Patent Description:
Checking tire pressure is an important part of the maintenance of a vehicle. Tire pressures should be maintained at predetermined pressures to ensure that a tire performs as intended by the manufacturer. <CIT> discloses a method of monitoring the pressure of a tyre of an aircraft that comprises taking two or more pressure readings from the tyre at different times, calculating an estimated deflation rate based on the pressure readings, and calculating a time for the tyre to deflate to a reference pressure level based on the estimated deflation rate. <CIT> discloses a method for estimating the condition or the pressure of an aircraft tire that includes measuring, during the same flight and/or after the landing which follows this flight, physical data characteristic of at least the pressure in the tire, at a first time, then at a second, separated by at least <NUM> minutes. Using the measured data, input data of an estimation model is formed, which provides at least one output data representative of the condition of the tire or the pressure (PPM) in the tire at a third time later than the second time. <CIT> discloses a tire pressure monitoring system for wheeled vehicles that uses a vehicle computer, a vehicle position sensor, a tire pressure sensor, and ambient temperature readings to determine whether the vehicle has remained stationary for a predetermined time period before determining and reporting cold tire pressure.

A first aspect of the present invention provides a computer-implemented method of predicting tire pressure of a tire of an aircraft, the method comprising: obtaining first data indicative of an inflation pressure of the tire; obtaining second data indicative of a brake temperature of a brake associated with an aircraft wheel to which the tire is mounted; obtaining third data indicative of a gas temperature of the tire; obtaining fourth data indicative of one or more predicted operational conditions of the aircraft during a future time period; and determining, based at least in part on the first, second, third and fourth data, predicted tire pressure characteristics of the tire during the future time period. Optionally, the fourth data is indicative of one or more of: a predicted future route of the aircraft within the future time period; a predicted turn-around time of the aircraft between future flight cycles within the future time period; a predicted flight time of one or more flight cycles of the aircraft within the future time period; a predicted ground time of the aircraft within the future time period; a predicted weight of the aircraft during one or more flight cycles of the aircraft within the future time period; a time of day during which the aircraft is predicted to be in-flight and/or on ground within the future time period; a predicted aircraft taxi distance within the future time period; and a predicted number of flight cycles of the aircraft within the future time period.

Optionally, wherein the fourth data is indicative of one or more of: a predicted runway surface condition for one or more runways on which the aircraft is predicted to operate within the future time period; a predicted weather forecast for an ambient environment of the aircraft at predicted geographic locations within the future time period.

Optionally, the fourth data is indicative of one or more of: a predicted inflation pressure of the tire within the future time period; a predicted brake temperature of the brake within the future time period; a predicted gas temperature of the tire within the future time period; a predicted tire carcass temperature of the tire within the future time period; a predicted pressure loss of the tire within the future time period; a predicted aircraft wheel temperature of the aircraft wheel within the future time period; a predicted usage of a brake cooling fan associated with the brake within the future time period; a predicted brake wear state of the brake within the future time period; a predicted ambient temperature within the future time period; a predicted aircraft position within the future time period; a predicted aircraft steering angle during one or more flight cycles within the future time period; a predicted aircraft speed during one or more flight cycles within the future time period; a predicted aircraft centre of gravity during one or more flight cycles within the future time period; and a predicted braking energy of the brake during one or more flight cycles within the future time period.

Optionally, the first data is indicative of one or more of a past inflation pressure of the tire, and a current inflation pressure of the tire.

Optionally, the second data is indicative of one or more of a past brake temperature of the brake and a current brake temperature of the brake.

Optionally, the third data is indicative of one or more of a past gas temperature of the tire and a current gas temperature of the tire.

Optionally, the method comprises determining, based at least in part on the predicted tire pressure characteristics of the tire, a maintenance action to be performed on the tire.

Optionally, the method comprises scheduling, based at least in part on the predicted tire pressure characteristics of the tire, a maintenance action to be performed on the tire.

Optionally, the method comprises updating the predicted tire pressure characteristics based at least in part on actual operational conditions of the aircraft experienced within at least part of the future time period.

