US20140359068A1 - Trigger-based data collection system - Google Patents
Trigger-based data collection system Download PDFInfo
- Publication number
- US20140359068A1 US20140359068A1 US14/464,342 US201414464342A US2014359068A1 US 20140359068 A1 US20140359068 A1 US 20140359068A1 US 201414464342 A US201414464342 A US 201414464342A US 2014359068 A1 US2014359068 A1 US 2014359068A1
- Authority
- US
- United States
- Prior art keywords
- data
- machine
- machines
- event
- fleet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000013480 data collection Methods 0.000 title description 27
- 238000000034 method Methods 0.000 claims description 30
- 230000007613 environmental effect Effects 0.000 claims description 2
- 230000004044 response Effects 0.000 description 14
- 230000003137 locomotive effect Effects 0.000 description 10
- 230000005540 biological transmission Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 230000000116 mitigating effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0081—On-board diagnosis or maintenance
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/04—Protocols specially adapted for terminals or networks with limited capabilities; specially adapted for terminal portability
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
- B61L27/57—Trackside diagnosis or maintenance, e.g. software upgrades for vehicles or trains, e.g. trackside supervision of train conditions
Definitions
- This disclosure relates generally to systems and methods monitoring operation and fault conditions of a machine, and more specifically, to systems and methods for collecting and communicating data associated with a fleet of machines in response to a triggering event.
- Machine downtime suffered as a result of a fault condition in a machine can be costly, so efficient diagnostics systems are desirable to minimize repair time.
- the data collected may be useful for maintenance of a fleet of machines, in addition to the particular machine from which the data is collected.
- the '478 patent is directed to an on-board monitor for a railroad locomotive that interfaces with the controller subsystems of the locomotive to collect parametric performance data.
- the specific data to be collected and the collection intervals are defined at a remote service center and transmitted to the on-board monitor.
- the on-board monitor also includes the capability to collect additional data or collect data more frequently in response to the results of certain triggering events.
- the system and method disclosed in the '478 patent may monitor and report operational data of a machine, the system and method disclosed may still suffer from a number of possible drawbacks.
- the system and method disclosed in the '478 patent only collects information from the particular locomotive that suffered the fault.
- collecting data from multiple machines may lead to faster and more accurate fault identification.
- data collection from a fleet of machines may result in more quickly recognizing a common problem about the machines, such that similar faults can be prevented in the remainder of the machines in the fleet.
- the system and method disclosed in the '478 patent in response to a fault trigger or a request from another system, transmits all the data collected from its machine.
- the presently disclosed systems and methods may be directed to mitigating or overcoming one or more of the possible drawbacks set forth above and/or other problems in the art.
- the present disclosure is directed to a system for collecting and communicating data associated with at least one of a plurality of machines and may include at least one sensor associated with at least one of the plurality of machines.
- the at least one sensor may be configured to monitor an operational condition of the at least one machine and provide signals indicative of the operational condition.
- the system may also include at least one local data system associated with the at least one machine.
- the at least one local data system may include a processor and may be configured to receive signals from the at least one sensor and detect an event affecting the at least one machine based on the signals received from the at least one sensor.
- the at least one local data system may also be configured to communicate data relevant to the detected event to a location remote from the at least one machine.
- the system may also include a central data system.
- the central data system may include a processor and be configured to receive the data relevant to the detected event and communicate with other machines of the plurality such that the other machines communicate data relevant to the detected event to the central data system.
- the present disclosure is directed to a processor-implemented method for collecting data from at least one machine.
- the method may include detecting via a first processor an event associated with an affected machine of the at least one machines and determining a relevant data portion of machine data based on the event.
- the method may also include commanding the affected machine to collect event-specific data and receiving via a second processor the event-specific data and the relevant data portion from the affected machine.
- the present disclosure is directed to a machine fleet.
- the machine fleet may include a plurality of machines and a data collection system.
- the data collection system may include at least one sensor associated with at least one of the plurality of machines.
- the at least one sensor may be configured to monitor an operational condition of the at least one machine and provide signals indicative of the operational condition.
