GB2577065A

GB2577065A - System and method for aircraft health and schedule maintenance

Info

Publication number: GB2577065A
Application number: GB1814784.3A
Authority: GB
Inventors: Durant Adam; Rix Antony
Original assignee: Satavia Ltd
Current assignee: Satavia Ltd
Priority date: 2018-09-11
Filing date: 2018-09-11
Publication date: 2020-03-18
Also published as: AU2019337807B2; GB201814784D0; WO2020053778A1; AU2019337807A1

Abstract

An aircraft atmospheric contamination determination system has a computing arrangement operable to access a database of atmospheric contamination data as a function of location and time, and to receive flight data relating to a target aircraft from an input interface. The computing arrangement executes a predictive aircraft health model to (i) determine a contaminant exposure measure for the target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time. Preferably, the computing arrangement provides an alert or schedules a maintenance intervention based on the health parameter. The health parameter may be indicative of engine health or remaining useful life. The model may be trained by machine learning and may also provide estimates of future contamination exposure of the target aircraft.

Description

(71) Applicant(s):

Satavia Ltd

Chesterton Road, Cambridge, CB4 3AZ, United Kingdom (72) Inventor(s):

Adam Durant Antony Rix (74) Agent and/or Address for Service:

Basck Ltd

50-60 Station Road, Cambridge, Cambridgeshire, CB1 2JH, United Kingdom

EP 3200039 A1 US 20180012423 A1 (56) Documents Cited:

EP 3290342 A1

EP 2562701 A1

US 20170323274 A1

Durant et al, 31 August 2015, DAEDALUS - Enhanced Weather Threat Awareness for Aviation, ESA, https:// business.esa.int/projects/daedalus, accessed 06/11/2018 (58) Field of Search:

INT CL G06Q, G08G

Other: EPODOC, WPI, Patent Fulltext (54) Title of the Invention: System and method for aircraft health and schedule maintenance

Abstract Title: Determining aircraft health and scheduling maintenance based on atmospheric contamination data (57) An aircraft atmospheric contamination determination system has a computing arrangement operable to access a database of atmospheric contamination data as a function of location and time, and to receive flight data relating to a target aircraft from an input interface. The computing arrangement executes a predictive aircraft health model to (i) determine a contaminant exposure measure for the target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time. Preferably, the computing arrangement provides an alert or schedules a maintenance intervention based on the health parameter. The health parameter may be indicative of engine health or remaining useful life. The model may be trained by machine learning and may also provide estimates of future contamination exposure of the target aircraft.

I <

LU

T

LU Z z

LU

Q

LU o

Q

LU cr CL

CYCLES, FROM WHEN LAST

SEEN

1/12

FIG. 1

2/12

FIG. 2

3/12

FIG. 3

4/12

PREDICTED ENGINE HEALTH

EXPECTED

ENGINE HISTORY

80“

60“

ENGINE

CYCLES SINCE LAST SHOP VISIT

1000 2000 3000 4000 5000

6000

SEEN

ENGINE EXPECTED TO BE AT END OF LIFE

EXPECTED REMAINING

USEFUL LIFE IN ¹CYCLES, FROM WHEN LAST SEEN

FIG. 4

5/12

PREDICTED ENGINE HEALTH

60 40 20 0 -

6000

1000

CYCLES SINCE LAST SHOP VISIT

2000 3000 4000

5000

ENGINE SUBJECT TO HIGH CONTAMINANT EXPOSURE

LIFESPAN EXPECTED IF CURRENT EXPOSURE CONTINUES

EXPECTED LIFESPAN OF ENGINE SUBJECT TO LOW CONTAMINANT EXPOSURE

LIFESPAN EXPECTED IF SERVICE MODIFIED TO REDUCE EXPOSURE AND/OR

OPTIMISE MAINTENANCE TO REDUCE EFFECTS OF CONTAMINANTS

FIG. 5

6/12

FLIGHT PHASE

DEPARTURE

DISTANCE

ARRIVAL

INTEGRATION OF CONTAMINANT DENSITY OR

MASS FLOW GIVES TOTAL CONTAMINANT EXPOSURE DURING FLIGHT

FIG. 6

7/12

------ HIGH RISK OF HAI ENCOUNTER ON ROUTE

INTEGRATED METHODOLOGY

FIG. 7A

8/12

MEDIUM RISK OF HAI ENCOUNTER ON ROUTE

HIGH RISK OF HAI ENCOUNTER ON ROUTE

MEDIUM RISK OF HAI ENCOUNTER ON ROUTE

LOW RISK OF HAI

ENCOUNTER ON ROUTE ! CA ΐ · 20% EXTRA ΐ FUEL ΐ·COST FOR ! AIRLINE IF ΐ FLIGHT ! CANCELED

CA •50% EXTRA

FUEL

CA • 20%

EXTRA

FUEL

CA • NO EXTRA

FUEL

RM •CONSIDER

ALTERNATIVE

FLIGHT • ROUTES

NO MITIGATION

RM ALERT FLIGHT PASSENGE RS (REBOOK OPTIONS) CANCEL FLIGHT

NO MITIGATION

RM - OPTION 1

RM

VALIDATION TO FLIGHT DATA

RM • DELAY FLIGHT BY 2 HOURS

NO MITIGATION

FORECASTED

COSTS

FORECASTED COST ACTUAL COST TURBULENCE ENCOUNTER( S)

PILOT REPORTS

ACTUAL COSTS x

x x

RM - OPTION 1

RM - OPTION 2

NO MITIGATION NEEDED

Q.QST ESTI MATED (TRADITIONALLY'

FIG. 7B

9/12

804

806

FIG. 8A

10/12

808

FIG.8B

11/12

FIG. 9

12/12

FIG. 10

- 1 SYSTEM AND METHOD FOR AIRCRAFT HEALTH AND SCHEDULE MAINTENANCE

TECHNICAL FIELD

The present disclosure relates generally to systems that predict health of an aircraft from sensed atmospheric contamination, wherein such health includes, for example, aircraft engine health, but not limited thereto; moreover, the present disclosure relates to methods for (of) predicting health of an aircraft from sensed atmospheric contamination, for example health of an aircraft, engine or its equipment. Moreover, the aforesaid system schedules maintenance interventions based on the aircraft health, for example based on the aircraft engine health. Furthermore, the present disclosure is concerned with computer program products comprising a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a computerized device comprising processing hardware to execute the aforesaid methods.

BACKGROUND

Environmental contaminants such as dust, ice, atmospheric aerosol particles and corrosive gases pose a hazard to aircrafts. The environmental contaminants accumulate inside aircraft bodies, aircraft wings, aircraft surfaces, aircraft control apparatus, aircraft undercarriage, aircraft ducts, aircraft instruments, aircraft engines and so forth; moreover, the environmental contaminants cause wear or blockage, adhering to components thus clogging them, damaging protective surfaces, and inducing corrosion. Certain environmental contaminants

-2may additionally or alternatively corrosively or abrasively attack such components. Such detrimental effects may damage the aircraft, systems, instruments and/or the engines of the aircrafts, thus reducing aircraft or engine life, and may even cause in-flight failures or incorrect performance or instrument readings, all with resulting cost impact and risk to human lives. It is therefore desirable to try to avoid exposure to such environmental contamination and also remove such contamination from aircraft before damage occurs; however, such removal, for example by way of washing of aircraft component parts, costs money and result in aircraft being grounded during such washing.

When operators execute aircraft engine maintenance, most of the operators conventionally assess a condition of a given aircraft engine solely from flight data, wherein the flight data include measurements and related engineering parameters such as engine control settings, engine cycle counts and engine operating hours. Flight data may be transmitted from the given aircraft when in flight, for example using a communication system such as ACAR.S, or collected on the ground when the given aircraft is at an airport gate or when data recordings pertaining to the given aircraft are periodically retrieved by personnel. Such flight data is monitored, and investigations or interventions are made when flight data values are found to be outside normal expected ranges.

One existing known system that is employed to assess an aircraft engine uses a digital twin of a twinned physical system that utilises measurement data from one or more sensors to quantify values of one or more designated parameters of the twinned physical system. Another existing known system applies a regression or modelling method to estimate remaining useful life (R.UL) or some other measure of condition of an

-3aircraft engine, or some components of the aircraft engine, and a control system for computing a dynamic optimization of an aircraft engine system at a present time, and over a configurable time interval to achieve constrained state estimation objectives or constraints of an operator or original equipment manufacturer (OEM). Another existing known control system estimates data that is used to optimise scheduling of maintenance over a configurable time interval to achieve constrained cost estimation objectives or constraints of an operator, an original equipment manufacturer (OEM), or a service provider.