Optionally, the future time period is at least <NUM> days, at least <NUM> days, or at least <NUM> days.

Optionally, the method comprises determining, based at least in part on the predicted tire pressure characteristics of the tire, a predicted remaining lifespan of the tire.

Optionally, the method comprises determining, based at least in part on the predicted tire pressure characteristics of the tire, a future inflation pressure for the tire.

Optionally, determining the predicted tire pressure characteristics of the tire during the future time period comprises utilising a model of the tire, and the brake, and one or more relationships between the tire and the brake.

Optionally, determining the predicted tire pressure characteristics of the tire during the future time period comprises utilising a machine learning algorithm.

Optionally, the method comprises obtaining fifth data indicative of historical tire condition, and determining, based at least in part on the first, second, third, fourth, and fifth data, the predicted tire pressure characteristics of the tire.

Optionally, the method comprises obtaining sixth data indicative of historical wheel condition, and determining, based at least in part on the sixth data, the predicted tire pressure characteristics of the tire.

Optionally, the method comprises displaying the predicted tire pressure characteristics of the tire to a user.

A second aspect of the present invention provides a computing system comprising one or more processors configured to perform the computer-implemented method according to the first aspect of the present invention.

A third aspect of the present invention provides a data carrier comprising machine readable instructions for the operation of one or more processors of the computing system according to the second aspect of the present invention to perform the computer-implemented method according to the first aspect of the present invention.

A fourth aspect of the present invention provides a method of predicting a pressure of a tire, the method comprising: obtaining historical tire inflation pressure data of the tire; obtaining historical brake temperature data of a brake configured to provide a braking force to a wheel associated with tire; obtaining historical gas temperature data of the tire; obtaining future aircraft operating data; and determining, based at least in part on the historical tire inflation pressure data, the historical brake temperature data, the historical gas temperature data and the future aircraft operating data, a future tire pressure of the tire.

A system <NUM> comprising an aircraft <NUM> and a computing system <NUM> is illustrated schematically in <FIG>. The aircraft <NUM> comprises a first group <NUM> of two nose wheels, and a second group <NUM> of four main landing gear wheels. It will be appreciated that the number of wheels may vary between aircraft in practice. The computing system <NUM> comprises one or more processors <NUM> that are configured to operate in a manner described in more detail hereafter. The computing system <NUM> can be located on-board the aircraft <NUM>, and/or located remotely from the aircraft <NUM>.

A wheel system <NUM> associated with an individual wheel <NUM>, such as a nose wheel or a main landing gear wheel, is illustrated schematically in <FIG>. The system <NUM> comprises a tire <NUM>, a brake <NUM>, a tire pressure sensor <NUM>, a tire temperature sensor <NUM>, and a brake temperature sensor <NUM>.

The computing system <NUM> is configured to obtain first data <NUM> indicative of an inflation pressure of the tire <NUM> from the tire pressure sensor <NUM>, for example a pressure to which the tire <NUM> was last inflated.

The computing system <NUM> is configured to obtain second data <NUM> indicative of a brake temperature of the brake <NUM> from the brake temperature sensor <NUM>, for example a brake temperature of the brake <NUM> following application of the brake <NUM> during landing of the aircraft <NUM> or during taxi of the aircraft <NUM>. The brake temperature may, in practice, be dependent on other factors such as wheel speed, brake pressure, brake torque, brake gain, brake wear state, reverse thrust, aerodynamic drag, and the like, and it will be appreciated that in some examples the brake temperature can be inferred from such parameters, rather than being directly measured by the brake temperature sensor <NUM>. It will be appreciated that the brake temperature can impact the tire gas temperature through its proximity to the tire.

