A SYSTEM AND METHOD FOR MONITORING A RAILWAY NETWORK

Abstract

The present invention relates to a system for monitoring at least one railway network, the system comprising: at least one sensor component configured to measure sensor data relate to the at least one railway network, at least one processing component configured to (pre)process the sensor data, at least one analyzing component configured to analyze the sensor data, and at least one interface configured to access at least one server configured to be bidirectionally connected to the system, wherein the sensor data comprises at least one data recorded during an inspection activity. The invention also relates to a method for monitoring a railway network, the method comprising: recording at least one railway network related data, pre-processing the at least one railway network related data, and generating at least one pre-processed railway network related data, wherein the pre-processing comprises structuring the at least one railway network related data and wherein the at least one railway related network comprises at least one data recorded during at least one inspection activity and/or at least one data recorded during at least one failure correction activity.

Claims

1. A system for monitoring at least one railway network, the system comprising at least one sensor component configured to measure sensor data relate to the at least one railway network, at least one processing component configured to (pre)process the sensor data, at least one analyzing component configured to analyze the sensor data, and at least one interface configured to access at least one server configured to be bidirectionally connected to the system, wherein the sensor data comprises at least one data recorded during an inspection activity.

2. The system according to claim 1, wherein the system is configured to correct the sensor data, and generate a corrected railway network related dataset.

3. The system according to claim 1, wherein the system is configured to generate a text form dataset, unify at least one technical word from a plurality of dataset, analyze the at least one dataset, and develop at least one language-agnostic framework.

4. The system according to claim 3, wherein the system is configured to collect incoming maintenance data from at least one railway network, process the incoming maintenance data from the at least one railway network to generate at least one processed incoming maintenance dataset, and generate at least one developing model based on the at least one processed incoming maintenance dataset, wherein the at least one developing model comprises at least one develop model with a structured data.

5. The system to according to claim 4, wherein the system is configured to model the incoming maintenance data from the at least one railway network based on the at least one processed incoming maintenance dataset, and generate at least one hypothesis based on the at least one processed incoming maintenance dataset, wherein the at least one hypothesis comprises at least one of a maintenance recommendation and/or an action.

6. The system according to claim 1, wherein the system is configured to identify and analyze at least one of a number of effective inspections, a malfunction event, and a trigger of a maintenance action.

7. The system according to claim 1, wherein the system is configured to provide analyzed data of at least a first railway network, analyzed data of at least a second railway network, and compared data of the at least first railway network and the at least second railway network, wherein the at least first railway network is different from the at least second railway network, and wherein the system is configured to identify at least one data comprising similar parameters between the at least first railway network and the at least second railway network.

8. A method for monitoring a railway network, the method comprising recording at least one railway network related data, pre-processing the at least one railway network related data, and generating at least one pre-processed railway network related data, wherein the pre-processing comprises structuring the at least one railway network related data and wherein the at least one railway related network comprises at least one data recorded during at least one inspection activity and/or at least one data recorded during at least one failure correction activity.

9. The method according to claim 8, wherein the method comprises correcting the at least one railway network related data generating a corrected railway network related dataset, generating a text form dataset, unifying at least one technical word from a plurality of dataset, analyzing the at least one dataset, and developing at least one language-agnostic framework.

10. The method according to claim 8, wherein the method comprises collecting incoming maintenance data from at least one railway network, and processing the incoming maintenance data from the at least one railway network to generate at least one processed incoming maintenance dataset.

11. The method according to claim 10, wherein the method comprises generating at least one developing model based on the at least one processed incoming maintenance dataset, modelling the incoming maintenance data from the at least one railway network based on the at least one processed incoming maintenance dataset, and generating at least one hypothesis based on the modelling, wherein the at least one hypothesis comprises at least one of a maintenance recommendation and/or an action.

12. The method according to claim 11, wherein the at least one hypothesis comprises at least one of a system recommendation, a system question answering, an inspection results related to at least one particular asset, and clustering maintenance of activities to at least one particular group of events.

