Field

The invention lies in the field of installation of sensors and particularly in the field of installation of sensors in a railway network. The goal of the invention is to provide system and a method for installation of sensors in a railway infrastructure for condition monitoring and predictive maintenance. More particularly, the present invention relates to a system for installation of sensors in the railway network, a method performed in such a system and corresponding use of such a system.

Background

The idea of mounting devices to railway infrastructure is not new. Most of the devices mounted on rails are for track safety and are used to ensure safe and secure traffic control. Attaching and/or mounting objects with magnets instead of drilling, screwing, using raw plugs, or gluing has many advantages, namely: higher flexibility, no damage to the material, and, for the most part, it leads to a much more elegant result.

However, the approaches typically implemented in the state of the art may exhibit a plurality of disadvantages, for instance, such approaches may generally require placement and mounting techniques that place them in an unsafe manner in close proximity to, for example, a wheel flange. Wheels with high flanges are especially destructive to many of these arrangements and wheels that are laterally shifted or containing thin flanges may create geometries that are more difficult to detect reliably. In addition, the high vibration environment has proven to be very challenging for any of the methods of mechanical attachment. Furthermore, wear of a rail head causes inherent geometric changes over time that require adjustments or more complex designs that can accommodate such variation.

Railway sensors have previously been disclosed. For example, NL2014693A discloses a system being attached to the rails using magnets. The invention relates to a train monitoring system for monitoring at least a portion of a rail track. The invention also relates to as well as a train detection unit, signaling unit, network router and safety helmet for such a train monitoring system. According to a first example of the invention, a train monitoring system is provided for monitoring at least a portion of a rail track for rail track workers. The train monitoring system comprises at least one train detection unit, comprising detection means adapted to detect the presence of a train in the section of the rail track, and communication means adapted to communicate information regarding the presence of the train. The system further comprises at least one signaling unit, comprising communication means adapted to communicate with the communication means of the at least one train detection unit, and signaling means adapted to deliver at least one warning signal to the rail track workers indicative of the presence of the train. The train monitoring system according to a first example is characterized in that the communication means of the at least one train detection unit and the at least one signaling unit are further adapted to communicate over a public telecommunications network.

US20200023871A1 discloses a wayside railway sensor package is provided to detect railway wheels for the purposes of assessing the speed and direction of a train in order to align any measured characteristic on said moving train with the proper vehicle. The stand-alone package is easily installed in the web of the rail using standard tools. When used in combination with recent processing techniques, the package can be used to replace one or more components or subsystems on all common wayside detectors while also providing enhanced capabilities and improved reliability. The package also contains sensors that provide data used for assessing additional rail, wheel, and vehicle conditions directly.

Summary

In light of the above, it is an object of the present invention to overcome or at least alleviate the shortcomings of the prior art. More particularly, it is an object of the present invention to provide a system and a method to install at least one sensor component in a railway infrastructure, particularly to a safest position.

In addition, it is a preferred object of the present invention to provide completely or at least partially wireless sensors, which can be self-powered or battery powered or a combination of both to obtain a longer lifetime.

In a first aspect the invention relates to a sensor system for installations at a railway network, the system comprising: at least one sensor component, at least one housing component configured to house the at least one sensor component, and at least one installing module configured to install the housing component and/or the at least one sensor component to at least one physical infrastructure of the railway network.

The at least one housing component and the at least one installing module may comprise an integrated component. The at least one sensor component may be configured to: measure at least one property related to at least one component of the railway network, and measure at least one sensor data, wherein the at least one sensor data may comprise a measure of the at least one property related to the at least one component of the railway network.

The system may further comprise at least one processing component configured to process the at least one sensor data.

The system may comprise at least one analyzing component configured to analyze the at least one sensor data.

The system may comprise at least one interface configured to access at least one server configured to be bidirectionally connected to the sensor system.

The at least one physical infrastructure of the railway network may comprise at least one of: railway infrastructure, and railway assets.

The at least one installing module may comprise at least one magnetic component.

The at least one installing module may be connected to the at least one housing component.

The at least one installing module may be connected to the at least one sensor component.

The at least one installing module may comprise a bolted connection to at least one of the at least one housing with at least one bolt with a size of at least 4mm in diameter.

The at least one installing module may comprise at least 1/3 of the cross-section surface area of the housing component.

The at least one installing module may be configured to be de-attached from the rail infrastructure means of applying a pull force. The pull force can be applied directly or with help of an external lever system.

