Method and system for estimating the remaining useful life of tyres for transport vehicles on the basis of telematic data

Abstract

The invention relates to an estimating method (200) implemented by a computer system (100) for estimating the remaining useful life (RUL) of a given tyre by aggregating influencing parameters obtained from the given tyre and telematic information obtained from a given vehicle fitted with the given tyre. The invention relates to a computer system (100) for implementing a method (200) for estimating the remaining useful life (RUL) of a given tyre by aggregating influencing parameters obtained from the given tyre and telematic information obtained from a given vehicle fitted with the given tyre.

Description

[0001] Description

[0002] Title: Method and System for Estimating the Residual Life of Transport Vehicle Tires Based on Telematics Data

[0003] Technical Field

[0004] The invention relates to a method implemented by a computer system for estimating a residual service life (RUL) of an identified tire by aggregating influential parameters obtained from the identified tire and telematics data obtained from an identified vehicle on which the identified tire is mounted. The invention also relates to a computer system for implementing a method for estimating a residual service life (RUL) of an identified tire by aggregating influential parameters obtained from the identified tire and telematics data obtained from an identified vehicle on which the identified tire is mounted.

[0005] Context

[0006] In the transportation industry, manufacturers and / or managers of transportation vehicles typically design maintenance plans based on parameters such as calendar time and / or mileage. These transportation vehicles (or "vehicles") may include trucks, light commercial vehicles (or "LCVs"), buses, vans, cars (e.g., taxis), military vehicles, and other types of ground transportation that make multiple trips per day between two or more predetermined locations. These vehicles are configured to perform operations related to various industries, such as transportation, mining, forestry, waste management, construction, quarrying, logistics, and agriculture. These vehicles are also configured to perform operations related to government and military operations.

[0007] Transportation vehicles encounter a variety of driving conditions, including varying road and environmental conditions. Road conditions may include, for example, inclines / declines, curves, and road quality, such as potholes, etc. Environmental conditions may include, for example, traffic congestion, road construction, and weather phenomena such as precipitation and temperature. As a result, transportation fleet managers must manage downtime and associated transportation costs. As used herein, the term "fleet" refers to one or more transportation vehicles, where a fleet may refer to the set of vehicles located or based at a given location, located or based at equivalent locations, used for a similar purpose, and / or used by an identified entity (e.g., a business, a military unit, etc.).

[0008] Tires contribute significantly to transportation costs. Therefore, it may be necessary to monitor the operations of the transportation vehicle(s) to determine their overall operational efficiency. In particular, tire management can have dramatic consequences in the event of a tire failure. However, transportation vehicle managers often lack this information for tires when vehicles are far from a vehicle management base. For example, there is no alert to warn of the need for immediate maintenance or visibility of the current status of a tire while in full operation (either loaded or empty).

[0009] A known solution to address this challenge involves the implementation of telematics. Based on operational information or data, it is possible to predict how and / or where a truck may be used. Data that can be monitored and recorded include, but are not limited to, actual miles driven, road surface type, user usage of the vehicle, time of day the vehicle is driven, acceleration / deceleration rate, braking rate, road condition, lateral acceleration or other characteristics indicating sharp turning maneuvers; driver identification, and temporal characteristics (e.g., period of inactivity).Telematics data may be generated by mobile applications, sensors installed on vehicles, data from one or more communication networks (or “Controller Area Network” or “CAN”), on-board diagnostic devices (or “OBD”) and their combinations and equivalents.

[0010] These data are likely to be used in a predictive maintenance method, which refers to maintenance actions based on the "health" of a tire and its environment. Relevant information (e.g., tire wear) can be estimated by applying artificial intelligence (AI) techniques. A machine learning (ML) approach can be used to develop a prediction model based on supervised learning (including semi-supervised learning), which is then applied to estimate the remaining useful mileage of transportation vehicle tires for a transportation application.ML methods can be used to describe the current state of a system, predict the future state and values ​​of the system, and / or recommend actions to maintain or improve the functionality of the system based on data-driven models, where these models are generated from a dataset consisting of historical data records. This historical data is used as a reference to validate the model's performance. The choice of prediction algorithms is based on the availability of this data and the requirements of the stakeholders (including, but not limited to, the driver, the fleet manager, and the maintenance manager) (see "Machine Learning Approach for Predictive Maintenance of Transport Systems", Mallouk, Issam et al., 2021 Third International Conference on Transportation and Smart Technologies, DOI: 10.1109 / TST52996, 2021, 00023 (2021) (or "the Issam reference").So, in a sense, predicting future maintenance needs can be achieved by monitoring equipment and detecting patterns that signal emerging failure.

[0011] Specifically, "Ensemble Learning" refers to a general approach to machine learning that aims to improve predictive performance by combining the predictions of multiple models. There are three main classes of Ensemble Learning methods (but it is understood that there are an unlimited number of ensembles one could develop for predictive modeling).

[0012] Boostrap aggregating (or "bagging") involves fitting multiple decision trees to different samples of the same dataset and averaging the predictions. The main idea of ​​bagging is to generate a series of independent observations with the same size and distribution as the original data. Given the series of observations, an ensemble predictor is generated that is better than the single predictor generated on the original data. There are several algorithms based on the bagging technique, including, but not limited to, Bagged Decision Trees and Random Forest. The Random Forest method uses the bagging method to improve the predictions of the base classifier (which is a decision tree).Boosting involves sequentially adding ensemble members that correct the predictions made by previous models and producing a weighted average of the predictions. Boosting is used to convert a weak learning model into one with better generalization. For example, techniques such as majority voting (in classification problems) or a linear combination of weak learners (in regression problems) can achieve better prediction. There are several algorithms based on the Boosting technique, including, but not limited to, AdaBoost and Gradient Boosting methods.

[0013] Stacking refers to fitting several different types of models on the same data and using another model to learn how to best combine the predictions. Stacking can be achieved either by combining the outputs of several base models or by using a method to choose the "best" base model. Stacking is one of the integration techniques in which the meta-learning model is used to integrate the output of the base models. There are several algorithms based on the Boosting technique, including, but not limited to, Blending and Stacked Models methods. The application of Ensemble Learning techniques in the transportation field is recognized in the prior art. For example, US Pat. No. 10,936,917 discloses a system that implements a machine learning algorithm to identify and classify recurring stops of a fleet of vehicles.The algorithm is capable of selecting a subset of features for identification and classification. The classification implements a Random Forest-type algorithm to classify a recurring stop into a depot, an employee's home, or other locations.