One common drawback to aforementioned known methods and systems is that while exposure to contaminants is known to damage aircraft, for example aircraft engines, it is only identified through their impact on measurements such as exhaust-gas temperature margin. Where such impact is observed, it may occur long after an original exposure to atmospheric and after damage due to the original exposure to the atmospheric contaminants has accumulated. Furthermore, some types of atmospheric contaminant exposure, such as corrosion or loss of protective coatings, may not be sensed until components fail.

Airlines often schedule aircraft washes, for example engine washes when an exhaust-gas temperature margin is found to be substantially worse than expected. Engine washes are used to restore exhaust gas temperature margin by cleaning out dust and other contaminants. However, the contaminants are removed only after they have been in the engine for a long time (for example, several months) and as a result, significant damage may have been caused to the aircraft engine. Airlines implement aircraft engine washes, despite an associated cost penalty, because exhaust gas temperature margin is a parameter that they can

-4measure, but they do not have a measure of when the aircraft engine is exposed to atmospheric contaminants.

Therefore, there arises a need to address the aforementioned technical drawbacks in existing known approaches for predicting an aircraft health parameter of an aircraft and scheduling maintenance interventions for the aircraft, mutatis mutandis for aircraft engines.

SUMMARY

According to a first aspect, there is provided an aircraft atmospheric contamination determination system, comprising:

(a) a computing arrangement including at least one input interface and at least one output interface, wherein (i) the computing arrangement executes when in operation a predictive aircraft health model, wherein the computing arrangement accesses when in operation a database of atmospheric contamination data defined as a function of location and time;

(ii) the at least one input interface receives flight data relating to a target aircraft to be evaluated and provides the flight data to the computing arrangement; and (iii) the at least one output interface provides at least one predicted aircraft health parameter; and (b) wherein, the computing arrangement uses the predictive aircraft health model and the inputs to (i) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time.

Optionally, in the aircraft atmospheric contamination determination system, the computing arrangement is trained by interrogating the database of contamination data to (i) obtain a set of training data by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data; and (ii) provide the historical contaminant exposure and the aircraft service data as inputs for use in training the predictive aircraft health model.

Optionally, in the aircraft atmospheric contamination determination system, the predictive aircraft health model includes a predictive aircraft engine health model representative of one or more engines of the target aircraft, and the at least one predictive aircraft health parameters includes at least one predictive aircraft engine health parameter.

Optionally, in the aircraft atmospheric contamination determination system, the at least one input interface receives historical data relating to the target aircraft engine to be analyzed.

-6Optionally, in the aircraft atmospheric contamination determination system, the system further comprises at least one storage medium coupled directly or indirectly to the computing arrangement and containing a database of historical data relating to a plurality of aircraft engines that the system may be used to evaluate.

Optionally, in the aircraft atmospheric contamination determination system, the predictive engine health model is trained by machine learning using the set of training data comprising historical data sets, wherein the historical data sets comprise aircraft flight data, atmospheric contaminant data over location and time and engine service data, wherein the predictive engine health model is applied to provide a prediction after initial training of the computing arrangement to define to predictive engine health model within the computing arrangement.

Optionally, in the aircraft atmospheric contamination determination system, the predicted engine health parameter is selected from a group comprising a cycle count, hour count, remaining cycles, remaining hours or health index.

Optionally, in the aircraft atmospheric contamination determination system, the computing arrangement further calculates and outputs at least one of: a standard deviation, variance or confidence interval for the predicted health parameter.

Optionally, in the aircraft atmospheric contamination determination system, training the predictive engine health model into the computing arrangement requires:

(i) at least one contaminant parameter for the set of training data; and (ii) at least one engine health parameter for each engine or aircraft associated with the set of training data.

Optionally, in the aircraft atmospheric contamination determination system, the flight data includes flight trajectory information that is selected from at least one of flight phase, estimation of airspeed, or engine mass flow, wherein the airspeed or the engine mass flow is estimated to weigh the at least one contaminant and to provide estimates of rate, density or mass of contaminants passing through the aircraft engine.

Optionally, in the aircraft atmospheric contamination determination system, the input interface further receives one or more input variables that are utilised by the computing arrangement in the engine health model, wherein the one or more input variables include one or more of: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates.

Optionally, in the aircraft atmospheric contamination determination system, the system comprises a distributed computing system and the control module and at least one data storage medium are centralised and at least one input interface and at least one output interface are provided at local nodes.

-8Optionally, in the aircraft atmospheric contamination determination system, the historic contaminant exposure is determined by:

(i) analyzing a plurality of historical trajectories of a plurality of aircraft engines associated the set of training data over location and time; and (ii) estimating contaminant concentration of the plurality of aircraft engines for each trajectory using an atmospheric model.

According to a second aspect, there is provided an aircraft atmospheric contamination determination system that comprises:

(ii) the at least one input interface receives flight data relating to a target aircraft to be evaluated and provides the flight data to the computing arrangement; and (iii) the at least one output interface provides at least one predicted aircraft health parameter to a user;

(b) wherein, the computing arrangement uses the predictive aircraft health model and the inputs to (I) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time; and (iii) provide an alert or schedule a maintenance intervention for the target aircraft based on the predicted aircraft health parameter.

Optionally, in the aircraft atmospheric contamination determination system, the computing arrangement is trained by interrogating the database of contamination data to:

(i) obtain a set of training data by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data; and (ii) provide the historical contaminant exposure and the aircraft service data as inputs for use in training the predictive aircraft health model.

Optionally, in the aircraft atmospheric contamination determination system, the computing arranging uses an engine health model to provide estimates of future contamination exposure of an engine of the target aircraft overtime by estimating at least one of: a probability, a probability density function, multiple parameters including at least one of an expected mean and a standard deviation of estimated contaminant exposure; wherein the contamination determination system utilises the estimates to schedule interventions.

Optionally, in the aircraft atmospheric contamination determination system, the system further schedules (i) a maintenance intervention when an engine contamination measure passes or forecasts to pass a threshold; and (ii) a maintenance intervention when an engine health measure passes or forecasts to pass a threshold, wherein the maintenance intervention comprises at least one of engine wash, engine inspection, engine removal or engine shop visit.

According to a third aspect, there is provided an asset maintenance system, comprising a computing arrangement including at least one input interface and at least one output interface, wherein the computing arrangement, when in operation, (I) accesses a database of atmospheric contamination data defined as a function of location and time;

(ii) receives flight data relating to an asset to be evaluated;

(ill) determines a flight trajectory specifying at least one location of the asset over time for each of a plurality of flights in the asset flight data;

(iv) retrieves the atmospheric contaminant data at a location and a time in proximity to each of a plurality of flight trajectories of the at least one flight;

(v) determines a contaminant exposure measure for the asset; and (vi) outputs a recommended maintenance intervention to the asset according to the value of at the contaminant exposure measure of the asset.

Optionally, in the asset maintenance system, the system schedules a maintenance intervention when a contaminant exposure measure passes or forecasts to pass a threshold.

-11 Optionally, in the asset maintenance system, the maintenance intervention comprises at least one of engine wash, engine inspection, engine removal or engine shop visit.

According to a fourth aspect, there is provided a method for (of) determining aircraft health using a computing arrangement coupled to a sensor arrangement, wherein the method comprises:

(a) providing a computing arrangement including at least one input interface and at least one output interface, wherein the method includes:

(I) arranging for the computing arrangement to execute when in operation a predictive aircraft health model, wherein the computing arrangement accesses when in operation a database of atmospheric contamination data defined as a function of location and time;

(ii) receiving at the at least one input interface flight data relating to a target aircraft to be evaluated and providing the flight data to the computing arrangement; and (iii) providing to the at least one output interface at least one predicted aircraft health parameter to a user; and (b) arranging for the computing arrangement to use the predictive engine health model and the inputs to (i) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and

- 12 (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time.

Optionally, the method further comprises training the computing arrangement by interrogating the database of contamination data to (i) obtain a set of training data by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data; and (ii) provide the historical contaminant exposure and the aircraft service data as inputs for use in training the predictive aircraft health model.

Optionally, the method further comprises training the aircraft health model to include an engine health model using machine learning to provide the predictive engine health model, wherein the predictive engine health model receives at least one contaminant parameter and to output at least one engine health parameter.

Optionally, in the method, the step (b) of training the engine health model utilises the aircraft flight data and the atmospheric contaminant data as inputs and engine health data derived from service data to provide at least one health parameter.

Optionally, in the method, the engine health parameter comprises at least one of an engine health index or a useful remaining life prediction.

- 13 Optionally, in the method, the machine learning comprises any one of: linear regression, neural network, decision tree, decision forest or gradient boosted decision tree, radial basis function, support vector machine, Gaussian process or principal component analysis.

Optionally, in the method, the machine learning includes at least one of time within a year or time of day as regression parameters.

Optionally, in the method, the predictive model provides at least one of: a standard deviation, a variance or confidence interval for the predicted engine health parameter.