13. The method according to claim 8, wherein the method comprises generating at least one report comprising at least one of a frequency of occurred failures, a cause of track infrastructure deterioration, a type of track infrastructure deterioration, and a temporal evolution of failures comprising a defined time range.

14. The method according to claim 9, wherein developing the framework comprises building at least one ingestion pipeline, ingesting at least one data, generating at least one ingested dataset, processing the at least one ingested dataset, generating at least one processed ingested dataset, modeling data based on at least one of the at least one ingested dataset, and the at least one processed ingested dataset, storing the at least one processed ingested dataset in a database, and retrieving data from the at least one database.

15. The method according to claim 8, wherein the method comprises training at least one model to yield at least one pre-trained embedding, applying the at least one pre-trained embedding to the at least one processed ingested dataset, classifying the at least one processed ingested dataset into at least one category, predicting the at least one category for the at least one processed ingested dataset based on at least one event, and translating at least one category comprised by the at least one processed ingested dataset into at least one language, wherein the at least one language is selectively defined, and generating at least one translated dataset.

Description

[0327] The present invention will now be described with reference to the accompanying drawings which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.

[0328] FIG. 1 depicts a schematic representation of a railway network and system arranged at the railway network;

[0329] FIG. 2 depicts a system for monitoring a railway network according to embodiments of the present invention;

[0330] FIG. 3 depicts a schematic of a computing device according to embodiments of the present invention;

[0331] FIG. 4 depicts a schematic of phases according to embodiments of the present invention;

[0332] FIG. 5 depicts steps of a use case according to embodiments of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

[0333] In the following description, a series of features and/or steps are described. The skilled person will appreciate that unless explicitly required and/or unless requires by the context, the order of features and steps is not critical for the resulting configuration and its effect. Further, it will be apparent to the skilled person that irrespective of the order of features and steps, the presence or absence of time delay between steps can be present between some or all of the described steps.

[0334] It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings.

[0335] FIG. 1 depicts a schematic representation of a railway network and system arranged at the railway network. In simple terms, the system may comprise a railway section with the railway 1 itself, comprising rails 10 and sleepers 3. Instead of the sleepers 3 also a solid bed for the rails 10 can be provided.

[0336] Moreover, a further example of constitutional elements is conceptually represented a mast, conceptually identified by reference numeral 6. Such constitutional elements are usually arranged at or in the vicinity of railways. Furthermore, a tunnel is shown, conceptually identified by reference numeral 5. It should be understood that other constructions, buildings etc. may be present and also used for the present invention as described before and below.

[0337] For instance, a first sensor 2 can be arranged on one or more of the sleepers. The sensor 2 can be an acceleration sensor and/or any other kind of railway specific sensor. Examples have been mentioned before.

[0338] Further, a second sensor 9 can also arranged on another sleeper distant from the first sensor 2. Although it seems just a small distance in the present example, those distances can range from the distance to the neighboring sleeper to one or more kilometers. Other sensors can be used for attachment to the sleepers as well. The sensors can further be of different kind-such as where the first sensor 2 may be an acceleration sensor, the second sensor 9 can be a magnetic sensor or any other combination suitable for the specific need. The variety of sensors are enumerated before.

[0339] In one embodiment, any of the sensors, for example, the first sensor 2 and/or the second sensor 9, can directly be attached to the rail.

[0340] The sensors, for example the first sensor 2 and/or the second sensor 9, further comprise a wireless sensor network. The sensor node can transmit data to a base station (not shown here). The base station can be installed to the railway infrastructure. The base station can also be installed in the surroundings of the railway infrastructure. The base station can also be a remote base station. The communication module between the base station and the sensor node(s) can comprise, for example Xbee with a frequency of 868 MHz, but is not limited to this.

[0341] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can also be installed in cases and inserted inside the railway infrastructure, for example inside a special hole carved into the concrete. The case can also be attached to the railway infrastructure using fixers. The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can be obtaining sensor data based on acceleration, inclination, distance, etc.