The pull force may at least be 100 N, preferably at least 150 N, more preferably at least The at least one installing module may be configured to automatically adjust the at least one sensor component frequency to at least one standard protocol.

The at least one sensor component may be configured to communicate with at least one server.

The at least one sensor component may further comprise at least one power component.

The at least one power component may be configured to derive energy from at least one of: solar power, thermal energy, wind energy, kinetic energy, and ambient energy.

The at least one power component may be configured to collect electrical energy from an electrical field around the rail during a passing train.

The at least one power component may further comprise at least one energy-storage component.

The at least one energy-storage component may comprise a capacitor module.

The capacitor module may comprise a hybrid-layer-capacitor.

The at least one energy-storage component may comprise a non-rechargeable module.

The non-rechargeable module may comprise a lithium-thionyl chloride cell.

The at least one energy-storage component may comprise a rechargeable module.

The system may comprise at least one antenna.

In one embodiment at least one of the at least one antenna may be an internal antenna.

In another embodiment, at least one of the at least one antenna may be an external antenna.

The system may comprise an outer dimension, wherein the outer dimension may comprise a longitudinal dimension, a vertical dimension and a thickness, wherein the longitudinal dimension may be less than 300 mm, preferably less than 250 mm, more preferably less than 200 mm, the vertical dimension may be less than 80 mm, preferably less than 70 mm, more preferably less than 60 mm, and the thickness may be less than 70 mm, preferably less than 60 mm, more preferably less than 50 mm.

The system may further comprise a calibrating component configured to calibrate a magnetic influence on the at least one sensor component.

The calibrating component adjust a frequency of the at least one sensor component.

The at least one sensor component may comprise at least one capacitance.

The at least one sensor component may comprise at least one piezoelectric component.

S34. The system according to any of the preceding embodiments, wherein the at least one sensor component may comprise at least one optical component.

In one embodiment, at least one of the at least one optical component may comprise at least one laser component.

The at least one sensor component may comprise at least one magnetic induction component.

The at least one sensor component may comprise at least one electromechanical servo- hydraulic component.

The at least one sensor component may comprise at least one resonance component.

The at least one sensor component may be configured to measure sensor data with a quality of at least 10 bits.

The at least one sensor component may be configured to execute a wireless transmission of the sensor data, wherein the wireless transmission may be performed with a bandwidth of at least 125 kBit/s, preferably at least 500 kBit/s, more preferably at least 1000 kBit/s.

The at least one sensor component may be configured to measure sensor data with a frequency of at least 500 Hz, preferably at least 1000 Hz, more preferably at least 2000 Hz. The at least one sensor component may be configured for timely unevenly distributed sampling with a total of at least 100 sample points per second.

The sampling may be randomly timely unevenly distributed.

The at least one processing component may be partially comprised by the at least one sensor component, wherein the at least one sensor component may be configured to: pre-process the sensor data and transmit, generate a pre-processed sensor data set, and transmit the pre-processed sensor data set to the at least one server.

The pre-processed sensor data set may comprise any at least one relevant to the railway infrastructure or of a passing train.

The at least one sensor component may comprise a single-measurement-node.

The at least one housing component may comprise at least one fixing component.

The at least one fixing component may comprise a magnetic fixing component.

The at least one sensor component may comprise at least one signal processing component.

The at least one signal processing component may comprise a digital output, wherein the digital output may be suitable to be directly read by a main computing unit (MCU).

The at least one signal processing component may comprise an analog output.

The at least one signal processing component may comprise an analog output processing module configured to convert the analog output from an analog-digital- converter (ADC) input.

The MCU may be configured to receive and pre-process sensor data.

The MCU may comprise at least one storage component configured to store at least 512 kB, preferably at least 4096 kB, more preferably at least 4096 MB.

In one embodiment, the at least one storage component may be a non-volatile storage component. The system may further comprise at least one communication component configured to bidirectionally communicate with other components of the system or at least one of the at least one server

The at least one communication component may be a wireless communication component.

The system may further comprise at least one trigger component configure to trigger the at least one sensor component to measure the sensor data when the passing train may be approaching the at least one sensor component.

The at least one trigger component may comprise a remote trigger module.

The remote trigger module may be a wireless remote trigger module.

The at least one fixing component may comprise at least one adhesive material.

The at least one adhesive material may comprise a two-component adhesive material.

The at least one adhesive material may comprise a conductive component.

The at least one adhesive material may comprise a semi conductive component.

The at least one adhesive material may comprise an isolating component.

