WO2024154208A1

WO2024154208A1 - Rail vehicle data analysis method, rail vehicle data analysis device and rail vehicle data analysis computer program

Info

Publication number: WO2024154208A1
Application number: PCT/JP2023/001060
Authority: WO
Inventors: Nikhil ADKAR; Shotaro MIYANAGA
Original assignee: Hitachi, Ltd.
Priority date: 2023-01-16
Filing date: 2023-01-16
Publication date: 2024-07-25

Abstract

Aspects relate to providing a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles and providing insight into the potential causes of stopping accuracy deterioration for a particular rail vehicle. A rail vehicle data analysis method includes acquiring a set of rail vehicle operation data; identifying a set of stop accuracy values for a rail vehicle; determining a set of abnormal stop accuracy values; defining a set of analysis variables that may influence influencing stop accuracy of the rail vehicle; defining a set of analysis functions for identifying the set of analysis variables; extracting data that corresponds to a timeframe that includes the set of analysis variables; identifying a set of feature values that indicate potential causes of the set of abnormal stop accuracy values; and generating a set of output data that indicates the set of feature values for each rail vehicle.

Description

RAIL VEHICLE DATA ANALYSIS METHOD, RAIL VEHICLE DATA ANALYSIS DEVICE AND RAIL VEHICLE DATA ANALYSIS COMPUTER PROGRAM

The present disclosure relates to a rail vehicle data analysis method, a rail vehicle data analysis device, and a rail vehicle data analysis computer program.

In recent years, with the increased sophistication of railway systems, the importance of reliably monitoring, collecting, and communicating information relating to the operation of the rail vehicles of a rail vehicle consist is similarly increasing. By analyzing operation information collected from rail vehicle systems, useful insights regarding operational efficiency and safety can be acquired.

Conventionally, techniques for analyzing rail vehicle operational data to detect abnormalities have been considered.
As an example of a rail vehicle data abnormality technique, Japanese Unexamined Patent Application Publication No. 2006-117190 (Patent Document 1) discloses “An object of the present invention is to provide an ATC data analysis system capable of automatically performing analysis processing such as detecting an abnormality in a railway vehicle using ATC data. An ATC analysis device (21) is disclosed that utilizes a database (200) storing ATC data including ATC chart data transmitted from an ATC device mounted on a railway vehicle. The ATC analysis device 21 uses ATC data to execute data analysis software that implements analysis functions including a railway vehicle abnormality detection function, a deceleration measurement function, and a slip/skid detection function.”

Japanese Unexamined Patent Application Publication No. 2006-117190

Stopping accuracy is one important index of operational efficiency in automatic train operation (ATO) systems. Typically, in ATO systems, train stopping control is performed manually to bring the train to a stop at a designated location, such as the embark/disembark areas of a railway station. Poor stopping accuracy can result in the train overshooting or undershooting its designated stop location, potentially inhibiting passenger movement and leading to safety concerns.

The stopping accuracy of a train may be influenced by any of a large number of factors, including brake performance, friction, weather conditions, voltage, train approach speed, and the like. Accordingly, in order to maintain operational efficiency and safety, it is desirable to detect deteriorations in stopping accuracy as well as diagnose the potential cause of the detected deterioration.

While Patent Document 1 discloses a technique for analyzing automatic train control (ATC) data to automatically detect the presence of abnormalities based on the speed of the train, emergency break usage, and the like, it does not consider or provide techniques for detecting deteriorations in stopping accuracy and diagnosing potential causes of any detected stopping accuracy deterioration. As a result, train operators are required to use trial-and-error approaches to estimate the causes of stopping accuracy deterioration in rail vehicle systems.

Accordingly, it is an object of the present disclosure to provide a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles and providing insight into the potential causes of stopping accuracy deterioration for a particular rail vehicle.

One representative example of the present disclosure relates to a rail vehicle data analysis method including acquiring a set of rail vehicle operation data relating to operation of a rail vehicle; identifying, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle; determining, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values; defining a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle; defining a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data; extracting, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables; identifying, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values; and generating a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.

According to the present disclosure it is possible to provide a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles and providing insight into the potential causes of stopping accuracy deterioration for a particular rail vehicle.

Problems, configurations, and effects other than those described above will be made clear by the following description in the embodiments for carrying out the invention.

FIG. 1 illustrates an example computing architecture for executing the embodiments of the present disclosure. FIG. 2 is a diagram illustrating an example hardware configuration of a rail vehicle data analysis system according to the embodiments of the present disclosure. FIG. 3 is a flowchart illustrating a rail vehicle data analysis method according to the embodiments of the present disclosure. FIG. 4 is a diagram illustrating an analysis variable table for storing a set of analysis variables according to the embodiments of the present disclosure. FIG. 5 is a diagram illustrating an analysis function table for storing a set of analysis functions according to the embodiments of the present disclosure. FIG. 6 is a diagram illustrating a compound analysis function table for storing a set of compound analysis functions according to the embodiments of the present disclosure. FIG. 7 is a diagram illustrating a stop accuracy condition table for storing a set of stop accuracy conditions according to the embodiments of the present disclosure. FIG. 8 is a diagram illustrating the configuration of the first subset of the rail vehicle operation data according to the embodiments of the present disclosure. FIG. 9 is a diagram illustrating the configuration of the second subset of the rail vehicle operation data according to the embodiments of the present disclosure. FIG. 10 is a diagram illustrating the configuration of the set of output data according to the embodiments of the present disclosure.

Description of Embodiment(s)

Herein, embodiments of the present invention will be described with reference to the Figures. It should be noted that the embodiments described herein are not intended to limit the invention according to the claims, and it is to be understood that each of the elements and combinations thereof described with respect to the embodiments are not strictly necessary to implement the aspects of the present invention.

Various aspects are disclosed in the following description and related drawings. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure.

The words “exemplary” and/or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and/or “example” is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term “aspects of the disclosure” does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation.

Further, many aspects are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be recognized that various actions described herein can be performed by specific circuits (e.g., an application specific integrated circuit (ASIC)), by program instructions being executed by one or more processors, or by a combination of both. Additionally, the sequence of actions described herein can be considered to be embodied entirely within any form of computer readable storage medium having stored therein a corresponding set of computer instructions that upon execution would cause an associated processor to perform the functionality described herein. Thus, the various aspects of the disclosure may be embodied in a number of different forms, all of which have been contemplated to be within the scope of the claimed subject matter.

Hereinafter, a detailed description of the embodiments of the present disclosure will be described with reference to the Figures.

Turning now to the Figures, FIG. 1 depicts a high-level block diagram of a computer system 100 for implementing various embodiments of the present disclosure, according to embodiments. The mechanisms and apparatus of the various embodiments disclosed herein apply equally to any appropriate computing system. The major components of the computer system 100 include one or more processors 102, a memory 104, a terminal interface 112, a storage interface 113, an I/O (Input/Output) device interface 114, and a network interface 115, all of which are communicatively coupled, directly or indirectly, for inter-component communication via a memory bus 106, an I/O bus 108, bus interface unit 109, and an I/O bus interface unit 110.

The computer system 100 may contain one or more general-purpose programmable central processing units (CPUs) 102A and 102B, herein generically referred to as the processor 102. In embodiments, the computer system 100 may contain multiple processors; however, in certain embodiments, the computer system 100 may alternatively be a single CPU system. Each processor 102 executes instructions stored in the memory 104 and may include one or more levels of on-board cache.