- the data collection system may also include at least one local data system associated with the at least one machine.
- the at least one local data system may include a processor and may be configured to receive signals from the at least one sensor and detect an event affecting the at least one machine based on the signals received from the at least one sensor.
- the at least one local data system may also be configured to communicate data relevant to the detected event to a location remote from the at least one machine.
- the data collection system may also include a central data system.
- the central data system may include a processor and be configured to receive the data relevant to the detected event and communicate with other machines of the plurality such that the other machines communicate data relevant to the detected event to the central
- FIG. 1 is a schematic depiction of an exemplary machine fleet.
- FIG. 2 is a block diagram of an exemplary data collection system.
- FIG. 3 is a flowchart of an exemplary method of collecting data from at least one machine.
- FIG. 1 shows an exemplary machine fleet 100 in which systems and methods for data collection may be implemented consistent with the disclosed embodiments.
- Machine fleet 100 may include any group of machines 110 defined by shared or similar characteristics.
- each machine 110 of machine fleet 100 may be the same type or the same model of machine.
- the three machines 110 comprising the exemplary machine fleet 100 shown in FIG. 1 are all locomotives.
- Machine fleet 100 may include other types of machines, including but not limited to fixed engine systems, construction machines, commercial machines, and marine-based machines, that may incorporate the systems and methods for data collection consistent with the embodiments disclosed herein.
- a machine fleet 100 may be defined by shared or similar characteristics among the plurality of machines 110 of machine fleet 100 . It may be desirable to define machine fleet 100 based on a common characteristic that makes machines 110 of machine fleet 100 particularly likely to experience common faults or malfunctions. For example, machines 110 that all work in similar environmental conditions, such as, for example, extremely hot temperatures or windy, dusty climates, are likely to experience similar operating conditions and suffer from faults related to those conditions. Therefore, it may be desirable to collect operational data from similar machines 110 or machines 110 operating in similar conditions to identify the cause of possible machine faults, thereby reducing downtime and providing data helpful to preventing similar faults among other machines 110 in a particular machine fleet 100 .
- machines 110 of a particular machine fleet 100 may also be defined by other common characteristics.
- machine fleet 100 may be a locomotive consist in which each machine 110 is a locomotive, and all machines 110 are connected together to form a train.
- machines 110 may be grouped in a particular machine fleet 100 by a similar type of load each machine 110 carries.
- machine fleet 100 may include a plurality of locomotives each pulling passenger cars.
- machine fleet 100 may be defined by a common geographic location of each machine 110 .
- machines 110 of a particular machine fleet 100 may all be operating at a single worksite.
- machines 110 of a particular machine fleet 100 may all be operating in a certain geographic area, such as within a predefined radius of an identified location, or within a certain geographic region.
- machines 110 of a particular machine fleet 100 may share similar working conditions.
- machines 110 of a particular machine fleet 100 may all work at construction sites that present similar problems, such as, for example, rocky soil.
- machines 110 of a particular machine fleet 100 may all have similar purposes.
- machines 110 of a particular machine fleet 100 may be of different types yet all be used to move heavy loads. It will be apparent that it may be beneficial to categorize machines 110 in a particular machine fleet 100 by one or more characteristics, including but not limited to those discussed above, in order to streamline troubleshooting and share useful operating information among machines 110 of the particular machine fleet 100 .
- Machine fleet 100 may also include a data collection system 115 , as illustrated in FIG. 2 .
- Data collection system 115 may gather specific machine data from a larger pool of data being collected in response to a triggering event or condition.
- a triggering event may include the fault of a subsystem of one or more of machines 110 .
- the event may include the fault of machine 110 .
- the triggering event may include a temperature rising above a threshold limit in one or more of machines 110 .
- the triggering event may be based on a sensor reading of one or more sensors associated with machines 110 .
- the triggering event may be a communication from a machine operator of a fault condition or breakdown.
- an operator of machine 110 may send a signal to data collection system 115 indicative of a fault.
- Machine data that data collection system 115 may collect includes two types of information.