The at least one adhesive component may be configured to support the at least one magnetic fixing component.

The system may further comprise at least one gateway configured to facilitate at least a monodirectional communication, preferably at least a bidirectional communication between at least two components of the system.

The system may comprise at least one self-testing module configured to execute selftesting step of at least one component of the system.

The system may comprise at least one internal energy meter component configured to measure at least one energy consumption data. The at least one internal energy meter component may comprise a real-time clock integrated circuit (RTC IC).

In a second aspect the invention relates to a method for operating a system for installations at a railway network, the method comprising: installing at least one sensor component to at least one physical infrastructure of the railway network, and adjusting a position of the at least one sensor component, wherein the step of installing the at least one sensor component precedes the step of adjusting the position of the at least one sensor component.

The method may further comprise horizontally mounting the at least one component to the at least one physical infrastructure of the railway network.

In one embodiment, the horizontal mounting may comprise aligning a vertical axis.

The aligning of the vertical axis may comprise rotating at least one axis, preferably at least one two axes, such as three axes.

The method may comprise snaping at least one of: the least one sensor component, and the at least one housing on the rail. In one embodiment, the snaping may succeed the aligning. Furthermore, the aligning may comprise using a screwing mechanism.

The method may comprise controlling at least one axis of at least one component of the system, wherein the controlling may comprise modifying the relative position of the at least one component of the system comprising at least one of: a proximal position, a distal position, and a horizontal axis, wherein the method may also comprise applying at least one rotational movement on at least one of: a proximal position, a distal position, and horizontal axis.

The method may comprise automatically checking a plurality of operation-relevant- values.

The plurality of operation-relevant values may comprise at least one of: internal voltage, and validation of sensor signal, wherein the method may comprise generating a functionality-status hypothesis based upon the automatic checking of the plurality of the operation-relevant-values.

The method may comprise generating a functionality-status finding based on the functionality-status hypothesis. The method may comprise measuring an energy consumption of the at least one sensor component via an energy meter.

The method may comprise automatically selecting at least one wireless standard to improve a connection quality between any of the component of the system to the at least one server.

The method may comprise assessing availability of the system comprising at least one feature of: transmission range, and energy efficiency, such as energy consumption per payload.

The method may comprise establishing at least one maintenance procedure.

The method may comprise performing a self-testing step, wherein the self-testing step execute a self-test function may comprise by at least one component of the system.

The self-testing step may comprise checking at least one operation relevant value

The at least one operation relevant value may comprise at least one of: internal voltage data, and sensor signal.

The method may comprise generating at least one functionality hypothesis, wherein the at least one functionality hypothesis may be based on the at least one operation relevant value.

The method may comprise measuring at least one energy consumption data.

The method may comprise measuring the least one energy consumption data via at least one internal energy meter component.

The method may comprise estimating a remaining useful lifetime based on the at least one energy consumption data, and generating at least one estimation dataset.

The method may comprise automatically initiating at least one component of the system based on the a least one estimation dataset.

The at least one estimation dataset may comprise at least one of: vibrational data, and temporal-based data. The method may comprise measuring at least one property related to at least one component of the railway network, and measuring at least one sensor data, wherein the at least one sensor data may comprise a measure of the at least one property related to the at least one component of the railway network.

The measuring may be performed via at least one sensor component.

The method may comprise processing the at least one the at least one sensor data via at least one processing component.

The method may comprise analyzing the at least one sensor data via at least one analyzing component.

The method may comprise granting access to at least one component of the system to at least one server.

The method may comprise de-attaching the at least one installing module from the rail infrastructure by means of applying a pull force.

The pull force may be at least 100 N, preferably at least 150 N, more preferably at least 200 N.

The at least one installing module may be configured to automatically adjust the at least one sensor component frequency to at least one standard protocol.

The method may comprise establishing a communication between the at least one sensor component with at least one server.

The method may comprise deriving energy via at least one power component from at least one of: solar power, thermal energy, wind energy, kinetic energy, and ambient energy.

The method may comprise colleting electrical energy via the at least one power component from an electrical field around the rail during a passing train.

The method may comprise storing energy in at least one energy-storage component. The method may further comprise calibrating a magnetic influence on the at least one sensor component via a calibrating component.

The method may comprise adjusting a frequency of the at least one sensor component via the at least one calibrating component.

The method may comprise measuring sensor data with a quality of at least 10 bits.