Optionally, in the method, the atmospheric contaminant data over location and time comprises an atmospheric model of the concentration or mixing ratio of contaminants at a plurality of locations and times.

Optionally, in the method, the contaminants include a plurality of contaminants selected from: dust, organic particles, volcanic ash, salt, sulphur dioxide and sulphate ions, or any combination thereof.

Optionally, in the method, the atmospheric model derives estimates of at least one of the mixing ratio or the concentration of contaminants at the plurality of locations and times and provides an expected mass of the respective contaminant per unit mass of air at a particular location, height measure and time.

Optionally, in the method, the atmospheric model provides at least one of: an estimated average of contaminants for each location by time-of

- 14 day and/or time-of-year; a standard deviation of contaminants for each location by time-of-day and/or time-of-year.

Optionally, the method further comprises scheduling maintenance interventions for the aircraft engine in response to the predicted engine health parameter.

Optionally, in the method, the aircraft flight data comprises flight phase and estimation of at least one of airspeed or engine mass flow and associated location, time and date information.

Optionally, in the method, using the predictive model to predict an engine health parameter comprises a Monte Carlo modeling.

Optionally, in the method, the predictive engine health model includes one or more further input variables selected from: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates.

According to fourth aspect, there is provided a computer program product comprising instructions to cause the system the first or second aspects to carry out the method of the third aspect.

It will be appreciated that the aforesaid present method is not merely a method of doing a mental act” as such, but has a technical effect in that the method employs measurement data and functions as a form of technical control using machine learning of a technical artificially

- 15intelligent system; the method involves building an artificially intelligent machine learning model and/or using the machine learning model to address, for example to solve, the technical problem of assessing aircraft health, for example aircraft engine health.

Moreover, it will be appreciated that patent authorities (for example the UKIPO and the EPO) regularly grant patent rights for data encoders, wherein input data to the encoders is often of an abstract nature (for example computer generated graphics) and encoding merely amounts to rearranging bits present in the input data, namely merely causing a change in data entropy (see for example, MPEG encoders, JPEG encoders, H. 264 type encoders and decoders). Moreover, the EPO has granted patent rights merely for methods of analyzing networks and producing graphical representations of the networks (for example, see EP2250763B1 {Arrangements for networks, Canright et al.), validated in the United Kingdom)(for example, see EP1700421B1 {A method of managing networks by analyzing connectivity, Can right et al.), also validated in the United Kingdom), wherein the patent rights have been validated in respect of the UK. Thus, to consider the method of the present disclosure to be subject matter that is excluded from patentability would be totally inconsistent with EPO and UKIPO practice in respect of inventions that are technically closely related to embodiments described in the present disclosure.

Embodiments of the present disclosure substantially eliminate or at least partially address the aforementioned drawbacks in existing approaches for predicting an aircraft health parameter of a target aircraft and scheduling maintenance interventions for the target aircraft, mutatis mutandis target aircraft engine.

- 16 Additional aspects, advantages, features and objects of the present disclosure are made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow.

It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

FIG. 1 is a schematic illustration of a system in accordance with an embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating steps of a method for (of) training the system of FIG. 1, namely a method for (of) training an engine health model for sequent use thereafter to predict an engine health parameter

- 17 of an aircraft engine in accordance with an embodiment of the present disclosure;

FIG. 3 is a flowchart illustrating steps of a method for (of) predicting an engine health parameter of the engine or the aircraft using a predictive engine health model in accordance with an embodiment of the present disclosure;

FIG. 4 is a graphical illustration of a predicted engine health in an ordinate Y-axis plotted against a number of cycles since last shop visit in an abscissa X-axis to predict an expected remaining useful life in accordance with an embodiment of the present disclosure;

FIG. 5 is a graphical illustration of a predicted engine health in the ordinate Y-axis plotted against a number of cycles since last shop visit in the abscissa X-axis to predict an expected lifespan of the engine for different levels of exposure to contaminants in accordance with an embodiment of the present disclosure;

FIG. 6 illustrates a graphical illustration of a flight trajectory model of flight phases and contaminant density at the engine at the flight phases in accordance with an embodiment of the present disclosure;

FIGS. 7A to 7B are graphical illustrations of an integrated method for (of) using the system of FIG.l to determine a cost awareness (CA) and a risk mitigation (RM) for predicting a risk of high altitude ice (HAI) being encountered on an aircraft route at different periods from a departure time, in accordance with an embodiment of the present disclosure;

FIGS. 8A to 8B are flow diagrams illustrating steps of a method for (of) training the system of FIG. 1 to provide a predictive engine health model

- 18using machine learning and using the predictive engine health model to predict an engine health in accordance with an embodiment of the present disclosure;

FIG. 9 is an illustration of steps of a computing arrangement for determining contaminant data using one or more servers, a supercomputing and/or a distributed computing platform according to an embodiment of the present disclosure; and

FIG. 10 is an illustration of an exploded view of a distributed or cloud computing implementation in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DETAILED DESCRIPTION OF EMBODIMENTS

The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.

(ii) the at least one input interface receives flight data relating to a target aircraft to be evaluated and provides the flight data to the computing arrangement; and (iii) the at least one output interface provides at least one predicted aircraft health parameter to a user; and (b) wherein, the computing arrangement uses the predictive aircraft health model and the inputs to (i) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time.

Optionally, in an aircraft atmospheric contamination determination system, the computing arrangement is trained by interrogating the database of contamination data to (I) obtain a set of training data by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data; and

-20 (ii) provide the historical contaminant exposure and the aircraft service data as inputs for use in training the predictive aircraft health model.

The present system, when in operation, thus determines a contaminant exposure of the target aircraft, for example target engine, and predicts an aircraft health parameter, for example engine health parameter, of the target aircraft, for example target aircraft engine, for scheduling maintenance interventions. The present system thus predicts, when in operation, engine health or remaining useful life of the target aircraft, for example target aircraft engine. The present system thus schedules maintenance interventions for the target aircraft, for example target aircraft engine, based on the predicted aircraft health parameter, for example aircraft engine parameter. The present system potentially assists to prioritise the maintenance interventions based on the predicted engine health parameter.

In embodiments of the present disclosure, the flight data, engine service data, atmospheric contaminant data at a location and a given time, an aircraft flight history, an assignment of engines to aircraft and engine service data are obtained from a server. A user device is communicatively connected to the aircraft engine contamination determination system for receiving flight data relating to the aircraft engine to be evaluated, as required by a user. The system may generate, for example, for ease of processing, a table of the data for the training set. For example, for sensor readings, a column of the table defines readings relating to a contaminant and rows of the table provide values for a plurality of flights. In an example embodiment, the system beneficially uses linear regression

-21 to calculate weights such that a weighted sum of the columns plus an optional offset weight provides a predictive engine health model. In another embodiment, the system beneficially uses a variant of regression such as Least Squares Regression or any suitable regression models to calculate weightings to provide a predictive engine health model. In yet another example embodiment, the generation of the predictive engine health model is beneficially generated using at least one of artificial intelligence (Al), Machine Learning or a neural network algorithm; such artificial intelligence (Al), Machine Learning and the neural network algorithm will be understood by a person skilled in the art of computer system design. In an alternative embodiment, the weights or parameters of a predictive engine health model are optionally determined or adjusted manually by a person skilled in the art to provide a prediction of an engine health parameter. It will be appreciated in embodiments of the present disclosure that a predictive model of aircraft health is trained on measurement data, to define various parameters of the model, and then, thereafter, the predictive model is applied to data pertaining to the target aircraft to predict its health, for example its aircraft engine health; in other words, there are two distinct phases in operation of embodiments of the present disclosure.

In an example embodiment, the computing arrangement includes a control module that is communicatively connected to a server via a communication network. The control module is connected to at least one input interface that receives, when in operation, flight data relating to a target aircraft engine to be evaluated and the at least one input interface provides the flight data to the control module. In one embodiment, the at least one input interface receives the flight data relating to the aircraft engine to be evaluated from a user device of a user. The control module

-22 is connected to at least one output interface that provides, when in operation, at least one predicted engine health parameter to the user through the user device. The user device optionally comprises a personal computer, a smartphone, a tablet, a laptop or an electronic notebook. The communication network may be a wired network or a wireless network. The server is optionally be a tablet, a desktop, a personal computer or an electronic notebook. In an embodiment, the server is optionally a cloud service.

The server may comprise a contaminant database of contamination data over location and time and a historical database of historical data relating to a plurality of aircraft engines. In one example embodiment, the server comprises a database of assignment of engines to aircraft and engine service data.

The system optionally determines historic contaminant exposure by analyzing a plurality of historical trajectories of a plurality of aircraft engines associated to the set of training data over (namely, as a function of) location and time and estimating contaminant concentration of the plurality of aircraft engines for each trajectory using an atmospheric model. In an embodiment, the contaminant exposure is optionally estimated using a trajectory or trajectory model.

