Method and system for determining a condition of a landing runway for an aircraft

Abstract

A method for determining a landing runway condition of an aircraft comprises the steps of: acquiring a set of different types of data groups (D1, D2) used to evaluate and monitor the runway deterioration condition; deriving a weighting coefficient (Ki) from each data group; filtering the data; determining a partial runway condition for each data group; modifying the weighting coefficient of each data group; and combining the partial runway conditions to derive a runway condition coefficient (RWYCC) associated with a confidence index (IC) derived from the modified weighting coefficients.

Description

Technical Field

[0001] This invention relates in general to optimizing airport traffic and reducing the number of runway closures, which can have very significant economic consequences for airport operators. Background Technology

[0002] More specifically, the present invention relates to determining the condition of airport landing runways in order to optimize runway use while meeting safety requirements.

[0003] Currently, airport operators are required to monitor runway conditions. This monitoring is conducted either through radio reports provided immediately after an aircraft lands, through friction coefficient measurements taken by test trucks traveling on the runway, through sensors embedded in the runway to determine the type and level of contaminants, through weather sensors, through human observation and measurements taken by runway inspectors, or through a manual combination of all these data sources.

[0004] Radio reports provided by the pilot during landing can be subjective. Measurements of the coefficient of friction are also inaccurate because the test trucks used for these measurements cannot effectively simulate the gliding motion of an aircraft (especially a large vehicle), thus the measurements are relatively imprecise. Furthermore, this data source requires the runway to be closed during the measurement. Measurements from buried sensors only represent 1 cm of friction over several kilometers of runway. 2 The same applies to manual measurements performed by runway inspectors.

[0005] It is also known that indicators of airport runway slip can be estimated by detecting the lateral deviation of an aircraft from a reference trajectory, or by monitoring the deceleration of an aircraft based on braking data. Summary of the Invention

[0006] In view of the above, the object of the present invention is to provide aircraft with a status of landing runway conditions that has increased reliability and relevance and can be used to optimize runway utilization by airport operators. Furthermore, the objective is to maintain consistency among all these available measurement sources and to incorporate new, more reliable methods that weight the relevance of each data source.

[0007] Therefore, according to a first aspect, the object of the present invention is a method for determining the conditions of an aircraft landing runway, the method comprising the following steps:

[0008] - Acquire a collection of different types of data sets that are used to assess and monitor deteriorating runway conditions;

[0009] - Calculate the weighting coefficients for each data group;

[0010] - Filter data;

[0011] - Determine partial runway conditions for each data set;

[0012] - Modify the weighting coefficients for each data group; and

[0013] - Combine partial runway conditions to generate runway coefficients associated with a confidence index derived from modified weighted coefficients.

[0014] Advantageously, during filtering, the data is grouped according to braking segment, and each braking segment is associated with the braking segment identification information, the date information of the data, the segment location information, and the modified weighting coefficient.

[0015] According to another feature of the method of the invention, during data acquisition, first data related to the braking parameters of the aircraft and second data related to the gliding status of the aircraft obtained from the ground are acquired.

[0016] It is advantageous to acquire first data when the aircraft is gliding on the landing runway at a speed below a threshold.

[0017] According to another feature, at the end of the filtering step, first data related to the braking parameters is provided to the calculation step, which is able to derive the runway timestamp friction coefficient and the modified weighting coefficient.

[0018] Advantageously, in the step of calculating the friction coefficient, the friction coefficient is calculated based on the first filter data and the second filter data.

[0019] The method may also include a step of decoding the first data and a step of decoding the second data.

[0020] For example, the second data includes aircraft position data, data related to runway conditions, and meteorological data.

[0021] Advantageously, the weighting coefficients derived from the filtering step can be modified based on time or the sampling frequency of the data.

[0022] The method may also include a prior step of initializing the weighting coefficients for each data set.

[0023] According to another feature of the method according to the invention, data from another data set is used during the step of determining partial runway conditions.

[0024] It is possible to specify runway coefficients for different sections of the runway, particularly for each third of the runway. Furthermore, the history of runway coefficients can be compiled.