The method may comprise executing a wireless transmission of the sensor data via the at least one sensor component, wherein the wireless transmission may be performed with a bandwidth of at least 125 kBit/s, preferably at least 500 kBit/s, more preferably at least 1000 kBit/s.

The method may comprise measuring sensor data with a frequency of at least 500 Hz, preferably at least 1000 Hz, more preferably at least 2000 Hz.

The method may comprise sampling via the at least one sensor component with a total of at least 100 sample points per second.

The sampling may be randomly timely unevenly distributed.

The method may comprise at least one of: pre-processing the sensor data, transmitting the sensor data, generating a pre-processed sensor data set, and transmitting the pre- processed sensor data set to the at least one server.

The method may comprise converting an analog output processing of at least one signal via an analog output processing module.

The method may comprise receiving and pre-processing sensor data via an MCU.

The method may further comprise establishing a bidirectional communication between at least one communication component and other components of the system or at least one of the at least one server.

The method may further comprise triggering a measurement of the sensor data when the passing train may be approaching the at least one sensor component. The method may further comprise facilitating at least a monodirectional communication, preferably at least a bidirectional communication between at least two components of the system.

The invention also relates to use of the system as recited herein for carrying out the method as recited herein.

In one embodiment, the invention may relate to the use of the method according as recited herein and the system as recited herein for installation of at least one sensor component on a railway network.

Furthermore, the invention relates to a computer-implemented program comprising instructions which, when executed by a user-device, causes the user-device to carry out the method steps as recited herein.

In one embodiment, the computer-implemented program may comprise instructions which, when executed by a server, may cause the at least one server to carry out the method steps as recited herein.

In another embodiment, the computer-implemented program may comprise instructions which, when executed causes by a user-device, may cause the user-device and a server to carry out the method steps as recited herein.

The approach of the present invention may also be advantageous, as it may allow implementing a wireless transmission wireless transmission comprising a bigger amount of data. While system known in the art are typically wired or comprises to certain extent a transmission system only capable of low amounts of data.

Moreover, the present invention may allow or at least facilitate to overcome some of the drawback of the prior art, such as, for example, that the prior art can only transmit a low amount of data.

Moreover, the present invention may allow to implement have a device that is able to transfer full measurements of acceleration, which may be particularly advantageous for railway monitoring as well as towards for predicting goals and maintenance.

Furthermore, the approach of the present invention may allow to attach one or more sensor components to the rail itself and accordingly, provide capability of obtaining sufficient diagnostic data from a sensor installation deployed in a least obtrusive position with a simple and robust installation methodology.

The invention is further described with the following numbered embodiments.

Below, system embodiments will be discussed. These embodiments are abbreviated by the letter "S" followed by a number. Whenever reference is herein made to "system embodiments", these embodiments are meant.

51. A sensor system for installations at a railway network, the system comprising: at least one sensor component; at least one housing component configured to house the at least one sensor component; at least one installing module configured to install the housing component and/or the at least one sensor component to at least one physical infrastructure of the railway network.

52. The system according to the preceding embodiment, wherein the at least one housing component and the at least one installing module comprise an integrated component.

53. The system according to any of the 2 preceding embodiments, wherein the at least one sensor component is configured to measure at least one property related to at least one component of the railway network, and measure at least one sensor data, wherein the at least one sensor data comprises a measure of the at least one property related to the at least one component of the railway network.

54. The system according to any of the preceding embodiments, wherein the system further comprises at least one processing component configured to process the at least one sensor data.

55. The system according to any of the preceding embodiments, wherein the system comprises at least one analyzing component configured to analyze the at least one sensor data. 56. The system according to any of the preceding embodiments, wherein the system comprises at least one interface configured to access at least one server configured to be bidirectionally connected to the sensor system.

57. The system according to any of the preceding embodiments, wherein the at least one physical infrastructure of the railway network comprises at least one of railway infrastructure, and railway assets.

58. The system according to any of the preceding embodiments, wherein the at least one installing module comprises at least one magnetic component.

59. The system according to any of the preceding embodiments, wherein the at least one installing module is connected to the at least one housing component.

510. The system according to any of the preceding embodiments, wherein the at least one installing module is connected to the at least one sensor component.

511. The system according to any of the preceding embodiments, wherein the at least one installing module comprises a bolted connection to at least one of the at least one housing with at least one bolt with a size of at least 4mm in diameter.

512. The system according to any of the preceding embodiments, wherein the at least one installing module comprises at least 1/3 of the cross-section surface area of the housing component.