In embodiments, the memory 104 may include a random-access semiconductor memory, storage device, or storage medium (either volatile or non-volatile) for storing or encoding data and programs. In certain embodiments, the memory 104 represents the entire virtual memory of the computer system 100, and may also include the virtual memory of other computer systems coupled to the computer system 100 or connected via a network. The memory 104 can be conceptually viewed as a single monolithic entity, but in other embodiments the memory 104 is a more complex arrangement, such as a hierarchy of caches and other memory devices. For example, memory may exist in multiple levels of caches, and these caches may be further divided by function, so that one cache holds instructions while another holds non-instruction data, which is used by the processor or processors. Memory may be further distributed and associated with different CPUs or sets of CPUs, as is known in any of various so-called non-uniform memory access (NUMA) computer architectures.

The memory 104 may store all or a portion of the various programs, modules and data structures for processing data transfers as discussed herein. For instance, the memory 104 can store a rail vehicle data analysis application 150. In embodiments, the rail vehicle data analysis application 150 may include instructions or statements that execute on the processor 102 or instructions or statements that are interpreted by instructions or statements that execute on the processor 102 to carry out the functions as further described below.
In certain embodiments, the rail vehicle data analysis application 150 is implemented in hardware via semiconductor devices, chips, logical gates, circuits, circuit cards, and/or other physical hardware devices in lieu of, or in addition to, a processor-based system. In embodiments, the rail vehicle data analysis application 150 may include data in addition to instructions or statements. In certain embodiments, a camera, sensor, or other data input device (not shown) may be provided in direct communication with the bus interface unit 109, the processor 102, or other hardware of the computer system 100. In such a configuration, the need for the processor 102 to access the memory 104 and the rail vehicle data analysis application 150 may be reduced.

The computer system 100 may include a bus interface unit 109 to handle communications among the processor 102, the memory 104, a display system 124, and the I/O bus interface unit 110. The I/O bus interface unit 110 may be coupled with the I/O bus 108 for transferring data to and from the various I/O units. The I/O bus interface unit 110 communicates with multiple I/

O interface units

112, 113, 114, and 115, which are also known as I/O processors (IOPs) or I/O adapters (IOAs), through the I/O bus 108. The display system 124 may include a display controller, a display memory, or both. The display controller may provide video, audio, or both types of data to a display device 126. Further, the computer system 100 may include one or more sensors or other devices configured to collect and provide data to the processor 102.
As examples, the computer system 100 may include biometric sensors (e.g., to collect heart rate data, stress level data), environmental sensors (e.g., to collect humidity data, temperature data, pressure data), motion sensors (e.g., to collect acceleration data, movement data), or the like. Other types of sensors are also possible. The display memory may be a dedicated memory for buffering video data. The display system 124 may be coupled with a display device 126, such as a standalone display screen, computer monitor, television, or a tablet or handheld device display.
In one embodiment, the display device 126 may include one or more speakers for rendering audio. Alternatively, one or more speakers for rendering audio may be coupled with an I/O interface unit. In alternate embodiments, one or more of the functions provided by the display system 124 may be on board an integrated circuit that also includes the processor 102. In addition, one or more of the functions provided by the bus interface unit 109 may be on board an integrated circuit that also includes the processor 102.

The I/O interface units support communication with a variety of storage and I/O devices. For example, the terminal interface unit 112 supports the attachment of one or more user I/O devices 116, which may include user output devices (such as a video display device, speaker, and/or television set) and user input devices (such as a keyboard, mouse, keypad, touchpad, trackball, buttons, light pen, or other pointing device). A user may manipulate the user input devices using a user interface in order to provide input data and commands to the user I/O device 116 and the computer system 100, and may receive output data via the user output devices. For example, a user interface may be presented via the user I/O device 116, such as displayed on a display device, played via a speaker, or printed via a printer.

The storage interface 113 supports the attachment of one or more disk drives or direct access storage devices 117 (which are typically rotating magnetic disk drive storage devices, although they could alternatively be other storage devices, including arrays of disk drives configured to appear as a single large storage device to a host computer, or solid-state drives, such as flash memory). In some embodiments, the storage device 117 may be implemented via any type of secondary storage device. The contents of the memory 104, or any portion thereof, may be stored to and retrieved from the storage device 117 as needed. The I/O device interface 114 provides an interface to any of various other I/O devices or devices of other types, such as printers or fax machines. The network interface 115 provides one or more communication paths from the computer system 100 to other digital devices and computer systems; these communication paths may include, for example, one or more networks 130.

Although the computer system 100 shown in FIG. 1 illustrates a particular bus structure providing a direct communication path among the processors 102, the memory 104, the bus interface 109, the display system 124, and the I/O bus interface unit 110, in alternative embodiments the computer system 100 may include different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface unit 110 and the I/O bus 108 are shown as single respective units, the computer system 100 may, in fact, contain multiple I/O bus interface units 110 and/or multiple I/O buses 108. While multiple I/O interface units are shown which separate the I/O bus 108 from various communications paths running to the various I/O devices, in other embodiments, some or all of the I/O devices are connected directly to one or more system I/O buses.

In various embodiments, the computer system 100 is a multi-user mainframe computer system, a single-user system, or a server computer or similar device that has little or no direct user interface, but receives requests from other computer systems (clients). In other embodiments, the computer system 100 may be implemented as a desktop computer, portable computer, laptop or notebook computer, tablet computer, pocket computer, telephone, smart phone, or any other suitable type of electronic device.

Next, with reference to FIG. 2, an example hardware configuration of a rail vehicle data analysis system according to the embodiments of the present disclosure will be described.

FIG. 2 is a diagram illustrating an example hardware configuration of a rail vehicle data analysis system 200 according to the embodiments of the present disclosure. The rail vehicle data analysis system 200 relates to an information processing system configured to collect a set of rail vehicle operation data relating to operation of a rail vehicle, identifying data for analysis relating to stopping accuracy of the rail vehicle, setting analysis parameters, and performing analysis to determine potential causes of stopping accuracy deterioration for the rail vehicle.

As illustrated in FIG. 2, the rail vehicle data analysis system 200 according to the embodiments of the present disclosure includes a rail vehicle 210, a user terminal 220, a communication network 230 and a rail vehicle data analysis device 240. In the rail vehicle data analysis system 200, the rail vehicle 210, the user terminal 220, and the rail vehicle data analysis device 240 may be communicatively connected via the communication network 230.
Here, the communication network 230 may include a Local Area Network (LAN) connection, the Internet, a Wide Area Network (WAN) connection, a Metropolitan Area Network (MAN) connection or the like.

In embodiments, the rail vehicle 210 may include one or more rail vehicles, such as trains, that are mechanically coupled or linked together to travel on a track that extends along a route. Alternatively, the rail vehicles may not be mechanically linked together, but may communicate with each other so that the vehicles coordinate movements and the group of vehicles move along a route together in a coordinated manner. The rail vehicle 210 may be used in operations described as freight rail, passenger rail, high speed rail, commuter rail, rail transit, metro, light rail, trams, tramways, or train-tram. In embodiments, the rail vehicle 210 may be configured to record a set of rail vehicle operation data that characterizes its operation and performance. This rail vehicle operation data may be transmitted to the user terminal 220 and rail vehicle data analysis device 240 periodically or in real time via the communication network 230, or may be stored on a local storage device in the rail vehicle 210 to be manually collected once operation has completed.