[0014] In another example, US10,878,328 discloses a process for building a classification model using the driving habits of drivers for whom the driver signature model is to be developed are identified. During the process, telematics data is generated, and, based on the consolidation of telematics data collected from various sources, an analytical platform of the system quantifies the risk factor of each driver in their manner of driving the vehicle. A Random Forest technique is used to distinguish a non-linear boundary between the driving patterns of the drivers so that the model can learn the dominant manner in which the driver drives the vehicle.

[0015] However, models can look for parameter changes related to actual component degradation, or they can examine vehicle usage patterns and indirectly infer component wear. Data-driven solutions can be based on real-time data transmitted during operation or on historical data collected (see Predicting the Need for Vehicle Compressor Repairs Using Maintenance Records and Logged Vehicle Data, Prytz, R. et al., Engineering Applications of Artificial Intelligence 41 (2015), 139-150)(“the Prytz reference”). Predicting future maintenance needs can therefore be achieved by predicting a residual useful life (or “RUL”). Transport vehicles (including heavy-duty vehicles) operate in diverse and often harsh environments. The possibilities for continuous monitoring are therefore limited.Thus, the disclosed invention estimates the remaining service life (RUL) of an identified tire that is mounted on an identified vehicle (e.g., an identified transport truck). The estimation of the residual service life involves a means of estimating the remaining mileage of a tire in a transport environment. This estimation performed by the invention is based on a machine learning algorithm that is fed solely by telematics information: no tread depth measurement is necessary. The mileage estimation is therefore used to provide actionable recommendations regarding the condition of the tire, including the date of its dismounting.

[0016] Summary of the invention

[0017] The invention relates to an estimation method implemented by a computer system for estimating a residual life of an identified tire by aggregating influential parameters obtained from the identified tire and telematic information obtained from an identified vehicle having the identified tire mounted, characterized in that the method comprises the following steps: a step of carrying out a process of building a prediction model, comprising the following steps: a step of introducing, into the system, influential parameters of the identified tire comprising data obtained by one or more communication devices of the system and transmitted to a server of the system and telematic information obtained from the identified vehicle;and a step of grouping, by one or more processors of the system, the obtained influential parameters and the obtained telematic information in order to assemble a plurality of independent journeys to develop at least one residual life profile of the identified tire; a step of training the prediction model which uses a grouped output to develop a plurality of predictions corresponding to journeys made by the identified vehicle having the identified tire mounted and to estimate the number of journeys to be made by this identified tire, in which a supervised learning model is used to predict the residual life corresponding to a prediction mileage of dismounting of the identified tire;a step of forecasting, by the processor(s), the number of journeys made in real time, in which the forecast comprises a forecast of a type of journey based on the telematic information comprising historical geographical coordinates; and a comparison step during which the residual service life before reaching the predicted mileage for dismantling the identified tire, resulting from the forecast model, is compared to a defined dismantling threshold value corresponding to a mileage planned for carrying out the dismantling of the identified tire; so that the system (100) creates a maintenance plan for the identified tire.;

[0018] In some embodiments of the method of the invention, the method further comprises a step of recording the influential parameters of the identified tire and the telematics information in a database of the system, in which the influential parameters of the identified tire comprise: the historical information of the identified tire comprising data corresponding to the historical journeys of the identified vehicle having mounted the identified tire; and the general information of the identified tire comprising data corresponding to the identified tire, including its mounting position on the identified vehicle.

[0019] In some embodiments of the method of the invention, the maintenance plan created by the system during the comparison step comprises: a maintenance project in which the identified tire remains mounted on the identified vehicle where the number of kilometers resulting from the prediction model is greater than the disassembly threshold value defined for the identified tire; and an inspection project in which the identified tire is inspected where the number of kilometers resulting from the prediction model is equal to or less than the disassembly threshold value defined for the identified tire.

[0020] In certain embodiments of the method of the invention, the introduction step further comprises a step of introducing into the system the telematic data originating from one or more communication networks.

[0021] In some embodiments of the method of the invention, the introducing step further comprises a step of introducing into the system the telematics data from a locating means of the system mounted on or in the identified vehicle, this introducing step comprising: introducing into the system the telematics information which corresponds to each visit of the identified vehicle to an intended location; and introducing into the system the data obtained by the locating means which corresponds to each journey of the identified vehicle.

[0022] In certain embodiments of the method of the invention, the locating means comprises one or more global positioning systems (GPS).

[0023] In some embodiments of the method of the invention, the method further comprises a step of identifying a plurality of intended locations, wherein the identifying comprises identifying coordinates of each intended location using historical GPS data obtained from the influential parameters incorporating the telematics information.

[0024] In some embodiments of the method of the invention, the identified vehicle comprises a truck with a cab at the front and a chassis in the form of a flat body for connecting a transport container.

[0025] In certain embodiments of the method of the invention, the historical information and / or the general information are generated and / or managed, at least in part, by one or more industrial vehicle managers, including one or more fleet companies to which the identified vehicle belongs and / or one or more producers including mining producers.

[0026] In some embodiments of the method of the invention, the introduction step comprises a step of creating a baseline of tires intended for mounting on the identified vehicle. In some embodiments of the method of the invention, the supervised learning model that is used to predict the residual life during the training step comprises an ensemble learning method.

[0027] In some embodiments of the method of the invention, the ensemble learning method comprises using the Random Forest technique to distinguish a non-linear boundary between the maximum mileages of different tires.

[0028] The invention also relates to a computer system for implementing a method for estimating the residual life of an identified tire by aggregating influential parameters obtained from the identified tire and telematic information obtained from an identified vehicle having the identified tire mounted, the system characterized in that the system comprises: at least one memory configured to store an application for analyzing data representative of a grouping of a plurality of telematic data and the influential parameters in order to assemble a plurality of independent paths to develop at least one residual life profile of the identified tire;and one or more communication servers each comprising at least one or more processors operatively connected to the memory, the processor(s) comprising an execution module of the analysis application which carries out the grouping of the influential parameters and the telematic information, in which the processor(s) are capable of executing programmed instructions stored in the memory to: carry out a process of building a prediction model, comprising the following steps: a step of introducing, to the system, the influential parameters of the identified tire comprising data obtained by one or more communication devices of the system and transmitted to a server of the system and the telematic information obtained from the identified vehicle;and a step of grouping, by one or more processors of the system, the obtained influential parameters and the obtained telematics information in order to assemble a plurality of independent journeys to develop at least one residual life profile of the identified tire; training the prediction model which uses a grouped output to develop a plurality of predictions corresponding to journeys made by the identified vehicle having the identified tire mounted and to estimate the number of journeys to be made by this identified tire, in which a supervised learning model is used to predict the residual life corresponding to a predicted mileage of dismounting of the identified tire; predicting, by the one or more processors, the number of journeys made in real time, in which the prediction comprises a prediction of a type of journey based on the telematics information comprising historical geographic coordinates;and making a comparison, during which the remaining service life before reaching the predicted mileage for dismantling the identified tire, resulting from the prediction model, is compared to a defined dismantling threshold value corresponding to a mileage expected to carry out the dismantling of the identified tire; so that the system creates a maintenance plan for the identified tire.;