513. The system according to any of the preceding embodiments, wherein the at least one installing module is configured to be de-attached from the rail infrastructure means of applying a pull force.

S14 The system according to the preceding embodiment, wherein the pull force is at least 100 N, preferably at least 150 N, more preferably at least 200 N.

S15. The system according to any of the preceding embodiment, wherein the at least one installing module is configured to automatically adjust the at least one sensor component frequency to at least one standard protocol.

S16. The system according to any of the preceding embodiments, wherein the at least one sensor component is configured to communicate with at least one server. S17. The system according to any of the preceding embodiments, wherein the at least one sensor component further comprises at least one power component.

518. The system according to any of the preceding embodiments and with features of the preceding embodiment, wherein the at least one power component is configured to derive energy from at least one of solar power, thermal energy, wind energy, kinetic energy, and ambient energy.

519. The system according to any of the preceding embodiments and with features of embodiment S17, wherein the at least one power component is configured to collect electrical energy from an electrical field around the rail during a passing train.

520. The system according to any of the preceding embodiments and with features of embodiment S17, wherein the at least one power component further comprises at least one energy-storage component.

521. The system according to the preceding embodiment, wherein the at least one energy-storage component comprises a capacitor module.

522. The system according to the preceding embodiment, wherein the capacitor module comprises a hybrid-layer-capacitor.

523. The system according to any of the preceding embodiments and with features of embodiment S21, wherein the at least one energy-storage component comprises a non- rechargeable module.

524. The system according to the preceding embodiment, wherein the non- rechargeable module comprises a lithium-thionyl chloride cell.

525. The system according to any of the preceding embodiments and with features of embodiment S21, wherein the at least one energy-storage component comprises a rechargeable module. 526. The system according to any of the preceding embodiments, wherein the system comprises at least one antenna.

527. The system according to the preceding embodiment, wherein at least one of the at least one antenna is an internal antenna.

528. The system according to embodiment S26 or S27, wherein at least one of the at least one antenna is an external antenna.

529. The system according to any of the preceding embodiment, wherein the system comprises an outer dimension, wherein the outer dimension comprises a longitudinal dimension, a vertical dimension and a thickness, wherein the longitudinal dimension is less than 300 mm, preferably less than 250 mm, more preferably less than 200 mm, the vertical dimension is less than 80 mm, preferably less than 70 mm, more preferably less than 60 mm, and the thickness is less than 70 mm, preferably less than 60 mm, more preferably less than 50 mm.

530. The system according to any of the preceding embodiment, wherein the system further comprises a calibrating component configured to calibrate a magnetic influence on the at least one sensor component.

531. The system according to any of the preceding embodiments and with features of the preceding embodiment and embodiments S16 or S17, wherein the calibrating component adjust a frequency of the at least one sensor component.

532. The system according to any of the preceding embodiment, wherein the at least one sensor component comprises at least one capacitance.

533. The system according to any of the preceding embodiments, wherein the at least one sensor component comprises at least one piezoelectric component.

534. The system according to any of the preceding embodiments, wherein the at least one sensor component comprises at least one optical component.

S35. The system according to the preceding embodiment, wherein at least one of the at least one optical component comprises at least one laser component. S36. The system according to any of the preceding embodiments, wherein the at least one sensor component comprises at least one magnetic induction component.

537. The system according to any of the preceding embodiment, wherein the at least one sensor component comprises at least one electromechanical servo-hydraulic component.

538. The system according to any of the preceding embodiment, wherein the at least one sensor component comprises at least one resonance component.

539. The system according to any of the preceding embodiments, wherein the at least one sensor component is configured to measure sensor data with a quality of at least 10 bits.

540. The system according to any of the preceding embodiments, wherein the at least one sensor component is configured to execute a wireless transmission of the sensor data, wherein the wireless transmission is performed with a bandwidth of at least 125 kBit/s, preferably at least 500 kBit/s, more preferably at least 1000 kBit/s.

541. The system according to any of the preceding embodiments, wherein the at least one sensor component is configured to measure sensor data with a frequency of at least 500 Hz, preferably at least 1000 Hz, more preferably at least 2000 Hz.

542. The system according to any of the preceding embodiments, wherein the at least one sensor component is configured for timely unevenly distributed sampling with a total of at least 100 sample points per second.

