SYSTEMS AND METHODS FOR MONITORING AND VALIDATING STATUS OF WHEEL DETECTOR DEVICES IN A CLASSIFICATION YARD
20250346265 ยท 2025-11-13
Assignee
Inventors
Cpc classification
B61L17/00
PERFORMING OPERATIONS; TRANSPORTING
B61L25/021
PERFORMING OPERATIONS; TRANSPORTING
International classification
B61L25/02
PERFORMING OPERATIONS; TRANSPORTING
Abstract
Methods and systems for determining a status of detectors in a classification yard. In particular embodiments, a set of car event data associated with a detector may be analyzed to determine the performance of the detector during operations of each car event. A status of the detector may be determined from the analysis of the performance of the detector during operations of each car event. In embodiments, the analysis may include thresholding analysis that may be configured to determine a relationship (e.g., a deviation relationship) between real-world speeds measured during the car events and predicted speeds expected during the car events for each car event associated with the detector. In embodiments, the status of the detector may be used to ensure corrective actions on the detector (e.g., deploy maintenance personnel, report the status of the detector, send a control signal to the detector to deactivate, etc.).
Claims
1. A method of determining a status of detectors in a classification yard, comprising: compiling a plurality of car events associated with a detector device in the classification yard, wherein each car event of the plurality of car events is associated with a predicted speed of a cut passing through the detector device during a respective car event, and wherein each car event includes real-world measurements of an actual speed of the cut at the detector device during the respective car event; generating a set of speed differences associated with the detector based on the plurality of car events associated with the detector; applying thresholding analysis to the set of speed differences associated with the detector to determine a status of the detector; and generating a corrective action signal including one or more of: an indication of the status of the detector; and a corrective action to be taken on the detector.
2. The method of claim 1, wherein generating the set of speed differences associated with the detector includes: calculating, for each car event in the plurality of car events associated with a detector, a speed difference by calculating a difference between the predicted speed of the cut passing through the detector device during the respective car event and the actual speed of the cut at the detector device during the respective car event; and aggregating each calculated speed difference for each car event into the set of speed differences associated with the detector.
3. The method of claim 2, wherein applying thresholding analysis to the set of speed differences associated with the detector includes applying one or more of a set of differential speed rules to the set of speed differences associated with the detector, wherein the set of differential rules includes one or more of: a first speed differential rule specifying that the status of the retarder device is based on whether a threshold percentage of the set of speed differences associated with the detector is outside of a range defined by plus or minus a speed threshold; a second speed rule specifying that the status of the retarder device is based on whether a median or average of the set of speed differences associated with the detector is within a range defined by plus or minus an average threshold; a third speed differential rule specifying that the status of the retarder device is based on whether a spread range of speed differences values within a middle percentage of the set of speed differences associated with the detector is less than a spread threshold, wherein the middle percentage of the set of speed differences is defined by a range of speed differences values including a top percentile threshold of the speed differences values in the set of speed differences and a bottom percentile threshold of the speed differences values in the set of speed differences; and a combination speed differential rule that includes a weighted combination of the results of one or more of the first speed differential rule, the second speed differential rule, and the third speed differential rule.
4. The method of claim 1, wherein applying the thresholding analysis to the set of speed differences associated with the detector to determine the status of the detector includes: obtaining a percentage result for the detector, the percentage result indicating a percentage status of the detector.
5. The method of claim 4, wherein determining the status of the detector includes flagging a status flag of the detector with one or more of: a bad status indication when the percentage status of the detector is at least 100%; a warning status indication when the percentage status of the detector is greater than a threshold percentage and less than 100%; and a good status indication when the percentage status of the detector is less than the threshold percentage.
6. The method of claim 5, wherein the warning status indication includes a section warning indication identifying a section of the detector determined to have failed the thresholding analysis.
7. The method of claim 1, further comprising: identifying that the indication of the status of the detector found to be bad has previously been reset a number of times; determining that the number of times exceeds a threshold amount; and flagging the detector as a potential misidentification of a bad detector.
8. A system for determining a status of detector devices in a classification yard, comprising: at least one processor; and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations including: compiling a plurality of car events associated with a detector device in the classification yard, wherein each car event of the plurality of car events is associated with a predicted speed of a cut passing through the detector device during a respective car event, and wherein each car event includes real-world measurements of an actual speed of the cut at the detector device during the respective car event; generating a set of speed differences associated with the detector based on the plurality of car events associated with the detector; applying thresholding analysis to the set of speed differences associated with the detector to determine a status of the detector; and generating a corrective action signal including one or more of: an indication of the status of the detector; and a corrective action to be taken on the detector.
9. The system of claim 8, wherein generating the set of speed differences associated with the detector includes: calculating, for each car event in the plurality of car events associated with a detector, a speed difference by calculating a difference between the predicted speed of the cut passing through the detector device during the respective car event and the actual speed of the cut at the detector device during the respective car event; and aggregating each calculated speed difference for each car event into the set of speed differences associated with the detector.
10. The system of claim 9, wherein applying thresholding analysis to the set of speed differences associated with the detector includes applying one or more of a set of differential speed rules to the set of speed differences associated with the detector, wherein the set of differential rules includes one or more of: a first speed differential rule specifying that the status of the retarder device is based on whether a threshold percentage of the set of speed differences associated with the detector is outside of a range defined by plus or minus a speed threshold; a second speed rule specifying that the status of the retarder device is based on whether a median or average of the set of speed differences associated with the detector is within a range defined by plus or minus an average threshold; a third speed differential rule specifying that the status of the retarder device is based on whether a spread range of speed differences values within a middle percentage of the set of speed differences associated with the detector is less than a spread threshold, wherein the middle percentage of the set of speed differences is defined by a range of speed differences values including a top percentile threshold of the speed differences values in the set of speed differences and a bottom percentile threshold of the speed differences values in the set of speed differences; and a combination speed differential rule that includes a weighted combination of the results of one or more of the first speed differential rule, the second speed differential rule, and the third speed differential rule.
11. The system of claim 8, wherein applying the thresholding analysis to the set of speed differences associated with the detector to determine the status of the detector includes: obtaining a percentage result for the detector, the percentage result indicating a percentage status of the detector.
12. The system of claim 11, wherein determining the status of the detector includes flagging a status flag of the detector with one or more of: a bad status indication when the percentage status of the detector is at least 100%; a warning status indication when the percentage status of the detector is greater than a threshold percentage and less than 100%; and a good status indication when the percentage status of the detector is less than the threshold percentage.
13. The system of claim 12, wherein the warning status indication includes a section warning indication identifying a section of the detector determined to have failed the thresholding analysis.
14. The system of claim 8, further comprising: identifying that the indication of the status of the detector found to be bad has previously been reset a number of times; determining that the number of times exceeds a threshold amount; and flagging the detector as a potential misidentification of a bad detector.