The user terminal 220 is a device that can be used by a user (e.g., a client) of the rail vehicle data analysis device 240. In embodiments, the user terminal 220 may be used to request analysis by the rail vehicle data analysis device 240 of the set of rail vehicle operation data recorded for the rail vehicle 210, and confirm the results of this analysis. Further, in embodiments, the user terminal may be used to allow a user to define a set of analysis variables and/or a set of analysis functions for analyzing the rail vehicle operation data. As examples, the user terminal 220 may be implemented using a personal computer, tablet computer, smart phone, or other computing device.

The rail vehicle data analysis device 240 is a device configured to analyze the set of rail vehicle operation data to detect stopping accuracy deterioration in the rail vehicle 210 and determine potential causes of said stopping accuracy deterioration. In embodiments, the rail vehicle data analysis device 240 may be implemented using the computer system 100 illustrated in FIG. 1 as part of a distributed computing architecture. For instance, the functions of the rail vehicle data analysis device 240 may be implemented using one or more computing devices (e.g., the computer system 100) comprising a cloud infrastructure.

As illustrated in FIG. 2, the rail vehicle data analysis device 240 may include a data acquisition unit 242, an analysis data extraction unit 244, an analysis management unit 246, and an analysis unit 248. In embodiments, the data acquisition unit 242, the analysis data extraction unit 244, the analysis management unit 246, and the analysis unit 248 may be implemented as software modules constituting the rail vehicle data analysis application 150 stored in the memory 104 of the computer system 100 illustrated in FIG. 1. In this way, the functions of the data acquisition unit 242, the analysis data extraction unit 244, the analysis management unit 246, and the analysis unit 248 can be performed by the processor 102 of the computer system 100 to realize the techniques of the present disclosure.

The data acquisition unit 242 is a functional unit for acquiring the set of rail vehicle operation data for the rail vehicle 210. In embodiments, the data acquisition unit 242 may acquire the set of rail vehicle operation data from the user terminal 220. For example, a user of the user terminal 220 may upload the set of rail vehicle operation data to the rail vehicle data analysis device 240 via a graphical user interface provided by the data acquisition unit 242. In embodiments, the data acquisition unit 242 may transmit a data acquisition request to the rail vehicle 210 directly in order to acquire the set of rail vehicle operation data. Alternatively, in certain embodiments, the rail vehicle 210 may be configured to automatically upload the set of rail vehicle operation data to the data acquisition unit 242. This automatic data upload may be performed at periodic time intervals, after occurrence of a particular event (e.g., departure or arrival of the rail vehicle 210 from a particular location), in response to collection of a certain amount of data, or the like.

The analysis data extraction unit 244 is a functional unit for extracting, from the set of rail vehicle operation data acquired by the data acquisition unit 242, a set of analysis data to be analyzed. In embodiments, the analysis data extraction unit 244 may identify, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle 210, and determine, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values. This second subset of the rail vehicle operation data may be used as the set of analysis data.

The analysis management unit 246 is a functional unit for setting parameters to facilitate analysis of the set of analysis data extracted by the analysis data extraction unit 244. In embodiments, the analysis management unit may define a set of analysis variables that relate to operation events that have a possibility of influencing the stop accuracy of the rail vehicle 210, and define a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data. In embodiments, the set of analysis variables and the set of analysis functions may be designated based on user input (e.g., user input received from the user of the user terminal 220).

The analysis unit 248 is a functional unit for performing analysis on the set of analysis data (e.g., the second subset of the rail vehicle operation data) using the parameters (e.g., the analysis variables and analysis functions) set by the analysis management unit 246. In embodiments, the analysis unit 248 may extract, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables, identify, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values, and generate a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data. The analysis unit 248 may subsequently provide the set of output data to the user terminal 220.

According to the rail vehicle data analysis device 240 illustrated in FIG. 2, it is possible to provide a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles and providing insight into the potential causes of stopping accuracy deterioration for a particular rail vehicle.

Next, with reference to FIG. 3, a rail vehicle data analysis method according to the embodiments of the present disclosure will be described.

FIG. 3 is a diagram illustrating a rail vehicle data analysis method 300 according to the embodiments of the present disclosure. The rail vehicle data analysis method 300 is a method for analyzing a set of rail vehicle operation data to detect stopping accuracy deterioration in a rail vehicle and determining potential causes of said stopping accuracy deterioration. The rail vehicle data analysis method 300 may be performed by the various functional units of the rail vehicle data analysis device 240 illustrated in FIG. 2.

First, at Step S305, the data acquisition unit 242 acquires a set of rail vehicle operation data relating to operation of a rail vehicle. The set of rail vehicle operation data may be a collection of structured information characterizing the operation of a rail vehicle. For instance, the set of rail vehicle operation data may include information relating to the speed, acceleration, brake usage, electrical parameters, stopping accuracy, signals, departure and arrival times, routes, and any other parameters related to the operation of the rail vehicle. In certain embodiments, the set of rail vehicle operation data may include a set of time series operation records that at least include a speed value of the rail vehicle at a particular time while traveling along a route, a stop accuracy value of the rail vehicle at a particular time while traveling along the route, and a next station tag at which the rail vehicle is scheduled to stop on the route. In certain embodiments, the set of rail vehicle operation may be ATC (automatic train control) data.

In embodiments, the data acquisition unit 242 may acquire the set of rail vehicle operation data from the user terminal 220. For example, a user of the user terminal 220 illustrated in FIG. 2 may upload the set of rail vehicle operation data to the rail vehicle data analysis device 240 via a graphical user interface provided by the data acquisition unit 242. In embodiments, the data acquisition unit 242 may transmit a data acquisition request to the rail vehicle 210 directly in order to acquire the set of rail vehicle operation data. Alternatively, in certain embodiments, the rail vehicle 210 may be configured to automatically upload the set of rail vehicle operation data to the data acquisition unit 242. This automatic data upload may be performed at periodic time intervals, after occurrence of a particular event (e.g., departure or arrival of the rail vehicle 210 from a particular location), in response to collection of a certain amount of data, or the like.

Next, at Step S310, the analysis data extraction unit 244 identifies, from among the set of rail vehicle operation data acquired in Step S305, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle. As described herein, the set of stop accuracy values are one or more numerical values that indicate the accuracy with which a rail vehicle stops (e.g., speed reaches zero) with respect to a designated stop location. In embodiments, the set of stop accuracy values may include distance values that indicate how far the rail vehicle stopped from the designated stop location. Negative distance values may indicate that a rail vehicle stopped before reaching the designated stop location (e.g., the rail vehicle undershot the stop location), a zero value may indicate that a rail vehicle stopped precisely at the designated stop location, and positive distance values may indicate that a rail vehicle exceeded the designated stop location (e.g., the rail vehicle overshot the stop location). In embodiments, the analysis data extraction unit 244 may identify a stop accuracy value for one or more rail vehicles for each of a number of predetermined stops of each rail vehicle along a route, and aggregate the identified stop accuracy values as the first subset of the rail vehicle operation data.