543. The system according to the preceding embodiment, wherein the sampling is randomly timely unevenly distributed.

544. The system according to any of the preceding embodiments and with features of embodiments S4, wherein the at least one processing component is partially comprised by the at least one sensor component, wherein the at least one sensor component is configured to pre-process the sensor data and transmit, generate a pre-processed sensor data set, and transmit the pre-processed sensor data set to the at least one server. S45. The system according to the preceding embodiment, wherein the pre-processed sensor data set comprises any at least one relevant to the railway infrastructure or of a passing train.

546. The system according to any of the preceding embodiments, wherein the at least one sensor component comprises a single-measurement-node.

547. The system according to any of the preceding embodiments, wherein the at least one housing component comprises at least one fixing component.

548. The system according to the preceding embodiment, wherein the at least one fixing component comprises a magnetic fixing component.

549. The system according to any of the preceding embodiments, wherein the at leaslt one sensor component comprises at least one signal processing component.

550. The system according the preceding embodiment, wherein the at least one signal processing component comprises a digital output, wherein the digital output is suitable to be directly read by a main computing unit (MCU).

551. The system according to embodiment S48, wherein the at least one signal processing component comprises an analog output.

552. The system according to the preceding embodiment, wherein the at least one signal processing component comprises an analog output processing module configured to convert the analog output from an analog-digital-converter (ADC) input.

553. The system according to any of the preceding embodiments and with features of embodiment S50, wherein the MCU is configured to receive and pre-process sensor data.

554. The system according to the preceding embodiment, wherein the MCU comprises at least one storage component configured to store at least 512 kB, preferably at least 4096 kB, more preferably at least 4096 MB.

555. The system according to the preceding embodiment, wherein the at least one storage component is a non-volatile storage component. 556. The system according to any of the preceding embodiments, wherein the system further comprises at least one communication component configured to bidirectionally communicate with other components of the system or at least one of the at least one server

557. The system according to the preceding embodiment, wherein the at least one communication component is a wireless communication component.

558. The system according to any of the preceding embodiments, wherein the system further comprises at least one trigger component configure to trigger the at least one sensor component to measure the sensor data when the passing train is approaching the at least one sensor component.

559. The system according to the preceding embodiment, wherein the at least one trigger component comprises a remote trigger module.

560. The system according to the preceding embodiment, wherein the remote trigger module is a wireless remote trigger module.

561. The system according to any of the preceding embodiment and with features of embodiment S48, wherein the at least one fixing component comprises at least one adhesive material.

562. The system according to the preceding embodiment, wherein the at least one adhesive material comprises a two-component adhesive material.

563. The system according to any of the 2 preceding embodiments, wherein the at least one adhesive material comprises a conductive component.

564. The system according to any of embodiments S60 or S61, wherein the at least one adhesive material comprises a semi conductive component.

565. The system according to any of embodiment S60 or S61, wherein the at least one adhesive material comprises an isolating component.

S66. The system according to any of the preceding 5 embodiments and with features of embodiment S49, wherein the at least one adhesive component is configured to support the at least one magnetic fixing component. 567. The system according to any of the preceding embodiments, wherein the system further comprises at least one gateway configured to facilitate at least a monodirectional communication, preferably at least a bidirectional communication between at least two components of the system.

568. The system according to any of the preceding embodiments, wherein the system comprises at least one self-testing module configured to execute self-testing step of at least one component of the system.

569. The system according to any of the preceding embodiments, wherein the system comprises at least one internal energy meter component configured to measure at least one energy consumption data.

570. The system according to the preceding embodiment wherein the at least one internal energy meter component comprises a real-time clock integrated circuit (RTC IC)

Below, method embodiments will be discussed. These embodiments are abbreviated by the letter "M" followed by a number. Whenever reference is herein made to "method embodiments", these embodiments are meant.

Ml. A method for operating a system for installations at a railway network, the method comprising: installing at least one sensor component to at least one physical infrastructure of the railway network, and adjusting a position of the at least one sensor component, wherein the step of installing the at least one sensor component precedes the step of adjusting the position of the at least one sensor component.

M2. The method according to the preceding embodiment, wherein the method further comprises horizontally mounting the at least one component to the at least one physical infrastructure of the railway network.

M3. The method according to the preceding embodiment, wherein the horizontal mounting comprises aligning a vertical axis.

M4. The method according to the preceding embodiment, wherein the aligning of the vertical axis comprises rotating at least one axis, preferably at least one two axes, such as three axes. M5. The method according to the preceding embodiment, wherein the method comprises snaping at least one of the least one sensor component, and the at least one housing on the rail.

M6. The method according to the 2 preceding embodiments, wherein the snaping succeed the aligning.