15. A computer-based tool for determining a status of detector devices in a classification yard, the computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations comprising: compiling a plurality of car events associated with a detector device in the classification yard, wherein each car event of the plurality of car events is associated with a predicted speed of a cut passing through the detector device during a respective car event, and wherein each car event includes real-world measurements of an actual speed of the cut at the detector device during the respective car event; generating a set of speed differences associated with the detector based on the plurality of car events associated with the detector; applying thresholding analysis to the set of speed differences associated with the detector to determine a status of the detector; and generating a corrective action signal including one or more of: an indication of the status of the detector; and a corrective action to be taken on the detector.
16. The computer-based tool of claim 15, wherein generating the set of speed differences associated with the detector includes: calculating, for each car event in the plurality of car events associated with a detector, a speed difference by calculating a difference between the predicted speed of the cut passing through the detector device during the respective car event and the actual speed of the cut at the detector device during the respective car event; and aggregating each calculated speed difference for each car event into the set of speed differences associated with the detector.
17. The computer-based tool of claim 6, wherein applying thresholding analysis to the set of speed differences associated with the detector includes applying one or more of a set of differential speed rules to the set of speed differences associated with the detector, wherein the set of differential rules includes one or more of: a first speed differential rule specifying that the status of the retarder device is based on whether a threshold percentage of the set of speed differences associated with the detector is outside of a range defined by plus or minus a speed threshold; a second speed rule specifying that the status of the retarder device is based on whether a median or average of the set of speed differences associated with the detector is within a range defined by plus or minus an average threshold; a third speed differential rule specifying that the status of the retarder device is based on whether a spread range of speed differences values within a middle percentage of the set of speed differences associated with the detector is less than a spread threshold, wherein the middle percentage of the set of speed differences is defined by a range of speed differences values including a top percentile threshold of the speed differences values in the set of speed differences and a bottom percentile threshold of the speed differences values in the set of speed differences; and a combination speed differential rule that includes a weighted combination of the results of one or more of the first speed differential rule, the second speed differential rule, and the third speed differential rule.
18. The computer-based tool of claim 15, wherein applying the thresholding analysis to the set of speed differences associated with the detector to determine the status of the detector includes: obtaining a percentage result for the detector, the percentage result indicating a percentage status of the detector.
19. The computer-based tool of claim 18, wherein determining the status of the detector includes flagging a status flag of the detector with one or more of: a bad status indication when the percentage status of the detector is at least 100%; a warning status indication when the percentage status of the detector is greater than a threshold percentage and less than 100%; and a good status indication when the percentage status of the detector is less than the threshold percentage.
20. The computer-based tool of claim 15, further comprising: identifying that the indication of the status of the detector found to be bad has previously been reset a number of times; determining that the number of times exceeds a threshold amount; and flagging the detector as a potential misidentification of a bad detector.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
[0026]
[0027]
[0028]
[0029]
[0030] It should be understood that the drawings are not necessarily to scale and that the disclosed embodiments are sometimes illustrated diagrammatically and in partial views. In certain instances, details which are not necessary for an understanding of the disclosed methods and apparatuses or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the particular embodiments illustrated herein.
DETAILED DESCRIPTION
[0031] The disclosure presented in the following written description and the various features and advantageous details thereof, are explained more fully with reference to the non-limiting examples included in the accompanying drawings and as detailed in the description. Descriptions of well-known components have been omitted to not unnecessarily obscure the principal features described herein. The examples used in the following description are intended to facilitate an understanding of the ways in which the disclosure can be implemented and practiced. A person of ordinary skill in the art would read this disclosure to mean that any suitable combination of the functionality or exemplary embodiments below could be combined to achieve the subject matter claimed. The disclosure includes either a representative number of species falling within the scope of the genus or structural features common to the members of the genus so that one of ordinary skill in the art can recognize the members of the genus. Accordingly, these examples should not be construed as limiting the scope of the claims.
[0032] A person of ordinary skill in the art would understand that any system claims presented herein encompass all of the elements and limitations disclosed therein, and as such, require that each system claim be viewed as a whole. Any reasonably foreseeable items functionally related to the claims are also relevant. The Examiner, after having obtained a thorough understanding of the disclosure and claims of the present application has searched the prior art as disclosed in patents and other published documents, i.e., nonpatent literature. Therefore, the issuance of this patent is evidence that: the elements and limitations presented in the claims are enabled by the specification and drawings, the issued claims are directed toward patent-eligible subject matter, and the prior art fails to disclose or teach the claims as a whole, such that the issued claims of this patent are patentable under the applicable laws and rules of this country.
[0033] Various embodiments of the present disclosure are directed to systems and techniques that provide functionality for determining a status of detector devices in a classification yard. In particular embodiments, a set of car event data associated with a detector may be analyzed to determine the performance of the detector to measure speed and/or arrival times of cuts passing through the detector. A status of the detector may be determined from analysis of the performance of the detector to measure speed and/or arrival times of cuts passing through the detector. In embodiments, the status of the detector may be used to ensure corrective actions is taken on the detector (e.g., deploy maintenance personnel, report the status of the detector, send a control signal to the detector to deactivate, etc.).
[0034]
[0035] In embodiments, functionality of server 110 may provide for determining, based on car events associated with a detector, a status of the detector. In embodiments, server 110 may include functionality for determining, based on car events associated with a detector, a status of the detector by compiling data related to car events associated with the detector, applying threshold analysis to the compiled data to determine a relationship between real-world measurements and expected (e.g., predicted or desired) results with respect to operation of the detector, to determine a status of the detector based on the thresholding analysis, and to generate a corrective action signal in response to the determination of the status of the detector. In embodiments, the thresholding analysis may include analysis that focuses on the relationship between real-world measurements and expected (e.g., predicted or desired) results for each car event associated with a detector. In some embodiments, the thresholding analysis may include applying one or more rules that may determine the amount and/or spread of deviations between the real-world measurements for determining the status of a detector. In some embodiments, the thresholding analysis may include determining, for each car event associated with a detector, a speed difference between a predicted speed and the actual speed (e.g., actual speed-predicted speed), and analyzing the relationship of the speed differences against thresholds to determine the status of the detector.
[0036] It is noted that the functional blocks, and components thereof, of system 100 of embodiments of the present invention may be implemented using processors, electronics devices, detectors, electronics components, logical circuits, memories, software codes, firmware codes, etc., or any combination thereof. For example, one or more functional blocks, or some portion thereof, may be implemented as discrete gate or transistor logic, discrete hardware components, or combinations thereof configured to provide logic for performing the functions described herein. Additionally, or alternatively, when implemented in software, one or more of the functional blocks, or some portion thereof, may comprise code segments operable upon a processor to provide logic for performing the functions described herein.
[0037] It is also noted that various components of system 100 are illustrated as single and separate components. However, it will be appreciated that each of the various illustrated components may be implemented as a single component (e.g., a single application, server module, etc.), may be functional components of a single component, or the functionality of these various components may be distributed over multiple devices/components. In such embodiments, the functionality of each respective component may be aggregated from the functionality of multiple modules residing in a single, or in multiple devices.
