COMPUTER-IMPLEMENTED METHODS

Abstract

A computer-implemented method. The method comprising: (a) obtaining a plurality of unique identifiers, each unique identifier being associated with a respective characterising code, each characterising code being indicative of character of an example of a same physical system; (b) accessing a database of characterising codes, and retrieving each respective characterising code; (c) determining a degree of similarity between each retrieved characterising code; and (d) providing, as an output, the determined degrees of similarity.

Claims

1. A computer-implemented method comprising: (a) obtaining a plurality of unique identifiers, each unique identifier being associated with a respective characterising code, each characterising code being indicative of character of an example of a same physical system; (b) accessing a database of characterising codes, and retrieving each respective characterising code; (c) determining a degree of similarity between each retrieved characterising code; and (d) providing, as an output, the determined degrees of similarity.

2. The computer-implemented method of claim 1, wherein in step (c), determining a degree of similarity between each retrieved characterising code may comprise determining one or more parameters, characterised by the characterising code, which are the same or within a threshold distance of the corresponding parameter of the other characterising codes.

3. The computer-implemented method of claim 1, wherein the characterising codes are formed of a concatenation of cluster labels for a plurality of parameters describing the physical system.

4. The computer-implemented method of claim 3, wherein if all cluster labels between a pair of retrieved characterising codes are determined to be identical, then the pair of characterising codes are determined to be a match to one another.

5. The computer-implemented method of claim 3, wherein each cluster label signifies an average value for the respective parameter.

6. The computer-implemented method of claim 1, wherein the system is an aircraft including a gas turbine engine.

7. The computer-implemented method of claim 6, wherein the characterising codes are indicative of operating conditions of the gas turbine engine.

8. The computer-implemented method of claim 7, wherein the characterising codes are indicative of an operation location and/or ambient conditions of the gas turbine engine.

9. The computer-implemented method of claim 7, wherein the characterising codes are indicative of an operating history of the gas turbine engine.

10. The computer-implemented method of claim 5, wherein the characterising codes are indicative of the following parameters of the gas turbine engine: a utilisation time of the gas turbine engine; a number of flights performed by the aircraft; a duration of a flight performed by the aircraft; a take-off altitude of a flight performed by the aircraft; a temperature of the engine during a start-up; a temperature at take-off during a flight performed by the aircraft; a temperature of the engine during a shut-down; a holding time during a flight performed by the aircraft; an average sulphur dioxide level during a flight performed by the aircraft; an average sulphate level during a flight performed by the aircraft; an average level of dust present during a flight performed by the aircraft; an average level of sand present during a flight performed by the aircraft; and one or more locations flown over by the aircraft.

11. The computer-implemented method of claim 6, wherein the method further comprises, based on the results of the comparison, modifying a maintenance schedule of the gas turbine engine.

12. The computer-implemented method of claim 6, wherein the method further comprises, based on the results of the comparison, modifying a stocking schedule of parts for the gas turbine engine.

13. The computer-implemented method of claim 1, wherein determining a degree of similarity between the candidate characterising code and the extant characterising codes includes assigning a weighting value to one or more components of each characterising code.

14. A computer-network comprising a processor, memory, and storage, wherein the memory contains machine executable instructions which, when run on the processor, cause the processor to: (a) obtain a plurality of unique identifiers, each unique identifier being associated with a respective characterising code, each characterising code being indicative of character of an example of a same physical system; (b) access a database of characterising codes, and retrieving each respective characterising code; (c) determine a degree of similarity between each retrieved characterising code; and (d) provide, as an output, the determined degree of similarity.

15. The computer-implemented method of claim 14, wherein in step (c), determining a degree of similarity between each retrieved characterising code may comprise determining one or more parameters, characterised by the characterising code, which are the same or within a threshold distance of the corresponding parameter of the other characterising codes.

16. The computer-implemented method of claim 14, wherein the characterising codes are formed of a concatenation of duster labels for a plurality of parameters describing the physical system.

17. The computer-implemented method of claim 16, wherein if all duster labels between a pair of retrieved characterising codes are determined to be identical, then the pair of characterising codes are determined to be a match to one another.

18. The computer-implemented method of claim 16, wherein each cluster label signifies an average value for the respective parameter.

19. The computer-implemented method of claim 14, wherein the system is an aircraft including a gas turbine engine.

20. The computer-implemented method of claim 19, wherein the characterising codes are indicative of operating conditions of the gas turbine engine.