In general, stop accuracy values for the rail vehicle may be continuously calculated in real time during operation of the rail vehicle and saved as time-series records. As an example, stop accuracy value records may be calculated and recorded at a fixed time interval on the order of several hundred milliseconds. Further, aspects of the disclosure relate to the recognition that, due to the time required for sensor data collection and stop accuracy calculation processing, the calculated stop accuracy values may be delayed with respect to the actual movement of the rail vehicle. Accordingly, stop accuracy record computation and generation may continue for several time intervals after the rail vehicle has actually come to a stop at a designated stop location. As a result, the stop accuracy record that most accurately represents the distance between the rail vehicle and the designated stop location may not be the stop accuracy record that corresponds to the time when the rail vehicle actually came to a stop, but the stop accuracy record several time intervals after the rail vehicle came to a stop.

Accordingly, in view of the above, aspects of the disclosure relate to using a set of stop accuracy conditions to identify a set of stop accuracy values for the rail vehicle. Here, the set of stop accuracy values may include one or more stop accuracy values that most accurately indicate the distance of the rail vehicle with respect to a particular stop location. The stop accuracy conditions refer to requirements that can be used to identify the stop accuracy record that most precisely indicates the stopping accuracy of the rail vehicle with respect to a particular stop location. As examples of the stop accuracy conditions will be described later with respect to FIG. 7, a description thereof will be omitted here.

In embodiments, the analysis data extraction unit 244 may identify the set of stop accuracy values for the rail vehicle by identifying, from among the set of time series operation records, a first time series operation record in which the speed value of the rail vehicle is zero, the speed value of the rail vehicle is non-zero in the immediately previous time series operation record, the speed value of the rail vehicle is zero in the immediately subsequent time serious operation record, and the next station tag changes in the immediately subsequent time series operation record. In this way, the analysis data extraction unit 244 can identify the stop accuracy value that corresponds to a time when the rail vehicle has reached a stop after traveling at a certain speed, remains at rest, and has not yet started operation toward the next station on the route. Subsequently, the analysis data extraction unit 244 may extract, as the first subset of the rail vehicle operation data, the next station tag from the first time series operation record and a stop accuracy value from a second time series operation record a predetermined number of records subsequent to the first time series operation record. This predetermined number of records may be determined based on the time series record computation time (that is, the time it takes for the stop accuracy values to be computed and a corresponding record to be created). In this way, the analysis data extraction unit 244 can identify a stop accuracy value that corresponds to a time after generation of stop accuracy value records for a particular designated stop location have completed, thus accounting for the time delay in record generation and making it possible to select the stop accuracy record that most precisely indicates the stopping accuracy of the rail vehicle with respect to a particular stop location.

Next, at Step S315, the analysis data extraction unit 244 determines, from among the first subset of the rail vehicle operation data identified in Step S310, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values. Here, the set of abnormal stop accuracy values may include stop accuracy values that indicate a potential error with the stopping capabilities of the rail vehicle. The set of abnormal stop accuracy values may be determined using a predetermined stop accuracy threshold. In embodiments, the stop accuracy threshold may indicate a range of acceptable stop accuracy values (e.g., -100 cm to +100 cm). Accordingly, the analysis data extraction unit 244 may determine, from among the first subset of the rail vehicle operation data, time series operation records with stop accuracy values that do not fall within the range specified by the stop accuracy threshold as the second subset of the rail vehicle operation data. It should be noted that, in addition to the set of abnormal stop accuracy values, the second subset of the rail vehicle operation data also includes the operational parameters (time series speed information, break usage information and the like) for the rail vehicle. In embodiments, the second subset of the rail vehicle operation data may be sent to a nested space for analysis. The nest space may be a logical space configured for temporary storage and analysis of the rail vehicle operation data.

Next, at Step S320, the analysis management unit 246 may define a set of analysis variables that relate to operation events that have a possibility of influencing the stop accuracy of the rail vehicle. The set of analysis variables refer to particular parameters within the set of rail vehicle operation data that relate to operation events that have a possibility of influencing the stop accuracy of the rail vehicle. Here, operation events refer to any state, incident, action, or situation that occurs during the operation of the rail vehicle. For instance, the analysis variables may include a slip variable that relates to an operation event of the wheels of the rail vehicle slipping on the rails, a speed variable that relates to the speed of the rail vehicle, an acceleration variable that relates to the acceleration of the rail vehicle, and the like. The set of analysis variables may be used by the analysis functions to be described later to identify the presence of operation events that have a possibility of influencing the stop accuracy of the rail vehicle.

In embodiments, the analysis management unit 246 may define the set of analysis variables based on the input of a user (e.g., a user of the user terminal 220 illustrated in FIG. 2). For example, a user may define the set of analysis variables based on those variables they believe may have an impact on the stop accuracy of the rail vehicle. In certain embodiments, the analysis management unit 246 may define the set of analysis variables to include all or a portion of the parameters defined in the rail vehicle operation data. As examples of the set of analysis variables will be described later with respect to FIG. 4, a description thereof will be omitted here.

Next, at Step S325, the analysis management unit 246 may define a set of analysis functions. The set of analysis functions refer to logical functions for identifying the set of analysis variables defined in Step S320 within the second subset of the rail vehicle operation data. As examples, the set of analysis functions may include a slip function for identifying a slip variable within the second subset of the rail vehicle operation data, or a speed function for identifying data corresponding to times when the rail vehicle traveled at a certain speed. In certain embodiments, the analysis management unit 246 may define a compound analysis function that includes a first analysis function for identifying a first analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data and a second analysis function for identifying a second analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data. As examples of the set of analysis functions and compound analysis functions will be described later with respect to FIG. 5 and FIG. 6, a description thereof will be omitted here.

Next, at Step S330, the analysis unit 248 extracts, from the second subset of the rail vehicle operation data using the set of analysis functions defined in Step S325, a third subset of the rail vehicle operation data that corresponds to a predefined timeframe that includes the set of analysis variables. As an example, the analysis unit 248 may extract a portion of the second subset of the rail vehicle operation data corresponding to a timeframe from 30 seconds before to 10 seconds after occurrence of an operation event corresponding to a particular analysis variable.
In this way, by determining rail vehicle operation data (the second subset) that includes abnormal stop accuracy values, and then extracting a portion of this rail vehicle operation data (the third subset) corresponding to a time period in which a particular operation event that may impact stop accuracy has occurred, analysis of the potential causes of the abnormal stop accuracy values can be facilitated.

Next, at Step S335, the analysis unit 248 identifies, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values. In embodiments, the analysis unit 248 may use a statistical analysis technique for identifying parameters associated with values classified as statistical outliers. In embodiments, the analysis unit 248 may identify the maximum value of one or more predetermined parameters from within the third subset of the rail vehicle operation data as the set of feature values. As examples, the analysis unit 248 may identify, as the set of feature values, the maximum speed value and the maximum brake voltage value for the rail vehicle within the third subset of the rail vehicle operation data. In embodiments, this analysis may be performed within a nest space.

Next, at Step S340, the analysis unit 248 may generate a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data. In embodiments, the analysis unit may visually emphasizing the feature values for each set of rail vehicle operation data in the set of output data. Further, in certain embodiments, the analysis unit may determine, based on the set of feature values, a candidate cause for the set of abnormal stop accuracy values, and include the candidate cause in the set of output data. The analysis unit may output the set of output data to the user terminal 220 via the communication network 230. As examples of the set of output data will be described later with respect to FIG. 12, a description thereof will be omitted here.