[0342] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, may further be divided into group, for example based on the distance. The sensor node(s), for example the first sensor 2 and/or the second sensor 9 lying within a pre-determined distance may be controlled by one base station. The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can also be installed on the moving railway infrastructure such as on-board of a vehicle. The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can comprise an amplifier to amplify any signal received by the base station.

[0343] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can be installed such that the sensor node lying within one group can communicate with their base station in one-hop. The base station can receive information from its neighbors and retransmit all the information to the server 800.

[0344] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can comprise sensor(s). The sensor can be accelerometers, such as Sensor4PRI for example ADCL 345, SQ-SVS etc. The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can comprise inclinometers, such as SQ-SI-360DA, SCA100T-D2, ADXL345 etc.

[0345] The sensor node can further comprise distance sensors. The distance sensors can be configured to at least measure the distance between slab tracks, using infrared and/or ultrasonic. The distance sensor can be for example, MB1043, SRF08, PING, etc.

[0346] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can comprise visual sensors, such as 3D cameras, speed enforcement cameras, traffic enforcement cameras, etc. It may be noted that sensor node(s) may comprise sensors to observe the physical environment of the infrastructure the sensor node(s) are installed in. For example, but not limited to, temperature sensor, humidity sensor, altitude sensor, pressure sensor, GPS sensor, water pressure sensor, piezometer, multi-depth deflectometers (MDD), acceleration.

[0347] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can be installed according to a protocol based on routing trees to be able to transmit information to the base station. Once the information has been received, a cellular network can be used to send sensor data to a remote server 800.

[0348] The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can comprise an analog-to-digital converter, a micro controller, a transceiver, power and memory. One or more sensor(s) can be embedded in different elements and can be mounted on boards to be attached to the railway infrastructure. The sensor node(s), for example the first sensor 2 and/or the second sensor 9, can also comprise materializing strain gauges, displacement transducers, accelerometers, inclinometers, acoustic emission, thermal detectors, among others. The analog signal outputs generated by the sensors can be converted to digital signals that can be processed by digital electronics. The data can then be transmitted to the base station by a microcontroller through a radio transceiver. All devices can be electric or electronic components supported by power supply, which can be provided through batteries or by local energy generation (such as solar panels), the latter mandatory at locations far away from energy supplies.

[0349] The sensor data 101 collected from the sensor node(s), for example the first sensor 2 and/or the second sensor 9, can be transferred to the base station using wireless communication technology such as Wi-Fi, Bluetooth, ZigBee or any other proprietary radio technologies suitable for the purpose. For example, the ZigBee network can be advantageous to consumes less power. On the other hand, for transmitting the input 101 data from the base station to the server 800 long-range communication such a cellular network or satellite can be used as well as wired technologies based on optical fiber.

[0350] Due to the short transmission range, communications from sensor nodes may not reach the base station, a problem that can be overcome by adopting relay nodes to pass the data from the sensor node(s), for example the first sensor 2 and/or the second sensor 9.

[0351] Another sensor 7, which may be different or the same kind of sensor, can be attached, for example, to the mast 6 or any other structure. This may be a different kind of sensor, such as, for example, an optical, temperature, even acceleration sensor, etc. A further kind of sensor, for example sensor 8, can be arranged above the railway as at the beginning or within the tunnel 5. This could, for example, be a height sensor for determining the height of a train, an optical sensor, a doppler sensor etc. It will be understood that all those sensors mentioned here and/or before are just non-limiting examples.

[0352] Furthermore, the sensors can be configured to submit the sensor data via a communication network, such as a wireless communication network. As the communication network bears several advantages and disadvantages regarding availability, transmittal distance, costs etc. the transmittal of sensor data is optimized as described herein before and below.

[0353] FIG. 2 depicts a system 100 for monitoring a railway network. In simple terms, the system 100 may comprise a sensor component 200, a processing component 300, a storing component 400, an analyzing component 500 and a server 600.