- Machines 110 may continuously collect data from various subsystems and sensors within machine. To analyze machine 110 in response to a triggering event, it may not be necessary for data collection system 115 to provide this large amount of data from the affected machine 110 or other machines 110 in fleet 100 . Instead, data collection system 115 may identify which portions of the total data is relevant. This first data set, which may include historical data that predates the occurrence of the triggering event, is “relevant data.” Data collection system 115 may want additional data collected in response to a triggering event. For example, data collection system 115 may want data collected for a period of time after the occurrence of the event. This type of data, measured in response to a triggering event, is “event-specific data.”
- Data collection system 115 may include a plurality of sensors 120 .
- Each sensor 120 may be configured to monitor a particular operational condition of a machine 110 .
- each machine 110 may include one or more sensors 120 configured to monitor the temperature at various locations of associated machine 110 .
- machine 110 may include sensors 120 to monitor the functionality of one or more subsystems of machine 110 .
- sensors 120 may monitor electric characteristics, such as current flow and/or voltage potential to determine whether an electronic subsystem is functioning normally.
- Sensors 120 may monitor any machine condition that is directly or indirectly indicative of the operability of machine 110 or its subsystems, including, but not limited to, pressure, density, emissions data, speed, fluid level, fluid flow, volume flow rate, vibration, torque, force, throttle position, mass air-fuel ratio, traction, rotary position, rotational motion, and speed.
- Data collection system 115 may include any combination of sensors 120 known in the art.
- Data collection system 115 may include at least one local data system 140 , each local data system 140 being associated with one of machines 110 .
- Local data system 140 may embody a single microprocessor or multiple microprocessors that include a means for receiving machine data from sensors 120 and/or other local data systems 140 and for communicating with other systems. Numerous commercially available microprocessors can be configured to perform the functions of local data system 140 . It should be appreciated that local data system 140 could readily embody a general machine or engine microprocessor capable of gathering machine data.
- Local data system 140 may include all the components required to run an application such as, for example, a memory, a secondary storage device, and a processor, such as a central processing unit or any other means known.
- Various other known circuits may be associated with local data system 140 , including power source circuitry (not shown) and other appropriate circuitry.
- each machine 110 in fleet 100 includes at least one local data system 140 , and local data system 140 may be associated with (e.g., located within) machine 110 .
- Local data system 140 may be configured to receive machine data from the associated machine 110 .
- local data system 140 may receive machine data from one or more of sensors 120 associated with the same machine 110 .
- Machine data may include any information related to the operation or the condition of machine 110 , including machine conditions measured and/or reported by sensors 120 .
- local data system 140 may continually overwrite machine data received from sensors 120 with new machine data.
- Some embodiments of local data system 140 may be configured to store the most recent machine data, such as, for example, data from a predefined period of time such as the last thirty seconds of operation. By overwriting outdated data to store recent machine data, the memory of local data system 140 can be smaller, as it will only store a predefined maximum amount of data from each sensor 120 .
- local data system 140 may be configured to stop storing and/or overwriting data from sensors 120 in response to a triggering event. This prevents overwriting data that may be useful in diagnosing the cause of the triggering events.
- local data system 140 may collect event-specific data in response to a triggering event. For example, once local data system 140 detects a triggering event, local data system 140 may be configured to continue to collect data from sensors 120 for a predefined period of time, such as for ten seconds after the triggering event is detected. Data collected after the triggering event that is detected may be called “event-specific data.” According to some embodiments, event-specific data may be collected at a faster rate than sensors 120 collect data prior to identifying a triggering condition. For example, sensors 120 may collect event-specific data at 5 millisecond intervals.
- Local data system 140 may be capable of detecting an event affecting machine 110 based on machine data that local data system 140 receives related to its associated machine 110 . Once the occurrence of an event is detected, local data system 140 may determine which portions of the collected machine data are relevant data based on the event. For example, the relevant data portion may depend upon the nature of the event. According to some embodiments, if the triggering event is an overheating condition, the relevant data portion may include temperature data that local data system 140 has received from one or more of sensors 120 . Alternatively or additionally, the relevant data portion may depend on the timing of the triggering event.