According to the rail vehicle data analysis method 300 illustrated in FIG. 3, by using the set of stop accuracy conditions, it is possible to account for the time delay in record generation and to identify a first subset of rail vehicle operation data that includes the stop accuracy records that most precisely indicates the stopping accuracy of the rail vehicle with respect to a particular stop location. Additionally, by using analysis functions to analyze a second subset of rail vehicle operation data having abnormal stop accuracy values, it becomes possible to extract a third subset of rail vehicle operation data that includes the occurrence of an operation event that may influence stop accuracy. Further, by analyzing the third subset of rail vehicle operation data with a statistical analysis technique, feature values that indicate potential causes of the abnormal stop accuracy values can be determined.
In this way, it is possible to provide a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles and providing insight into the potential causes of stopping accuracy deterioration for a particular rail vehicle.

Next, with reference to FIG. 4, the set of analysis variables according to the embodiments of the present disclosure will be described.

FIG. 4 is a diagram illustrating an analysis variable table 400 for storing the set of analysis variables according to the embodiments of the present disclosure. As illustrated in FIG. 4, the analysis variable table 400 may include a variable number 402, a variable name 404, a variable source address 406, a variable destination address 408, conversion data 410, a memo 412, and an author 414.
The variable number 402 indicates a number to serve as a unique identifier for each analysis variable in the analysis variable table 400.

The variable name 404 indicates the name of a particular analysis variable. As described herein, the analysis variables according to the embodiments of the present disclosure refer to particular parameters within the set of rail vehicle operation data that relate to operation events that have a possibility of influencing the stop accuracy of the rail vehicle. For instance, as illustrated in FIG. 4, the analysis variables may include a slip variable that relates to the wheels of the rail vehicle slipping on the rails, a speed variable that relates to the speed of the rail vehicle, an acceleration variable that relates to the acceleration of the rail vehicle, an overstop variable that relates to the rail vehicle overshooting its stop location, an emergency break variable that relates to the usage of an emergency break by the rail vehicle, and a voltage variable that relates to the voltage supplied to the rail vehicle (e.g., the voltage supplied to the breaks of the rail vehicle).
As described herein, the analysis variables designated by the variable names 404 may be used by the analysis functions to be described later to identify the presence of operation events that have a possibility of influencing the stop accuracy of the rail vehicle. In embodiments, a user (e.g., a user of the user terminal 220 illustrated in FIG. 2) may designate the analysis variables in the analysis variable table 400 based on those variables they believe may have an impact on the stop accuracy of the rail vehicle. In embodiments, the analysis variable table 400 may be structured to include all or a portion of the parameters defined in the rail vehicle operation data.

The variable source address 406 is information that defines the source location of a particular analysis variable within the data file containing the set of rail vehicle operation data. For instance, in the event that the data file containing the set of rail vehicle operation data is formatted as a proprietary data file (e.g., a PCF file, CSV file, or other tabular data format), the variable source address 406 may designate a row and a column of a table in the data file that includes a particular analysis variable.
The variable destination address 408 is information that defines the destination location of a particular analysis variable to be saved in within the data file containing the set of rail vehicle operation data after numerical system conversion (base 2 to base 10, etc.) or other pre-processing operation has been completed.

The conversion data 410 is information for specifying the numerical system conversion to be applied to a particular analysis variable. As examples, the conversion data may indicate a numerical conversion from base 2 to base 10, base 2 to base 16, base 16 to base 10, or the like.

The memo 412 is information for indicating the purpose of a particular analysis variable.
The author 414 indicates the user who defined a particular analysis variable.

Next, with reference to FIG. 5, a set of analysis functions according to the embodiments of the present disclosure will be described.

FIG. 5 is a diagram illustrating an analysis function table 500 for storing the set of analysis functions according to the embodiments of the present disclosure. As illustrated in FIG. 5, the analysis function table 500 may include a function number 502, a function name 504, a function variable 506, a function operator 508, a bit change value (before) 510, a bit change value (after) 512, a range value (low end) 514, a range value (high end) 516, a memo 518 and an author 520.

The function number 502 indicates a number to serve as a unique identifier for each analysis function in the analysis function table 500.

The function name 504 indicates the name of a particular analysis function. As described herein, the set of analysis functions are logical functions for identifying the set of analysis variables of the analysis variable table 400 illustrated in FIG. 4 within the rail vehicle operation data (the second subset of the rail vehicle operation data). As examples, as illustrated in FIG. 5, the set of analysis functions may include a slip function for identifying a slip variable within the second subset of the rail vehicle operation data, or a speed function for identifying data corresponding to times when the rail vehicle traveled at a certain speed. Multiple analysis functions may be defined for a particular analysis variable. For instance, the analysis variable table 400 may include a first speed function for identifying rail vehicles with a speed of 60 kilometers per hour or greater, and a second speed function for identifying rail vehicles with a speed of between 20-60 kilometers per hour.
In embodiments, a user (e.g., a user of the user terminal 220 illustrated in FIG. 2) may designate the analysis functions in the analysis function table 500 based on those variables (operation events) they wish to identify within the set of vehicle operation data.

The function variable 506 indicates the analysis variable to be operated on (that is, identified) by a particular analysis function. As examples, the function variable 506 may include slip, speed, emergency brake usage, or the like.

The function operator 508 indicates a state to be identified with respect to a particular analysis variable. For instance, as illustrated in FIG. 5, the function operator 508 may include “change,” “greater than or equal to,” “less than or equal to,” “between,” or the like. As an example, a “slip function” with a function operator of “change” may be configured to identify any change in a binary bit that indicates the presence or absence of slip with respect to a rail vehicle.

The bit change value (before) 510 indicates an initial state of a particular bit corresponding to a particular function variable 506 prior to a change, and the bit change value (after) 512 indicates a final state of a particular bit corresponding to a particular function variable 506 after a change. The bit change value (before) 510 and the bit change value (after) 512 can be used to monitor for change in a binary bit that indicates the presence or absence of slip with respect to a rail vehicle, for instance.

The range value (low end) 514 indicates the low end of a numerical range for a particular bit corresponding to a particular function variable 506, and the range value (high end) 516 indicates the high end of a numerical range for a particular bit corresponding to a particular function variable 506. The range value (low end) 514 and the range value (high end) 516 can be used to monitor for rail values associated with a function variable 506 that satisfies a particular numerical range, such as a rail vehicle with a speed greater than or equal to 60 kilometers per hour or a rail vehicle with a speed between 20 and 60 kilometers per hour.

The memo 518 is information for indicating the purpose of a particular analysis function.
The author 520 indicates the user who defined a particular analysis function.

Next, with reference to FIG. 6, a set of compound analysis functions according to the embodiments of the present disclosure will be described.

FIG. 6 is a diagram illustrating a compound analysis function table 600 for storing a set of compound analysis functions according to the embodiments of the present disclosure. As illustrated in FIG. 6, the compound analysis function table 600 may include a compound analysis function name 602, compound analysis function logic 604, a memo 606 and an author 608.