The computing system <NUM> is configured to obtain third data <NUM> indicative of a gas temperature of the tire <NUM> from the tire temperature sensor <NUM>. The tire temperature sensor <NUM> may in practice indirectly measure the gas temperature of the tire <NUM>, for example by measuring the temperature of a wheel component located internally within the tire <NUM> and inferring the gas temperature using appropriate mathematical relationships. For example, as the gas temperature of the tire <NUM> changes, so will the gas pressure of the tire <NUM>. Although gas loss due to diffusion / leakage may be constantly occurring, we can assume over a relatively short period of time that the effect of pressure loss is nominal and that the relationship between pressure and temperature follows the ideal gas law. The ideal gas law can be given as PV=nRT, where P is pressure, V is volume, n is amount of gas, R is the ideal gas constant, and T is temperature. For example, the ideal gas law can be used to map a measured temperature of a wheel component to a gas temperature through knowledge of the tire gas pressure at that measured temperature.

Details of the form of the tire pressure sensor <NUM>, the brake temperature sensor <NUM>, and the tire temperature sensor <NUM>, are not pertinent to the present invention, and so are not described here for the sake of brevity. It will be appreciated, however, that any appropriate tire pressure sensor, brake temperature sensor, and tire temperature sensor, can be utilised by a person skilled in the art. Although the sensors <NUM>, <NUM> and <NUM> are depicted separately for clarity, some or all of them may be combined into a single element in some examples, for example a combined tire pressure and tire temperature sensor may be provided.

It will further be appreciated that although the first <NUM>, second <NUM> and third <NUM> data are described here as being obtained directly from the corresponding sensors, in other examples the first <NUM>, second <NUM> and third <NUM> data can be obtained indirectly by the computing system <NUM>, for example by inference from other parameters without the use of the respective sensor, and/or by obtaining an appropriate value stored in memory or the like. In some examples the first <NUM>, second <NUM> and third <NUM> data can be obtained by the computing system <NUM> in real-time, for example such that the first <NUM>, second <NUM> and third <NUM> data are indicative of current inflation pressure, current brake temperature and current tire gas temperature, respectively. In some examples, first <NUM>, second <NUM> and third <NUM> data can comprise historical data, for example data indicative of past inflation pressure, past brake temperature, and past tire gas temperature, respectively.

The computing system <NUM> is further configured to obtain fourth data <NUM> indicative of one or more predicted operational conditions of the aircraft <NUM> during a future time period. The form of the fourth data <NUM> can vary in practice, with illustrative examples discussed later herein.

The computing system <NUM> is configured to determine, based at least in part on the first <NUM>, second <NUM>, third <NUM> and fourth <NUM> data, predicted tire pressure characteristics of the tire <NUM> during the future time period. Such determination may take place via appropriate processing of the first <NUM>, second <NUM>, third <NUM> and fourth <NUM> data by the one or more processors <NUM> of the computing system <NUM>. The processing can take many forms.

In some examples, processing of the first <NUM>, second <NUM>, third <NUM> and fourth <NUM> data may utilise a pre-determined model of the wheel system <NUM>, for example a model of the tire <NUM>, brake <NUM>, and the relationships between them, for example with respect to pressure and/or temperature. In other examples, processing of the first <NUM>, second <NUM>, third <NUM> and fourth <NUM> data may utilise a machine learning algorithm having the first <NUM>, second <NUM>, third <NUM> and fourth <NUM> data as inputs, and the predicted tire pressure characteristics of the tire <NUM> during the future time period as an output. Such a machine learning algorithm may be trained to provide its output based on a set of training data, for example a set of training data labelled with ground truth values in a supervised learning process. In another example, measured data may form a training data set - for example a pressure at time t<NUM> can be used as the ground truth and the data at time t<NUM> forms training data, where t<NUM> is a time a predetermined period before t<NUM>, such as <NUM> hour, <NUM> hours, <NUM> hours, <NUM> hours, <NUM> hours, <NUM> hours, <NUM> hours, <NUM> hours, <NUM> days, <NUM> days, <NUM> days, <NUM> days, <NUM> days, <NUM> days, <NUM> days, <NUM> month, <NUM> months, and so on. A same measured data set can be used as training data across different time periods and some examples may comprise machine learning algorithms for respective different time periods. In some examples, the machine learning algorithm can be updated in real-time based on data obtained by the aircraft <NUM>. In some examples the machine learning algorithm can comprise a neural network.

A method <NUM> in accordance with the system <NUM> is illustrated schematically in <FIG>.