[0354] In one embodiment, the sensor component 200 may comprise a plurality of sensor units, and each may comprise a plurality of sensor nodes. Therefore, the sensor component 200 may also be referred to as a plurality of sensor components 200.

[0355] Additionally or alternatively, the sensor component may be configured to sample information relevant to a railway network, for instance, electric current based information of a given component and/part of a railway network.

[0356] In one embodiment, the processing 300 component may comprise a standalone component configure to retrieve information from the sensor 200. Additionally or alternatively, the processing component may be configured to bidirectionally communicate the storing component 300 and the analyzing component 500. For instance, the processing component 300 may transfer raw sensor data to the storing component 400, wherein the raw sensor data may be stored until the processing component 300 may require said data for processing to generate a processed sensor data. In another embodiment, the processing component 300 may also transfer processed sensor data to the storing component 400. In a further embodiment, the processing component may also retrieve data from the storing component 400.

[0357] In one embodiment, the analyzing component 500 may be configured to bidirectionally communicate with the processing component 300, the storing component 400 and/or the server 600. It will be understood that the communication of the analyzing component 500 with the other components may take place independent and/or simultaneously one from another.

[0358] In one embodiment, the processing component 300 may also be integrated with at least one of the sensors 200. In other words, the processing component 300 may also comprise an imbedded module of the sensors 200.

[0359] In embodiment, the analyzing component 500 may be configured to process sensor data based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0360] The server 600 may comprise one or more modules configured to receive information from the analyzing component 500.

[0361] In another embodiment of the presentation invention, the sensor 200, the processing component 300, the storing component 400 and the analyzing component may comprise an integrated module configured to execute subsequently the tasks corresponding to each individual component, and transfer a final processed analyzed sensor data to the server 600. In simple words, in one embodiment the sensor 200, the processing component 300, the storing component 400 and the analyzing component 500 may comprises modules of a single component.

[0362] In one embodiment, the server 600 may retrieve information from the analyzing component 500, and further may provide information to the analyzing component 500, for example, operation parameters. It will be understood that each component may receive a plurality of operation parameters, for instance, the processing component 300 may be commanded to execute a preprocessing of the data received from the sensors 200.

[0363] Alternatively or additionally, the processing component 300 may be instructed to transmit the original data received from the sensors 200, i.e., the data coming from the sensors 200 can be transferred directly to the next component without executing any further task. It will be understood that the component may also be configured to perform a plurality of tasks at the same time, e.g., processing the data coming from the sensor 200 before transferring to the next component and transferring the data coming from the sensors 200 without any processing.

[0364] In one embodiment, the server 600 may comprise a cloud server, a remote server and/or a collection of different type of servers. Therefore, the server 600 may also be referred to as cloud server 600, remote server 600, or simple as servers 500. In another embodiment, the servers 500 may also converge in a central server.

[0365] It will be understood that the server 600 may also be in bidirectional communication with the storing component 400, the processing component or the sensor component 200 without passing through the analyzing component 500 or any other intermediate component. For this purpose, each component may also comprise a remote communication unit configured to establish a remote communication between a component, e.g., sensor component 200, with the server 600.

[0366] The storing component 400 may be configured to receive information from the server 600 for storage. In simple words, the storing component 400 may store information provided by the servers 600. The information provided by the server 600 may include, for example, but not limited to, data obtained by sensors 200, data processed by the processing component 500 and any additional data generated in the servers 600. It will be understood that the servers 600 may be granted access to the storing component 400 comprising, inter alia, the following permissions, reading the data allocated in the storing component 400, writing and overwriting the data stored in the storing component 400, control and modify the storage logic and the data distribution within the storing component 400.