- the relevant data portion may include data related to the start up, including data related to subsystems that had been operating at the time of the triggering event and data related to the operability of those subsystems.
- Local data system 140 may identify the relevant data by the sensor 120 from which the data originates.
- Data collection system 115 may also include a central data system 150 .
- Central data system 150 may be associated with (e.g., located on) one of the plurality of machines 110 . Alternatively, central data system 150 may be located remotely with respect to machines 110 .
- Central data system 150 may embody a single microprocessor or multiple microprocessors that include a means for receiving relevant data from and/or for sending instructions to local data systems 140 .
- Central data system 150 may be configured to receive machine data from at least one of the plurality of local data systems 140 .
- Central data system 150 may also be configured to analyze and/or process the machine data to determine the cause of or solution to the triggering event. Numerous commercially available microprocessors can be configured to perform the functions of central data system 150 .
- central data system 150 could readily embody a general machine or engine microprocessor capable of gathering machine data.
- Central data system 150 may include all the components required to run an application such as, for example, a memory, a secondary storage device, and a processor, such as a central processing unit or any other means known.
- Various other known circuits may be associated with central data system 150 , including power source circuitry (not shown) and other appropriate circuitry.
- Central data system 150 may be configured to receive the relevant data portion from at least one of the plurality of local data systems 140 . Once central data system 150 has received the relevant data portion, this data may be analyzed and/or processed (e.g., by central data system 150 ) to determine the cause or solution of the triggering event. According to some embodiments, central data system 150 may receive the relevant data portion from all machines 110 in fleet 100 at the same time. Alternatively, central data system 150 may receive the relevant data portion from the affected machine 110 and then request the relevant data portion from other machines 110 . The order of data receipt may be configured by the user according to the particular application.
- Local data system 140 may be configured to transmit the relevant data portion and the event-specific data to central data system 150 . According to some embodiments, local data system 140 may transmit event-specific data to central data system 150 automatically. Alternatively, local data system 140 may transmit the event-specific data to central data system 150 in response to a request from central data system 150 . In a similar manner, local data system 140 may transmit the relevant data portion to central data system 150 based on the triggering event. Alternatively, central data system 150 may identify the relevant data portion and communicate this to local data system 140 . For example, central data system 150 may identify the relevant data portion by the sensor(s) 120 from which the data originated.
- central data system 150 may be further configured to receive a first signal from one or more of local data systems 140 indicative of the nature of the event.
- central data system 150 may send a second signal to local data system 140 identifying the event-specific data and requesting local data system 140 to begin collecting the event-specific data to transmit to central data system 150 .
- the second signal may also identify the relevant data portion, such that local data system 140 is able to identify and collect the relevant data portion for central data system 150 .
- Central data system 150 may share information among different machines 110 .
- central data system 150 may be configured to identify a plurality of machines 110 that share one or more characteristics with other machines 110 .
- the plurality of machines 110 sharing one or more characteristics may make up a portion or all of machine fleet 100 .
- central data system 150 may be configured to communicate the identity of the relevant data portion to other local data systems 140 in machine fleet 100 .
- Central data system 150 may receive the relevant data portion from each of the local data systems 140 in machine fleet 100 .
- local data system 140 not associated with that machine may be configured to collect event-specific data.
- central data system 150 may be configured to send a signal to all local data systems 140 in a particular machine fleet 100 indicative of the occurrence of an event on at least one machine 110 of machine fleet 100 .
- central data system 150 may request event-specific data from all machines 110 in response to an event associated with one machine 110 .
- local data system 140 may be configured to collect event-specific data and transmit this data to central data system 150 .
- Central data system 150 may be configured to receive the event-specific data and the relevant data portion from each machine 110 in machine fleet 100 .
- Central data system 150 may analyze event-specific data from all machines 110 in machine fleet 100 to determine any anomalies particular to machine 110 on which the event occurred to narrow down the cause of or solution to the event.
- FIG. 3 is a flowchart of an exemplary method for collecting data from at least one machine 110 .