M7. The method according to any of the preceding embodiment and with features of embodiment M3, wherein the aligning comprises using a screwing mechanism.

M8. The method according to any of the preceding method embodiments, wherein the method comprises controlling at least one axis of at least one component of the system, wherein the controlling comprises modifying the relative position of the at least one component of the system comprising at least one of a proximal position, a distal position, and a horizontal axis, wherein the method also comprises applying at least one rotational movement on at least one of a proximal position, a distal position, a vertical axis, and horizontal axis.

M9. The method according to any of the preceding method embodiments, wherein the method comprises automatically checking a plurality of operation-relevant-values.

MIO. The method according to the preceding embodiment, wherein the plurality of operation-relevant values comprises at least one of internal voltage, and validation of sensor signal, wherein the method comprises generating a functionality-status hypothesis based upon the automatic checking of the plurality of the operation-relevant-values.

Mil. The method according to the any of the preceding method embodiments, wherein the method comprises generating a functionality-status finding based on the functionality-status hypothesis. M12. The method according to any of the preceding method embodiments, wherein the method comprises measuring an energy consumption of the at least one sensor component via an energy meter.

M13. The method according to any of the preceding method embodiments, wherein the method comprises automatically selecting at least one wireless standard to improve a connection quality between any of the component of the system to the at least one server.

M14. The method according to the preceding embodiment, wherein the method comprises assessing availability of the system comprising at least one feature of transmission range, and energy efficiency, such as energy consumption per payload.

M15. The method according to any of the preceding method embodiments, wherein the method comprises establishing at least one maintenance procedure.

M16. The method according to any of the preceding method embodiment, wherein the method comprises performing a self-testing step, wherein the self-testing step execute a self-test function comprises by at least one component of the system.

M17. The method according to the preceding embodiment, wherein the self-testing step comprises checking at least one operation relevant value

M18. The method according to the preceding embodiment, wherein the at least one operation relevant value comprises at least one of internal voltage data, and sensor signal.

M19. The method according to any of the 3 preceding embodiments, wherein the method comprises generating at least one functionality hypothesis, wherein the at least one functionality hypothesis is based on the at least one operation relevant value.

M20. The method according to any of the preceding method embodiments, wherein the method comprises measuring at least one energy consumption data. M21. The method according to the preceding embodiment, wherein the method comprises measuring the least one energy consumption data via at least one internal energy meter component.

M22. The method according to the 2 preceding embodiments, wherein the method comprises estimating a remaining useful lifetime based on the at least one energy consumption data, and generating at least one estimation dataset.

M23. The method according to the preceding embodiment, wherein the method comprises automatically initiating at least one component of the system based on the a least one estimation dataset.

M24. The method according to the 2 preceding embodiments, wherein the at least one estimation dataset comprises at least one of vibrational data, and temporal-based data.

M25. The method according to any of the preceding method embodiments, wherein the method comprises measuring at least one property related to at least one component of the railway network, and measuring at least one sensor data, wherein the at least one sensor data comprises a measure of the at least one property related to the at least one component of the railway network.

M26. The method according to the preceding embodiment, wherein the measuring is performed via at least one sensor component.

M27. The method according to any of the preceding method embodiments, wherein the method comprises processing the at least one the at least one sensor data via at least one processing component.

M28. The method according to any of the preceding method embodiments, wherein the method comprises analyzing the at least one sensor data via at least one analyzing component.

M29. The method according to any of the preceding method embodiments, wherein the method comprises granting access to at least one component of the system to at least one server. M30. The method according to any of the preceding method embodiments, wherein the method comprises de-attaching the at least one installing module from the rail infrastructure by means of applying a pull force.

M31. The method according to the preceding embodiment, wherein the pull force is at least 100 N, preferably at least 150 N, more preferably at least 200 N.

M32. The method according to any of the preceding embodiment, wherein the at least one installing module is configured to automatically adjust the at least one sensor component frequency to at least one standard protocol.

M33. The method according to any of the preceding method embodiments, wherein the method comprises establishing a communication between the at least one sensor component with at least one server.

M34. The method according to any of the preceding method embodiments, wherein the method comprises deriving energy via at least one power component from at least one of solar power, thermal energy, wind energy, kinetic energy, and ambient energy.

M35. The method according to the preceding embodiment, wherein the method comprises colleting electrical energy via the at least one power component from an electrical field around the rail during a passing train.

M36. The method according to any of the 2 preceding embodiments, wherein the method comprises storing energy in at least one energy-storage component.