[0029] In some embodiments of the system of the invention, the system further comprises: a communication network that manages the incoming data to the system, the communication network comprising: at least one communication server with at least one processor that manages the data corresponding to the influential parameters of the identified tire; and one or more communication devices that obtain the data corresponding to the influential parameters of the identified tire and transmit them to the server; a database in which the influential parameters of the identified tire are recorded, this data comprising the telematics information corresponding to each visit of the identified vehicle to a planned location;and a database having recorded data aggregated therefrom to construct at least one residual service life (RUL) profile of the identified tire from a plurality of trips made by the identified vehicle having the identified tire mounted thereon. In some embodiments of the system of the invention, the data recorded in the database includes data obtained from a location means mounted on or in the identified vehicle, such data including: telematics information corresponding to each visit of the identified vehicle to an intended location; and data obtained by the global positioning system corresponding to each trip of the identified vehicle.;

[0030] In some embodiments of the system of the invention, the server is associated with one or more transport vehicle managers.

[0031] In some embodiments of the system of the invention, the influential parameters of the identified tire include: historical information of the identified tire including data corresponding to the historical journeys of the identified vehicle having mounted the identified tire; and general information of the identified tire, including its mounting position on the identified vehicle.

[0032] In some embodiments of the system of the invention, the supervised learning model that is used to predict the remaining life during training of the prediction model comprises an ensemble learning method. In some embodiments of the system of the invention, the ensemble learning method comprises the use of the Random Forest technique to distinguish a non-linear boundary between the maximum mileages of different tires.

[0033] Other aspects of the invention will become apparent from the following detailed description.

[0034] Brief description of the drawings

[0035] The nature and various advantages of the invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which like reference numerals designate like parts throughout, and in which:

[0036] [Fig 1] Figure 1 represents a schematic view of an embodiment of a system of the invention which implements a method for estimating a residual life of an identified tire.

[0037] [Fig 2] Figure 2 represents a flow diagram of an embodiment of an estimation method of the invention carried out by the system of Figure 1.

[0038] [Fig 3] Figure 3 represents an exemplary grouping of data accumulated at a predicted point representing multiple journeys made by an identified tire between consecutive visits.

[0039] [Fig 4] Figure 4 represents an exemplary analysis of total mileage data of dismounted tires that are used to train an algorithm based on the Ensemble Learning technique.

[0040] Detailed description

[0041] The solution proposed by the invention aims to carry out an estimation of a residual service life (or "remaining useful life" in English, or "RUL") of an identified tire mounted on an associated transport vehicle (or "vehicle"). This estimation is carried out using only the historical information of the journeys made by the vehicle (including, for example, in cases where the vehicle comprises a truck or a bus, data corresponding to the data obtained by means of a GPS global positioning system) (or "GPS system") that the tire concerned has made as well as general information on this tire (including, for example, data corresponding to its retreading rank, its mounting position on the vehicle, the theoretical average number of journeys at this position and the performance of similar tires).This data, containing a significant part of the use of the identified tire, will be used in a machine learning model to predict the remaining mileage of the identified tire based on the parameters influencing its lifespan.

[0042] The constitution of a tire is typically described by a representation of its constituents in a meridian plane, that is, a plane containing the tire's axis of rotation. The radial, axial, and circumferential directions respectively designate the directions perpendicular to the tire's axis of rotation, parallel to the tire's axis of rotation, and perpendicular to any meridian plane. The expressions "radially," "axially," and "circumferentially" respectively mean "in a radial direction," "in the axial direction," and "in a circumferential direction" of the tire. The expressions "radially inner" and "radially outer" respectively mean "closer, respectively further, from the tire's axis of rotation, in a radial direction.

[0043] Referring now to the figures, in which like numbers identify like elements, Figure 1 shows an embodiment of a computer system (or "system") 100 for implementing a residual life estimation method RUL (or "RUL estimation method" or "method") 200 of the invention (see Figure 2). The RUL estimation method 200 of the invention takes advantage of methods and tools based on artificial intelligence (or "AI") to associate with each identified tire a RUL prediction obtained from the data recorded during the travel cycles performed by the associated vehicle. Thus, the system 100 allows continuous improvement across all tires fitted in a fleet of vehicles, ensuring that the system 100 improves from the experience it acquires from each tire and each associated vehicle.

[0044] The estimation method 200 is based on the use of an identified tire, being defined via a residual service life (RUL) profile of the identified tire coming out of a prediction model based on a machine learning model. It is understood that its state of use and its corresponding mileage are represented by a set of values. These values ​​represent the gap that exists between the new state (represented by a tire that has never been mounted on the identified vehicle) and the worn state (represented by a tire removed from use due to reaching the disassembly threshold by wear corresponding to a mileage planned to carry out the disassembly of the tire). The state of use can take the form of a unit value such as the remaining mileage as a function of the height of the tread (without the need for measuring the depth of the tread).

[0045] As used herein, the “residual service life” (or “RUL”) of an identified tire refers to its mileage potential based on the values ​​of the parameters influencing its longevity. The residual service life may be determined continuously or at regular, predefined, or sporadic intervals by collecting data corresponding to the parameters influencing the service life of the identified tire. Based on the collected data, the RUL estimation method 200 of the invention may define a current use state of the identified tire to determine its residual service life.

[0046] Referring again to Figure 1, the system 100 includes a communications network (or "network") 102 that manages incoming data to the system 100 from various sources. The communications network 102 incorporates at least one communications server (or "server") 102a with at least one processor that manages data corresponding to the historical information 104 and the general information 106 regarding an identified tire. The term "identified tire" (in the singular or plural) is used herein to refer to a particular tire present in the physical environment of the system 100 and which is mounted on an identified vehicle (being a tire still in service on the identified vehicle) (see, for example, the identified tire Pio mounted on the transport truck 10 in Figure 1).

[0047] The term "historical information" (in the singular or plural) is used herein to refer to data corresponding to the historical journeys of the identified vehicle having mounted the identified tire. This data may include, without limitation, data corresponding to the times and / or dates of departure and / or arrival of the identified vehicle at a predetermined location (e.g., a vehicle distribution base, a truck fleet, a warehouse, etc.), the journeys made by the vehicle (this data being likely to be collected from various sources, including data of the historical journeys attributed to a truck 10 and / or to one or other vehicles belonging to a fleet of transport vehicles) (see Figure 1), data on the identified vehicle (including, without limitation, the manufacturer, the model of the identified vehicle and its version, its identification code, etc.) and environmental, weather and / or climatic conditions during historical journeys.