[0367] In one embodiment of the present invention the server 600 may be configured transmit a signal to other component of the railway system based upon health status information retrieved from sensors 200. For instance, a giving health status data is provided by the server 600 and subsequently the server 600 generates a signal containing instructions, which are transmitted to the railway system for implementation. The set of instructions may comprise, inter alia, generating a hypothesis as regards the health status of the railway network and/or a failure hypothesis, which may comprise instructions to be implemented before a failure occurs on the railway network, such as switching rolling unit from on track to another. Furthermore, the signal may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0368] In one embodiment, the sensors 200 may, inter alia, adopt a configuration that allows identifying trains, their speeds and their wear effect on the tracks. The data gathered by the sensors 200 may constitute the basis for the server 600 to generate instructions for the activation of the switches. In simple words, if a train is approaching this part of the network, the sensors 200 may retrieve data that may allow activating the switches in order to redirect the trains, for example, from track 1 to track 2, according to their speed and/or wear effect. The data gathered by the sensors 200 may be communicated to the server 600, which may subsequently transmit the information and the corresponding instructions to the nearest assets, for example, the nearest switch, which may consequently be activated to control the traffic on the tracks. Furthermore, in one embodiment of the present invention, the system 100 may estimate the health status of components of the railway network and may further generate a health status and/or failure hypothesis that may allow to forecast the suitability of the component of the railway network to allocate rolling units. Such hypothesis may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0369] In another embodiment of the present invention, the system 100 may determine that a particular part and/or component of the railway network, for instance, a given section of track and/or a switch, is required to be replaced and/or maintain before a given date to avoid failure of the railway.

[0370] In one embodiment of the present invention, the system 100 may also determine that a particular rolling stock may pass through a component or portion of the railway network requiring maintenance, reparation or replacement, however, due to work schedule it may be prompt to failure if an inadequate rolling unit passes through. This approach may be advantageous, as it may allow to reduce failure of railway networks, which may be achieved by monitoring, evaluating and forecasting optimal operation conditions of the railway network.

[0371] Furthermore, the system 100 may be configured to predict a future status of the railway network and based on that may determine an optimal operation conditions using data analysis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0372] In more simple words, determinations of the system 100 may directly be used forecast point machine failure, which may be advantageous for planning and execution of maintenance and/or inspections of railway network, which may further allow to minimize downtime of single machines and more importantly an adjacent railway network. Such monitoring, analyzing and forecasting may be based on machine learning comprising predicting health status hypothesis and/or failure hypothesis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0373] FIG. 3 depicts a schematic of a computing device 1000. The computing device 1000 may comprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C.

[0374] The computing device 1000 can be a single computing device or an assembly of computing devices. The computing device 1000 can be locally arranged or remotely, such as a cloud solution.

[0375] On the different data storage units 30 the different data can be stored, such as the genetic data on the first data storage 30A, the time stamped data and/or event code data and/or phenotypic data on the second data storage 30B and privacy sensitive data, such as the connection of the before-mentioned data to an individual, on the thirds data storage 30C.

[0376] Additional data storage can be also provided and/or the ones mentioned before can be combined at least in part. Another data storage (not shown) can comprise data specifying for instance, air temperature, rail temperature, position of blades, model of point machine, position of point machine and/or further railway network related information. This data can also be provided on one or more of the before-mentioned data storages.

[0377] The computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B and the third data storage unit 30C through the internal communication channel 160, which can comprise a bus connection 160.

[0378] The computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programmable gate array). The first data storage unit 30A may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0379] The second data storage unit 30B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0380] The third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0381] It should be understood that generally, the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory. That is, only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A), the respective data element share (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A).

[0382] In some embodiments, the third data storage unit 30C can be a secure memory device 30C, such as, a self-encrypted memory, hardware-based full disk encryption memory and the like which can automatically encrypt all of the stored data. The data can be decrypted from the memory component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like. In some embodiments, the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure a secure storing and handling of the encryption key (i.e., private key) stored in the third data storage unit 30C.

[0383] In some embodiments, the second data storage unit 30B may not be provided but instead the computing device 1000 can be configured to receive a corresponding encrypted share from the database 60. In some embodiments, the computing device 1000 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60.