[0038] It is further noted that functionalities described with reference to each of the different functional blocks of system 100 described herein is provided for purposes of illustration, rather than by way of limitation and that functionalities described as being provided by different functional blocks may be combined into a single component or may be provided via computing resources disposed in a cloud-based environment accessible over a network, such as one of network 145.
[0039] As noted above, in typical operations of a classification yard, such as a hump yard, a stock train that includes train cars to be marshalled to their assigned train may be pushed by a hump push engine at a set speed along the approach section of the hump to the crest of the hump. As the train cars roll past the hump crest, gravity may begin pulling the train cars towards the bottom of the hump. In embodiments, the train cars are cut from the stock train and the cut is allowed to roll down the hump and is marshalled to the destination train. Ensuring that the cut reaches the assigned destination train at the appropriate coupling speed is very important. As such, in embodiments, a cut may be tracked and controlled as the cut moves along the marshalling tracks of the classification yard. In particular, the route and the speed of the cut from the hump to its destination track or train may be controlled using various components of the classification yard. For example, classification yard may implement switches, detectors, and retarders, among other components. In embodiments, the cooperative operation of the various components of the classification yard may enable the classification yard to ensure that various cuts traverse the marshalling tracks and arrive at the destination coupling point at the appropriate coupling speed.
[0040] In embodiments, detectors 140 may include one or more detectors configured to detect a speed of a cut. In embodiments, detectors 140 may be laid out at different points along the tracks of the classification yard. In this manner, detectors 140 may be configured to detect the speed of a cut at various points along the route of the cut through the marshalling tracks of the classification yard.
[0041] In embodiments, detectors 140 may be configured to detect the presence of a cut at various points along the route of the cut through the marshalling tracks of the classification yard. For example, as a cut passes through a detector a particular time, the detector may detect the presence of the cut at the particular time, and may generate a detection, including an identification of the cut, a timestamp indicating date/time of the detection, the location of the detection (e.g., an identification of the detector), etc.
[0042] In some embodiments, detectors 140 may include one or more detectors configured to detect one or more wheels, or wheel axles (e.g., one or more wheels, or wheel axles, of a train car, or of a cut, passing through the one or more detectors). In embodiments, detectors 140 may detect more than one wheel (or wheel axle) of the train car, or cut, passing through detectors 140. For example, a train car passing through a detector may include multiple wheel axles. In this case, the detector may be configured to detect one or more of the multiple wheel axles, and in some embodiments may identify the wheel axle detected. For example, for a train car including four wheels, the detector may identify that a detection includes detection of wheel three of four, when the detector detects the third wheel in the train car. In another example, for a cut traveling through the detector that includes two train cars each including four wheels, the detector may identify that a detection includes detection of wheel five of eight, when the detector detects the fifth wheel in the cut. In some embodiments, the wheel detector may detect more than one wheel.
[0043] In some embodiments, detectors 140 may detect a speed of a cut by detecting multiple wheels of the cut, measuring the time between the detections, and calculating a speed based, at least in part, on the characteristics of the cut. For example, a cut may travel through a detector of detectors 140. The detector may detect a first wheel of the cut at a first time. A first detection of a first wheel of the cut may be generated with a timestamp equal to the first time. The detector may detect a second wheel of the cut at a second time. A second detection of the second wheel of the cut may be generated with a timestamp equal to the second time. In embodiments, the speed of the cut may be calculated by comparing the first and the second time, and determining a speed based on the distance between the first and the second wheel, which may be a known characteristic of the cut. For example, the distance between the first and second wheel traveled by cut over the difference between the first and second detection times may provide the speed of the cut over the detector.
[0044] In some embodiments, detectors 140 may detect a speed of a cut by detecting a cut at multiple detectors and measuring the time between the detections. For example, a cut may travel through a route along the classification yard and may pass a first detector of detectors 140 at a first time. The first detector may detect the cut (e.g., a wheel of the cut) at the first time. A first detection of the cut may be generated with a timestamp equal to the first time. The cut may continue travelling through the route and may pass a second detector of detectors 140 at a second time. The second detector may detect the cut (e.g., a wheel of the cut) at the second time. A second detection of the cut may be generated with a timestamp equal to the second time. In embodiments, the speed of the cut may be calculated by comparing the first and the second time, and determining a speed based on the distance between the first and the second detector, which may be a known characteristic of detectors 140. For example, the distance between the first and second detector traveled by cut over the difference between the first and second detection times may provide the speed of the cut between the first and second detectors.
[0045] In embodiments, the components of classification yard 140 may include occupancy devices that may include track circuits, light detectors, presence detectors, etc., and may be configured to detect occupancy of a track and/or track segment, such as to detect a presence of a vehicle within the track and/or track segment. In some embodiments, occupancy devices may be configured to detect when a track or track segment has been filled to a safe capacity, which may enable a system to prevent overfilling of the track or track segment. In embodiments, occupancy devices may be used in long sections of track that may not include a wheel detector, a retarder, etc. In embodiments, an occupancy device may be configured with predicted on and off times. If the on and off times are exceeded, a segment protected by the occupancy device may be temporarily protected and if the condition persists, the protection may remain. In embodiments, protecting a track or track segment may include routing away from the protected track or track segment, such as in response to a determination that sufficient time exists to route the vehicle away from the protected track or track segment. However, the railroad vehicle may be routed into the protected track segment (e.g., to prevent a side-swipe) in response to a determination that there is not sufficient time to route the vehicle away from the protected track or track segment.
[0046] Data collector 150 may be configured to capture and store (e.g., in a database, such as database 124) car event data related to car events associated with detectors (e.g., the various detectors of the classification yard). In embodiments, during operations, predictions of the speed and/or arrival times of a cut at a detector (e.g., a detector of detectors 140) may be made during classification operations of a classification yard. For example, a production prediction manager may generate production predictions (e.g., using control parameters including tuning coefficients, characteristics of the cut, characteristics of the track or device, etc.) of a speed and/or arrival times of a cut at a particular detector. In embodiments, the production predictions may include production predictions for a plurality of cuts scheduled to be classified through the classification yard and for many detectors of the classification yard. Operations and techniques of a system for generating production predictions is described in related U.S. Patent Application Docket No. [BNSF-00163], the entire contents of which are herein incorporated by reference for all purposes.
[0047] In embodiments, as a car event occurs (e.g., as cut passes through a detector), real-world measurements related to the car event at the detector may be generated and may be captured by data collector 150. The captured real-world measurements related to the car event at the detector may be included into the car event associated with the detector. In embodiments, a car event associated with a detector may include an actual speed of the cut as it passed through the detector, and may also include the predicted speed for the cut (e.g., the speed at which the cut was predicted to pass at the detector for the car event). In embodiments, data collector 150 may store the car events associated with a detector into a database (e.g., database 124) for subsequent analysis.