- the method may include detecting an event associated with an affected machine 110 of the at least one machine 110 .
- step 210 may include determining a relevant data portion of machine data based on the event.
- local data system 140 may determine the relevant data portion.
- central data system 150 may send a first signal to local data system 140 of the affected machine identifying the relevant data portion.
- Central data system 150 may also send the first signal to other machines 110 identified as having a common characteristic with the affected machine 110 .
- central data system 150 may command affected machine 110 to collect event-specific data at step 220 . According to some embodiments, central data system 150 may communicate this command to the local data system 140 associated with the affected machine 110 . Central data system 150 may also request event-specific data from other machines 110 identified as having a common characteristic with the affected machine 110 . At step 230 , central data system 150 may receive the event-specific data and the relevant data portion. Optionally, central data system 150 may receive similar data from other machines 110 in machine fleet 100 .
- the disclosed systems and methods may provide a robust solution for maintenance and diagnostics of a fleet of machines. As a result of event-based data collection, the disclosed systems and methods may provide a more refined solution to data collection, decreasing the amount of unnecessary data transmission.
- the disclosed data collection methods may more accurately diagnose a problem by collecting data related to the particular event.
- This solution may allow for customized data collection in response to a particular fault or triggering event, which helps collect more data that is likely to provide the key to correcting the identified fault.
- the disclosed systems and methods may provide a more efficient solution for data communication, which may be particularly useful when time spent transmitting or processing data may result in additional downtime.
- this downtime may be costly and prevent operators from meeting deadlines.
- the key issue may no longer be collecting enough information to identify the problem. Rather, the key to efficient repair of machine fleets may be identifying what portions, if any, of the gathered operational data are useful in solving a particular problem.
- the systems and methods may facilitate identifying a cause, rather than just a solution, of particular faults. For example, collecting event-specific and relevant machine data from all machines in a fleet may help an engineer determine which values are particular to the broken machine and which characteristics are within a normal range for that machine. Furthermore, sharing known problems among a fleet can help identify likely problems of a particular machine within the fleet before that machine data is analyzed.
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Mechanical Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
A system for collecting and communicating data associated with at least one of a plurality of machines includes a sensor. The sensor is configured to monitor an operational condition of the at least one associated machine and provide signals indicative of the operational condition. The system includes at least one local data system, including a processor, associated with the machine and configured to receive signals from the sensor, detect an event affecting the at least one machine based on the signals received from the sensor, and communicate data relevant to the detected event to a location remote from the at least one machine. A central data system including a processor and configured to receive the data relevant to the detected event and communicate with other machines of the plurality of much such that the other machines communicate data relevant to the detected event to the central data system is included.
Description
- This disclosure relates generally to systems and methods monitoring operation and fault conditions of a machine, and more specifically, to systems and methods for collecting and communicating data associated with a fleet of machines in response to a triggering event.
- Machine downtime suffered as a result of a fault condition in a machine such as, for example, a locomotive, can be costly, so efficient diagnostics systems are desirable to minimize repair time. In complex machines with multiple subsystems, it may be difficult to determine which subsystem is suffering from a fault or the particular cause of that fault without extensive analysis of the affected machine.
- For maintenance and diagnostic purposes, it may be desirable to collect data relating to a machine during real-time operation for later retrieval. In some diagnostic systems, the data collected often includes much more information than what may be desired for a specific purpose or application. However, the data collected may be useful for maintenance of a fleet of machines, in addition to the particular machine from which the data is collected.
- One solution for monitoring a locomotive is described in U.S. Pat. No. 6,487,478 B1 (“the '478 patent”). The '478 patent is directed to an on-board monitor for a railroad locomotive that interfaces with the controller subsystems of the locomotive to collect parametric performance data. The specific data to be collected and the collection intervals are defined at a remote service center and transmitted to the on-board monitor. The on-board monitor also includes the capability to collect additional data or collect data more frequently in response to the results of certain triggering events.