The system optionally determines at least one of: an average, a total and cumulative estimate of exposure to each contaminant type, per cycle and/or per time interval, and incorporates one or more of these estimates in the engine health model. The system beneficially determines estimates of contaminant exposure by flight phase and incorporates the estimate for at least one flight phase in the engine health model. The system optionally determines values for aggregates of types of contaminant, for

-23 example totaling dust of different particle sizes, with an optional weighting of different parameters. The system optionally estimates a cost associated with actual engine usage to determine a cost of contaminant exposure of the aircraft engine.

The system optionally estimates an expected contamination exposure of the aircraft engine over a period of time by estimating a probability, a probability density function, or multiple parameters including expected mean and standard deviation of estimated contaminant exposure. The system optionally determines either of, or both of, the mean and standard deviation of the contaminant exposure or density for at least one of a time within year and time of day. For example, the historical data sets are grouped by calendar month and hour of the day, thus providing an estimate of the mean and standard deviation of the engine health parameter that varies from month to month and over the course of a day. The system optionally determines a mean or standard deviation, or both a mean and a standard deviation, of future contaminant exposure or density by using the machine learning or regression methods, such as linear regression, neural network, decision tree, decision forest or gradient boosted decision tree, where time within a year and/or a time of day are included as regression parameters. In an example embodiment, the system determines a probability that the contaminant is present, rather than the average concentration for certain types of contaminant, such as ice crystals.

The system optionally determines at least one contaminant parameter for the set of training data and at least one engine health parameter for each aircraft engine associated with the set of training data. The system optionally determines the at least one target health parameter based on

-24 the at least one contaminant parameter, using techniques known in art of statistics, regression and machine learning.

The input interface further receives one or more input variables that are utilised by a processor of the computing arrangement in the engine health model. The one or more input variables include one or more of: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates. In one embodiment, the target input variable of the aircraft engine includes one or more of accumulated cycles or hours, count of excursions outside an operating threshold, for example engine service data. The one or more input variables may be determined per flight, flight phase, or at a finer resolution such as every minute. In an example embodiment, transformation and feature engineering methods known in the art of machine learning may be employed to determine at least one of the input variables or output variables. The feature engineering methods known in the art of machine learning may include calculating a moving average, moving standard deviation, L-norm, cumulative value, non-linear transform of a variable, product or weighted combination of variables.

The predictions are optionally be made using any known machine learning or regression method, such as linear regression, neural network, decision tree, decision forest or gradient boosted decision tree, radial basis function, support vector machine, Gaussian process, principal component analysis. In an embodiment, an expected lifetime of the aircraft engine may be determined from an output of the predictive engine health model. The expected lifetime of the aircraft engine is optionally used to plan when

-25the engine requires replacement or removal for a shop visit. The predictive engine health model may additionally output a confidence interval on its remaining useful life estimate, so that an aircraft engine may prepare for when the end of life may be likely to occur, for example, to ensure that a spare aircraft is available.

The system may calculate the remaining useful life (RUL) of the aircraft engine or the aircraft using a table, or example to assist processing or understand of data. The table includes recorded data of the aircraft engine and the last row indicates a target life when the aircraft engine is ending. For example, the row corresponding to when an aircraft engine is, or is due to be, removed for service shows with a zero (0) and the second last row shows with a one (1), and hence the third last row shows a two (2). The system may use the RUL to provide servicing and schedule maintenance accordingly when an aircraft has an aircraft engine at 50% health and may swap the aircraft engine if needed or for those events to coincide with the end of life (0) of the aircraft engine. For example, an aircraft engine El is presently in poor condition and an aircraft engine E2 is in good condition, at the present time engine El has a RUL value that is lower than the RUL value of engine E2, and at the end of life both the aircraft engines El, E2 would have approximately the same RUL measure such as zero.

In one embodiment, the system determines the engine health index using a transform. For example, when all aircraft engines are new it is considered as hundred (100). The end of life of an aircraft engine is assigned an index of zero (0). For training of the model, a target engine health parameter is calculated for each training engine by means of interpolation between the new value (such as 100) and the end of life

-26 value (such as 0) according to the cycles since new of the engine. Where CSN(flight, engine) is the cycles since new of the engine in each flight in the training set, and CEOL(engine) is cycles at end of life, that is the value of CSN(engine) where the engine is at, or is estimated to be at, an end of remaining useful life or requires a major service, the target engine health index EHI is beneficially determined using the following or an equivalent formula:

EHI (flight, engine) = 100 * [1 - csn» engine) / _{CE0L(engine)]}

In another example embodiment, such calculation may also, or alternatively, be based on the hours since new of each engine. In an embodiment, the determination of health Index requires an extrapolation step to be performed to determine a remaining useful life of the aircraft engine.

The system is optionally used to schedule maintenance interventions for the aircraft engine in response to the predicted engine health parameter. In an embodiment, an engine is considered to be due for maintenance when an engine health parameter computed for the engine falls below a threshold. The predicted remaining useful life and/or an extrapolation of engine health index determines the number of cycles before such maintenance is required. The system optionally schedules maintenance interventions including both short-term interventions, such as engine washes to clean out dust, and long-term interventions such as major maintenances or shop visits. Engine washing is described in the Appendix provided below.

The system may adjust one or more quantities indicative of engine health to consider maintenance events. In an example embodiment, when an

-27 engine has a major service, its engine health parameter is reset to a value representative of an engine that has undergone such a major service, and for subsequent modelling counts of cycles since such major service, and accumulated input variables, are reset to zero or some other representative value. On account of engine performance being improved after such a major, corresponding savings in carbon dioxide emissions are beneficially attributed to carbon offsets that can assist towards paying for costs of the major service.

The system further prioritises maintenance for the aircraft engine according to the determined engine health and/or lifetime. In an example embodiment, an engine that has a value of an engine health parameter that is below the expected value of such a parameter, given the same cycle count, would be prioritised for maintenance ahead of an engine with above expected engine health parameter.

According to an embodiment, the at least one input interface receives historical data relating to the target aircraft engine to be analysed.

According to another embodiment, the system further comprises at least one storage medium coupled directly or indirectly to the control module and containing a database of historical data relating to a plurality of aircraft engines that the system may be used to evaluate.

According to yet another embodiment, the predictive engine health model is trained by machine learning using the set of training data comprising historical data sets. In an embodiment, the historical data sets comprise aircraft flight data, atmospheric contaminant data over location and time and engine service data.

-28According to yet another embodiment, the predicted engine health parameter is selected from a group comprising a cycle count, hour count, remaining cycles, remaining hours or health index.

According to yet another embodiment, the processor further calculates and outputs at least one of a standard deviation, variance or confidence interval for the predicted health parameter.

According to yet another embodiment, the predictive engine health model is trained to determine:

Once trained, the predictive engine health model may be used to calculate the at least one aircraft health parameters, for example engine health parameter, corresponding to the at least one contaminant parameter for an engine, aircraft or flight not present in the training set.

According to yet another embodiment, the flight data includes flight trajectory information that is selected from at least one of flight phase, estimation of airspeed or engine mass flow. In an embodiment, the airspeed or engine mass flow is estimated to weigh the at least one contaminant and to provide estimates of rate, density or mass of contaminants passing through the aircraft engine.

According to yet another embodiment, the input interface further receives one or more input variables that are utilized by the processor in the engine health model, wherein the one or more input variables include one or

-29more of: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates.

According to yet another embodiment, the system comprises a distributed computing system and the control module and at least one data storage medium are centralised and at least one input interface and at least one output interface are provided at local nodes.

According to yet another embodiment, the historic contaminant exposure is determined by:

According to a second aspect, there is provided an aircraft atmospheric contamination determination system, comprising:

-30(ii) the at least one input interface receives flight data relating to a target aircraft to be evaluated and provides the flight data to the computing arrangement; and (ill) the at least one output interface provides at least one predicted aircraft health parameter to a user; and (b) wherein, the computing arrangement uses the predictive aircraft health model and the inputs to (I) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time; and (ill) provide an alert or schedule a maintenance intervention for the target aircraft based on the predicted aircraft health parameter.

-31 Optionally, in the aircraft atmospheric contamination determination system, the system further schedules (I) a maintenance intervention when an engine contamination measure passes or forecasts to pass a threshold; and (ii) a maintenance intervention when an engine health measure passes or forecasts to pass a threshold, wherein the maintenance intervention comprises at least one of engine wash, engine inspection, engine removal or engine shop visit.

The system may generate a notification or provide alerts to the user device through the at least one output interface based on the predicted engine health parameter.

According to an embodiment, a processor of the computing arrangement uses the engine health model to provide estimates of future contamination exposure of the aircraft engine over time by estimating a probability, a probability density function, or multiple parameters including the expected mean and standard deviation of estimated contaminant exposure and the contamination determination system utilises the estimates to schedule interventions.