[0048] User terminal 130 may include a mobile device, a smartphone, a tablet computing device, a personal computing device, a laptop computing device, a desktop computing device, a computer system of a vehicle, a personal digital assistant (PDA), a smart watch, another type of wired and/or wireless computing device, or any part thereof. In embodiments, user terminal 130 may provide a user interface that may be configured to provide an interface (e.g., a graphical user interface (GUI)) structured to facilitate an operator interacting with system 100, e.g., via network 145, to execute and leverage the features provided by server 110. In embodiments, the operator may be enabled, e.g., through the functionality of user terminal 130, to provide configuration parameters that may be used by system 100 to provide functionality for determining the status of detectors, as well as to interact with results (e.g., selection, confirmation, verification of results, etc.). In embodiments, user terminal 130 may be configured to communicate with other components of system 100. In embodiments, the functionality of user terminal 130 may include presenting results of detector status determination operations to an operator. In embodiments, the results of detector status determination operations may be presented to an operator via the GIU of user terminal 130.
[0049] In embodiments, server 110, classification yard 140 (and its various components), and user terminal 130 may be communicatively coupled via network 145. Network 145 may include a wired network, a wireless communication network, a cellular network, a cable transmission system, a Local Area Network (LAN), a Wireless LAN (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), the Internet, the Public Switched Telephone Network (PSTN), etc.
[0050] Server 110 may be configured to facilitate operations for determining a status of detectors in a classification yard in accordance with embodiments of the present disclosure. In embodiments, functionality of server 110 to facilitate determination of a status of detectors in a classification yard may be provided by the cooperative operation of the various components of server 110, as will be described in more detail below.
[0051] Although
[0052] As shown in
[0053] Memory 112 may comprise one or more semiconductor memory devices, read only memory (ROM) devices, random access memory (RAM) devices, one or more hard disk drives (HDDs), flash memory devices, solid state drives (SSDs), erasable ROM (EROM), compact disk ROM (CD-ROM), optical disks, other devices configured to store data in a persistent or non-persistent state, network memory, cloud memory, local memory, or a combination of different memory devices. Memory 112 may comprise a processor readable medium configured to store one or more instruction sets (e.g., software, firmware, etc.) which, when executed by a processor (e.g., one or more processors of processor 111), perform tasks and functions as described herein.
[0054] Memory 112 may also be configured to facilitate storage operations. For example, memory 112 may comprise database 124 for storing various information related to operations of system 100. For example, database 124 may store analysis models, threshold data, configuration information, etc., to be used for configuring system 100, etc. In embodiments, database 124 may store characteristics of various and different train cars, such as rolling resistance characteristics, weights, aerodynamic characteristics (e.g., drag coefficient, coupler overhang status, articulation status of the etc. In embodiments, database 124 may store car event data related to speed, energy, and/or arrival times measurements of various cuts at various points (e.g., devices and/or segments) of the classification yard.
[0055] Database 124 is illustrated as integrated into memory 112, but in some embodiments, database 124 may be provided as a separate storage module or may be provided as a cloud-based storage module. Additionally, or alternatively, database 124 may be a single database, or may be a distributed database implemented over a plurality of database modules.
[0056] Data compiler 121 may be configured to retrieve data related to car events associated with detectors, and to filter the data according to various configuration parameters for subsequent thresholding analysis (e.g., by threshold analysis manager 122). In some embodiments, data compiler 121 may include functionality to format and/or present car event data to an operator.
[0057] In embodiments, data compiler 121 may provide a robust mechanism for configuring, controlling, and executing data retrieval and compilation functionality in order to obtain a most appropriate set of car events and leverage the car event data to determine a status of a detector or detectors. For example, in embodiments, data compiler 121 may be configured to retrieve and compile the car event data by configuring and scheduling one or more analysis jobs that include configuration associated with the analysis job itself as well as the car event data against which thresholding analysis is to be performed.
[0058] In embodiments, configuring an analysis job may include specifying values for various parameters defining characteristics of the job as well as characteristics of the car events to be included in the thresholding analysis. In a sense, the configuration of an analysis job may serve to filter car events in the database so that data compiler 121 may compile those car events that meet the configuration (e.g., pass the filters). The resulting set of car events may be used in the thresholding analysis (e.g., performed by thresholding analysis manager 122) to determine a status of one or more detectors. For example, in embodiments, the various parameters defining characteristics of the job may include parameters for specifying attributes of the job, such as a schedule name, a starting time of the job, a minimum time period that the job should run, how often the job is to be scheduled (e.g., daily, weekly, monthly, etc.), how often the job is to recur (e.g., re-perform analysis of particular car events), etc. In embodiments, the various parameters defining characteristics of the car events to be included in the analysis may include parameters for specifying a detector for which car events are to be compiled (e.g., an identification of the detector), a minimum car event count, a type of precipitation associated with the car events, such as the precipitation status when the car event was recorded (e.g., dry, wet, rain, snow, rust, all, etc.), a maximum temperature at the time of the car event, a minimum temperature at the time of the car event, an indication of a time period that each task performing the analysis job may be available after completion, etc.
[0059] In some embodiments, data compiler 121 may be configured to provide a user interface (UI) via which an operator may specify values for the various configuration parameters of an analysis job. For example, data compiler 121 may present the UI (e.g., via user terminal 130) to the operator and may include various fields via which the operator may specify values for respective configuration parameters. In some embodiments, the values for the various configuration parameters of an analysis job may be automatically selected by data compiler 121, such as by retrieving values stored in database 124, which may be previously specified by an operator or may be default values.
[0060] In embodiments, scheduling an analysis job may include defining a period over which the analysis job may be executed, as well as the recursive configuration of the analysis job. In embodiments, an analysis job may be scheduled, which may make the analysis job available to be executed. The execution of the analysis job may be performed using tasks, in which case the job may be executed against a set of car events currently available via a task execution. In this manner, a task for an analysis job may execute the analysis job based on the analysis job configuration on car event data current available. In some embodiments, a second task may be started for the same analysis job, in which case the second task may execute the analysis job (e.g., in accordance with the analysis job configuration) against a set of car events as available with respect to the second task. In this manner, as car event data in the database changes (e.g., more data is collected), a task may permit the analysis job to be executed for the data as it changes. For example, an analysis job may be scheduled to be run weekly, in which case, every week, data compiler 121 may compile car events (e.g., from database 124) that meet the analysis job configuration into a task which is executed to perform the determination of the status of one or more detectors in the classification yard. This functionality may allow data retriever and compiler 121 to ensure that the analysis job for detector status determination may be performed recursively, and in this manner the results of a first iteration of the analysis job may be leveraged in a subsequent execution (e.g., via a second task). In addition, the use of tasks may also enable the analysis to be performed over different sets of car event data, which may facilitate a diverse thresholding analysis of the data.
[0061] In embodiments, the functionality of data compiler 121 to enable the configuration of analysis jobs may provide a mechanism to configure different analysis jobs with different sets of configuration parameter values, which may combine for a full deployment of configurations that may cover a large set of situations, conditions, and/or configurations. In some embodiments, data compiler 121 may include functionality to combine jobs, such as by defining a field for specifying that an analysis job is combinable. In embodiments, combining analysis jobs may include combining the results (e.g., detector status determination results) of the analysis jobs, such as for presentation to an operator for verification, confirmation, and/or corrective actions to address detector status results.