[0048] The historical information is obtained from the telematics information (being telematics data) of the identified vehicle (obtained, for example, by one or more sensors installed on the identified vehicle, from one or more CAN communication networks, by means of a GPS global positioning system, and / or by their combination and their known equivalents). The telematics information of the identified vehicle includes, without limitation, the following data elements:

[0049] Information from the identified vehicle's engine speed (e.g., transmission setting such as park, drive, neutral, throttle position, coolant temperature, intake air temperature, barometric pressure, vehicle speed, manifold absolute pressure, oxygen sensor, coolant sensor);

[0050] Information from electrical sensors (e.g., visual / audio systems, brake lights, turn signals, headlights, hazard lights, reverse lights, parking lights, windshield wipers, doors locked, key in ignition / door lock, dump body operated, battery voltage, fuel level, mileage, occupant weight, cargo weight);

[0051] Information from the state of the identified vehicle (e.g., the speed of the identified vehicle, its location, the distance traveled, the relative distance of the identified vehicle to other vehicles and / or other objects);

[0052] Calculated information (e.g., acceleration and / or deceleration of the identified vehicle, its lateral acceleration, loss of pressure in its tires); and

[0053] Driver identification (e.g., by voice recognition, code, biometrics, retina, etc.).

[0054] The term “general information” (in the singular or plural) is used herein to refer to data corresponding to the identified tire. This data may include, without limitation, its size (which may be represented by the tire type and / or its nomenclature), its construction code (e.g., “R” for radial), its production source (e.g., the name and / or brand of the producer of the identified tire, its date of manufacture and its place of manufacture, distribution and / or storage), its unique identification number (or “serial number”), its load index (or “load index”), its speed symbol (e.g., “A5” which represents 25 km / h), and / or its expected mileage.For example, for a tire of size 250 / 70 R 15, the number "250" represents the nominal section width of the tire in millimeters, the number "70" represents the height / aspect ratio of the tire, the letter "R" represents a radial tire, and the number "15" represents the rim diameter in inches.

[0055] General information may also include the mounting position (including mounting history) of the identified tire. It is understood that the mounting position of an identified tire Pio shown in Figure 1 is given as an example.

[0056] General information may also include the retread rank (if applicable) of the identified tire. The term "retread" is used here to refer to the method of restoring a worn tire to serviceable condition by renewing the tread compound and ply(ies). During the retreading process, old tread materials are removed and replaced with new materials. Retreading is a method that is known and regulated in the industry. Data corresponding to the retread rank of an identified tire is managed by an identified vehicle manager and / or the identified tire producer.

[0057] The server 102a may be associated with one or more transport vehicle managers 108, including, without limitation, one or more fleet companies to which the identified vehicle(s) belong. In one embodiment of the invention, the historical information and / or the general information may be generated and / or managed, at least in part, by one or more sites served by one or more vehicles having the identified tires mounted (or by one or more networks of sites of which a specific site is a part). In this embodiment, the system 100 may simulate destination sites for the identified tire from the historical information. It is understood that the managers 108 manage their vehicles in different ways, and their management methods are therefore subject to modification.Thus, in this embodiment, the general information can also be used to specify the link between the historical information and the properties of the identified tire by making a personalized simulation in relation to the destination sites (for example, by Monte Carlo means).

[0058] The server 102a may include (or may access) a database 110 of the system 100, data in which incorporating the historical information 104 and the general information 106 are recorded to construct the residual service life (RUL) profile of the identified tire. This data includes telematics information corresponding to each visit of the identified vehicle to a planned location (e.g., each visit of the truck 10 to a loading site of a transport container) (see FIG. 1). The data for each visit includes information specifying the condition of the identified tire at the time of the visit, this information including, without limitation, the date of the visit, the mileage of the identified tire on that date, the type of the vehicle concerned, damage noted during the visit, etc.It is understood that a measurement of the depth of the sculpture may be part of this data to serve as a validation basis, but this measurement is not critical to the performance of the method 200.

[0059] The data recorded in the database 110 may include data obtained by a locating means mounted on the identified vehicle (for example, in the cab 10a of the truck 10). This data includes data that corresponds to each journey of the identified vehicle (for example, a journey made by the truck 10 between the loading site of the transport container and the filling site of the container). It is understood that a journey could include a one-way journey, one or more round-trip journeys, and variable journeys.

[0060] Furthermore, the server 102a may comprise (or may access) a database 112 of which data recorded in the database 110, including the telematics information, are grouped to develop at least one residual life (RUL) profile of the identified tire. To construct the residual life (RUL) profile, this grouping, which is carried out by the processor(s) of the server 102a, assembles a plurality of independent journeys. Thus, the database 112 incorporates the obtained telematics information together with the general information, which allows the manager 108 of the identified vehicle to understand the (RUL) profile of an identified tire and to estimate the remaining mileage of this tire in order to predict a maintenance plan.

[0061] The communication network 102 of the system 100 comprises one or more communication devices (or "devices") 114 that capture and transmit the obtained data to the server 102a. The one or more communication devices 114 comprise one or more portable devices such as a mobile network device 14a (e.g., a mobile phone, a laptop computer, one or more portable devices connected to the network, including devices of the "augmented reality" and / or "virtual reality" type, and / or any combinations and / or any equivalents). In any case, the communication devices may comprise clothing and / or portable devices connected to the network, and worn by one or more operators (where each operator is a human, a device known as a robot and / or an autonomous vehicle).For example, a monitoring device worn by a driver of the vehicle may monitor by video the conditions of a journey and send the corresponding data (being the historical information of the identified tire) to the server 102a of the communication network 102.

[0062] The communication device(s) 114 may also include one or more remote computers 114b capable of transferring data via the communication network 102. For example, a portable device 114a of the system 100 may transmit the historical information and / or the general information of the identified tire Pio to a remote computer 114b of the system 100. Based on the transmitted data, the remote computer 114b may transmit to the portable device 114a the maintenance record of the identified tire, indicating a planned maintenance project (for example, an advice to remove the identified tire from service because a prediction model of the invention indicates a remaining mileage too low to complete the upcoming cycle).