The method <NUM> comprises obtaining <NUM> first data indicative of an inflation pressure of the tire.

The method <NUM> comprises obtaining <NUM> second data indicative of a brake temperature of a brake associated with an aircraft wheel to which the tire is mounted.

The method <NUM> comprises obtaining <NUM> third data indicative of a gas temperature of the tire.

The method <NUM> comprises obtaining <NUM> fourth data indicative of one or more predicted operational conditions of the aircraft during a future time period.

The method <NUM> comprises determining <NUM>, based at least in part on the first, second, third and fourth data, predicted tire pressure characteristics of the tire during the future time period.

Once the computing system <NUM> has determined the predicted tire pressure characteristics of the tire <NUM>, a plurality of actions may be taken by the computing system <NUM>. In some examples the computing system <NUM> can determine based at least in part on the predicted tire pressure characteristics of the tire <NUM>, a maintenance action to be performed on the tire. In some examples the computing system <NUM> can schedule, based at least in part on the predicted tire pressure characteristics of the tire <NUM>, a maintenance action to be performed on the tire <NUM>. In some examples, a determined and/or scheduled maintenance action can be displayed to a user by the computing system <NUM>. By utilising the computing system <NUM> in such a manner, actions such as future required inflation of the tire <NUM> can be predicted. This may allow for longer maintenance free operating periods for the aircraft <NUM>, for example in comparison to existing systems where tire pressures are required to be checked more regularly. For example, the future time period can be at least <NUM> days, at least <NUM> days, or at least <NUM> days, which may enable a corresponding maintenance free operating period for the aircraft <NUM>. This may also allow for improved planning of routes of the aircraft <NUM>, for example by enabling routes to be planned to guide the aircraft <NUM> to an appropriate maintenance center when required. Prediction of the tire pressure characteristics of the tire <NUM> may also facilitate reduction of aircraft turn-around time, for example by reducing or eliminating the need to wait for tires to cool for a direct pressure measurement to be made.

In some examples the computing system <NUM> is configured to utilise the predicted tire pressure characteristics of the tire <NUM> to determine a remaining lifespan of the tire <NUM>. For example the computing system <NUM> can utilise data relating to any of tire deflection, tire rolling distances, number of flight cycles, brake energy, brake temperature, wheel temperature, tire gas temperature, tire carcass temperature, and the predicted tire pressure characteristics of the tire <NUM> to determine a remaining lifespan of the tire <NUM>.

In some examples the predicted tire pressure characteristics can be updated by the computing system <NUM> based at least in part on actual operational conditions of the aircraft <NUM> experienced within at least part of the future time period. In such a manner accuracy of the predicted tire pressure characteristics for the remainder of the future time period may be improved.

As noted above, the fourth data <NUM> can take a plurality of forms.

In some examples, the fourth data <NUM> is indicative of one or more of: a predicted runway surface condition for one or more runways on which the aircraft is predicted to operate within the future time period; and a predicted weather forecast for an ambient environment of the aircraft at predicted geographic locations within the future time period.

Runway surface conditions and/or weather conditions can impact on the length of time the brake <NUM> is required to be applied to bring the aircraft <NUM> to a stop on a runway. For example, where a runway is contaminated by rain, snow, or ice, an increased amount of braking, e.g. an increased brake application time, may be required to bring the aircraft <NUM> to a stop. This can impact on the temperature of the brake <NUM>, which can in turn impact on the predicted tire pressure characteristics of the tire <NUM>.

Similarly, where runway surface friction effects result in a low coefficient of friction between the runway and the tire <NUM>, an increased amount of braking, e.g. an increased brake application time, may be required to bring the aircraft <NUM> to a stop. This can impact on the temperature of the brake <NUM>, which can in turn impact on the predicted tire pressure characteristics of the tire <NUM>. Similarly, where runway surface friction effects result in a high coefficient of friction between the runway and the tire <NUM>, the tire <NUM> itself, e.g. the tire carcass, may experience increased heating, which can impact on the predicted tire pressure characteristics of the tire <NUM>. Brake gain can also impact on the coefficient of friction between the runway and the tire <NUM>, which can impact on the predicted tire pressure characteristics of the tire <NUM>.