- Although the system and method disclosed in the '478 patent may monitor and report operational data of a machine, the system and method disclosed may still suffer from a number of possible drawbacks. For example, the system and method disclosed in the '478 patent only collects information from the particular locomotive that suffered the fault. When machines in a fleet have similar operating conditions, collecting data from multiple machines may lead to faster and more accurate fault identification. Furthermore, data collection from a fleet of machines may result in more quickly recognizing a common problem about the machines, such that similar faults can be prevented in the remainder of the machines in the fleet. Additionally, the system and method disclosed in the '478 patent, in response to a fault trigger or a request from another system, transmits all the data collected from its machine. In complex machines such as locomotives, this may result in transmission of a significantly large amount of data, a large portion of which may be wholly unrelated to the fault condition that triggered the data collection or transmission. When a particular event triggers a fault condition, it may be preferable to send only relevant data to decrease the time and cost of data analysis.
- The presently disclosed systems and methods may be directed to mitigating or overcoming one or more of the possible drawbacks set forth above and/or other problems in the art.
- According to one aspect, the present disclosure is directed to a system for collecting and communicating data associated with at least one of a plurality of machines and may include at least one sensor associated with at least one of the plurality of machines. The at least one sensor may be configured to monitor an operational condition of the at least one machine and provide signals indicative of the operational condition. The system may also include at least one local data system associated with the at least one machine. The at least one local data system may include a processor and may be configured to receive signals from the at least one sensor and detect an event affecting the at least one machine based on the signals received from the at least one sensor. The at least one local data system may also be configured to communicate data relevant to the detected event to a location remote from the at least one machine. The system may also include a central data system. The central data system may include a processor and be configured to receive the data relevant to the detected event and communicate with other machines of the plurality such that the other machines communicate data relevant to the detected event to the central data system.
- In accordance with another aspect, the present disclosure is directed to a processor-implemented method for collecting data from at least one machine. The method may include detecting via a first processor an event associated with an affected machine of the at least one machines and determining a relevant data portion of machine data based on the event. The method may also include commanding the affected machine to collect event-specific data and receiving via a second processor the event-specific data and the relevant data portion from the affected machine.
- According to another aspect, the present disclosure is directed to a machine fleet. The machine fleet may include a plurality of machines and a data collection system. The data collection system may include at least one sensor associated with at least one of the plurality of machines. The at least one sensor may be configured to monitor an operational condition of the at least one machine and provide signals indicative of the operational condition. The data collection system may also include at least one local data system associated with the at least one machine. The at least one local data system may include a processor and may be configured to receive signals from the at least one sensor and detect an event affecting the at least one machine based on the signals received from the at least one sensor. The at least one local data system may also be configured to communicate data relevant to the detected event to a location remote from the at least one machine. The data collection system may also include a central data system. The central data system may include a processor and be configured to receive the data relevant to the detected event and communicate with other machines of the plurality such that the other machines communicate data relevant to the detected event to the central data system.
-
FIG. 1 is a schematic depiction of an exemplary machine fleet. -
FIG. 2 is a block diagram of an exemplary data collection system. -
FIG. 3 is a flowchart of an exemplary method of collecting data from at least one machine. -
FIG. 1 shows anexemplary machine fleet 100 in which systems and methods for data collection may be implemented consistent with the disclosed embodiments.Machine fleet 100 may include any group ofmachines 110 defined by shared or similar characteristics. According to some embodiments, eachmachine 110 ofmachine fleet 100 may be the same type or the same model of machine. For example, the threemachines 110 comprising theexemplary machine fleet 100 shown inFIG. 1 are all locomotives.Machine fleet 100 may include other types of machines, including but not limited to fixed engine systems, construction machines, commercial machines, and marine-based machines, that may incorporate the systems and methods for data collection consistent with the embodiments disclosed herein. - A