As described herein, in certain embodiments, in addition to the set of analysis functions described above with reference FIG. 5, a set of compound analysis functions may be used to identify the set of analysis variables within the rail vehicle operation data. For instance, a compound analysis function may be defined that includes a first analysis function for identifying a first analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data and a second analysis function for identifying a second analysis variable of the set of analysis variables within the rail vehicle operation data. Compound analysis functions may be used to identifier more granular operation events within the set rail vehicle operation data.

The compound analysis function name 602 indicates the name of a particular compound analysis function. For instance, the compound analysis function name 602 may include “high speed slip” or “high speed and overstop,” for instance.

The compound analysis function logic 604 indicates the logic for implementing a particular compound analysis function. In embodiments, the compound analysis function logic 604 may be specified in terms of the individual analysis functions it includes. For instance, the compound analysis function logic 604 may include a “slip function and speed function (60+)”. This “high speed slip” function can be used to identify slip events among rail vehicles traveling at greater than 60 kilometers per hour. As another example the “high speed and overstop” function defined by “speed function (60+) and overstop” can be used to identify overstop events among rail vehicles traveling at greater than 60 kilometers per hour.

The memo 606 is information for indicating the purpose of a particular compound analysis function.
The author 608 indicates the user who defined a particular compound analysis function.

Next, with reference to FIG. 7, the set of stop accuracy conditions according to the embodiments of the present disclosure will be described.

FIG. 7 is a diagram illustrating a stop accuracy condition table 700 for storing a set of stop accuracy conditions according to the embodiments of the present disclosure. As described herein, aspects of the present disclosure relate to using the set of stop accuracy conditions to identify a set of stop accuracy values for the rail vehicle that most accurately indicate the distance of the rail vehicle with respect to a particular stop location, taking into account the delay in time stop accuracy record generation. Accordingly, the stop accuracy condition table 700 shown in FIG. 7 illustrates an example of the stop accuracy conditions that can be used to identify the set of stop accuracy values. It should be noted that the stop accuracy conditions illustrated in FIG. 7 are mere examples, and the stop accuracy conditions used to identify the set of stop accuracy values for the rail vehicle are not particularly limited herein, such that any stop accuracy conditions or logic capable of determining a set of stop accuracy values that reliably indicate the distance of the rail vehicle with respect to a particular stop location may be utilized.

As illustrated in FIG. 7, the stop accuracy condition table 700 may include stop accuracy conditions 715 for identifying a particular record n from among a set of time series operation records to include in the first subset of the rail vehicle operation data that includes the set of stop accuracy values and a recording action 720 for indicating the information to be recorded when a particular record n that satisfies the stop accuracy conditions 715 has been identified.

In embodiments, the stop accuracy conditions 715 may include a first condition that specifies that the speed value of the rail vehicle be zero in a particular first time series operation record n, a second condition that specifies that the speed value of the rail vehicle be non-zero in a previous time series operation record n-1, a third condition that specifies that the speed value of the rail vehicle be zero in an immediately subsequent time serious operation record n+1, and a fourth condition that specifies that the next station tag in the immediately subsequent time series operation record n+1 is not the same as the next station tag in the first time series operation record n. By identifying a first time series operation record n that satisfies these conditions, it is possible to identify a stop accuracy value that corresponds to a time when the rail vehicle has reached a stop after traveling at a certain speed, remains at rest, and has not yet started operation toward the next station on the route (e.g., the last time series operation record before the rail vehicle starts operation toward the next station on the route).

Once a first time series operation record n that satisfies the stop accuracy conditions 715, the action specified by the recording action 720 may be performed. In embodiments, the recording action 720 may specify that the next station tag for the first time series operation record n and a stop accuracy value from a second time series operation record a predetermined number of records x subsequent to the first time series operation record be extracted and recorded as the first subset of the rail vehicle operation data. The value of x may be determined based on the average time delay in time series record computation and generation. As an example, in the case that the stop accuracy calculation takes 400 milliseconds and time series operation recording is performed at an interval of 100 milliseconds, the value of x may be specified to be “4.” As a result, a time series operation record that is 4 records subsequent to the first time series operation record n (corresponding to a time when 400 milliseconds has passed from when the train has reached a stop and the stop accuracy computation has completed) will be identified as the second time series operation record. In this way, it becomes possible to identify a stop accuracy value that corresponds to a time after generation of stop accuracy value records for a particular designated stop location have completed, thus accounting for the time delay in record generation and making it possible to select the stop accuracy record that most precisely indicates the stopping accuracy of the rail vehicle with respect to a particular stop location.

Next with reference to FIG. 8, the first subset of the rail vehicle operation data according to the embodiments of the present disclosure will be described.

FIG. 8 is a diagram illustrating the configuration of the first subset of the rail vehicle operation data 800 according to the embodiments of the present disclosure. As described herein, the first subset of the rail vehicle operation data 800 is a subset of the rail vehicle operation data that includes at least a set of stop accuracy values. As illustrated in FIG. 8, the first subset of the rail vehicle operation data 800 may include a record number 802, a rail vehicle number 804, a data file name 806, a record time 808, a stop accuracy value 810 and a station code 812.

The record number 802 is a number that uniquely identifies a particular data record within the first subset of the rail vehicle operation data 800.
The rail vehicle number 804 is a number that uniquely identifies a particular rail vehicle.

The data file name 806 is a file name for identifying the data file including the rail vehicle operation data for a particular rail vehicle.
The record time 808 indicates the time and date at which a particular data file was recorded.

The stop accuracy values 810 indicates the stop accuracy values identified from each set of rail vehicle operation data (e.g., each data file). As illustrated in FIG. 8, the set of stop accuracy values 810 may include normal stop accuracy values together with abnormal stop accuracy values (illustrated in bold in FIG. 8) that do not satisfy a predetermined stop accuracy threshold.

The station code 812 indicates the station code that corresponds to the identified set of stop accuracy values 810. That is, with reference to FIG. 8, each of the stop accuracy values 810 was identified for the station code of “Tokyo.”

Next, with reference to FIG. 9, the second subset of rail vehicle operation data according to the embodiments of the present disclosure will be described.

FIG. 9 is a diagram illustrating the configuration of the second subset of the rail vehicle operation data 900 according to the embodiments of the present disclosure. As described herein, the second subset of the rail vehicle operation data 900 is a subset of the first subset of the rail vehicle operation data that includes at least a set of abnormal stop accuracy values. As illustrated in FIG. 9, the second subset of the rail vehicle operation data 900 may include a record number 902, a data file name 904, and stop accuracy values 906.

The record number 902 is a number that uniquely identifies a particular data record within the second subset of the rail vehicle operation data 900.

The data file name 904 is a file name for identifying the data file including the rail vehicle operation data for a particular rail vehicle.

The stop accuracy values 906 indicate the set of abnormal stop accuracy values determined from the first subset of the rail vehicle operation data. As described herein, in embodiments, the set of abnormal stop accuracy values may be stop accuracy values within the first subset of the rail vehicle operation data that do not fall within the range of acceptable stop accuracy values specified by the stop accuracy threshold. As an example, in the case that the stop accuracy threshold specifies a range of -100 centimeters to 100 centimeters, the stop accuracy values of 150, 300,-200, 350, 230 and 400 may be identified as abnormal stop accuracy values.

Next, with reference to FIG. 10, a set of output data according to the embodiments of the present disclosure will be described.