[0384] The computing device 1000 may comprise a further memory component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The memory component 140 may also be connected with the other components of the computing device 1000 (such as the computing component 35) through the internal communication channel 160.

[0385] Further the computing device 1000 may comprise an external communication component 130. The external communication component 130 can be configured to facilitate sending and/or receiving data to/from an external device (e.g., backup device 10, recovery device 20, database 60). The external communication component 130 may comprise an antenna (e.g., WIFI antenna, NFC antenna, 2G/3G/4G/5G antenna and the like), USB port/plug, LAN port/plug, contact pads offering electrical connectivity and the like. The external communication component 130 can send and/or receive data based on a communication protocol which can comprise instructions for sending and/or receiving data. Said instructions can be stored in the memory component 140 and can be executed by the computing unit 35 and/or external communication component 130. The external communication component 130 can be connected to the internal communication component 160. Thus, data received by the external communication component 130 can be provided to the memory component 140, computing unit 35, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C. Similarly, data stored on the memory component 140, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C and/or data generated by the commuting unit 35 can be provided to the external communication component 130 for being transmitted to an external device.

[0386] In addition, the computing device 1000 may comprise an input user interface 110 which can allow the user of the computing device 1000 to provide at least one input (e.g., instruction) to the computing device 100. For example, the input user interface 110 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like.

[0387] Additionally, still, the computing device 1000 may comprise an output user interface 120 which can allow the computing device 1000 to provide indications to the user. For example, the output user interface 110 may be a LED, a display, a speaker and the like.

[0388] The output and the input user interface 100 may also be connected through the internal communication component 160 with the internal component of the device 100.

[0389] The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP, APU, or FPGA. The memory may be singular or plural, and may be, but not limited to, being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.

[0390] The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and/or microcontrollers. The data processing device 20 can comprise memory components, such as, main memory (e.g., RAM), cache memory (e.g., SRAM) and/or secondary memory (e.g., HDD, SDD). The data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the memory components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as: [0391] output user interface, such as: [0392] screens or monitors configured to display visual data (e.g., displaying graphical user interfaces of railway network status), [0393] speakers configured to communicate audio data (e.g., playing audio data to the user), [0394] input user interface, such as: [0395] camera configured to capture visual data (e.g., capturing images and/or videos of the user), [0396] microphone configured to capture audio data (e.g., recording audio from the user), [0397] keyboard configured to allow the insertion of text and/or other keyboard commands (e.g., allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard) and/or trackpad, mouse, touchscreen, joystick-configured to facilitate the navigation through different graphical user interfaces of the questionnaire.

[0398] The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, memory components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and/or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper-mentioned user interfaces).

[0399] FIG. 4 schematically depicts 3 phases according to embodiments of the present invention. In simple terms, FIG. 4 depicts: ingestion, data processing and storage. A system according to embodiment of the present invention is configured to collect, retrieve and/or receive data 210, for example, from a user of railway network, for instance, from the user's external tool via, inter alia, API in a plurality of formats 222 comprising, but not limited to, JSON, XML. Furthermore, the system is configured to collect, retrieve and/or receive data from a different user, such as a local and/or internal user, which may be granted permission to upload the data via a plurality of tools 224, for example, via a web application. Such data may also be referred to as uploaded data. In one embodiment, the uploaded data may be collected or picked up, and store in a raw data bucket 240, i.e., function of code block data normalization 220, which may be adapted to provide an identification to each data and/or identify the origin of such as a data, for example, to identify which user is the data coming from. The user(s) may also be referred to as clients, and it is intended to refer to a one or more user and/or client of the railway network, but this term is not intended to refer to passenger using a railway network. Additionally or alternatively, the system may also be configured to determine into which storage bucket the data have to be allocated. In on embodiment, to autonomously perform each step.

[0400] Whenever data is in the system, a trigger function 250 may pick the data and may convert the structure, i.e., columns according to the meta store. In simple terms, this process may change the naming of columns to form a common structure for the clients, for instance, for all clients. Once the data is structured, the system may be configured to store the data in at least one database, which may serve an input source for further processes such as, for example, for further use case of creating models.