[0063] In embodiments of the system 100, the communication device(s) 114 of the communication network 102 may also include one or more capture devices 114c that capture and transmit data from the identified tire to the server 102a. The communication device(s) 114c could be disposed in or on the identified vehicle (e.g., in a cab 10a of a truck 10) (see FIG. 1). In embodiments, the capture device(s) 114c could include an imaging system (not shown) for capturing images and transmitting the corresponding data to the server 102a.The imaging system may include at least one camera that captures images of objects that enter the field of view of the at least one camera during at least one journey made by the identified vehicle (e.g., images of the ground, images of debris along the journey, images of the condition of the journey, images of the identified tire in use, etc.). These objects could be captured in the images together with influencing parameters. The corresponding data is recorded in the database 110 of the system 100 to facilitate the construction of a prediction model of the estimation method 200. This data is updated on a continuous basis or on an intermittent basis.

[0064] In embodiments of the system 100 where a manager 108 of the identified vehicle generates and / or manages the historical information and / or the general information, this data (including data corresponding to one or more images) may be generated, at least in part, by one or more communication devices 114.

[0065] The communications network 102 may include wired or wireless connections and may implement any data transfer protocol known to those skilled in the art. Examples of wireless connections may include, but are not limited to, radio frequency (RF), satellite, cellular telephones (analog or digital), Bluetooth®, Wi-Fi, infrared, ZigBee, local area network (LAN), wireless local area network (WLAN), wide area network (WAN), near field communication (NFC), other wireless communication configurations and standards, their equivalents, and a combination thereof.

[0066] It is understood that the communication network 102 (including the server 102a) involves the use of one or more processors as understood by those skilled in the art. The term "processor" (or, alternatively, the term "programmable logic circuit") (in the singular or plural) refers to one or more devices capable of processing and analyzing data and including one or more software programs for their processing (e.g., one or more integrated circuits known to those skilled in the art as being included in a computer, one or more controllers, one or more microcontrollers, one or more microcomputers, one or more programmable logic controllers (or "PLCs"), one or more application-specific integrated circuits, one or more neural networks, and / or one or more other known equivalent programmable circuits).The processor includes software for processing data captured by the elements associated with the system 100 (and the corresponding data obtained) as well as software for identifying and locating variances and identifying their sources to correct them.

[0067] The invention takes advantage of methods and tools based on artificial intelligence (or "AI") to complete partial information provided (based on the historical information and the general information obtained). A machine learning analysis is performed using a machine learning model such as an ensemble learning model. The machine learning analysis receives as input the processed data from the communication network 102, being the historical information 104 and the general information 106 of the identified tire. In embodiments, data is obtained as inputs in different layers of the machine learning model.The algorithm enables continuous improvement across all tires fitted to a fleet of vehicles, ensuring that the system 100 improves from the experience it acquires, particularly on the choice between a maintenance project and an inspection project for the identified tire.

[0068] Referring further to Figures 1 and 2, embodiments are disclosed of a RUL estimation method (or "estimation method" or "method") 200 of the invention performed by the system 100. In each embodiment, the estimation method 200 is computer-implemented (e.g., by the server 102a) so that the system 100 can build the prediction model that estimates the remaining mileage of identified tires based on its predetermined removal mileage (referred to as "demount prediction mileage").

[0069] As used herein, the term “method” or “process” may include one or more steps performed by at least one computer system having one or more processors to execute instructions that perform the steps. Any sequence of steps is given by way of example and does not limit the described methods to any particular sequence. In the following description, an identified vehicle that is associated with the identified tire is represented by a truck 10. The truck 10 includes a cab 10a at the front and a chassis 10b in the form of a flat body for connecting a known transport container (not shown). The truck 10 is of the type well known in the trade for transporting products over distances.However, it is understood that the truck 10 is given as an example and that the system 100 could be implemented with other types of vehicles having one or more identified tires fitted (for example, any vehicle that makes repetitive journeys).

[0070] The identified vehicle (e.g., truck 10) includes an acceleration sensing means, which could be a known accelerometer (not shown). The accelerometer may be a triaxial accelerometer that is capable of measuring acceleration in all three spatial dimensions. Accelerometers may be micro-electromechanical sensors ("MEMS") that are widely available at a low cost and remain reliable in operation. In operation, the accelerometer senses vertical and horizontal shocks experienced by the identified vehicle on which it is mounted (e.g., vertical and horizontal accelerations produced when the vehicle travels on uphill or downhill surfaces, as well as on rough surfaces). The accelerometer may be mounted anywhere on or in the identified vehicle so that it can sense the gaits and stops of the identified vehicle.For example, the accelerometer may be located under or in the cab 10a or on the chassis 10b of the truck 10 of FIG. 1. It is understood that the acceleration data may also be obtained from various other sources, including, without limitation, from one or more CAN communication networks, by means of a GPS global positioning system, and / or by their combination and known equivalents.

[0071] By starting the estimation method 200 shown in Figure 2, the method comprises a step of carrying out a process of building a prediction model (or "model"). To carry out the construction of the prediction model, the process comprises a step of introducing 202, to the system (100), influential parameters of the identified tire and the telematic information obtained from the identified vehicle. During this step, influential parameters of the identified tire include data obtained by the communication devices 114 of the system 100 and transmitted to the 102a. The influential parameters obtained include data corresponding to the historical information 104 and the general information 106 of the identified vehicle.To train the prediction model, this data is stored (for example, in a database 110 of the system 100) (see FIG. 1), and it is updated during the duration of the process (either on a continuous basis or on an intermittent basis).

[0072] In one embodiment of the estimation method 200 of the invention, the prediction model can predict a number of residual journeys of the identified tire (corresponding to its remaining mileage) to determine its residual life. In this embodiment, the introduction step 202 comprises the introduction into the database 110 of the telematic information which corresponds to each visit of the identified vehicle.

[0073] In this embodiment of the method 200, the input step 202 also comprises inputting into the database 110 the data obtained from a locating means of the system mounted on or in the identified vehicle (e.g., on or in the transport truck 10). The locating means may detect the position of the identified vehicle by various techniques known in the art, including by means of a GPS global positioning system, an inertial navigation system and / or other equivalent locating means. The data obtained by the locating means and recorded in the database 110 includes data that corresponds to each trip of the identified vehicle (e.g., a trip made by the transport truck 10 between the loading site 20 and the unloading site 30). It is understood that a trip could include a one-way trip, one or more round-trip trips or one or more variable trips.The data obtained by the location means can be transmitted by the network 102 to the server 102a in order to consolidate and process this data. It is understood that the data obtained could also be recorded in the database 112 to determine an operating state of the identified vehicle.

[0074] In this embodiment, the introducing step 202 further comprises a step of identifying a plurality of intended locations (e.g., one or more vehicle management bases and / or one or more destinations frequented by the identified vehicle). The identification comprises identifying coordinates of each intended location using historical GPS data obtained from the influential parameters incorporating the telematics information.