Description

DESCRIPTION OF THE DRAWINGS

[0072] Embodiments of the disclosure will now be described by way of example with reference to the accompanying drawings in which:

[0073] FIG. 1 shows a flow diagram of a computer-implemented method;

[0074] FIG. 2 shows a computer network;

[0075] FIG. 3 shows a generated characterising code;

[0076] FIG. 4 shows a graphical display of the characterising code of FIG. 3;

[0077] FIG. 5 shows a database of characterising codes;

[0078] FIG. 6 shows a flow diagram of a computer-implemented method; and

[0079] FIG. 7 shows a flow diagram of a computer-implemented method.

DETAILED DESCRIPTION

[0080] Aspects and embodiments of the present disclosure will now be discussed with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art.

[0081] FIG. 1 shows a flow diagram of a computer-implemented method. In a first step, S100, data is received which includes the values for a plurality (n) parameters. The parameters are associated with the physical system for which characterising codes are being generated. In this example, the physical system is an aircraft including a gas turbine engine. Accordingly, the parameters are any of those discussed above. However the system could, instead, be an aircraft including any one or more of: a windscreen; doors; windows; tyres; flaps; and paint. The parameters may be descriptive of the state or usage of any of those parts of the aircraft (for example flight time during which the windscreen has been installed, or the number of times the doors have been opened).

[0082] Once the data has been received in step S100, the method moves to step S102, where a corresponding data cluster from a plurality of data clusters is identified for an i.sup.th parameter of the n parameters. The data clusters each define a range of possible values for the respective parameter. This identification can be performed using a K-means clustering method to identify the corresponding data cluster. The size of the data clusters may be determined by a preliminary step (not shown) of splitting the entire possible range of values for the respective parameter into clusters using a silhouette method.

[0083] After the corresponding data cluster has been identified, parameter i is assigned to that data cluster in step S104. Subsequently, a determination is made as to whether all n parameters have had data clusters determined and assigned, i.e. does i=n. If the determination is false ‘N’, the method moves to step S108, where i is incremented, and the method then returns to step S102 for the next value of i.

[0084] Once all parameters have had data clusters identified and assigned (i=n, ‘Y’) the method moves to step S110 where a characterising code is generated. Preferably, generating the characterising code comprises concatenating each of the identified and assigned data clusters. For example, assigning the parameter i to its identified data cluster may comprise producing a label: [Parameter type][Identified Cluster]. To take sector length as an example, if the value of the sector length parameter placed it in the 2.sup.nd data cluster, the label “SL2” may be produced. Similarly, if the value of the utilisation time placed it in the 5.sup.th data cluster, the label “UT5” may be produced. Therefore a partial example of a characterising code, formed by the concatenation of the labels, would be “UT5SL2”.

[0085] The characterising code, generated in step 110, is then stored as per step S112.

[0086] The method may further include a step, after step S110, and before S112, of comparing a distance between the characterising code generated in step S110 and one more existing characterising codes for examples of the same physical system. If the distance is less than a threshold distance, the generated characterising code may be associated with the existing characterising code and this association may also be stored in the database. The distance may be a Euclidean distance. The distance may be determined by subtracting the label for the cluster from one characterising code from the corresponding label of another characterising code. For example, to compare the label “SL4” with “SL2”, the labels are subtracted to determine a distance of 2 from one to the other. The distances can then be summed for all labels, to determine an overall degree of similarity. Codes with a total difference of zero are identical, and in some examples a threshold distance of 2 is used where codes with a total difference of 2 or less are considered close matches. The total differences between each pair of characterising codes may be stored, and for each characterising code a top 10 closest matches may be determined and provided as an output. In further examples, weightings may be applied to determined differences. For example, such that a cluster 5 to cluster 4 difference (with a distance of 1) is considered more significant than a cluster 4 to cluster 3 difference (also with a distance of 1).

[0087] FIG. 2 shows a computer network 200. An input/output device 202 of the network 200, for example a network interface, communicates with a plurality of data sources. These sources include an aircraft 204, which communicates directly with the network 200; an external weather database 206; and an external flight information database 208. The network interface is connected to one or more processors 212, which are connected to memory 210 and storage 214. The memory 210 contains machine executable instructions 216, which when performed on the one or more processors 212 cause the processors to carry out the method discussed with relation to FIG. 1, and/or the method discussed with relation to FIG. 6, and/or the method discussed with relation to FIG. 7.