[0401] The structured data may be used by different use cases 260, 280, 270. For instance, a first use case 260 may comprise a database processing 262, a data modelling 264 and/or translation processing 266. Thus, the system may be configured to implement the first use case 260, and once the use case 260 is implemented and its task or processing 262, 264, 266 has been completed, the system may further store the first use case 260 in the database 242, 248, for example, in a separate database, from which the data may be further be use, for example, for analysis 286, plugging the data to a visualization tool 282 or be a data source for chatbot 284. Additionally or alternatively, the system may log each execute process, for example, all steps mentioned above, as logged data by means of a log engine 230. The system may use such logged data for a plurality of further method steps such as for continuous monitoring, for collecting errors produced by the system.

[0402] FIG. 5 depicts a diagram schematically representing steps of a use case according to embodiments of the present invention. In simple terms, FIG. 5 depicts steps within a given use case. Typically, working with data 310, such as text data, may comprise a plurality of steps such as the one depicts in FIG. 5. It should be understood that these steps are only exemplary, and therefore, not limiting the present invention, and that for sake of simplicity possible steps are exhaustively depict in FIG. 5.

[0403] As explained in FIG. 4, the system may be configured to receive, collect and/or retrieve data such as data 310 related to railway networks and/or data of at least one user. Further, as depicted in FIG. 5, the present invention may also comprise, inter alia, the following steps:

[0404] Data pre-processing 320, which may comprise at least one phase. In simple terms, the data preprocessing may comprise reading data, for example, contained within a sentence such a text and/or words. Moreover, the data pre-processing may also comprise: dividing the text into, but not limited to, at least one sequence of words, and normalizing the words. It should be understood that the present invention may also further implement other known processes in language processing. The system may further be configured to generate at least one pre-processed data; and

[0405] Data modelling 340, which may comprise implementing state of art models on the pre-processed data, such as, for example, ELMo, BERT. Hence, the system may also be configured to generate at least one modelled data.

[0406] Once all data modelling 340 has be implemented and application of models has yield at least one modelled data, the system may be configured to fine tune a neural network 360, such as, at least one neural network model, which may, inter alia, be used for a plurality of use cases, for instance, for prediction 362 of recurrent neural network like long short term memory model (LSTM). Thus, the system may also be configured to further adapt existing models to data used by embodiments of the present invention. The plurality of use case, may comprise, but not limited to, at least one of: classification messages 362, maintenance messages, predicting 364 action in case of a failure, creating chatbots 366, creating virtual assistants 366 where the user(s) can query the system or which may prompt an input from the at least one user. Furthermore, it should be understood that query may also comprise requesting data by an user to as regards failure(s) has occurred such as in which asset as well as actions to be taken in case of occurrence of such failure(s). It should be understood that the system may also be configured to automatically and/or autonomously provide such a query to at least one user.

[0407] In one embodiment the data pre-processing 320 may further comprises a reading step 322 to read data to a data frame, a tokenization step 324 to divide the text into a sequence of words, a normalizing step 326 to perform a normalization of tokens, a stemmening and lemmatization step 328, an extraction step 332 for the extraction of token types and to to extract unique strings of df, a counting step 334 to calculate occurrence of cells, and a maintenance step 336 for maintenance of data.

[0408] While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims.

[0409] Reference numbers and letters appearing between parentheses in the claims, identifying features described in the embodiments and illustrated in the accompanying drawings, are provided as an aid to the reader as an exemplification of the matter claimed. The inclusion of such reference numbers and letters is not to be interpreted as placing any limitations on the scope of the claims.

[0410] The term at least one of a first option and a second option is intended to mean the first option or the second option or the first option and the second option.

[0411] Whenever a relative term, such as about, substantially or approximately is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., substantially straight should be construed to also include (exactly) straight.

[0412] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), . . . , followed by step (Z). Corresponding considerations apply when terms like after or before are used.