FIG. 10 is a diagram illustrating the configuration of the set of output data 1000 according to the embodiments of the present disclosure. As described herein, the set of output data 1000 is a set of data that includes a set of feature values indicating potential causes of the set of abnormal stop accuracy values together with the set of rail vehicle operation data. As illustrated in FIG. 10, the set of output data 1000 may include a record number 1002, a rail vehicle number 1004, a data file name 1006, a time 1008, a first feature value 1010 and a second feature value 1012.

The record number 1002 is a number that uniquely identifies a particular data record within the set of output data 1000.

The rail vehicle number 1004 is a number that uniquely identifies a particular rail vehicle.

The data file name 1006 is a file name for identifying the data file including the rail vehicle operation data for a particular rail vehicle.
The time 1008 indicates the time and date at which a particular data file was recorded.

The first feature value 1010 and the second feature value 1012 are values corresponding to particular parameters that are associated with potential causes of the set of abnormal stop accuracy values. In embodiments, the first feature value 1010 and the second feature value 1012 may be the maximum values of predetermined parameters present within the set of rail vehicle operation data. In embodiments, the first feature value 1010 and the second feature value 1012 may be identified using a statistical analysis technique for to identify parameters associated with values classified as statistical outliers. As examples, the first feature value 1010 may be the maximum speed value of a rail vehicle within the third subset of the rail vehicle operation data, and the second feature value 1012 may be the maximum brake voltage value for the rail vehicle within the third subset of the rail vehicle operation data.

In embodiments, one or more feature values within the set of output data may be visually emphasized. For instance, in certain embodiments, feature values that are do not satisfy a normal operational range threshold specified for the corresponding parameter may be bolded, highlighted, displayed in a different color, or otherwise visually emphasized. As examples, with reference to FIG. 10, voltage values of “10 volts” and “20 volts” may fail to satisfy a normal operational range threshold of “80 volts to 300 volts,” and be visually emphasized with a bold font. Similarly, a speed value of “1 kilometer per hour” may fail to satisfy a normal operational range threshold of “30 kilometers per hour to 80 kilometers per hour,” and similarly be visually emphasized with a bold font.

Further, in certain embodiments, the analysis unit 248 may be configured to analyze the set of feature values to determine a candidate cause for abnormal stop accuracy value to which a particular feature value corresponds, and include the candidate cause in the set of input data. In embodiments, determining the candidate cause may include using a look-up table that indicates potential causes of stop accuracy deterioration for given feature values. As an example, using the look-up table, the analysis unit 248 may determine that a feature value for a parameter of speed that falls below a normal operational range threshold is associated with a candidate cause of stop accuracy deterioration of “excessive deceleration during breaking distance.” This determined candidate cause 1014 may be appended to the set of output data in the form of associated metadata, a callout indicating the corresponding feature value, or the like.

As described herein, aspects of the present disclosure relate to a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles by analyzing stop accuracy values for a rail vehicle.
However, as described herein, due to the time required for sensor data collection and stop accuracy calculation processing, the calculated stop accuracy values may be delayed with respect to the actual movement of the rail vehicle, such that stop accuracy record computation and generation may continue for several time intervals after the rail vehicle has actually come to a stop at a designated stop location. As a result, the stop accuracy record that most accurately represents the distance between the rail vehicle and the designated stop location may not be the stop accuracy record that corresponds to the time when the rail vehicle actually came to a stop, making it difficult to ascertain reliable stop accuracy values for stopping accuracy evaluation.

Accordingly, in view of the above, aspects of the disclosure relate to using a set of stop accuracy conditions to identify a set of stop accuracy values for the rail vehicle. By using the set of stop accuracy conditions, it becomes possible to account for the time delay in record generation and to identify a first subset of rail vehicle operation data that includes the stop accuracy records that most precisely indicate the stopping accuracy of the rail vehicle with respect to a particular stop location.

Additionally, by using a set of analysis functions to analyze a second subset of rail vehicle operation data having abnormal stop accuracy values identified from among the first subset of rail vehicle operation data, it becomes possible to extract a third subset of rail vehicle operation data that includes the occurrence of an operation event that may influence the stop accuracy of the rail vehicle. These operation events may include, for instance, emergency brake usage, slip events, speed above a normal operational range, or the like. In this way, it becomes possible to pinpoint the portion of rail vehicle operation data in which the operation event that resulted in deterioration of the stop accuracy occurred.

Further, by analyzing the third subset of rail vehicle operation data with a statistical analysis technique, feature values that indicate potential causes of the abnormal stop accuracy values can be determined. These feature values may be values corresponding to parameters associated with stop accuracy deterioration, such as abnormal speed or voltage values. Based on these feature values, candidate causes of the stop accuracy deterioration of a rail vehicle can be identified.

In this way, according to the embodiments of the present disclosure, it is possible to provide a rail vehicle data analysis technique for facilitating the detection of stopping accuracy deterioration in rail vehicles and providing insight into the potential causes of stopping accuracy deterioration for a particular rail vehicle.

As described herein, the present disclosure relates to the following embodiments.

(Aspect 1)
A rail vehicle data analysis method comprising:
acquiring a set of rail vehicle operation data relating to operation of a rail vehicle;
identifying, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle;
determining, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
defining a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle;
defining a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data;
extracting, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables;
identifying, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values; and
generating a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.

(Aspect 2)
The rail vehicle data analysis method according to aspect 1, wherein the set of rail vehicle operation data includes a set of time series operation records at least including:
a speed value of the rail vehicle at a particular time while traveling along a route,
a stop accuracy value of the rail vehicle at a particular time while traveling along the route, and
a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route.

(Aspect 3)
The rail vehicle data analysis method according to aspect 2, wherein identifying the first subset of the rail vehicle operation data includes:
identifying, from among the set of time series operation records, a first time series operation record in which:
the speed value of the rail vehicle is zero,
the speed value of the rail vehicle is greater than zero in a previous time series operation record,
the speed value of the rail vehicle is zero in an immediately subsequent time serious operation record, and
the next station tag differs from the next station tag in the immediately subsequent time series operation record; and
extracting, as the first subset of the rail vehicle operation data, the next station tag from the first time series operation record and a stop accuracy value from a second time series operation record a predetermined number of records subsequent to the first time series operation record.

(Aspect 4)
The rail vehicle data analysis method according to aspect 3, wherein the predetermined number of records subsequent to the first time series operation record is based on a time series record computation time.

(Aspect 5)
The rail vehicle data analysis method according to any one of aspects 1 to 4, wherein the set of analysis variables are selected from the group consisting of slip, speed, acceleration, overstop, and emergency break use.

(Aspect 6)
The rail vehicle data analysis method according to any one of aspects 1 to 5, wherein defining the set of analysis functions further includes:
defining a compound analysis function that includes a first analysis function for identifying a first analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data and a second analysis function for identifying a second analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data.

(Aspect 7)
The rail vehicle data analysis method according to any one of aspects 1 to 6, further comprising visually emphasizing the feature value for each set of rail vehicle operation data in the set of output data.

(Aspect 8)
The rail vehicle data analysis method according to any one of aspects 1 to 7, further comprising:
determining, based on the set of feature values, a candidate cause for the set of abnormal stop accuracy values; and
including the candidate cause in the set of output data.