Another runway condition that may impact on the on the predicted tire pressure characteristics of the tire <NUM> is a temperature of the runway. For example, ambient heat stored in the runway, e.g. as a result of sunlight heating the runway surface, may be passed to the tire <NUM> as a result of contact between the tire <NUM> and the runway. Similarly, the runway surface may provide cooling to the tire <NUM>. This may impact on the predicted tire pressure characteristics of the tire <NUM>.

A further weather condition that may impact on the predicted tire pressure characteristics of the tire <NUM> is sunlight, for example with direct sunlight on the tire <NUM> heating the tire carcass and the wheel <NUM>, thereby increasing the gas temperature within the tire <NUM>. This may impact on the predicted tire pressure characteristics of the tire <NUM>.

Another weather condition that may impact on the predicted tire pressure characteristics of the tire <NUM> is cross-winds experienced by the aircraft <NUM> during a flight cycle. For example, cross-winds can cause temperature variations in the wheels <NUM> and the tires <NUM>, including variation between different ones of the wheels <NUM> and the tires <NUM>. This may impact on the predicted tire pressure characteristics of the tire <NUM>.

By taking into account the runway surface conditions and/or weather conditions within the future time period, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

In some examples, the fourth data <NUM> is indicative of a predicted brake wear state of the brake within the future time period. Brake wear state may impact the temperature of the brake <NUM>, for example via brake energy absorption and thermal dissipation, and this may in turn impact on the predicted tire pressure characteristics of the tire <NUM>. For example, a relatively new brake, e.g. a relatively unworn brake may not reach as high a temperature as a relatively old brake, e.g. a relatively worn brake, but the relatively new brake may take longer to cool, which may result in more heat being applied to the gas within the tire <NUM>. Similarly, a relatively old brake may reach a higher temperature than a relatively new brake, but the relatively old brake may cool at a quicker rate, resulting in less heat being applied to the gas within the tire <NUM>. By taking into account the predicted brake wear state of the brake within the future time period, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy. In some examples, the predicted brake wear state may be determined based on one or more of brake energy, brake temperature, and brake heating and cooling rate, with use of past, current and predicted values envisaged.

In some examples, the aircraft <NUM> comprises a brake cooling fan associated with the brake <NUM>, and the fourth data <NUM> is indicative of a predicted usage of the brake cooling fan associated with the brake <NUM> within the future time period. Brake cooling fans may enable rapid cooling of the brake <NUM> by drawing air over the brake <NUM> in use. Cooling of the brake may have an impact on the temperature of the wheel <NUM>, and hence of the tire <NUM>, which may then impact on the predicted tire pressure characteristics of the tire <NUM>. By taking into account the predicted usage of the brake cooling fan within the future time period, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

The fourth data <NUM> can be indicative of a predicted aircraft steering angle during one or more flight cycles within the future time period. For example, steering of the wheel <NUM> when manoeuvring on a runway can cause friction between the runway and the tire <NUM>. This can generate heat in the tire carcass, which can also cause an increase in gas temperature within the tire <NUM>. This may then impact on the on the predicted tire pressure characteristics of the tire <NUM>. By taking into account the predicted aircraft steering angle, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

In some examples, the fourth data can be indicative of a predicted ambient temperature of the tire <NUM>, for example a predicted ambient gas temperature within the tire <NUM>, and/or a predicted ambient temperature of the brake <NUM>. A predicted ambient temperature may comprise a temperature to which a particular component will heat/cool to when not in use. It will be appreciated that such ambient component temperatures may be influenced by ambient weather conditions, and that the aircraft <NUM> may comprise temperature sensors for measuring ambient temperature, and/or that the aircraft may receive information regarding ambient temperature from off-board sources. Ambient conditions may also vary throughout the day, for example with warmer ambient conditions during daytime, and cooler ambient conditions at night, as well as varying throughout a flight cycle of the aircraft, e.g. with wheels <NUM> experiencing different conditions depending on whether they are extended or retracted through a landing gear bay of the aircraft <NUM>. By taking into account the predicted ambient temperatures, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy. In some examples the fourth data <NUM> may be indicative of predicted flight cycles of the aircraft <NUM> within the future time period, for example indicative of predicted aircraft operation within future flight cycles within the future time period.