M37. The method according to any of the preceding embodiment, wherein the method further comprises calibrating a magnetic influence on the at least one sensor component via a calibrating component.

M38. The method according to the preceding embodiment, wherein the method comprises adjusting a frequency of the at least one sensor component via the at least one calibrating component. M39. The method according to any of the preceding method embodiments, wherein the method comprises measuring sensor data with a quality of at least 10 bits.

M40. The method according to any of the preceding method embodiments, wherein the method comprises executing a wireless transmission of the sensor data via the at least one sensor component, wherein the wireless transmission is performed with a bandwidth of at least 125 kBit/s, preferably at least 500 kBit/s, more preferably at least 1000 kBit/s.

M41. The method according to any of the preceding method embodiments, wherein the method comprises measuring sensor data with a frequency of at least 500 Hz, preferably at least 1000 Hz, more preferably at least 2000 Hz.

M42. The method according to any of the preceding method embodiments, wherein the method comprises sampling via the at least one sensor component with a total of at least 100 sample points per second.

M43. The method according to the preceding embodiment, wherein the sampling is randomly timely unevenly distributed.

M44. The method according to any of the preceding method embodiments, wherein the method comprises at least one of pre-processing the sensor data, transmitting the sensor data, generating a pre-processed sensor data set, and transmitting the pre-processed sensor data set to the at least one server.

M45. The method according to any of the preceding method embodiments, wherein the method comprises converting an analog output processing of at least one signal via an analog output processing module.

M46. The method according to any of the preceding method embodiments, wherein the method comprises receiving and pre-processing sensor data via an MCU.

M47. The method according to any of the preceding method embodiments, wherein the method further comprises establishing a bidirectional communication between at least one communication component and other components of the system or at least one of the at least one server. M48. The method according to any of the preceding method embodiments, wherein the method further comprises triggering a measurement of the sensor data when the passing train is approaching the at least one sensor component.

M49. The method according to any of the preceding method embodiments, wherein the method further comprises facilitating at least a monodirectional communication, preferably at least a bidirectional communication between at least two components of the system.

Below, use embodiments will be discussed. These embodiments are abbreviated by the letter "U" followed by a number. Whenever reference is herein made to "use embodiments", these embodiments are meant.

Ul. Use of the system according to any of the preceding embodiments, for carrying out the method according to any of the preceding method embodiments.

U2. Use of the method according to any of the preceding method embodiments and the system according to any of the preceding embodiments, for installation of at least one sensor component on a railway network.

Below, program embodiments will be discussed. These embodiments are abbreviated by the letter "C" followed by a number. Whenever reference is herein made to "program embodiments", these embodiments are meant.

Cl. A computer-implemented program comprising instructions which, when executed by a user-device, causes the user-device to carry out the method steps according to any of the preceding method embodiments.

C2. A computer-implemented program comprising instructions which, when executed by a server, causes the at least one server to carry out the method steps according to any of the preceding method embodiments.

C3. A computer-implemented program comprising instructions which, when executed causes by a user-device, causes the user-device and a server to carry out the method steps according to any of the preceding method embodiments. Brief description of the drawings

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.

Fig. 1 depicts a schematic representation of a railway network and system arranged at the railway network;

Fig. 2 depicts a system for monitoring a railway network according to embodiments of the present invention;

Fig. 3 depicts a schematic of a computing device.

Detailed description of the drawings

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.

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.

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.

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. 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.

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.

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.

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.

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.

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. 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 600.

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.

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, SR.F08, PING, etc.

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, multidepth deflectometers (MDD), acceleration.

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 600.

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.

The sensor data 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 data from the base station to the server 600 long-range communication such a cellular network or satellite can be used as well as wired technologies based on optical fiber.

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.

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.

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.

Fig. 2 depicts a system 100 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.

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. 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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (applicationspecific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programable 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).

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). 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).

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).

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.

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.

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.

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.

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.

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. 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.

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.

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:

■ output user interface, such as: o screens or monitors configured to display visual data (e.g., displaying graphical user interfaces of railway network status), o speakers configured to communicate audio data (e.g., playing audio data to the user),

■ input user interface, such as: o camera configured to capture visual data (e.g., capturing images and/or videos of the user), o microphone configured to capture audio data (e.g., recording audio from the user), o 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.

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).

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.

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.

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".

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 (Yl), ..., followed by step (Z). Corresponding considerations apply when terms like "after" or "before" are used.