[0075] In all embodiments of the method 200 of the invention, the introduction step 202 may comprise an optional step of creating a learning base (or "base") which is introduced into the prediction model. The created learning base may comprise a reference base of the tires intended for mounting on the identified vehicle. The base may comprise an already created reference (for example, a table of residual life spans of the tires at several kilometers achieved before their disassembly). The base may include parameters corresponding to a plurality of commercially available tires (including parameters which are part of the general information discussed above). The created base may include images of the worn tires at known usage states and corresponding numbers of trips made.The learning base may therefore include expected images (and therefore data) corresponding to the profiles (3D, 2D, 1D) of the used tires and the associated vehicle journeys (including the number of kilometers covered during these journeys). The specific source is not essential for the method described here.

[0076] The system 100 implements the method 200 of the invention to assist a manager 108 of the identified vehicle in transmitting information about the progress of the use of the identified tire (and the corresponding mileage) to personnel at a site served by the manager, such as during an inspection of the identified vehicle (e.g., an inspection of the transport truck 10). The system 100 implements the estimation method 200 to prepare customized reminders to be sent to an operator of the identified vehicle (either the manager or a site served by the manager) to schedule maintenance of the identified tires. As used herein, “operator” (or “user” or “participant” or “subject”) refers to a single operator or a group of operators of one or more identified vehicles.An operator includes, but is not limited to, an individual operator of an identified vehicle (e.g., a driver of the truck 10), an individual member of a team or group that manages a particular vehicle, a digital community associated with the management of a particular activity in which an identified vehicle participates, and combinations and equivalents thereof. The operator may be a spectator who attends a ride and / or tour, either physically or digitally (e.g., by remotely managing the identified vehicle). As used herein, “operator” may also refer to one or more others who analyze data collected in another environment having conditions similar to the environment of the identified vehicle (e.g., a site having comparable operating conditions to the conditions to which the truck 10 is subjected).As used herein, “operator”) may also refer to any electronic system or device configured to receive command input and configured to automatically send data to at least one other operator.

[0077] Referring again to Figures 1 and 2, the estimation method 200 of the invention further comprises a step 204 of grouping, by one or more processors of the server 102a of the system 100, the influential parameters obtained and the telematic information obtained in order to assemble a plurality of independent journeys to develop at least one residual life (RUL) profile of the identified tire. By way of example, a GPS-type location means mounted on the identified vehicle provides the data necessary to obtain a correlation between the data recorded in the database 110 and the data recorded in the database 112. Referring to the example of Figure 3, the data, obtained and accumulated at a planned point (represented at the points “Travel D”, “Travel G” and “Travel H”), represents several journeys made by an identified tire between two consecutive visits.

[0078] Referring again to Figures 1 and 2, the estimation method 200 of the invention further comprises a step 206 of training the prediction model to predict the residual service life RUL corresponding to the achievement of the disassembly prediction mileage of the identified tire. As used herein, the “disassembly prediction mileage” of an identified tire refers to a maximum expected distance (for example, the number of kilometers remaining) before replacing it. During this step, a machine learning method takes as input the obtained influential parameters (being the historical information 104 and the general information 106 of the identified tire) as well as the data of the created learning base.After the system 100 has obtained the historical information and general information corresponding to the identified tire, the processor can retrieve the known residual lives which correspond to the number of trips made by the identified tire to build the prediction model.

[0079] In an embodiment where the training step 206 comprises a supervised learning method, the supervised learning method employed comprises a supervised learning method of the Ensemble Learning type (as discussed above). In an embodiment employing a supervised learning method of the Ensemble Learning type, the Ensemble Learning method employed could include, without limitation, one or more techniques of the bagging type (including Bagged Decision Trees and Random Forest), Boosting and / or Stacking.

[0080] In one embodiment employing a supervised ensemble learning method, the Random Forest technique is used to distinguish a nonlinear boundary between the maximum mileages of different tires. The Random Forest technique involves a collection of independent decision trees in which each tree receives the same input sample and classifies it by propagating it down the tree, from the root node to a leaf node. By presenting an initial untrained decision tree with many input and output correspondences, the parameters of its internal splitting functions will gradually evolve and produce similar input and output correspondences. This learning process is made possible by defining an information gain criterion. Parameters that produce maximum information gain may be rewarded (NOI Random Forest, Leo Breiman, UC Berkeley (January 2001)).

[0081] Random Forest is a machine learning model capable of distinguishing between observations based on the characteristics of a travel level. Referring to Figure 4, the data corresponding to a mileage of a dismounted tire is represented for three (3) managers A, B, C according to the applicable axle (rear axle, trailer axle, steering axle). Since most of the trips are made by the vehicle identified for each manager, there is a dense group of observations in a predefined area and the other observations that are randomly selected from other vehicles incorporating a tire of the type mounted on the identified vehicle. The Random Forest technique is therefore used as a regression algorithm for predictive maintenance of these vehicles, proposed as a machine learning-based approach to perform predictive maintenance.

[0082] In one embodiment of the method that uses data obtained by a GPS-type location means, the trip-level features take the geolocation data and construct variables such as average speed, maximum speed, standard deviation of speed, acceleration, etc. These trip features are fed into the prediction model to learn the signature of the identified tire and the operator's usage. In this embodiment, the estimation algorithm employed requires GPS data at at least 1 Hz to obtain information on time, speed, position, and altitude. From these values, other features such as lateral acceleration, longitudinal acceleration, road slope, and total distance can be calculated. All these features (GPS and calculated) along with their compiled statistics (mean, median, standard deviation, etc.) are used to feed the prediction model to perform its training and validation. The total distance traveled by the identified tire is also required to train and validate the prediction model, and this is what the deployed model predicts.

[0083] With this estimation algorithm, physical measurements are not necessary to estimate the dismounting of an identified tire due to its complete wear at the end of its life. It is understood that it is possible to use the tread depth associated with each mileage to improve the accuracy of the algorithm used, if the data are available (but this element is not essential to the realization of the method 200). Thus, the algorithm used does not involve dismounting an identified tire due to aggression, irregular wear (for example, of the central, shoulder and / or diagonal type), accidents or the robustness of the carcass. Furthermore, the algorithm used does not involve a change in the position of the identified tire (for example, a change between axles and within the same axle after rolling).

[0084] The system 100, by performing the method 200, is therefore capable of automatically identifying recurring stops from GPS tracks, satellite images or a combination thereof. The data comes from a large set of actual vehicles in normal operation with different operators.