[0025] Another object of the present invention is a system for determining the conditions of an aircraft landing runway, the system comprising:

[0026] - A device for acquiring a collection of different types of data sets, which is used to assess and monitor deteriorating runway conditions;

[0027] - A device for assigning weighting coefficients to each data group;

[0028] - Devices for filtering data; and

[0029] - A computing device configured to determine partial runway conditions for each data set and modify the weighting coefficients for each data set;

[0030] - A calculation device suitable for combining partial runway conditions to generate runway coefficients associated with a confidence index derived from modified weighting coefficients. Attached Figure Description

[0031] Other objects, features, and advantages of the invention will become apparent from the following description, given only by way of non-limiting embodiments and with reference to the accompanying drawings, in which:

[0032] [ Figure 1 The overall architecture of a system for determining landing runway conditions according to the present invention is shown;

[0033] [ Figure 2 This is an overview of a system for determining landing runway conditions according to the present invention, showing the main functional platform of the system;

[0034] [ Figure 3 This is a flowchart illustrating the main stages of data acquisition and processing used to derive runway coefficients;

[0035] [ Figure 4 This is a flowchart illustrating the acquisition of data related to the aircraft's braking parameters;

[0036] [ Figure 5 This demonstrates the filtering of the data;

[0037] [ Figure 6 The calculation of the friction coefficient μ experienced by the aircraft is shown; and

[0038] [ Figure 7 An example of a report generated by the method for determining runway conditions according to the present invention is shown schematically. Detailed Implementation

[0039] Figure 1 An exemplary embodiment of a system for determining the conditions of an aircraft landing runway according to the present invention is shown.

[0040] This system is designed to calculate and provide Runway Condition Codes (RWYCCs) for different sections of an airport's landing runway, and to provide runway conditions to airport operators to achieve optimal runway utilization, particularly by reducing runway closures. The RWYCCs comply with the Global Reporting Format (GRF) regulations in effect under RTM.0704.

[0041] The RWYCC coefficients are derived from various data sources and have a confidence index that reflects the reliability of the calculated coefficients.

[0042] The RWYCC coefficient is derived in particular from the following data: first data related to the aircraft's braking parameters and second data related to the aircraft's gliding conditions obtained on the ground.

[0043] Also refer to Figure 2 After landing, the aircraft transmitted the first data D1 in the form of a radio report.

[0044] The first data may include, for example, the following: aircraft type, aircraft weight, wheel speed relative to the ground, hydraulic braking pressure, flight stage, thrust reverser status, brake pedal depressed, GPS location, etc.

[0045] The second data, D2, relates more specifically to runway conditions and is provided by sensors C, radar data Rd, weather forecasts W, or measurements Tt from test trucks.

[0046] For example, sensors are used to determine potential contaminants (such as water, snow, stagnant water, mud, etc.), the thickness of the contaminants, the surface condition of the runway (such as dry, wet, slippery), and the ground temperature, etc.

[0047] The radar data was specifically designed to determine the aircraft's position, while the test truck provided the coefficient of friction.

[0048] The system for determining runway conditions essentially comprises a data acquisition and computation platform 1. This platform acquires various data sets D1 and D2 for assessing and monitoring the deterioration of airport runways, and calculates runway coefficients associated with confidence indices for each segment of the runway, such as each third. Platform 1 also provides a human-machine interface, for example, accessible by an airport manager G via an API computer application, which provides this information to the air traffic controller Ctrl to provide the information to aircraft A in flight.

[0049] Therefore, Platform 1 includes: an interface I for collecting and processing flight data, which receives data D1 provided by the aircraft; a data storage and decoding step II, which receives second data D2 and the first data D1 decoded by interface I; and a calculation step III, which receives the data decoded by storage and decoding step II and is configured to calculate runway coefficients based on the decoded data in a format suitable for presentation on the API interface, each runway coefficient being associated with a confidence index.

[0050] Now refer to Figure 3 The main steps of the method for determining runway conditions according to the present invention are described.

[0051] As can be seen, the method includes two data acquisition and processing stages that are executed in parallel for the acquisition and processing of the first data group D1 and the second data group D2.

[0052] First, regarding the aircraft's braking data D1, the raw data is obtained in step 2.

[0053] like Figure 4 As shown, Step 2 is initiated upon landing as soon as the aircraft touches the runway (Step 3).

[0054] If this is the case, then brake data is recorded via interface I (step 4). This data is recorded as long as the aircraft is on the landing runway and its speed relative to the ground is greater than the speed limit (e.g., ten knots) (step 5). Outside of these conditions, recording stops (step 6). In the next step, step 7, it is checked whether the data transmission conditions are met. If so, the recorded raw data is transmitted and stored in storage and decoding step II (step 8).

[0055] Transmission conditions can be determined based on aircraft type, airline, and destination airport.

[0056] At the end of the data acquisition phase, the raw data is decoded through data storage and decoding step II (step 9), and then filtered and processed by the third calculation step III (step 10).

[0057] It should be noted that the collection and processing interface I and the storage step II assign a weighting coefficient to each data group: K1 for the first data group D1 and K2 for the second data group D2. Therefore, as a non-limiting example, radar data is associated with weighting coefficient 25, sensor data with coefficient 30, meteorological data with coefficient 20, and braking data with coefficient 25.