(ii) receives flight data relating to an asset to be evaluated;

(iii) determines a flight trajectory specifying at least one location of the asset over time for each of a plurality of flights in the asset flight data;

Optionally, in the asset maintenance system, wherein the maintenance intervention comprises at least one of engine wash, engine inspection, engine removal or engine shop visit.

Optionally, the asset comprises any of an aircraft body, a flight surface, an engine, an instrument, s sensor, a control equipment or a landing gear, or a component of the foregoing.

According to a fourth aspect, the present disclosure also provides a method for (of) determining aircraft health using a computing arrangement coupled to a sensor arrangement, wherein the method comprises:

(ii) receiving at the at least one input interface flight data relating to a target aircraft to be evaluated and providing the flight data to the computing arrangement; and (iii) providing to the at least one output interface at least one predicted aircraft health parameter to a user; and (b) arranging for the computing arrangement to use the predictive engine health model and the inputs to (i) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time.

Optionally, training the computing arrangement is achieved by interrogating the database of contamination data to:

(i) obtain a set of training data by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data; and

-34(ii) provide the historical contaminant exposure and the aircraft service data as inputs for use in training the predictive aircraft health model,

According to an embodiment, the method further comprises training the engine health model using machine learning to provide a predictive engine health model, wherein the predictive engine health model receives at least one contaminant parameter and to output at least one engine health parameter.

According to another embodiment, the step of training the engine health model utilises the aircraft flight data and the atmospheric contaminant data as inputs and engine health data derived from service data to provide at least one health parameter. Such training is undertaken before the engine health model is subsequently used to predict aircraft health of a given target aircraft.

According to yet another embodiment, the engine health parameter comprises at least one of an engine health index or a useful remaining life prediction.

According to yet another embodiment, the machine learning comprises any one of: linear regression, neural network, decision tree, decision forest or gradient boosted decision tree, radial basis function, support vector machine, Gaussian process, or principal component analysis.

According to yet another embodiment, the machine learning includes at least one of time within a year or time of day as regression parameters.

-35According to yet another embodiment, the predictive model provides a standard deviation, variance or confidence interval for the predicted engine health parameter.

According to yet another embodiment, the atmospheric contaminant data over location and time comprises an atmospheric model of the concentration or mixing ratio of contaminants at a plurality of locations and times.

According to yet another embodiment, the contaminants include a plurality of contaminants selected from: dust, organic particles, volcanic ash, salt, sulphur dioxide and sulphate ions, or any combination thereof.

According to yet another embodiment, the atmospheric model derives estimates of at least one of the mixing ratio or the concentration of contaminants at the plurality of locations and times and provides an expected mass of the respective contaminant per unit mass of air at a particular location, height measure and time.

According to yet another embodiment, the atmospheric model provides at least one of estimated average or standard deviation of contaminants for each location by time-of-day, by time-of-year, or by both time-of-day and time-of-year.

According to yet another embodiment, the method further comprises scheduling maintenance interventions for the aircraft engine in response to the predicted engine health parameter.

According to yet another embodiment, the aircraft flight data comprises flight phase and estimation of at least one of airspeed or engine mass flow and associated location, time and date information.

-36According to yet another embodiment, using the predictive model to predict an engine health parameter comprises a Monte Carlo modeling. Monte Carlo modelling is especially useful for determining boundary limits for a given mathematical model by employing a plurality of computations with stochastic variation to map a solution space pertaining the Monte Carlo modelling.

According to yet another embodiment, the predictive engine health model includes one or more further input variables selected from: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates.

The advantages of the present method are thus identical to those disclosed above in connection with the present system and the embodiments listed above in connection with the system apply mutatis mutandis to the method.

It will be appreciated that the aforesaid present method is not merely a method of doing a mental act, but has a technical effect in that the method functions as a form of technical control using machine learning of a technical artificially intelligent system. The method involves building an artificially intelligent machine learning model and/or using the machine learning model to solve a technical problem of predicting an aircraft health parameter of a target aircraft, for example for predicting an engine health parameter of an engine of a target aircraft, and scheduling maintenance interventions for the target aircraft, for example for the target aircraft engine.

-37The present disclosure also provides a computer program product that executable on the aforementioned computing arrangement, wherein the computer program product comprises instructions to cause the above system to carry out the aforesaid method.

The advantages of the present computer program product are thus identical to those disclosed above in connection with the present system and the embodiments listed above in connection with the present system apply mutatis mutandis to the computer program product.

In one example embodiment, the system for scheduling aircraft operations is provided and the system comprising:

a control module, comprising a processor; and a memory, containing executable instructions for generating a predictive engine contamination model for use by the processor; and at least one storage medium coupled directly or indirectly to the control module and containing a database of contamination data over location and time;

a route input interface configured to receive schedule information related to a plurality of aircraft flights;

a fleet input interface configured to receive aircraft flight data and aircraft engine data relating to a target aircraft fleet being scheduled; and wherein the control module configured to receive the schedule information from the route input interface and the aircraft flight data and the aircraft engine

-38data from the fleet input interface and the processor interrogates the database of contamination data to provide inputs for use in the predictive engine contamination model, the processor using the predictive engine contamination model and inputs to derive:

an estimated historic contamination for an aircraft or each aircraft engine to at least one contaminant;

an estimated expected contaminant exposure for the aircraft or each aircraft engine; and the control module is further configured to identify at least one aircraft engine with higher estimated historical contamination or with lower estimated historical contamination;

identify at least one route with lower expected exposure or highest expected exposure to the at least one contaminant; and perform recommendations on route allocation by assigning an aircraft with the higher estimated historical contamination being scheduled to at least one route with lower expected exposure to ensure that total exposure to contamination, based upon the estimated historical contamination and expected contaminant exposure, is reduced or maintained within a required range;

the system further comprising an output interface configured to receive recommended route allocation information from the control module.

The system thus helps to schedule aircraft operations by generating a model for estimating aircraft engine contamination based upon known historical data sets including aircraft flight data, atmospheric contaminant

-39data over location and time, and engine service data. The system helps to schedule aircraft operations based on the estimation of contamination exposure, thereby managing the cost of operating aircraft or engines. The system optionally associates a fee, a charge or a contractual payment to estimated contaminants exposure and/or to provide recommendations derived from contaminants exposure; for example, carbon dioxide emission savings achieved by performing engine maintenance is beneficially recorded as a carbon offset and subject to financial reward payment that assists to improve aircraft maintenance and operating safety. The system helps to adjust the service of aircraft engines to increase overall engine cycles between shop visits. This practice may reduce an engine vendor's or owner's maintenance costs, and part of this saving may be shared with the airline or maintenance organization to compensate for the cost of making the adjustment and to motivate them to adopt the practice.

In one embodiment, the system optionally preferentially assigns aircraft with lower estimated historical contamination to routes with higher expected exposure to the at least one contaminant during route allocation.

In another embodiment, the expected contaminant exposure of the aircraft or the aircraft engine is estimated by determining an exposure index which is a measure of a contaminant exposure or an amount of contaminant material that passes through the aircraft or the aircraft engine in the aircraft fleet based on its trajectory. The control module allocates the exposure index to each aircraft engine in the fleet based upon the estimated historic contamination.

-40 In yet another embodiment, the system further comprises a maintenance interface that is communicated with the control module to input data for adjusting the estimated historic contamination for the aircraft or each aircraft engine as a result of maintenance interventions and to output alerts for required interventions.

In yet another embodiment, the historic contamination of the aircraft is estimated by determining at least one of an average contamination exposure of the aircraft or each aircraft engine per cycle, an average contamination exposure of the aircraft or each aircraft engine per time interval or a cumulative contamination exposure of the aircraft or each aircraft engine and determining historic contamination of at least one flight phase of the aircraft or each aircraft engine from the database of contamination data.

Embodiments of the present disclosure concern aircraft flight planning as a function of measured atmospheric contaminants that are potentially encountered that potentially adversely affect aircraft operating performance and reliability, for example aircraft engine operating performance and reliability.

In one embodiment, the system for aircraft flight planning is provided, wherein the system comprises:

a first storage medium including a measure of at least one atmospheric contaminant with respect to a location, an altitude or a pressure, and a time;

a control module in communication with the first storage medium, the control module having a processor and a memory containing executable instructions to provide the indications related to an estimated

-41 contamination risk to at least one aircraft at selected locations and altitudes or pressures;

an input interface in communication with the control module for receiving at least one aircraft flight plan data including at least one of a time, a pressure or an altitude, a trajectory and a location representing at least one aircraft flight; and an output interface in communication with the control module for providing indications related to the estimated atmospheric contamination risk to the at least one aircraft, wherein the control module receives at least one aircraft flight plan data from the input interface;

determines an estimated contamination risk using the measure of the at least one atmospheric contaminant for the at least one aircraft flight based upon a location, an altitude or pressure, a trajectory and a time information extracted from the at least one aircraft flight plan data; and provides a resultant indication related to the estimated contamination risk of the at least one aircraft to the output interface.

