustic waves 86. This is carried out using the same method described at steps 64 to 68 of the calibration of the mapping function. Specifically, a signal is transmitted from the processor to instruct the first acoustic device to transmit an input acoustic wave. The remaining acoustic devices in the array detect the transmitted acoustic signal originating from the first acoustic device. The waveforms detected at the acoustic devices which did not transmit the input acoustic wave are transmitted to the processor. The detected waveforms can be saved in the memory.

As described at step 68, each acoustic device will transmit an input acoustic wave which is detected as an output acoustic wave at the remaining acoustic devices. The transmission of input acoustic waves and the detection of output acoustic waves as described in steps 64, 66, and 68, can occur in sequence, as described, or simultaneously. If carried out simultaneously, the characteristics of the input waveforms can be adjusted so that the input acoustic wave of each acoustic device is unique. This enables the various input waveforms to be distinguished.

The transmitted input acoustic waves and detected output acoustic waves 86 are processed with an inverse of the mapping function 88. The inverse mapping function 88 provides a prediction 90 of the applied conditions 82. Therefore, this system provides a prediction of unknown applied conditions.

The input acoustic wave can be an acoustic wave originating from a point external to the component. In this example, the acoustic devices detect output acoustic waves only. In this example, the mapping function provides the relationship between the acoustic devices. Therefore, unknow applied conditions can be predicted using the mapping function and variations in the detection of the output acoustic waves between the acoustic devices.

The detected output acoustic wave signals can be negatively impacted by noise or unwanted reflections of signals within the components. Therefore, additional processing of the output acoustic wave signals may be required before being input into the inverse mapping function. For example, windowing techniques, filtering, and/or time or frequency domain separation could be implemented.

The memory 54 can store the transmitted input acoustic waves and detected output acoustic waves for transmission to an external device 58 which stores the mapping function. Alternatively, the memory 54 can store the mapping function such that a prediction of applied unknown conditions is output to an external device 58.

The memory 54 can store information regarding the material of the component so that the speed of acoustic waves within the material can be accounted for.

Therefore, in an example, an array of acoustic devices is installed in an assembly including a plurality of individual components connected at joints. Joints can be considered as discontinuities in the acoustic device transmission path and therefore a multi-dimensional map of signal "distortions", i.e., a multidimensional mapping function, can be generated to characterise the material condition of the assembly between the acoustic devices for different applied conditions. Having built this multi-dimensional relationship, it can be used inversely, through device measurements, to determine input conditions that have created the current material properties being detected. An example of a material property being detected is the change in transmission path length due to strain.

An example application could be to monitor a region of a main landing gear fitting that includes stressed regions sensitive to all input ground load directions, along with shock absorber closure, pressure, and temperature to create a mapping function using 5 or 6 acoustic devices for ground load inputs. The acoustic devices and mapping function can be used in an operational environment to extract out ground loads. This could provide an alternative approach to traditional flight testing information using easier to install and easier to replace sensors.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be capable of designing many alternative embodiments without departing from the scope of the invention as defined by the appended claims. In the claims, any reference signs placed in parenthesis shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps other than those listed in any claim or the specification as a whole. The singular reference of an element does not exclude the plural reference of such elements and vice-versa. Parts of the invention can be implemented by means of hardware comprising several distinct elements. In a device claim enumerating several parts, several of these parts can be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

CLAIMS1. An aircraft landing gear system comprising: a plurality of sensors arranged to detect a known acoustic input signal within one or more components of an aircraft landing gear structure; and a processor configured to: receive data from the plurality of sensors, the data corresponding to the detected known acoustic input signal; compare the data to a mapping function, wherein the mapping function defines a relationship between a detected acoustic signal and a known load on the one or more components of the aircraft landing gear structure; and estimate the load experienced by the one or more components of the aircraft landing gear structure based on the data and the mapping function.
2. The aircraft landing gear system according to claim 1, wherein the plurality of sensors are further arranged to transmit an acoustic signal..
3. The aircraft landing gear system according to claim 2, wherein the known acoustic input signal is generated by at least one of the plurality of sensors.
4. The aircraft landing gear system according to any preceding claim, wherein the mapping function is determined through a calibration phase and a training phase.
5. The aircraft landing gear system according to claim 4, wherein the calibration phase comprises: detecting a plurality of calibration acoustic signals when the one or more components of an aircraft landing gear structure are in a known condition.
6. The aircraft landing gear system according to claim 4, wherein the training phase comprises: providing the calibration acoustic signals to a machine learning algorithm to produce the mapping function.
7. A method for monitoring loads on one or more components of an aircraft landing gear structure, the method comprising: detecting a plurality of acoustic signals within one or more components of an aircraft landing gear structure, wherein the acoustic signals are produced when the one or more components experience a load; comparing the detected plurality of acoustic signals to a mapping function, wherein the mapping function defines a relation between a detected acoustic signal and a known load on the one or more components; and estimating the load experienced by the one or more components based on the detected plurality of acoustic signals and the mapping function.
8. The method according to claim 7, further comprising transmitting a plurality of acoustic signals within the one or more components of the aircraft landing gear structure.
9. The method according to claim 8, wherein transmitting a plurality of acoustic signals comprises transmitting the plurality of acoustic signals in sequence or simultaneously.
10. The method according to claim 8, wherein transmitting a plurality of acoustic signals comprises transmitting the plurality of acoustic signals during at least one phase of flight.
11. The method according to any of claims 7 to 10, wherein the mapping function is determined through a calibration phase and a training phase.
12. The method according to claim 11, wherein the calibration phase comprises: maintaining the one or more components of the aircraft landing gear structure in a known condition; transmitting a first acoustic signal using a first acoustic device; detecting the first acoustic signal at a second acoustic device; transmitting a second acoustic signal using the second acoustic device; and detecting the second acoustic signal at the first acoustic device.
13. The method according to claim 12, wherein the calibration phase further comprises: maintaining the one or more components of the aircraft landing gear structure in the known condition; disabling at least one of the plurality of acoustic devices; transmitting a third acoustic signal using an acoustic device which has not been disabled; and detecting the third acoustic signal at a further acoustic device which has not been disabled.
14. The method according to any of claims 11-13, wherein the training phase comprises: providing the first and second acoustic signals to a machine learning algorithm to produce the mapping function.
15. The method according to any of claims 7 to 14, wherein the estimated load is stored on a database.