(Aspect 9)
A rail vehicle data analysis device comprising:
a data acquisition unit configured to acquire a set of rail vehicle operation data relating to operation of a rail vehicle;
an analysis data extraction unit configured to:
identify, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle, and
determine, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
an analysis management unit configured to:
define a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle, and
define a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data; and
an analysis unit configured to:
extract, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables,
identify, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values, and
generate a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.

(Aspect 10)
A rail vehicle data analysis computer program comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method including:
acquiring a set of rail vehicle operation data relating to operation of a rail vehicle, wherein the set of rail vehicle operation data includes a speed value of the rail vehicle at a particular time while traveling along a route, a stop accuracy value of the rail vehicle at a particular time while traveling along the route, and a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route;
identifying, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle, by:
identifying, from among the set of time series operation records, a first time series operation record in which:
the speed value of the rail vehicle is zero,
the speed value of the rail vehicle is greater than zero in a previous time series operation record,
the speed value of the rail vehicle is zero in an immediately subsequent time serious operation record, and
the next station tag differs from the next station tag in the immediately subsequent time series operation record; and
extracting, as the first subset of the rail vehicle operation data, the next station tag from the first time series operation record and a stop accuracy value from a second time series operation record a predetermined number of records subsequent to the first time series operation record;
determining, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
defining a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle;
defining a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data;
extracting, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables;
identifying, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values; and
generating a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.

(Aspect 11)
A rail vehicle data analysis system comprising:
a rail vehicle;
a user terminal; and
a rail vehicle data analysis device,
wherein the rail vehicle data analysis device includes:
a data acquisition unit configured to acquire a set of rail vehicle operation data relating to operation of the rail vehicle;
an analysis data extraction unit configured to:
identify, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle, and
determine, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
an analysis management unit configured to:
define a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle, and
define a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data; and
an analysis unit configured to:
extract, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables,
identify, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values, and
generate a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data and output the set of output data to the user terminal.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

While the foregoing is directed to exemplary embodiments, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. “Set of,” “group of,” “bunch of,” etc. are intended to include one or more. It will be further understood that the terms “includes” and/or “including,” when used in this specification, specify the presence of the stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. In the previous detailed description of exemplary embodiments of the various embodiments, reference was made to the accompanying drawings (where like numbers represent like elements), which form a part hereof, and in which is shown by way of illustration specific exemplary embodiments in which the various embodiments may be practiced. These embodiments were described in sufficient detail to enable those skilled in the art to practice the embodiments, but other embodiments may be used and logical, mechanical, electrical, and other changes may be made without departing from the scope of the various embodiments. In the previous description, numerous specific details were set forth to provide a thorough understanding the various embodiments. But, the various embodiments may be practiced without these specific details. In other instances, well-known circuits, structures, and techniques have not been shown in detail in order not to obscure embodiments.

200... Rail vehicle data analysis system
210... Rail vehicle
220... User terminal
230... Communication network
240... Rail vehicle data analysis device
242... Data acquisition unit
244... Analysis data extraction unit
246... Analysis management unit
248... Analysis unit

Claims

A rail vehicle data analysis method comprising:
acquiring a set of rail vehicle operation data relating to operation of a rail vehicle;
identifying, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle;
determining, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
defining a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle;
defining a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data;
extracting, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables;
identifying, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values; and
generating a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.
The rail vehicle data analysis method according to claim 1, wherein the set of rail vehicle operation data includes a set of time series operation records at least including:
a speed value of the rail vehicle at a particular time while traveling along a route,
a stop accuracy value of the rail vehicle at a particular time while traveling along the route, and
a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route.
The rail vehicle data analysis method according to claim 2, wherein identifying the first subset of the rail vehicle operation data includes:
identifying, from among the set of time series operation records, a first time series operation record in which:
the speed value of the rail vehicle is zero,
the speed value of the rail vehicle is greater than zero in a previous time series operation record,
the speed value of the rail vehicle is zero in an immediately subsequent time serious operation record, and
the next station tag differs from the next station tag in the immediately subsequent time series operation record; and
extracting, as the first subset of the rail vehicle operation data, the next station tag from the first time series operation record and a stop accuracy value from a second time series operation record a predetermined number of records subsequent to the first time series operation record.
The rail vehicle data analysis method according to claim 3, wherein the predetermined number of records subsequent to the first time series operation record is based on a time series record computation time.
The rail vehicle data analysis method according to claim 1, wherein the set of analysis variables are selected from the group consisting of slip, speed, acceleration, overstop, and emergency break use.
The rail vehicle data analysis method according to claim 1, wherein defining the set of analysis functions further includes:
defining a compound analysis function that includes a first analysis function for identifying a first analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data and a second analysis function for identifying a second analysis variable of the set of analysis variables within the second subset of the rail vehicle operation data.
The rail vehicle data analysis method according to claim 1, further comprising visually emphasizing, in the set of output data, a feature value of the set of feature values that fails to achieve a normal operational range threshold.
The rail vehicle data analysis method according to claim 1, further comprising:
determining, based on the set of feature values, a candidate cause for the set of abnormal stop accuracy values; and
including the candidate cause in the set of output data.
A rail vehicle data analysis device comprising:
a data acquisition unit configured to acquire a set of rail vehicle operation data relating to operation of a rail vehicle;
an analysis data extraction unit configured to:
identify, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle, and
determine, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
an analysis management unit configured to:
define a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle, and
define a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data; and
an analysis unit configured to:
extract, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables,
identify, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values, and
generate a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.
A rail vehicle data analysis computer program comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method including:
acquiring a set of rail vehicle operation data relating to operation of a rail vehicle, wherein the set of rail vehicle operation data includes a speed value of the rail vehicle at a particular time while traveling along a route, a stop accuracy value of the rail vehicle at a particular time while traveling along the route, and a next station tag indicating a subsequent station at which the rail vehicle is scheduled to stop on the route;
identifying, from among the set of rail vehicle operation data using a set of stop accuracy conditions, a first subset of the rail vehicle operation data that includes a set of stop accuracy values for the rail vehicle, by:
identifying, from among the set of time series operation records, a first time series operation record in which:
the speed value of the rail vehicle is zero,
the speed value of the rail vehicle is greater than zero in a previous time series operation record,
the speed value of the rail vehicle is zero in an immediately subsequent time serious operation record, and
the next station tag differs from the next station tag in the immediately subsequent time series operation record; and
extracting, as the first subset of the rail vehicle operation data, the next station tag from the first time series operation record and a stop accuracy value from a second time series operation record a predetermined number of records subsequent to the first time series operation record;
determining, from among the first subset of the rail vehicle operation data using a predetermined stop accuracy threshold, a second subset of the rail vehicle operation data that includes a set of abnormal stop accuracy values;
defining a set of analysis variables that relate to operation events that have a possibility of influencing stop accuracy of the rail vehicle;
defining a set of analysis functions for identifying the set of analysis variables within the second subset of the rail vehicle operation data;
extracting, from the second subset of the rail vehicle operation data using the set of analysis functions, a third subset of the rail vehicle operation data that corresponds to a timeframe that includes the set of analysis variables;
identifying, from the third subset of the rail vehicle operation data using a statistical analysis technique, a set of feature values that indicate potential causes of the set of abnormal stop accuracy values; and
generating a set of output data that indicates the set of feature values in association with the set of rail vehicle operation data.