In one embodiment, the system thus reduces or mitigates a risk of contaminant exposure to the aircraft engine. The system thus allows operators to determine, before an aircraft takes off, whether it may be appropriate to alter the service (for example, to cancel, delay or re-route a flight) to avoid a contamination hazard. The system may permit operators or automated systems to determine possible changes to the aircraft flight path, height or speed when an aircraft is in flight, to avoid or reduce the risk of encountering a hazard. The system may run Weather

-42 Prediction Model (WR.F) using boundary conditions (e.g. dust boundary data) for predicting estimated contamination, and the output is an estimate of weather, water, and contaminants by locations, altitudes and time. The system may produce three-dimensional (3D) matrix for each contaminant separately and store or combine them to obtain four dimensional (4D) weather model using time.

In one embodiment, the system may calculate at least one modified aircraft flight plan for the at least one aircraft flight, an estimated contamination risk for the modified aircraft flight plan being generated for comparison with the estimated contamination for at least one aircraft flight plan.

Embodiments of the present disclosure may incorporate an effect of a wide variety of contaminants and may avoid aircraft sensors to measure exposure of contaminants and avoid the weight, drag, and cost of such sensors. Embodiments of the present disclosure may monitor engine condition with greater accuracy. Embodiments of the present disclosure may monitor the substantial variations in environmental contaminant exposure over time and as aircraft operate different routes, to improve accuracy of engine condition modeling. Embodiments of the present disclosure may permit the aircraft engine condition to be modeled with improved accuracy and only limited access, or no access, to measurement data from an engine. Embodiments of the present disclosure may recommend or schedule maintenance events that optimise aircraft engine life, including both short-term interventions such as engine washes to clean out dust, and long-term interventions such as major maintenances or shop visits. Embodiments of the present disclosure may recommend interventions such as engine washes when ad they are most needed (e.g.

-43 immediately after contaminant exposure) rather than when the results of damage of the aircraft engine are observed by sensors. Embodiments of the present disclosure may improve the accuracy of predictions of when maintenance is due by modeling the effect of corrosive contaminants such as salt and sulphate ion that are not typically sensed. Embodiments of the present disclosure may consider variations by time of day and year, and long-term trends, and enables airlines to make changes such as scheduling take-offs at times of day when contaminants are lower to reduce the impact or costs of contaminant exposure. Embodiments of the present disclosure may enable a user to resell the contaminant data to engine manufacturers and eventually selling into the Airlines in a Data as a service model (DaaS).

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system in accordance with an embodiment of the present disclosure. The system comprises a computing arrangement, wherein the computing arrangement includes a control module 102 that includes a processor 104 and a memory 106, an input interface 108, an output interface 110, a communication network 112, a server 114, a contamination database 116 of contamination data over location and time and a historical database 118 of historical data. The functions of these parts are as has been described above.

FIG. 2 is a flowchart illustrating steps of a method for (of) training an engine health model to predict an engine health parameter of an aircraft engine in accordance with an embodiment of the present disclosure. Such training is performed and concluded before the model is subsequently applied to data, for example measured sensor data, to provide

-44 predictions. At a step 202 of the method of training, one or more input variables that include engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates, are obtained. At a step 204 of the method of training, atmospheric contaminant data over location and time are obtained from a contamination database. At a step 206 of the method of training, aircraft flight history is obtained from a historical database. At a step 208 of the method of training, assignment data of aircraft engines and engine service data are obtained from the historical database. At a step 210 of the method of training, historical aircraft trajectories are determined from the historical database. At a step 212 of the method of training, contaminant exposure measure for a target aircraft engine is determined by retrieving atmospheric contaminant data at a location and a time in proximity to at least one target aircraft engine; such real-time data acquisition requires use of one or more sensors for sensing physical engine characteristics and engine environs. At a step 214 of the method of training, engine run time, cycles and hours are determined from the assignment data of engines to the aircraft, the engine service data, and the aircraft flight history. At a step 216 of the method of training, inputs such as the historical contaminant exposure, the engine service data and the one or more input variables are provided to an engine health model. At a step 218 of the method of training, engine health and engine life data are derived, namely is computed, from maintenance records. At a step 220 of the method of training, output variables are selected for the engine. At a step 222 of the method of training, the engine health model is trained to provide a predictive model using machine learning. At a step 224 of

-45the method of training, an engine health parameter of the target aircraft engine is predicted, namely is computed, and the effect of contaminants is incorporated using the predictive model.

FIG. 3 is a flowchart illustrating steps of a method for (of) predicting an engine health parameter of the engine or the aircraft using a predictive engine health model in accordance with an embodiment of the present disclosure; in other words, FIG. 2 concerns training the model, whereas FIG. 3 concerns subsequently applying the model. At a step 302 of the method of predicting, one or more input variables are obtained. At a step 304 of the method of predicting, atmospheric contaminant data over location and time are obtained from a contamination database of contamination data over location and time. At a step 306 of the method of predicting, aircraft flight history is obtained from a historical database of historical data. At a step 308 of the method of predicting, assignment data of engines to aircrafts and engine service data are obtained. At a step 310 of the method of predicting, historic aircraft trajectories are determined from the historical database. At a step 312 of the method of predicting, contaminant exposure measure for a target aircraft engine is determined by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft engine; contaminant exposure measure is determined, for example, by employing a sensor arrangement including one or more sensors. At a step 314 of the method of predicting, engine run time, cycles and hours are determined, namely are computed, from the assignment of engines to the aircraft and the engine service data and the aircraft flight history. At a step 316 of the method of predicting, inputs such as the historical contaminant exposure and the engine service data and the one or more input variables are provided to an engine health model. At a step 318 of

-46 the method of predicting, an engine health parameter of the target aircraft engine is predicted, namely is computed, and an effect of contaminants are incorporated using the predictive model. At a step 320 of the method of predicting, aircraft health is predicted using the predictive model, for example engine health and/or remaining useful life are predicted using the predictive model.

FIG. 4 is a graphical illustration of a predicted engine health in an ordinate Y-axis plotted against a number of cycles since last shop visit in an abscissa X-axis to predict an expected remaining useful life in accordance with an embodiment of the present disclosure. An expected end of life of an aircraft engine are beneficially determined from an output of the predictive engine health model. The expected end of life of an aircraft engine is beneficially used to plan when the aircraft engine requires replacement or removal or a shop visit. In an example embodiment, a life of the aircraft, for example aircraft engine, is reduced from a last service / shop visit if the environmental contaminant exposure affects negatively the aircraft, for example aircraft engine. The life of the aircraft, for example aircraft engine, is extended as expected to be at the end of life of the aircraft, for example aircraft engine, by predicting, namely computing, an engine health parameter using the predictive engine health model. For example, a number of cycles since last shop visit is increased from 5000 to 6000 when the engine health parameter of at least one aircraft engine is predicted. In an embodiment, an expected remaining useful life in cycles for an aircraft engine is automatically determined, namely is computed, from the last shop visit or service. The engine health index is calculated on a scale of 0 to 100. In an embodiment, a value of 100 in the scale indicates a new engine with an ideal performance and a value of 0 indicates that the aircraft engine requires a shop visit.

-47 FIG. 5 is a graphical illustration of a predicted engine health in an ordinate Y-axis plotted against a number of cycles since last shop visit in an abscissa X-axis to predict an expected lifespan of the engine for different levels of exposure to contaminants in accordance with an embodiment of the present disclosure. The graphical illustration demonstrates an effect of an intervention on an aircraft engine that had previously been subjected to high contaminant exposure is indicated. An expected lifespan of the aircraft engine is reduced if a current exposure continues (for example, a lifespan of the of the aircraft engine may be 4000 cycles when the engine is subjected to high contaminant exposure). The expected lifespan of the aircraft engine increases if a service is modified or a maintenance practice is improved to reduce future contaminant exposure and optimise maintenance to reduce effects of contaminants (for example, the lifespan of the engine or the aircraft is increased from 4000 cycles to 5000 cycles). The graphical illustration further elucidates that the expected lifespan of an aircraft engine is increased when the aircraft engine is subject to a low contaminant exposure.

FIG. 6 is a graphical illustration of a flight trajectory model of flight phases and contaminant density at the engine at the flight phases in accordance with an embodiment of the present disclosure. The graphical illustration elucidates that the distance is plotted in an abscissa X-axis and the airspeed is plotted in an ordinate Y-axis. The graphical illustration further elucidates that the distance is plotted in the X-axis and the height is plotted in the Y-axis. The graphical illustration elucidates that the airspeed at different flight phase includes take-off, climb, cruise, descent and landing of a flight and the height of the flight which is varied at departure, cruise and arrival of the flight. A control module beneficially determines, namely computes, an exposure index which is a measure of

-48a contaminant exposure or an amount of contaminant material that passes through the aircraft engine in the aircraft fleet based on its flight trajectory. The total contaminant exposure during flight is obtained using integration of contaminant density or mass flow at the aircraft engine.