[0062] In embodiments, analysis jobs may include configuration that may enable car event data to be classified based on various factors. For example, a particular car event may be classified into a particular classification (e.g., a particular data bucket) based on environmental conditions (e.g., weather, etc.), train car type, bearing type of the train car, etc. In embodiments, the various classifications, or buckets, may include a cold bucket (e.g., that may be used to classify car events that occur during cold weather), a warm bucket (e.g., that may be used to classify car events that occur during warm weather), a hot bucket (e.g., that may be used to classify car events that occur during hot weather), a wet bucket (e.g., that may be used to classify car events that occur during wet weather), a snow bucket (e.g., that may be used to classify car events that occur during snowy weather), a rain bucket (e.g., that may be used to classify car events that occur during rainy weather), a dry bucket (e.g., that may be used to classify car events that occur during dry weather), a resilience type bucket (e.g., that may be used to classify car events based on whether the cut includes train cars with a resilience type bearing), etc. It will be appreciated that the description of the above data buckets that may be used to classify car events are provided for illustrative purposes and not by way of limitation. As such, additional and/or different data buckets may be used to classify car events.
[0063] In embodiments, an analysis job may be configured as corresponding to a particular data bucket (e.g., an analysis job may be configured to be used for determining status of detectors using car event data associated with a data bucket). In these embodiments, the results of the analysis jobs may be specifically tailored to the conditions associated with the particular data bucket with which the analysis job is configured. In this manner, this functionality to configure a job with a data bucket may enable system 100 to perform operations for determining a status of detectors in a classification yard in which the status determination is tailored for particular conditions (e.g., environmental conditions, train car type, bearing type of the train car, etc.). In embodiments, this functionality may allow system 100 to ensure that device status determinations are made based on prevailing conditions. For example, in some cases, some detectors may be known to operate inefficiently in cold weather, as compared with the efficiency of the detectors in warm weather. In this case, by tailoring the thresholding analysis to a particular prevailing condition, the results may take into account the currently prevailing conditions, which may prevent misidentification of detector status (e.g., a good detector may be labeled as bad during cold weather if the thresholding analysis does not take into account the cold weather conditions).
[0064] In embodiments, the configuration of the analysis job may specify the data bucket applicable to the analysis job. For example, an analysis job may be configured as applicable to the cold bucket. In this example, data compiler 121 may operate to compile a set of car events for thresholding analysis that includes car events that occur during cold weather. In this manner, the results of the thresholding analysis using the results of this analysis job may be specifically tailored to cold weather conditions. In another example, an analysis job may be configured as applicable to the dry bucket. In this example, data compiler 121 may operate to compile a set of car events for thresholding analysis that includes car events that occur during dry weather, such as car events occurring while no rain or snow was present. In this manner, the results of the thresholding analysis using the results of this analysis job may be specifically tailored to dry weather conditions. In yet another example, an analysis job may be configured as applicable to the dry and cold bucket. In this example, data compiler 121 may operate to compile a set of car events for thresholding analysis that includes car events that occur during dry and cold weather, such as car events occurring during cold weather while no rain or snow was present. In this manner, the results of the thresholding analysis using the results of this analysis job may be specifically tailored to dry and cold weather conditions. In still another example, an analysis job may be configured as applicable to a resilience bearing true bucket. In this example, data compiler 121 may operate to compile a set of car events for thresholding analysis that includes car events in which each cut generating each of the car events including one or more train cars having resilient bearing. In this manner, the results of the thresholding analysis using the results of this analysis job may be specifically tailored to reliance bearing conditions.
[0065] In some embodiments, current or recent car event data may include car event data associated with a particular data bucket representing currently prevailing conditions. For example, currently prevailing conditions may include cold weather (e.g., currently may be winter in the location of classification yard 140). In this case, the current and/or recent car event data in the database may include mostly, if not exclusively, cold weather car events (e.g., car events belonging to the cold data bucket) due to the currently prevailing weather conditions. In embodiments, analysis jobs that may executed to be used for thresholding analysis may include these mostly, if not exclusively, cold weather car events, which may result in detector status determination results (e.g., based on thresholding analysis) that may be biased and/or tailored to the prevailing weather conditions (e.g., cold weather conditions in this example).
[0066] Following the example above, if and when the weather changes to warmer conditions, a next car event (or a series of next car events) may be warm car events. In this case, it may be desirable to ensure that the thresholding analysis for determining a status of detectors may be tailored to the new warmer prevailing condition. However, as most of the previous car events may include cold weather car events, due the previously prevailing cold weather conditions, tuning the thresholding analysis for determining a status of detectors to the current warm weather conditions may be challenging, as the current data is biased to cold weather (e.g., previously prevailing condition). To address this, data compiler 121 may provide functionality to specify alternate data buckets into which car events may be classified. In embodiments, an analysis job may be configured with parameters that may specify a main data bucket and one or more alternate data buckets. In these embodiments, the analysis job may be executed against a set of car events that includes car events associated with the main data bucket. However, if no car events associated with the main data bucket exist in the database, or if less than a threshold number of car events associated with the main data bucket exist in the database, data retriever and compiler 121 may operate to include car events associated with the one or more alternate data buckets. For example, configuration of an analysis job may specify a warm bucket, with a cold bucket as alternative. However, the weather conditions recently change from cold to warm. As a result, there may be no or limited warm weather car events. In this case, the warm bucket analysis job still may be executed, but against a set of car events that may include cold weather car events. The results of the analysis job may be used to automatically tune the thresholding analysis for determining a status of detectors. This functionality to specify alternative data buckets for an analysis job may enable system 100 to provide a mechanism to leverage the benefits of the thresholding analysis for determining a status of detectors functionality of embodiments, even when the car event data may not be perfectly fit to the current prevailing conditions.
[0067] In embodiments, data compiler 121 may be configured to provide the compiled set of car events for a particular analysis job to thresholding analysis manager 122. In embodiments, each car event may be associated with a detector, may represent an event in which a cut passes through a detector, and may include information related to the actual speed and/or arrival time of the cut at the detector.
[0068] In embodiments, a flag may be provided in configuration for a detector that may be used to specify the status of the detector. For example, the flag may be used to indicate a good status for the detector indicating that the status of the detector is good, which may indicate that the detector is able to detect speeds and/or arrival times of a cut with an acceptable level of performance, a bad status for the detector indicating that the status of the detector is bad, which may indicate that the detector is not able to detect speeds and/or arrival times of a cut with an acceptable level of performance and may be fully or close to fully degraded, and/or a warning status indicating that the status of the detector is cautionary, and may indicate that although the detector is able to detect speeds and/or arrival times of a cut with an acceptable level of performance, the performance of the detector has degraded significantly and may be close to failing.