[0058] For each data set, partial runway conditions are calculated, and the evolution of runway conditions is determined in relation to the estimates of confidence indices based on the calculated runway conditions.

[0059] The weighting coefficients K1 or K2 of the subset are adjusted over time during the method by the weighting of the data set, the correlation of the analyzed data, the sampling frequency of the data, and the date of data acquisition. The confidence index deteriorates over time in the absence of new data.

[0060] If data from another set is considered, the weighting coefficients are also modified.

[0061] In fact, the processing of each data group can use the raw data from another original decoded data group as input.

[0062] For example, braking data can be combined with radar data to correlate the position seen by the aircraft with the position given by the radar. Similarly, braking data can be combined with sensor data to optimize and correlate calculations based on context.

[0063] For example, if radar data is unavailable, the aircraft's positioning data available on the onboard computer is used.

[0064] It should be noted that, by default, radar data is not available, and data from the aircraft other than wheel speed, aircraft speed, pedal pressure, etc., is used.

[0065] In fact, negative temperatures and snowfall predicted by sensors will mean that the runway coefficient associated with contaminated runways will dominate.

[0066] The runway coefficients are subsequently calculated by combining the results of each data set weighted by the modified weighting coefficients, and by combining the average confidence index generated by the weighting of the coefficients of each data subset.

[0067] refer to Figure 5 The filtering algorithm is applied in parallel to various data configurations, namely, standard condition data or company-specific condition data. In this diagram, for clarity, only the processing performed in one data configuration is shown; otherwise, the processing would be performed on other data configurations.

[0068] For each data type, the runway braking zone is divided into multiple braking zones (step 11). Then, for each braking zone, it is determined whether the braking is manual or automatic. However, the data evaluation differs depending on whether the braking is manual (step 12) or automatic (step 13). In the case of manual braking (step 12), recorded and unrecorded data are collected separately via Quick Access Records (QAR) (steps 14 and 15). In the case of automatic braking, it is checked whether the maximum braking coefficient has been reached in each braking zone (step 16).

[0069] During the filtering process, the weighting coefficients are updated based on the nature of the braking. Automatic braking typically involves braking with a lower braking coefficient, which involves an increase in the weighting coefficient K'1, while data recorded via QAR involves a decrease in the weighting coefficient K'1.

[0070] Therefore, braking segments are determined based on the nature of the braking and the recording type (step 17).

[0071] For each braking segment, a vector is generated, each vector identifying the segment, the segment type, and the modified weighting coefficient K'1 assigned to the data.

[0072] In addition, data, timestamps, locations, and braking phases are associated with each segment.

[0073] In addition, the filtering used is time-based and specific to each type of data.

[0074] Furthermore, filtering is performed based on the sampling frequency of the data so that the sampling frequency of the data acquisition can be standardized by applying different weights to the data according to the sampling frequency.

[0075] At the end of the filtering step, the first data D1 related to the aircraft's braking parameters is processed by calculation step III to calculate the runway's friction coefficient μ (step 18).

[0076] refer to Figure 6 The calculation is based on filtered data Df identified by runway segmentation and runway type, and, if necessary, on filtered data Df' from another data set.

[0077] Regarding braking data, this braking data is integrated with data type information.

[0078] The data type is a vector, which allows us to know the braking type, accuracy type, type of aircraft or material, etc.

[0079] If other data (such as data from sensors or radar) is available, that data is also integrated into the calculation, and the Boolean variable Var is set to "true" to indicate that the weighting coefficients associated with that data can be improved.

[0080] If this is not the case, set the Boolean variable Var to "false" to indicate that the weighting coefficients will not be affected.

[0081] For example, the coefficient of friction μ experienced by the aircraft can be calculated using prior knowledge obtained from data from already landed aircraft or from experimental data, and the coefficient of friction can be obtained by using a model estimated from that data, which is then predicted from filtered data.

[0082] Of course, as new input data is added to the training library, the predictions will also be updated.

[0083] This can be achieved using various types of technologies.

[0084] For example, the random forest algorithm can be used to predict the value of the friction coefficient from decoded data.

[0085] In step 18 of calculating the friction coefficient, the weighting coefficient K1 is modified according to the location of the braking zone.

[0086] In the next step 19, the partial runway condition RCC is defined locally based on the value of the friction coefficient, and after step 19, the weighting coefficient K1” is modified according to time (K1”'), where the oldest data is assigned a downgraded confidence coefficient.

[0087] like Figure 3 As shown, the second data D2 related to the aircraft's taxiing status obtained from the ground also undergoes acquisition step 2, decoding step 9, and filtering step 10. However, the filtered data is directly used to calculate the partial runway condition RCC (step 19). Only after the step of calculating the partial runway condition is the weighting coefficient modified according to time (K'2).