FIGS. 7A to 7B are graphical illustrations of an integrated method pertaining to computing a cost awareness (CA) and a risk mitigation (RM) for predicting a risk of high-altitude ice (HAI) being encountered on an aircraft route at different periods from a departure time, in accordance with an embodiment of the present disclosure. The graphical illustrations elucidate forecasting, nowcasting and post-validation of an airline I aircraft engine. A system beneficially determines a possibility of a specific route at risk by assembling a numerical weather prediction (NWP) model and a trajectory models for forecasting a future of the aircraft engine. In an embodiment, the system provides a method for (of) forecasting in future using a specially-configured atmospheric model. The system may determine nowcasting of the aircraft engine by integration of various nowcasting products (implemented at ~10-minute updates). The system may determine post-validation to estimate a risk of a hazard being present at a location and time in future, by applying a spatial and/or temporal uncertainty calculation to an indication related to the estimated atmospheric contamination risk. For example, an uncertainty in future movement of air due to wind is beneficially represented with a standard deviation in units of distance, and a kernel function such as a radial Gaussian with a radius proportional to such standard deviation are beneficially applied to a matrix of hazard probability estimates to derive a spatially smoothed probability. In an example embodiment, the postvalidation of the aircraft engine is determined using machine learning, namely algorithms that adaptively modify their operating parameters in

-49response to training data being processed by the algorithms. In an embodiment, the uncertainty estimation may use an approximation to Bayes'Theorem. In an embodiment, the graphical illustration shows that a prediction uncertainty decreases closer to a departure of an aircraft. The graphical illustrations further elucidate an event indication that has occurred before the departure of the aircraft. The event indication comprises a high risk of HAI being encounter on route, a medium risk of HAI being encounter on route and a low risk of HAI being encounter on route (en route). The indications for the estimated risk on a specific route comprise cost awareness (CA) and risk mitigation (RM). The graphical illustration further elucidates actual costs for the aircraft with forecasted costs of the aircraft. In the medium risk of a HAI encounter on the route, the cost of the aircraft increases by 20% due to extra fuel cost, if the flight is canceled. The system may prefer a risk mitigation as alternative flight routes for passengers to reduce the cost for aircraft. In the high risk of a HAI encounter on route, the cost of the airline increases by 50% due to extra fuel, if the flight is canceled. The system may provide alerts including a risk mitigation to the flight passengers to rebook or cancel the flight. In the medium risk of a HAI encounter on the route, the cost for the aircraft increases by 20% due to extra fuel cost, if the flight is canceled and the system may delay the flight by two hours. The cost awareness and the risk mitigation option for the low risk of HAI encounter on route comprise no extra fuel and no mitigation needed respectively. The cost awareness and the risk mitigation may validate flight data that comprises forecasted cost, actual cost, one or more turbulence encounters and pilot reports. Such fuel savings also represent an energy efficiency of the aircraft when in operation, and are optionally recorded

-50as carbon offsets subject to financial repayment to assist to address costs of, for example, executing engine maintenance.

FIGS. 8A to 8B are flow diagrams illustrating steps of a method for (of) training a predictive engine health model using machine learning and using the predictive engine health model to predict an engine health in accordance with an embodiment of the present disclosure. At a step 802 of the method of training, a set of training data comprising historical data sets for a training set of at least one aircraft engine is provided. At a step 804 of the method of training, at least one measure of historic contaminant exposure of the set of training data is determined by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data. At a step 806 of the method of training, the historical contaminant exposure and the engine service data are provided to an engine health model. At a step 808 for prediction purposes, a contaminant exposure measure for a target aircraft engine is determined by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft engine. At a step 810 for prediction purposes, a predictive engine health model is used to predict an engine health parameter of the target aircraft engine based on at least one of target aircraft engine flight history and contaminant data over location and time.

FIG. 9 is an illustration of steps of a method for (of) determining contaminant data using one or more servers, a supercomputing and/or a distributed computing platform according to an embodiment of the present disclosure. At a step 902, satellite data is obtained. At a step 904, flight monitoring data is obtained. At a step 906, surface measures are obtained. At a step 908, radiosondes data is obtained. At a step 910,

-51 the satellite data is processed. At a step 912, the flight monitoring data is processed. At a step 914, the surface measures are processed. At a step 916, the radiosondes data is processed; it will be appreciated that radiosondes data is equivalent to sensor data. At a step 918, import data is calibrated. At a step 920, boundary conditions for the import data are determined and configured. At a step 922, the determined boundary conditions are provided to a supercomputing or a distributed computing platform. At a step 924, the determined boundary conditions are validated. At a step 926, weather models are determined. At a step 928, the boundary conditions, the weather models and the validated boundary conditions are obtained and processed by the supercomputing or distributed computing platform. At a step 930, the processed data is stored in a storage or distributed storage. At a step 932, the processed data are stored in a working memory. At a step 934, contaminant distribution of at least one of Latitude and longitude, altitude and/or pressure level or time are forecasted using the supercomputing or the distributed computing platform. In an embodiment, the forecast of contaminant data is generated for various latitude, longitude, altitude, pressure level or time.

FIG. 10 is an illustration of an exploded view of a distributed computing system or cloud computing implementation in accordance with an embodiment of the present disclosure; such computing elements are convenient regarding as being a computing arrangement. The exploded view comprises an input interface 1002, a control module that comprises a processor 1004, a memory 1006 and a non-volatile storage 1008, processing instructions 1010, a shared/ distributed storage 1012, a server that comprises a server processor 1014, a server memory 1016 and a server non-volatile storage 1018 and an output interface 1020.

-52The function of the server processor 1014, the server memory 1016 and the server non-volatile storage 1018 are thus identical to the processor 1004, the memory 1006 and the non-volatile storage 1008 respectively. The functions of these parts are as has been described above.

Additionally, it should be noted that the present system and method helps to minimize or eliminate certain aircraft emissions through improved maintenance and therefore reduces carbon dioxide (CO2) emissions.

Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present 10 disclosure as defined by the accompanying claims. Expressions such as including, comprising, incorporating, have, is used to describe and claim the present disclosure are intended to be construed in a nonexclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is 15 also to be construed to relate to the plural.

-53APPENDIX

It will be appreciated that aircraft engine washing is beneficial to curb aircraft fuel use. For example, embodiments of the present disclosure provide a new approach to quantity greenhouse gas benefits (carbon offsets') of curbing aircraft engine fuel consumption (kerosene production) by washing engines. Based on the quantification of emission reductions, proponents can issue and sell verified GHG credits known as Verified Carbon Units (VCU's). The method is beneficially used to quantify and credit the greenhouse gas emission reductions that occur when jet engine efficiency is increased, and fuel consumption reduced, through the washing of engines while on the wings of aircraft.

Whereas aircraft engines are periodically scheduled for off-wing overhauling and maintenance, less than five percent of aircraft engines are actually washed while on the wings of aircraft. This engine washing results in increased propulsive efficiency, thereby reducing fuel consumption.

An operating airflow through an aircraft engine is enormous. However, when the airflow is contaminated by sand, salt, chemicals and unburned hydrocarbons, amongst others, these arise particles that adhere to surfaces of aircraft engine parts leading to a phenomenon known as compressor fouling. A given contaminated engine has to work harder to compress a defined amount of air. Therefore, aircraft engine temperatures potentially rise and more fuel must be injected to achieve a same given thrust from the aircraft engine. This consequentially leads to faster engine deterioration, for example as aforementioned in connection with aforesaid embodiments of the present disclosure.

-54Washing a jet or turboprop engine on a customized cycle leads to a cleaner and therefore more efficient compressor. To achieve such washing, there is beneficially employed a dual nozzle arrangement that sprays water heated to 70 °C with up 5 to 70 bar directly into the core engine. Contrary to conventional known washing methods, a fine and evenly distributed water mist follows the gas path. The amount of water injected is optimized for each aircraft engine type. This optimization ensures efficient cleaning of the compressor and at the same time reduces, for example minimizes, an amount of residual io water remaining within the aircraft engine. The advanced equipment ensures easy preparation, short washing times and improved washing results. Cleaner aircraft engines run at lower temperatures, need less fuel and therefore emit less carbon dioxide (CO2) and other harmful greenhouse gases(NOX). An improved overall performance in aircraft 15 engine performance achieved saves money on MR.0 expenses during a given aircraft engine's life cycle.