[0069] In embodiments, data compiler 121 may be configured to filter car events to be compiled based on the status flag of a detector. For example, data compiler 121 may determine whether the status flag of a detector has been reset from a bad status or warning status to a good status. In response to a determination that the status flag of a detector has been reset from a bad status or warning status to a good status, data compiler 121 may filter out any car events that occurred (e.g., with a timestamp) before the status flag of the detector was reset from the compiled set of car events to be provided to thresholding analysis manager 122. In this manner, the compiled set of car events to be provided to thresholding analysis manager 122 may include car events for detectors occurring from the time the status flag of the detector was reset to a current time. This functionality may allow system 100 to ensure that the thresholding analysis is iterative with respect to corrective actions. For example, system 100 may cause a corrective action to be applied to a detector determined to have a bad status or warning status. After the corrective action, however, further thresholding analysis may be performed against the detector. In the case where the further thresholding analysis is performed on car events including the previous car events, the analysis may be biased toward a bad status finding. By resetting the set of car events that are considered in the determination of the status of a detector to include only car events after corrective action was taken on the detector, status results may be more reliable. In addition, resetting the set of car events that are considered in the determination of the status of a detector to include only car events after corrective action was taken on the detector may allow system 100 to determine whether a finding of a bad status detector may be actually caused by the prediction scheme (e.g., the prediction as to the expected measurements for a car event), rather than an actual bad status of the detector. In this case, if a detector is found to have a bad status numerous times after corrective action, system 100 may make a determination that the problem may be not that the detector is bad, but that the prediction scheme may be out of tune and not as accurate as should be.
[0070] In embodiments, data compiler 121 may be configured to calculate, based on the compiled set of car events, a speed difference for each car event in the compiled set of car events. In embodiments, data compiler 121 may calculate, for each car event in the compiled set of car events, a speed difference by calculating a difference between predicted speeds and actual speeds. For example, for a car event associated with a detector, data compiler 121 may subtract the predicted speed indicated in the car event from the actual (e.g., real-world) speed measured at the detector during car event. The result of the subtraction is the speed difference associated with the car event. Data compiler 121 may perform the same calculations for each car event in the compiled set of car events to obtain a speed difference for each car event in the compiled set of car events. The result may be a set of speed differences associated with the set of compiled set of car events, where each speed difference in the set of speed differences represents a difference between a predicted speed and an actual speed at a detector. In embodiments, a negative speed difference may indicate that the actual speed measured was lower than the predicted speed, and a positive speed difference may indicate that the actual speed measure was greater than the predicted speed. In embodiments, the set of speed differences may be stored in database 124 and/or may be provided to thresholding analysis manager 122.
[0071] In embodiments, data compiler 121 may be configured to generate a visual representation of the set of speed differences, and to present the generated visual representation to an operator. For example, in some embodiments, data compiler 121 may generate a box-and-whisker graph from the set of speed differences against the deviation represented by a speed difference from zero. In this case, a non-zero value for a speed difference may represent a deviation from zero, with zero indicating that there is no speed difference (e.g., the actual speed and the predicted speed in the car event are the same). In this example, a large deviation from zero may represent a car event in which the detector was not very accurate (e.g., the actual speed at the detector was far off from the predicted speed). On the other hand, a small deviation from zero, or a zero deviation, may represent a car event in which the detector was very accurate (e.g., the detected actual speed was very close or equal to the predicted speed).
[0072] In embodiments, the box-and-whisker graph may then be presented to the operator using a UI (e.g., via user terminal device 130).
[0073] As seen in
[0074] In embodiments, each box-and-whisker entry of box-and-whisker chart 200 may include an indication of the median value of the spread of each box-and-whisker entry. For example, for detector 212, the median value 222 of box-and-whisker entry 220 is approximately-1 MPH, which may indicate that many of the cuts passing through detector 212 had a speed approximately 1 MPH less than the predicted speed. In this same example, for detector 214, the median value 232 of box-and-whisker entry 230 is approximately-5 MPH, which may indicate that many of the cuts passing through detector 214 had a speed approximately 5 MPH less than the predicted speed, much farther off the predicted speed than detector 212.
[0075] In some embodiments, an operator presented with box-and-whisker chart 200 may gain insight into the operational performance of the detectors represented in box-and-whisker chart 200, and may even make maintenance decisions to ensure that corrective action is taken on detectors that may not be operating efficiently.
[0076] With reference back to
[0077] In embodiments, a first set of rules may include speed differential rules that may be applied to speed differences associated with a detector. The speed differential rules may be applied to the set of speed differences generated by data compiler 121 and may be configured to determine the status of a detector based on the rule application.
[0078] In embodiments, a first speed differential rule may include a rule that determines that the status of a detector is good when no more than a threshold percentage of actual speed data points associated with the detector are outside of a plus or minus speed difference from a predicted speed. Put another way, the first speed differential rule determines that the status of a detector is good when no more than a threshold percentage of speed differences fall outside the range defined between +speed threshold and speed threshold. In embodiments, thresholding analysis manager 122 may apply this first speed differential rule against the set of speed differences to determine, for each detector, the percentage of speed differences associated with each detector that outside the range defined between +speed threshold and speed threshold. Thresholding analysis manager 122 may then compare the percentage of speed differences associated with each detector that outside the range defined between +speed threshold and speed threshold with the threshold percentage. If, for a particular detector, the percentage of speed differences associated with each detector that outside the range defined between +speed threshold and speed threshold is less than the threshold percentage, thresholding analysis manager 122 may determine that the particular detector has passed the first speed differential rule. On the other hand, if, for the particular detector, the percentage of speed differences associated with each detector that outside the range defined between +speed threshold and speed threshold is not less than the threshold percentage, thresholding analysis manager 122 may determine that the particular detector has failed the first speed differential rule.
[0079] For example, a threshold percentage of 75% and a plus or minus speed threshold of 1 MPH may be specified for an application of the first speed differential rule. In this example, thresholding analysis manager 122 may determine, based on the set of speed differences, for a first detector, the percentage of speed differences associated with the first detector that are outside plus or minus 1 MPH. Thresholding analysis manager 122 may then compare the percentage of speed differences associated with the first detector that are outside of plus or minus 1 MPH with the threshold percentage of 75%. In this example, if the percentage of speed differences associated with the first detector that are outside of plus or minus 1 MPH is less than 75%, thresholding analysis manager 122 may determine that the first detector passed the first speed differential rule. However, if the percentage of speed differences associated with the first detector that are outside of plus or minus 1 MPH is not less than 75%, thresholding analysis manager 122 may determine that the first detector failed the first speed differential rule. Thresholding analysis manager 122 may continue applying the first speed differential rule to all detectors represented in the set of speed differences.
[0080] In embodiments, thresholding analysis manager 122 may apply the first speed differential rule to a detector with a parameter that ensures that the result of the application will be a percentage ratio between the percentage of speed differences associated with the detector that are outside of plus or minus the speed threshold and the parameters, such that the detector may be considered as a bad detector by thresholding analysis manager 122 when the percentage ratio is at least 100%. In these embodiments, the parameter may include a value by which the percentage of speed differences associated with the detector that are outside of plus or minus the speed threshold is divided. For example, thresholding analysis manager 122 may calculate the percentage of speed differences associated with the detector that are outside of plus or minus the speed threshold and may then divide the percentage by the parameter. The result may be a value configured to be 1 (or 100%) when the percentage of speed differences associated with the detector that are outside of plus or minus the speed threshold is above the threshold percentage. In embodiments, a detector may be determined to not fail the first differential rule, but the percentage ratio may be close to 100%, in which case, even though the detector may not be flagged as a bad detector, the detector may be flag as needing attention, as it may be close to becoming a bad detector. In this manner, thresholding analysis manager 122 may be configured to determine how close a detector may be to failing. In some embodiments a percentage ratio threshold may be used to determine to flag a detector as needing attention (e.g., a warning) even though the detector may not be flagged as a bad detector. In some embodiments, the percentage ratio threshold may be a value above 70% but less than 100%.