In some examples, the fourth data <NUM> can be indicative of a predicted wheel temperature of the wheel <NUM> within the future time period, for example a predicted temperature of a hub of the wheel <NUM> or the like. Heat from the wheel <NUM> may be transferred from the wheel <NUM> to the carcass of the tire <NUM> and/or the gas within the tire <NUM>, which may impact on the on the predicted tire pressure characteristics of the tire <NUM>. Heating of the wheel <NUM> may also have an impact on deflection of the carcass of the tire <NUM> and/or sealing/gas loss of the tire <NUM>. By taking into account the predicted wheel temperature of the wheel <NUM>, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

The fourth data <NUM> can be indicative of a predicted weight of the aircraft <NUM> during one or more flight cycles of the aircraft <NUM> within the future time period. For example, aircraft weight may impact on loading of the tire <NUM>, which in turn impacts on tire deflection. This may impact heating of the carcass of the tire <NUM>, and hence also gas temperature and pressure within the tire <NUM>. By taking into account the predicted weight of the aircraft <NUM>, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy. In some examples the fourth data <NUM> can comprise a load distribution between two or more tires <NUM> of the aircraft <NUM>.

Similarly, the fourth data <NUM> can be indicative of a predicted aircraft centre of gravity during one or more flight cycles within the future time period. As in relation to predicted aircraft weight, the predicted center of gravity may impact on loading of the tire <NUM>, which in turn impacts on tire deflection. This may impact heating of the carcass of the tire <NUM>, and hence also gas temperature and pressure within the tire <NUM>. By taking into account the predicted center of gravity of the aircraft <NUM>, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

In some examples the fourth data <NUM> can be indicative of one or more of a predicted aircraft speed during one or more flight cycles within the future time period, a predicted future route of the aircraft <NUM> within the future time period, a predicted turn-around time of the aircraft <NUM> between future flight cycles within the future time period, a predicted flight time of one or more flight cycles of the aircraft <NUM> within the future time period, a predicted ground time of the aircraft <NUM> within the future time period, a predicted aircraft taxi distance within the future time period, and a predicted number of flight cycles of the aircraft <NUM> within the future time period. Factors such as speed, ground time and number of flight cycles may impact tire temperature, for example as a result of the impact these factors have on deflection of the tire <NUM>, which may impact on pressure loss of the tire <NUM>.

In some examples, the fourth data <NUM> can be indicative of a predicted tire carcass temperature of the tire <NUM> within the future time period. Tire carcass temperature can impact on the predicted tire pressure characteristics of the tire <NUM> within the future time period. Heat can be generated within a tire carcass in a plurality of ways, including as a result of friction when the tire <NUM> slides under a heavy load (e.g. slip during braking), as a result of the stress deformation cycle as the tire <NUM> expands and contracts through tire deflection (e.g. as a result of free rolling and yaw, which may cause lateral forces through the tire <NUM>), and as a result of ambient conditions. The rate at which the tire carcass cools can also be impacted by a plurality of factors, including any of a rolling speed of the wheel <NUM>, ambient conditions, and temperature of a surface with which the tire <NUM> is in contact. For example, convective heat loss from the tire carcass to internal and external gas may occur, conductive heat loss from the tire carcass to a runway may occur, conductive heat transfer between the tire carcass and the wheel <NUM> may occur, and radiated heat loss to internal and external gas may occur. In some examples the predicted tire carcass temperature can be inferred based at least in part on any of a temperature of the wheel <NUM>, and a gas temperature within the tire <NUM>.