Claims

1. An aircraft atmospheric contamination determination system, comprising:

(ii) the at least one input interface receives flight data relating to a target aircraft to be evaluated and provides the flight data to the computing arrangement; and (iii) the at least one output interface provides at least one predicted aircraft health parameter; and (b) wherein the computing arrangement uses the predictive aircraft health model and the inputs to (i) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time.

2. An aircraft atmospheric contamination determination system, wherein the predictive aircraft health model is trained by (i) interrogating a database of contamination data to obtain a set of training data by retrieving the atmospheric contaminant data at a

-56location and a time in proximity to at least one flight of at least one training set aircraft; and (ii) providing the historical contaminant exposure and the training set aircraft service data as inputs for use in training the predictive aircraft health model.

3. An aircraft atmospheric contamination determination system of claim 1 or 2, wherein the predictive aircraft health model includes a predictive aircraft equipment health model representative of one or more equipment of a target aircraft, and the at least one predictive aircraft health parameters includes at least one predictive aircraft equipment health parameter; and wherein the aircraft equipment may be at least one of an aircraft body, flight surfaces, engine, instrument, sensor, control equipment or landing gear, or a component of the foregoing.

4. An aircraft atmospheric contamination determination system of claim 1, 2 or 3, wherein the at least one input interface receives historical data relating to the target aircraft engine to be analyzed.

5. An aircraft atmospheric contamination determination system of any one of claim 1, 2 or 3, wherein the system further comprises at least one storage medium coupled directly or indirectly to the computing arrangement and containing a database of historical data relating to a plurality of aircraft or equipment that the system may be used to evaluate.

6. An aircraft atmospheric contamination determination system of any one of claims 1 to 5, wherein the predictive aircraft or equipment health model is trained by machine learning using a set of training data

-57comprising historical data sets, wherein the historical data sets comprise aircraft flight data, atmospheric contaminant data over location and time and aircraft or equipment service data.

7. An aircraft atmospheric contamination determination system of any one of claims 1 to 6, wherein the predicted aircraft or equipment health parameter is selected from a group comprising a cycle count, hour count, remaining cycles, remaining hours or health index.

8. An aircraft atmospheric contamination determination system of any one of claims 1 to 7, wherein the computing arrangement further calculates and outputs at least one of: a standard deviation, variance or confidence interval for the predicted health parameter.

9. An aircraft atmospheric contamination determination system of any one of claims 1 to 8, wherein training the predictive aircraft or equipment health model into the computing arrangement requires:

(i) at least one contaminant parameter for the set of training data; and (ii) at least one aircraft or equipment health parameter for each aircraft or equipment associated with the set of training data.

10. An aircraft atmospheric contamination determination system of any one of claims 1 to 9, wherein the flight data includes flight trajectory information that is selected from at least one of flight phase, estimation of airspeed, or engine mass flow, wherein the airspeed or the engine mass flow is estimated to weigh the at least one contaminant and to provide estimates of rate, density or mass of contaminants passing through the aircraft engine.

-se-

11. An aircraft atmospheric contamination determination system of any one of claims 1 to 10, wherein the input interface further receives one or more input variables that are utilised by the computing arrangement in the aircraft or equipment health model, wherein the one or more input variables include one or more of: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, fuel flow rates, vibration measures, inspection results or wear estimates.

12. An aircraft atmospheric contamination determination system of any one of claims 1 to 11, wherein the system comprises a distributed computing system and the control module and at least one data storage medium are centralised and at least one input interface and at least one output interface are provided at local nodes.

13. An aircraft atmospheric contamination determination system of any one of claims 1 to 12, wherein the historic contaminant exposure is determined by:

(I) analyzing a plurality of historical trajectories of a plurality of aircraft or equipments in a set of training data over location and time; and (ii) estimating contaminant concentration of the plurality of aircraft engines for each trajectory using an atmospheric model.

14. An aircraft atmospheric contamination determination system, comprising:

(ii) the at least one input interface receives flight data relating to a target aircraft to be evaluated and provides the flight data to the computing arrangement; and (ill) the at least one output interface provides at least one predicted aircraft health parameter to a user; and (b) wherein, after training, the computing arrangement uses the predictive aircraft health model and the inputs to (I) determine a contaminant exposure measure for a target aircraft by retrieving atmospheric contaminant data at a location and a time in proximity to at least one flight of the target aircraft; and (ii) derive at least one predicted aircraft health parameter for the target aircraft based upon at least the target aircraft flight history and contaminant data as a function of location and time; and (ill) provide an alert or schedule a maintenance intervention for the target aircraft based on the predicted aircraft health parameter.

15. An aircraft atmospheric contamination determination system of claim 14, wherein the predictive aircraft health model is trained by (i) interrogating the database of contamination data to obtain a set of training data by retrieving the atmospheric contaminant data at a

-60location and a time in proximity to at least one flight of at least one training set aircraft; and (ii) providing the historical contaminant exposure and the training set aircraft service data as inputs for use in training the predictive aircraft health model

16. An aircraft atmospheric contamination determination system of claim 14 or 15, wherein the computing arranging uses an aircraft or equipment health model to provide estimates of future contamination exposure of the target aircraft or equipment over time by estimating at least one of: a probability, a probability density function, multiple parameters including at least one of an expected mean and a standard deviation of estimated contaminant exposure; wherein the contamination determination system utilises the estimates to schedule interventions.

17. A method for (of) determining aircraft health using a computing arrangement coupled to a sensor arrangement, wherein the method comprises:

18. A method of claim 17, wherein the method includes training the computing arrangement by:

(i) interrogating the database of contamination data to obtain a set of training data by retrieving the atmospheric contaminant data at a location and a time in proximity to at least one flight in the aircraft flight data; and (ii) providing the historical contaminant exposure and the aircraft service data as inputs for use in training the predictive aircraft health model.

19. A method of claim 17 or 18, wherein the predictive aircraft health model includes a predictive aircraft engine equipment health model representative of one or more equipment engines of the a target aircraft, and the at least one predictive aircraft health parameters includes at least one predictive aircraft equipment engine health parameter; and wherein

-62the aircraft equipment may be at least one of an aircraft body, flight surfaces, engine, instrument, sensor, control equipment or landing gear, or a component of the foregoing.

20. A method of claim 19, wherein the step of training the aircraft or equipment health model utilises the aircraft flight data and the atmospheric contaminant data as inputs and aircraft or equipment health data derived from service data to provide at least one health parameter.

21. A method of any one of claims 17 or 19, wherein the aircraft or equipment health parameter comprises at least one of an aircraft or equipment health index or a useful remaining life prediction.

22. A method of any one of claims 17 to 20, wherein the machine learning comprises any one of: linear regression, neural network, decision tree, decision forest or gradient boosted decision tree, radial basis function, support vector machine, Gaussian process or principal component analysis.

23. A method of claim 22, wherein the machine learning includes at least one of time within a year or time of day as regression parameters.

24. A method of one of claims 17 to 23, wherein the predictive model provides at least one of: a standard deviation, a variance or confidence interval for the predicted aircraft or equipment health parameter.

25. A method of any one of claims 17 to 24, wherein the atmospheric contaminant data over location and time comprises an atmospheric model

-63of the concentration or mixing ratio of contaminants at a plurality of locations and times.

26. A method of claim 25, wherein the contaminants include a plurality of contaminants selected from: dust, organic particles, volcanic ash, salt, sulphur dioxide and sulphate ions, or any combination thereof.

27. A method of claim 25 or 26, wherein the atmospheric model derives estimates of at least one of the mixing ratio or the concentration of contaminants at the plurality of locations and times and provides an expected mass of the respective contaminant per unit mass of air at a particular location, height measure and time.

28. A method of any one of claims 17 to 27, wherein the atmospheric model provides at least one of: an estimated average of contaminants for each location by time-of-day and/or time-of-year; a standard deviation of contaminants for each location by time-of-day and/or time-of-year.

29. A method of any one of claims 17 to 28, wherein the method further comprises scheduling maintenance interventions for the aircraft aircraft or equipment in response to the predicted aircraft or equipment health parameter.

30. A method of any of claims 17 to 29, wherein the aircraft flight data comprises flight phase and estimation of at least one of airspeed or engine mass flow and associated location, time and date information.

31. A method of any of claims 17 to 30, wherein using the predictive model to predict an aircraft or equipment health parameter comprises a Monte Carlo modeling.

5

32. A method of any one of claims 17 to 31, wherein the predictive aircraft or equipment health model includes one or more further input variables selected from: engine or aircraft control, loading or operational parameters, engine or aircraft model and type information, sensor data, weather parameters, temperatures, speeds, altitudes, mass flow rates, io fuel flow rates, vibration measures, inspection results or wear estimates.

33. A computer program product comprising instructions to cause the system of any one of claims 1 to 17 to carry out the method of any one of claims 18 to 32.

Intellectual

Property

Office

Application No: GB1814784.3