[0081] In embodiments, a second speed differential rule may include a rule that determines that the status of a detector is good when a median of the speed differences associated with the detector is not outside of a range defined by plus or minus an average threshold. In embodiments, thresholding analysis manager 122 may apply this second speed differential rule against the set of speed differences to determine, for each detector, the median of the speed differences associated with each detector. Thresholding analysis manager 122 may then compare the median of the speed differences associated with each detector with the range defined by plus or minus the average threshold. If, for a particular detector, the median of the speed differences associated with the particular detector is within the range defined by plus or minus the average threshold, thresholding analysis manager 122 may determine that the particular detector has passed the first speed differential rule. On the other hand, if, for the particular detector, the median of the speed differences associated with the particular detector is outside the range defined by plus or minus the average threshold, thresholding analysis manager 122 may determine that the particular detector has failed the second speed differential rule.
[0082] For example, an average threshold of 2 MPH may be specified for an application of the second speed differential rule. In this example, thresholding analysis manager 122 may determine, based on the set of speed differences, for a first detector, the median of the speed differences associated with the first detector. Thresholding analysis manager 122 may then compare the median of the speed differences associated with each detector with the range defined by plus or minus the average threshold of 2 MPH (e.g., [2 MPH to 2 MPH]). In this example, if the median of the speed differences associated with the first detector is between 2 MPH and 2 MPH, thresholding analysis manager 122 may determine that the first detector passed the second speed differential rule. However, if the median of the speed differences associated with the first detector is outside of 2 MPH to 2 MPH, thresholding analysis manager 122 may determine that the first detector failed the second speed differential rule. Thresholding analysis manager 122 may continue applying the second speed differential rule to all detectors represented in the set of speed differences.
[0083] In embodiments, thresholding analysis manager 122 may apply the second speed differential rule to a detector with a parameter that ensures that the result of the application of the second speed differential rule will be a percentage ratio, such that the detector may be considered as a bad detector by thresholding analysis manager 122 when the percentage ratio is at least 100%. In embodiments, a detector may be determined to not fail the second differential rule, but the percentage ratio may be close to 100%, in which case, even though the detector may not be flagged as a bad detector, the detector may be flag as needing attention, as it may be close to becoming a bad detector. In this manner, thresholding analysis manager 122 may be configured to determine how close a detector may be to failing. In some embodiments a percentage ratio threshold may be used to determine to flag a detector as needing attention (e.g., a warning) even though the detector may not be flagged as a bad detector. In some embodiments, the percentage ratio threshold may be a value above 70% but less than 100%.
[0084] In embodiments, a third speed differential rule may include a rule that determines that the status of a detector is good when a defined middle percentage of the speed differences associated with the detector is not spread over a spread range greater than a spread threshold. In embodiments, the defined middle percentage may be defined as a threshold and may include a range of speed differences defined by a top percentile threshold and a bottom percentile threshold. For example, a defined middle percentage may include a range of 50% of the speed differences associated with the detector, and may be defined as the range excluding the bottom 25 percentile and the top 75 of data points. In embodiments, thresholding analysis manager 122 may apply this third speed differential rule against the set of speed differences to determine, for each detector, the spread range of the defined middle percentage of speed differences associated with each detector. For example, thresholding analysis manager 122 may determine, for each detector, the difference between the lowest speed difference value in the speed differences within the defined middle percentage and the highest speed difference value in the speed differences within the defined middle percentage to determine the spread range of the defined middle percentage of speed differences associated with each detector. Thresholding analysis manager 122 may then compare the spread range of the defined middle percentage of speed differences associated with each detector with the spread threshold. If, for a particular detector, the spread range of the defined middle percentage of speed differences associated with each detector is less than the spread threshold, thresholding analysis manager 122 may determine that the particular detector has passed the third speed differential rule. On the other hand, if, for the particular detector, the spread range of the defined middle percentage of speed differences associated with each detector is not less than the spread threshold, thresholding analysis manager 122 may determine that the particular detector has failed the third speed differential rule.
[0085] For example, a spread threshold of 3 MPH may be specified for the third speed differential rule. In addition, a middle percentage that includes the bottom 25% and the top 75% of data points may be defined for the third speed differential rule. In embodiments, thresholding analysis manager 122 may apply this third speed differential rule against the set of speed differences and may identify speed differences associated with a detector falling within the middle percentage of all the speed differences associated with the detector. Analysis manager 122 may then determine a spread range of the speed differences within the middle percentage. For example, thresholding analysis manager 122 may determine the difference between the lowest speed difference value within the middle percentage and the highest speed difference value within the defined middle percentage. Analysis manager 122 may determine the spread range of the middle percentage as the difference between the highest speed difference value and the lowest speed difference value. In this example, thresholding analysis manager 122 may compare the spread range of the middle percentage to the spread threshold of 3 MPH. In this example, if the spread range of the middle percentage is less than the spread threshold of 3 MPH, thresholding analysis manager 122 may determine that the first detector passed the third speed differential rule. However, if the spread range of the middle percentage is not less than the spread threshold of 3 MPH, thresholding analysis manager 122 may determine that the first detector failed the third speed differential rule. Thresholding analysis manager 122 may continue applying the third speed differential rule to all detectors represented in the set of speed differences.
[0086] In embodiments, thresholding analysis manager 122 may apply the third speed differential rule to a detector with a parameter that ensures that the result of the application of the third speed differential rule will be a percentage ratio, such that the detector may be considered as a bad detector by thresholding analysis manager 122 when the percentage ratio is at least 100%. In embodiments, a detector may be determined to not fail the third speed differential rule, but the percentage ratio may be close to 100%, in which case, even though the detector may not be flagged as a bad detector, the detector may be flag as needing attention, as it may be close to becoming a bad detector. In this manner, thresholding analysis manager 122 may be configured to determine how close a detector may be to failing. In some embodiments a percentage ratio threshold may be used to determine to flag a detector as needing attention (e.g., a warning) even though the detector may not be flagged as a bad detector. In some embodiments, the percentage ratio threshold may be a value above 70% but less than 100%.