In some examples, the fourth data <NUM> can be indicative of any of a predicted gas temperature of the tire <NUM> within the future time period, and a predicted brake temperature of the brake <NUM> within the future time period. By taking into account the predicted gas temperature of the tire <NUM>, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

In some examples, the fourth data <NUM> can be indicative of a predicted pressure loss of the tire <NUM> within the future time period. Pressure loss may occur either via leakage through seals and fittings, or through gas diffusion. Pressure loss may be impacted by any of a number of flight cycles within a given time period, gas temperature of the tire <NUM>, tire carcass temperature, and a difference between tire gas pressure and ambient pressure.

In some examples the computing system <NUM> can obtain fifth data indicative of historical tire condition, and the computing system can determine, based at least in part on the fifth data, the predicted tire pressure characteristics of the tire <NUM>. Data indicative of historical tire condition can comprise any of historical tire re-tread events, historical wheel overhaul, historical tire UV exposure, and tire age. For example, UV exposure may impact deflection of the tire <NUM> due to its impact on structural properties of the tire <NUM>, and may also affect gas loss from the tire <NUM>. Similarly age of the tire may impact tire deflection, which can in turn impact on temperature of the carcass of the tire <NUM> and gas temperature within the tire <NUM>, and/or age of the tire can impact on gas loss from the tire <NUM>. By taking into account historical tire condition, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

In some examples the computing system <NUM> can obtain sixth data indicative of historical wheel condition, and the computing system <NUM> can determine, based at least in part on the sixth data, the predicted tire pressure characteristics of the tire <NUM>. For example, the sixth data may be indicative of wheel age. Wheel age may impact on gas loss from within the tire <NUM>, which may in turn impact on the predicted tire pressure characteristics of the tire <NUM>. By taking into account the historical wheel condition within the future time period, the predicted tire pressure characteristics of the tire <NUM> within the future time period may be obtained with greater accuracy.

An illustrative model <NUM> of the factors described above, and their relationships and connections, is shown schematically in <FIG>. Such a representation of the wheel system <NUM>, as utilised by the computing system <NUM>, can be thought of as a so-called digital twin of the wheel system <NUM>. The model <NUM> comprises surface contamination data <NUM>, data relating to friction between tires and a runway <NUM>, brake wear state data <NUM>, brake cooling fan status data <NUM>, brake temperature data <NUM>, aircraft weight and centre of gravity data <NUM>, wheel load sharing data <NUM>, tire rolling and deflection data <NUM>, tire life data <NUM>, wheel life data <NUM>, wheel temperature data <NUM>, ambient temperature data <NUM>, tire carcass temperature data <NUM>, tire gas temperature data <NUM>, tire gas leakage data <NUM>, tire inflation pressure data <NUM>, and tire pressure data <NUM>.

It will be appreciated that the model <NUM> can be implemented at various levels depending on the data available. For example, brake temperature data <NUM> may use measured brake temperature where available and predict future brake temperature based on estimated future surface contamination data <NUM>, data relating to friction between tires and a runway <NUM>, brake wear state data <NUM>, and brake cooling fan status data <NUM>.

In some applications, only the future tire pressure is required and this may be determined using combinations of input data without intervening relationships, so that the tire pressure is predicted without requiring formal intermediate models. This may be appropriate for some forms of machine learning algorithms.

Claim 1:
A computer-implemented method (<NUM>) of predicting tire pressure of a tire (<NUM>) of an aircraft (<NUM>), the method (<NUM>) comprising:
obtaining (<NUM>) first data (<NUM>) indicative of an inflation pressure of the tire (<NUM>);
obtaining (<NUM>) second data (<NUM>) indicative of a brake temperature of a brake (<NUM>) associated with an aircraft wheel (<NUM>) to which the tire (<NUM>) is mounted;
obtaining (<NUM>) third data (<NUM>) indicative of a gas temperature of the tire (<NUM>); and
obtaining (<NUM>) fourth data (<NUM>) indicative of one or more predicted operational conditions of the aircraft (<NUM>) during a future time period;
characterized in that the method comprises determining, (<NUM>) based at least in part on the first (<NUM>), second (<NUM>), third (<NUM>) and fourth (<NUM>) data, predicted tire pressure characteristics of the tire (<NUM>) during the future time period.