[0088] FIG. 3 shows a generated characterising code. The characterising code includes cluster identifiers for 12 parameters: utilisation time; cycles; sector length; heat map; take-off altitude; start temperature; take-off temperature; shut-down temperature; holding time; sulphur dioxide content; sulphate content; and dust and sand content. FIG. 4 shows a graphical display of the characterising code of FIG. 3. Notably, it shows where the aircraft described by the characterising code sits relative to its peers. For example, with regards to the utilisation parameter, the aircraft described by this particular characterising code sits towards an upper end of the range of utilisation times.

[0089] FIG. 5 shows a database of characterising codes. The database is indexed by a unique identification code, in this instance the aircraft tail number. Each UID is associated with its corresponding characterising code.

[0090] FIG. 6 shows a flow diagram of a computer-implemented method of comparing characterising codes. In a first step, S600, a candidate characterising code is obtained. This can be either by generating the characterising code as per the method discussed above, or alternatively by selection of an extant characterising code within a database.

[0091] Next, in a step shown in step S602, a database of n extant characterising codes is accessed. This can be the database from which the candidate characterising code is obtained. If so, then clearly the candidate characterising code will be excluded in subsequent comparisons.

[0092] Subsequently, in step S604, the candidate characterising code is compared to an i.sup.th extant characterising code. An identical characterising code, i.e. one where all duster labels are identical, would be considered a 100% match, and so the examples would be considered identical twins. Next closest matches are assessed by the number of parameters which are in the same cluster, and then for those parameters which are not in the same cluster the distance away from the respective clusters. For example, a parameter within the UT3 cluster would be considered closer to the UT4 cluster than the UT1 cluster. In a further example, some parameters are weighted more than others such that there are considered more important when comparisons are made.

[0093] Once the i.sup.th extant characterising code has been compared, the method moves to step S606 where it is determined if all characterising codes in the database (bar the candidate, in the example where the candidate was obtained from the database) have been compared with the candidate characterising code i.e. i=n. If not, ‘N’, the method moves to step S608 where i is incremented and a comparison is performed for the next extant characterising code.

[0094] When all extant characterising codes have been compared, ‘Y’, the method moves to step S610 whereby the results of the comparison are provided. This provision may be, for example, through a graphical interface indicating those extant characterising codes which are identical matches or those characterising codes that fall within a threshold distance from the candidate characterising code. The threshold distance may be set by the operators, for example to be at least 90% similar, at least 80% similar, or at least 70% similar.

[0095] In a further method of comparing characterising codes, shown in FIG. 7, a user inputs a plurality of unique identifiers (for example tail numbers for aircraft) in step S700. Using these unique identifiers, the method accesses the database described previously and retrieves the characterising code associated with each of the unique identifiers in step S702. Next, in step S704-S708, a degree of similarity (as discussed previously) is determined between each retrieved characterising code. The result of the determination is then provided to the user in step S710. This can allow a user to quickly identify, between sets of aircraft, which parameters are most shared within the selected sample. This is particularly advantageous when the characterising codes describe a number of parameters higher than would be otherwise analysable. For example, where a set of aircraft have shared the same fault, the method allows the determination of which parameters are most shared between the aircraft and so can provide insight during fault determination.

[0096] This allows for a very fast, and computationally easy, comparison of examples of physical systems. For example, where the physical system is an aircraft with a gas turbine engine, it allows for a clear comparison of different flight properties, and a cause of failure or damage can be more readily identified. As an example, if seven engines all had failures, and all had characterising codes indicating the same cluster for flight duration and altitude of take-off, these are likely to be the cause of the issue.

[0097] Moreover, this allows the maintenance of aircraft having identical or very similar characterising codes to be optimised. If a particular characterising code pattern is identified as suffering relatively little damage and/or wear in service, then the service intervals for all aircraft containing that characterising code pattern can be increased. If a particular characterising code pattern is identified as suffering relatively high damage and/or wear in service, then the service intervals for all aircraft containing that characterising code pattern can be decreased. This can further provide enhanced control over stock levels.

[0098] While the disclosure has been described in conjunction with the exemplary embodiments described above, many equivalent modifications and variations will be apparent to those skilled in the art when given this disclosure. Accordingly, the exemplary embodiments of the disclosure set forth above are considered to be illustrative and not limiting. Various changes to the described embodiments may be made without departing from the spirit and scope of the disclosure.