RAILWAY POINT MANAGING SYSTEM AND METHOD

Abstract

The present invention relates to a system for monitoring a railway network, the system comprising at least one sensor component configured to sample sensor data relevant to the railway network, at least one processing component configured to process the sensor data, at least one storing component configured to store the sensor data relevant to the railway network and the processed sensor data, and/or at least one analyzing component. The present invention also refers to a method for monitoring a railway network, the method comprising the steps of: retrieving at least one point machine data, processing the least one point machine data to generate at least one processed point machine data, and generating at least one railway health hypothesis based on the at least one processed point machine data.

Claims

1. A system for monitoring a railway network, the system comprising: at least one sensor component configured to sample sensor data relevant to the railway network, at least one processing component configured to process the sensor data, at least one storing component configured to store the sensor data relevant to the railway network and the processed sensor data, at least one analyzing component, and at least one server.

2. The system according to claim 1, wherein the at least one analyzing component is configured to at least one of: receive the sensor data from the at least one sensor component, monitor at least one railway health status of at least one component of the railway network, forecast at least one railway health status of at least one component of the railway network, and/or generate at least one railway health status hypothesis comprising at least one cause for the at least one railway health status of the at least one component of the railway network.

3. The system according to claim 1, wherein the sensor data relevant to the railway network comprises at least one railway infrastructural feature, wherein the at least one railway infrastructural feature comprises at least one feature based on electric current (EC) records.

4. The system according to claim 1, wherein the at least one analyzing component comprises a self-learning module configured to at least one of: analyze the at least one infrastructural feature, determine changes of the at least one infrastructural feature over time, and/or correlate changes of the at least one infrastructural feature with at least one railway health status hypothesis.

5. The system according to claim 4, wherein self-learning module, in the step of correlating changes of the last one infrastructural feature with at least one railway health status hypothesis, is further configured to execute at least one simulation model.

6. The system according to claim 1, wherein the at least one analyzing component is configured to execute at least one analytical approach and at least one server configured to at least one of: receive sensor data relevant to the railway network, monitor the sensor data, and/or generate an optimizing routing of rolling stocks on the railway network based on sensor data related to the railway network, wherein the at least one server is configured to generate an optimizing routing of rolling stocks by means of the least one analytical approach.

7. A method for monitoring a railway network, the method comprising the steps of retrieving at least one point machine data; processing the least one point machine data to generate at least one processed point machine data; and generating at least one railway health hypothesis based on the at least one processed point machine data.

8. The method according to claim 7, further comprising the step of forecasting at least one railway health status of at least one component of the railway network based on the at least one railway health hypothesis.

9. The method according to claim 8, wherein the step of forecasting at least one railway health status of the at least one component the railway network comprises using trends in at least one feature based on electric current (EC) records.

10. The method according to claim 7, wherein the method comprises the step of generating at least one railway failure hypothesis, wherein the at least one railway failure hypothesis is based on the at least one railway health hypothesis, and wherein the at least one railway failure hypothesis is based on the at least one processed point machine data.

11. The method according to claim 10, further comprising the step of forecasting at least one railway failure of at least one component of the railway network based on the at least one railway failure hypothesis, wherein the method further comprises using at least one feature based on at least one transformation of traces comprising at least one of: functional principal component analysis scores, reductions of wavelet transformation, and/or deviations from at least one average curve.

12. The method according to claim 7, further comprising the step of calculating at least one feature based on at least one complete trace based on at least one specific part of at least one trace, splitting the at least one trace into at least one time interval comprising equal-length time intervals, splitting the least one trace into at least one phase comprising at least one of a ramp-up phase, an unlocking phase. a moving phase, wherein the moving phase comprises at least one of moving a first blade, and/or moving a second blade. locking phase.

13. The method according to claim 8, wherein the step of forecasting at least one railway health status of the at least one component the railway network comprises using trends in at least one feature not based on electric current (EC) records comprising at least one of: air temperature, rail temperature, position of blades, model of point machine, and/or position of point machine.

14. The method according to claim 13, further comprising the step of generating at least one hypothesis as regards the position of blades, wherein the method comprises outputting a first finding comprising a first position of the blades, outputting a second finding comprising a second position of the blades, contrasting the first finding with the second finding, and/or generating a cause for the difference between the first finding and second finding, wherein the first position of the blades is a left locking position and the second position of the blades is a right blocking position.

15. The method according to claim 8, wherein the step of forecasting at least one railway health status of the least one component of the railway network is based on at least one analytical approach, and wherein the method further comprises the step of retrieving a first data of a first occurrence of a feature, processing the first data of the first occurrence of the feature, retrieving a n-th data of a n-th occurrence of the feature, processing the n-th data of the n-th occurrence of the feature, generating a data difference finding, wherein the data difference finding is based on at least one parameter difference between the first data of the first occurrence and the n-th data of the n-th occurrence, and outputting an interpreted data difference finding.

Description

[0319] 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.

[0320] FIG. 1 depicts a schematic representation of a railway network and system arranged at the railway network;

[0321] FIG. 2 depicts a system for monitoring a railway network according to embodiments of the present invention;

[0322] FIG. 3 depicts a schematic of a computing device.

[0323] 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.

[0324] 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.

[0325] 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 is needless to say that other constructions, buildings etc. may be present and also used for the present invention as described before and below.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] In one embodiment, the processing component 300 may also be integrated with at least one of the sensors 200. In order words, the processing component 300 may also comprise an imbedded module of the sensors 200.

[0336] 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.

[0337] The server 600 may comprise one or more modules configured to receive information from the analyzing component 500.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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).

[0356] 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).

[0357] 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).

[0358] 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.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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: [0367] output user interface, such as: [0368] screens or monitors configured to display visual data (e.g. displaying graphical user interfaces of railway network status), [0369] speakers configured to communicate audio data (e.g. playing audio data to the user), [0370] input user interface, such as: [0371] camera configured to capture visual data (e.g. capturing images and/or videos of the user), [0372] microphone configured to capture audio data (e.g. recording audio from the user), [0373] 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.

[0374] 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).

[0375] 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.

[0376] 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”.

[0377] 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.