[0087] In embodiments, a fourth speed differential rule may include a weighted combination of the first, second, and third speed differential rules. In embodiments, thresholding analysis manager 122 may apply the fourth rule by combining the results of the first, second, and third speed differential rules in a weighted combination. The weighted combination may include applying a respective multiplier to the results of each of the first, second, and third speed differential rules. For example, weighted factors of 3, 2, and 2.25 may be configured for the results of the first, second, and third speed differential rules, respectively. In this example, thresholding analysis manager 122 may apply the respective weighted factor to each of the first, second, and third speed differential rules and may then sum the results to obtain a single result for the combination rule. In embodiments, applying the respective weighted factor to each of the first, second, and third speed differential rules may include raising each of the of the first, second, and third speed differential rules results by a power equal to the respective weighted factors. For example, in this example, the results of the first rule may be raised to a power of 3, the results of the second rule may be raised to a power of 2, and the results of the third rule may be raised to a power of 2.5. In this example, the results of each exponential operation may be summed to obtain an overall result for the fourth rule. In some embodiments, as the results of the first, second, and third speed differential rules may include a percentage value, the result of the combination rule (e.g., fourth speed differential rule) may also be a percentage value. It is noted that the particular weighted factors of 3, 2, and 2.25 are described by way of example and not intended to be limiting in any way. Indeed, in some embodiments, other weighted factors may be used. For example, in another particular example, weighted factors of 2, 3, and 1.1 may be configured for the results of the first, second, and third differential rules, respectively.
[0088] In embodiments, thresholding analysis manager 122 may determine a status of a detector based on whether the detector failed or passed each of differential speed rules. For example, if a detector failed any of the differential speed rules, the status of the detector may be set as bad. On the other hand, if the detector passed all applied differential speed rules, the status of the detector may be set as good. In particular embodiments, a detector may fail the fourth speed differential rule (e.g., the combination rule) if the detector has not failed any one of the individual rules in the combination.
[0089] Results manager 123 may be configured to report the results of the thresholding analysis by thresholding analysis manager 122 and to generate, track, and/or control corrective actions performed in response to the determination of the status of detectors. For example, in some embodiments, results manager 123 may be configured to automatically generate a corrective action without operator intervention. In embodiments, results manager 123 may issue a notification to operators of the status of a detector, in which case the operators may take corrective action. In some embodiments, results manager 123 may, in response to a determination that the status of a detector is bad, send a control signal to the deactivate the detector in order to avoid potential damage.
[0090] In embodiments, results manager 123 may be configured to generate and provide one or more status reports indicating the determined status of one or more detectors based on the thresholding analysis by thresholding analysis manager 122. In embodiments, the status report may be interactive and may allow an operator to modify entries on the report. Modified entries in the report may be detected by results manager 123, which may cause results manager 123 to store the modified entries in the respective configurations of the corresponding detector. For example, in some embodiments, an operator may reset a status indicator flag of a detector (e.g., after corrective action has been taken on the detector) from a bad detector or a section warning status indicator to a good detector status indicator. In these cases, the configuration of the corresponding detector may include an indication that the status flag for the detector has been reset. In embodiments, car events associated with the detector having a timestamp with a value before the time the status flag was reset may be scrubbed or removed from the database. In this manner, subsequent thresholding analysis performed for the detector may only include car events after the status flag reset. The same process of removing car events associated with a detector occurring prior to a status flag reset for the detector may be performed every time the status flag for the detector is reset. However, in embodiments, results manager 123 may determine that the status flag of a detector found in a current thresholding analysis iteration to be a bad detector or having a section warning has been reset a number of times before, and that the number of times exceeds a threshold number of times. In this case, results manager 123 may determine to include an indication indicating that there is a likelihood that the detector is not actually bad, even though it has been found to be bad in the current thresholding analysis iteration, and there may be a possibility that the problem (e.g., the cause of the detector being found to be a bad detector) may be another device (e.g., a detector used to collect speed information associated with the detector) or the prediction scheme used to determine the expected speeds at the detector.
[0091]
[0092] In embodiments, UI 300 may include a section 310 for including an indication of the number of detector failures (e.g., the number of detectors whose status was determined as bad) included in the report.
[0093] As illustrated in
[0094] It is also note that the report presented in
[0095]
[0096] At block 402, a plurality of car events associated with a detector device in the classification yard is compiled. In embodiments, each car event of the plurality of car events may be associated with a predicted speed of a cut passing through the detector device during a respective car event, and each car event may include real-world measurements of an actual speed of the cut at the detector device during the respective car event. In embodiment, functionality of a data compiler (e.g., data compiler 121 in
[0097] At block 404, a set of speed differences associated with the detector is generated based on the plurality of car events associated with the detector. In embodiment, functionality of a data compiler (e.g., data compiler 121 in
[0098] At block 406, thresholding analysis is applied to the set of speed differences associated with the detector to determine a status of the detector. In embodiment, functionality of a threshold analysis manager (e.g., threshold analysis manager 122 in
[0099] At block 408, a corrective action signal including an indication of the status of the detector and/or a corrective action to be taken for the detector is generated. In embodiment, functionality of a results manager (e.g., results manager 123 in
[0100] Persons skilled in the art will readily understand that advantages and objectives described above would not be possible without the particular combination of computer hardware and other structural components and mechanisms assembled in this inventive system and described herein. Additionally, the algorithms, methods, and processes disclosed herein improve and transform any general-purpose computer or processor disclosed in this specification and drawings into a special purpose computer programmed to perform the disclosed algorithms, methods, and processes to achieve the aforementioned functionality, advantages, and objectives. It will be further understood that a variety of programming tools, known to persons skilled in the art, are available for generating and implementing the features and operations described in the foregoing. Moreover, the particular choice of programming tool(s) may be governed by the specific objectives and constraints placed on the implementation selected for realizing the concepts set forth herein and in the appended claims.
[0101] The description in this patent document should not be read as implying that any particular element, step, or function can be an essential or critical element that must be included in the claim scope. Also, none of the claims can be intended to invoke 35 U.S.C. 112 (f) with respect to any of the appended claims or claim elements unless the exact words means for or step for are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) mechanism, module, device, unit, component, element, member, apparatus, machine, system, processor, processing device, or controller within a claim can be understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and can be not intended to invoke 35 U.S.C. 112 (f). Even under the broadest reasonable interpretation, in light of this paragraph of this specification, the claims are not intended to invoke 35 U.S.C. 112 (f) absent the specific language described above.
[0102] The disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. For example, each of the new structures described herein, may be modified to suit particular local variations or requirements while retaining their basic configurations or structural relationships with each other or while performing the same or similar functions described herein. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive. Accordingly, the scope of the inventions can be established by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Further, the individual elements of the claims are not well-understood, routine, or conventional. Instead, the claims are directed to the unconventional inventive concept described in the specification
[0103] Those of skill in the art would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. Skilled artisans will also readily recognize that the order or combination of components, methods, or interactions that are described herein are merely examples and that the components, methods, or interactions of the various embodiments of the present disclosure may be combined or performed in ways other than those illustrated and described herein.
[0104] Functional blocks and modules in
[0105] The steps of a method or algorithm described in connection with the disclosure herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal, base station, a sensor, or any other communication device. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
[0106] In one or more exemplary designs, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. Computer-readable storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, a connection may be properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL), then the coaxial cable, fiber optic cable, twisted pair, or DSL, are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
[0107] Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods, and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.