[0085] The estimation method 200 of the invention further comprises a step 208 of forecasting, by the processor(s) of the server 102, the residual service life (RUL) of the identified tire before reaching its removal prediction mileage. In one embodiment of the method 200, this step comprises a step of forecasting the number of journeys made in real time (corresponding to the number of remaining kilometers), in which the forecast comprises a forecast of a type of journey based on telematics information comprising historical geographic coordinates. In this embodiment, an Ensemble Learning machine learning model is used to predict the residual service life (RUL) corresponding to the removal mileage of the identified tire.The RUL of the identified tires (determined, for example, in terms of number of kilometers or other equivalent terms) can be calculated from the data corresponding to the influential parameters of future cycles. It is understood that the data of future cycles can be hypothetical or real depending on the type of management applied by the manager 108 in his fleet of vehicles and his ability to know at a given time the routes to which a vehicle will be assigned.

[0086] In one embodiment, a difference between the true residual service life and the predicted number of kilometers is denoted by a calculated error. The calculated errors can be entered into the learning base (described above) to improve the predictive capacity of the prediction model. The estimation method 200 of the invention further comprises a comparison step 210 during which the service life RUL before reaching the predicted mileage for disassembly of the identified tire, resulting from the prediction model, is compared to a value of the disassembly threshold. This disassembly threshold is to be defined (for example, by the manager 108 and / or by the manufacturer of the identified tire) to organize the maintenance operations.The maintenance operations for the identified tyre include a choice between a 212 maintenance project (where the identified tyre remains fitted to the identified vehicle) and a 214 inspection project (where the identified tyre is inspected to ensure its removal at the right time, either for retreading or for introduction into the end-of-life circuit, including recycling).

[0087] During the comparison step 210, the system 100 indicates a maintenance project 212 of the identified tire if the number of kilometers resulting from the prediction model is greater than the disassembly threshold value defined for the identified tire. In a similar manner, the system 100 indicates an inspection project 214 of the identified tire if the number of kilometers resulting from the prediction model is equal to or less than this disassembly threshold value. In the case where the identified tire has reached, or is close to reaching, the disassembly threshold, the system 100 takes into account the residual service lives (RULs) of the used tires obtained (obtained, for example, from RUL duration readings taken on tires received in a factory after use).In the event that the identified tire is to be replaced by another tire of the same type, the forecast model is updated when the worn tire is replaced by an identified tire with a tread considered new (see number 216 of Figure 2). It is understood that the replacement of the identified tire may include either retreading or recycling (or other end-of-life treatment) of the identified tire.

[0088] In all embodiments of the estimation method 200 of the invention, one or more steps may be performed iteratively.

[0089] In all embodiments of the estimation method 200 of the invention, each method, in whole or in part, may be controlled over a network (e.g., network 102) via one or more network-connected devices (e.g., devices 114 of system 100), examples of which include, but are not limited to, one or more mobile phones, one or more laptops and / or tablets, or one or more other network-connected devices (including devices with augmented reality, virtual reality, and mixed reality capabilities), one or more network-connected and wearable articles of clothing and / or jewelry (including watches), and combinations and equivalents thereof).Any wearable network-connected device can be used to record and save all data corresponding to a cycle of an identified vehicle (e.g., transport truck 10). This data can be used in a monitoring mode by the same network-connected device or by any other network-connected device. Residual Life (RUL) estimation is advantageous because tire failure remains a more common issue than tire wear estimation. Therefore, there is a lot of data and resources available to the public. Furthermore, the advantage of a model without tread depth measurement is to reduce physical intervention, which has a positive impact on uptime and operational cost.The invention therefore offers the prediction of a longevity (and therefore the forecast of the maintenance plan) of an identified tire using easily accessible data, allowing the creation of a reliable forecasting model. Even if human specialists are flexible in making decisions regarding the maintenance project or the inspection project of a tire, they are not capable of analyzing large amounts of data necessary for real-time decision-making to determine a maintenance operation for the identified vehicle.

[0090] In all embodiments of the system 100, the system 100 evolves over time to improve the algorithm and, as a result, optimize the service life of the tires fitted to the transport vehicles. For example, truck 10 may be different from truck 10' with respect to the producer of each truck, the model concerned, the year of production, etc. Trucks 10 and 10' may be of the same make, model, weight, engine type, age, etc. In both scenarios, truck 10' therefore "learns" from truck 10. Truck 10' may, in addition to receiving the recommended operational setting values ​​(corresponding to the fitted tires), monitor its performance based on the received data.

[0091] Thus, as the amount of data received increases with the number of vehicles monitoring and transmitting their performance, the system 100 provides more relevant information for an identified vehicle. The system 100 may include pre-programming of the management information. For example, a setting of the estimation method 200 may be associated with the parameters of the typical physical environments in which the system 100 operates (e.g., the parameters of the locations served by an identified vehicle). In embodiments, the system 100 (and / or a facility incorporating the system 100) may receive voice commands or other audio data representing, for example, a start or stop of capturing data corresponding to the historical information and / or the general information of the identified tires, or a start or stop of movement of the communication device.A request to the system 100 may include a request for the current status of a cycle performed by an identified vehicle. A generated response may be represented audibly, visually, tactilely (e.g., using a haptic interface), and / or virtually and / or augmented. This response, together with corresponding data, may be recorded in the neural network.

[0092] For all embodiments of the system 100, a monitoring system could be implemented. At least a portion of the monitoring system may be provided in a portable device such as a mobile network device (e.g., a mobile phone, a laptop, one or more network-connected wearable devices (including “augmented reality” and / or “virtual reality” devices, network-connected wearables, and / or any combinations and / or equivalents). It is envisaged that detection and comparison steps may be performed iteratively.

[0093] The terms "at least one" and "one or more" are used interchangeably. Ranges that are presented as "between a and b" encompass the values ​​"a" and "b".

[0094] Although particular embodiments of the disclosed apparatus have been illustrated and described, it will be understood that various changes, additions, and modifications may be practiced without departing from the spirit and scope of the present disclosure. Accordingly, no limitations should be imposed on the scope of the disclosed invention except those set forth in the appended claims.

Claims

Demands 1. Estimation method (200) implemented by a computer system (100) to estimate a remaining life (RUL) of an identified tire by aggregating influential parameters obtained from the identified tire and telematics information obtained from an identified vehicle having the identified tire mounted, characterized in that the method comprises the following steps: a step of carrying out a process of constructing a prediction model, comprising the following steps: a step of introducing (202), into the system (100), the influential parameters of the identified tire comprising data obtained by one or more communication devices (114) of the system (100) and transmitted to a server (102a) of the system and telematics information obtained from the identified vehicle;and a grouping step (204), by one or more processors of the system (100), of the influential parameters obtained and the telematic information obtained in order to assemble a plurality of independent journeys to develop at least one residual life profile (RUL) of the identified tire; a training step (206) of the prediction model which uses a grouped output to develop a plurality of predictions corresponding to journeys made by the identified vehicle having the identified tire mounted and to estimate the number of journeys to be made by this identified tire, in which a supervised learning model is used to predict the residual life (RUL) corresponding to a predicted mileage of removal of the identified tire;a forecasting step (208), by the processor(s), of the number of trips made in real time, in which the forecast includes a forecast of a type of trip based on telematics information including historical geographic coordinates; and a comparison step (210) during which the remaining life (RUL) before reaching the predicted mileage for removing the identified tire, as determined by the forecasting model, is compared to a defined removal threshold value corresponding to a predicted mileage for removing the identified tire; so that the system (100) creates a maintenance plan for the identified tire.