[0088] In the next step, 20, the runway coefficient is calculated, which is correlated with a confidence index calculated from the modified weighted coefficients. This calculation is performed based on the weighted sum of local runway conditions.

[0089] In other words, the runway coefficient RWYCC is derived from the following formula:

[0090]

[0091] Therefore, global runway coefficients and a confidence index (IC) are provided for each section of the runway. The value of the confidence index depends on the time of data acquisition and the location of the braking zone.

[0092] Finally, in the final step 21, the runway coefficients and confidence indices for each section of the runway are transmitted to the API computer application, which is advantageously accessible online.

[0093] Figure 7 An example of an application that produces this type of report is shown.

[0094] The API application provides, for example, windows that display different runways. In this case, there are two runways, each consisting of multiple segments S1, S2, S3, S4, S5, and S6, with each segment identified.

[0095] Each segment is associated with the runway coefficient RWYCC, which in this case is Rwycc1 to Rwycc6 associated with the confidence index IC (IC1 to IC6 in this case). i .

[0096] In addition, each section is connected to input D1 ij Up to D6 ij The list is associated with each input and a weighting coefficient K1. ij To K6 ij Related. Runway coefficient RWYCC can also be provided. i The history of (t), each runway coefficient and their confidence index IC i (t) is correlated to allow for the determination of changes in runway coefficients, in order to provide decision support based on the history of runway coefficient changes.

Claims

1. A method for determining the conditions of an aircraft landing runway, characterized in that, The method includes the following steps: - Acquire a set of data groups of different types (D1, D2) for assessing and monitoring deteriorating runway conditions; - Derive the weighting coefficients (K1, K2) for each data group; - Then filter the data in the set of said data groups; - Determine partial runway condition (RCC) for each data set; - After filtering the data, modify the weighting coefficients for each data group; and - Combine the aforementioned partial runway conditions to derive the runway coefficient (RWYCC) associated with the confidence index (IC) derived from the modified weighted coefficients.

2. The method according to claim 1, wherein, During the filtering process, the data is grouped according to braking segment, and each braking segment is associated with braking segment identification information, date information of the data, segment location information, and modified weighting coefficients.

3. The method according to claim 1 or 2, wherein, During data acquisition, first data (D1) related to the braking parameters of the aircraft and second data (D2) related to the gliding status of the aircraft obtained from the ground are acquired.

4. The method according to claim 3, wherein, The first data (D1) is acquired as long as the aircraft is gliding on the runway at a speed below a threshold.

5. The method according to claim 3, wherein, At the end of the filtering step, the first data related to the braking parameters is provided to a calculation step adapted to derive the timestamp friction coefficient (µ) of the runway and the modified weighting coefficient.

6. The method according to claim 5, wherein, During the step of calculating the friction coefficient (µ), the friction coefficient is calculated based on the first filter data and the second filter data.

7. The method according to claim 3, wherein the method includes the step of decoding the first data and the second data.

8. The method according to any one of claims 4 to 6, the method comprising the step of decoding the first data and the second data.

9. The method according to claim 3, wherein, The second data includes aircraft position data, data on the condition of the runway, and meteorological data.

10. The method according to any one of claims 4 to 7, wherein, The second data includes aircraft position data, data on the condition of the runway, and meteorological data.

11. The method according to any one of claims 1, 2, 4, 5, 6, 7, and 9, wherein, The weighting coefficients derived from the filtering step are modified based on time or the sampling frequency of the data.

12. The method according to any one of claims 1, 2, 4, 5, 6, 7 and 9, the method comprising a prior step of initializing the weighting coefficients for each data set.

13. The method according to any one of claims 1, 2, 4, 5, 6, 7, and 9, wherein, During the step of determining the condition of the aforementioned portion of the runway, data from another data set is used.

14. The method according to any one of claims 1, 2, 4, 5, 6, 7, and 9, wherein, For different sections of the runway, the runway coefficient (RWYCC) is derived, and the history of the runway coefficient is compiled.

15. The method according to any one of claims 1, 2, 4, 5, 6, 7, and 9, wherein, For each third of the runway, the runway coefficient (RWYCC) is derived, and the history of the runway coefficient is compiled.

16. A system for determining the conditions of an aircraft landing runway, characterized in that, The system includes: - A device for acquiring a collection of different types of data sets, the collection of data sets being used to assess and monitor deteriorating runway conditions; - A device for assigning weighting coefficients to each data group; - A means for filtering data in the set of said data group; - A computing device configured to determine partial runway conditions for each data set and to modify the weighting coefficients of each data set after filtering the data; and - A calculation device suitable for combining partial runway conditions to generate runway coefficients associated with a confidence index derived from modified weighting coefficients.

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