2. A method (200) of claim 1, further comprising a step of recording the influential parameters of the identified tire and telematics information in a database (110) of the system (100), wherein the influential parameters of the identified tire include: historical information (104) of the identified tire comprising data corresponding to the historical journeys of the identified vehicle that fitted the identified tire; and general information (106) of the identified tire comprising data corresponding to the identified tire, including its fitting position on the identified vehicle.

3. A method (200) of claim 1 or claim 2, wherein the maintenance plan created by the system (100) during the comparison step (210) comprises: a maintenance plan (212) in which the identified tire remains mounted on the identified vehicle where the number of kilometers resulting from the prediction model is greater than the removal threshold value defined for the identified tire; and an inspection plan (214) in which the identified tire is inspected where the number of kilometers resulting from the prediction model is equal to or less than the removal threshold value defined for the identified tire.

4. A method (200) of any one of the preceding claims, wherein the introduction step (202) further comprises an introduction step to the system (100) of telematics data from one or more communication networks (CAN).

5. A method (200) of any one of the preceding claims, wherein the introduction step (202) further comprises an introduction step to the system (100), the telematics data from a system location means (100) mounted on or in the identified vehicle, this introduction step comprising: the introduction to the system (100) of the telematics information corresponding to each visit of the identified vehicle to a planned location; and the introduction to the system (100) of the data obtained by the location means corresponding to each journey of the identified vehicle.

6. Method (200) of claim 5, wherein the localization means comprises one or more global positioning systems (GPS).

7. Method (200) of claim 6, further comprising a step of identifying a plurality of intended locations, wherein the identification includes the identification of coordinates of each intended location using historical GPS data obtained from influential parameters incorporating telematics information.

8. Method (200) of any one of claims 5 to 7, wherein the identified vehicle comprises a truck (10) with a cab (10a) at the front and a chassis (10b) in the form of a flat body for connecting a transport container.

9. Method (200) of any one of claims 2 to 8, wherein the historical information (104) and / or general information (106) are generated and / or managed, at least in part, by one or more managers (108) of industrial vehicles, including one or more fleet companies to which the identified vehicle belongs and / or one or more producers including mining producers.

10. Method (200) of any one of claims 1 to 9, wherein the introduction step (202) includes a step of creating a reference base of tires intended for mounting on the identified vehicle.

11. Method (200) of any one of claims 1 to 10, wherein the supervised learning model which is used to predict the residual life (RUL) during the training step (206) includes an Ensemble Learning method.

12. Method (200) of claim 11, wherein the learning method together comprises the use of the Random Forest technique to distinguish a non-linear boundary between the maximum mileages of different tires.

13. Computer system (100) for implementing a method for estimating (200) the remaining life (RUL) of an identified tire by aggregating influential parameters obtained from the identified tire and telematics information obtained from an identified vehicle having the identified tire mounted, the system (100) characterized in that the system comprises: at least one memory configured to store a data analysis application representative of a grouping of a plurality of telematics data and influential parameters in order to assemble a plurality of independent journeys to develop at least one remaining life (RUL) profile of the identified tire;and one or more communication servers (102a), each comprising at least one or more processors operationally connected to memory, the processor(s) comprising an analysis application execution module which performs the grouping of influential parameters and telematics information, in which the processor(s) are capable of executing programmed instructions stored in memory to: perform a predictive model construction process, comprising the following steps: an introduction step (202) to the system (100) of the influential parameters of the identified tire, including data obtained by one or more communication devices (114) of the system (100) and transmitted to a server (102a) of the system, and telematics information obtained from the identified vehicle;and a grouping step (204), by one or more processors of the system (100), of the influential parameters obtained and the telematics information obtained in order to assemble a plurality of independent journeys to develop at least one residual life profile (RUL) of the identified tire; train the forecasting model which uses a grouped output to develop a plurality of forecasts corresponding to journeys made by the identified vehicle having the identified tire mounted and to estimate the number of journeys to be made by this identified tire, in which a supervised learning model is used to predict the residual life (RUL) corresponding to a predicted mileage of removal of the identified tire; forecast, by the processor(s), the number of journeys made in real time, in which the forecast includes a forecast of a journey type based on the information; telematics including historical geographic coordinates; and make a comparison, during which the remaining life (RUL) before reaching the predicted mileage for dismantling the identified tire, derived from the prediction model, is compared to a defined dismantling threshold value corresponding to a predicted mileage for dismantling the identified tire; so that the system (100) creates a maintenance plan for the identified tire.

14. System (100) of claim 13, further comprising: a communication network (102) which manages the data entering the system (100), the communication network (102) comprising: at least one communication server (102a) with at least one processor which manages the data corresponding to the influential parameters of the identified tire; and one or more communication devices (114) which obtain the data corresponding to the influential parameters of the identified tire and transmit them to the server (102a); a database (110) in which the influential parameters of the identified tire are recorded, this data including telematics information corresponding to each visit of the identified vehicle to a designated location;and a database (112) from which recorded data are grouped to construct at least one residual life profile (RUL) of the identified tire from a plurality of journeys made by the identified vehicle having the identified tire fitted.

15. System (100) of claim 13 or claim 14, wherein the data recorded in the database (110) includes data obtained from a location means mounted on or in the identified vehicle, such data including: telematics information corresponding to each visit of the identified vehicle to a planned location; and data obtained by the global positioning system corresponding to each journey of the identified vehicle.

16. System (100) of any one of claims 13 to 15, in which the server (102a) is associated with one or more transport vehicle managers (108).

17. System (100) of any one of claims 13 to 16, wherein the influencing parameters of the identified tire include: historical information (104) of the identified tire comprising data corresponding to the historical journeys of the identified vehicle that fitted the identified tire; and general information (106) of the identified tire, including its fitting position on the identified vehicle.

18. System (100) of any one of claims 13 to 17, wherein the supervised learning model which is used to predict the residual life (RUL) during the training of the prediction model includes an Ensemble Learning method.

19. System (100) of claim 18, wherein the learning method together comprises the use of the Random Forest technique to distinguish a non-linear boundary between the maximum mileages of different tires.

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