machine fleet 100 may be defined by shared or similar characteristics among the plurality ofmachines 110 ofmachine fleet 100. It may be desirable to definemachine fleet 100 based on a common characteristic that makesmachines 110 ofmachine fleet 100 particularly likely to experience common faults or malfunctions. For example,machines 110 that all work in similar environmental conditions, such as, for example, extremely hot temperatures or windy, dusty climates, are likely to experience similar operating conditions and suffer from faults related to those conditions. Therefore, it may be desirable to collect operational data fromsimilar machines 110 ormachines 110 operating in similar conditions to identify the cause of possible machine faults, thereby reducing downtime and providing data helpful to preventing similar faults amongother machines 110 in aparticular machine fleet 100. - According to some embodiments,
machines 110 of aparticular machine fleet 100 may also be defined by other common characteristics. For example,machine fleet 100 may be a locomotive consist in which eachmachine 110 is a locomotive, and allmachines 110 are connected together to form a train. According to some embodiments,machines 110 may be grouped in aparticular machine fleet 100 by a similar type of load eachmachine 110 carries. For example,machine fleet 100 may include a plurality of locomotives each pulling passenger cars. According to some embodiments,machine fleet 100 may be defined by a common geographic location of eachmachine 110. For example,machines 110 of aparticular machine fleet 100 may all be operating at a single worksite. Alternatively or additionally,machines 110 of aparticular machine fleet 100 may all be operating in a certain geographic area, such as within a predefined radius of an identified location, or within a certain geographic region. - According to some embodiments,
machines 110 of aparticular machine fleet 100 may share similar working conditions. For example,machines 110 of aparticular machine fleet 100 may all work at construction sites that present similar problems, such as, for example, rocky soil. Alternatively or additionally,machines 110 of aparticular machine fleet 100 may all have similar purposes. For example,machines 110 of aparticular machine fleet 100 may be of different types yet all be used to move heavy loads. It will be apparent that it may be beneficial to categorizemachines 110 in aparticular machine fleet 100 by one or more characteristics, including but not limited to those discussed above, in order to streamline troubleshooting and share useful operating information amongmachines 110 of theparticular machine fleet 100. -
Machine fleet 100 may also include adata collection system 115, as illustrated inFIG. 2 .Data collection system 115 may gather specific machine data from a larger pool of data being collected in response to a triggering event or condition. For example, a triggering event may include the fault of a subsystem of one or more ofmachines 110. Alternatively, the event may include the fault ofmachine 110. Alternatively or additionally, the triggering event may include a temperature rising above a threshold limit in one or more ofmachines 110. The triggering event may be based on a sensor reading of one or more sensors associated withmachines 110. According to some embodiments, the triggering event may be a communication from a machine operator of a fault condition or breakdown. For example, an operator ofmachine 110 may send a signal todata collection system 115 indicative of a fault. - Machine data that
data collection system 115 may collect includes two types of information.Machines 110 may continuously collect data from various subsystems and sensors within machine. To analyzemachine 110 in response to a triggering event, it may not be necessary fordata collection system 115 to provide this large amount of data from the affectedmachine 110 orother machines 110 infleet 100. Instead,data collection system 115 may identify which portions of the total data is relevant. This first data set, which may include historical data that predates the occurrence of the triggering event, is “relevant data.”Data collection system 115 may want additional data collected in response to a triggering event. For example,data collection system 115 may want data collected for a period of time after the occurrence of the event. This type of data, measured in response to a triggering event, is “event-specific data.” -
Data collection system 115 may include a plurality ofsensors 120. Eachsensor 120 may be configured to monitor a particular operational condition of amachine 110. For example, eachmachine 110 may include one ormore sensors 120 configured to monitor the temperature at various locations of associatedmachine 110. Additionally or alternatively,machine 110 may includesensors 120 to monitor the functionality of one or more subsystems ofmachine 110. For example,sensors 120 may monitor electric characteristics, such as current flow and/or voltage potential to determine whether an electronic subsystem is functioning normally.Sensors 120 may monitor any machine condition that is directly or indirectly indicative of the operability ofmachine 110 or its subsystems, including, but not limited to, pressure, density, emissions data, speed, fluid level, fluid flow, volume flow rate, vibration, torque, force, throttle position, mass air-fuel ratio, traction, rotary position, rotational motion, and speed.Data collection system 115 may include any combination ofsensors 120 known in the art. -
Data collection system 115 may include at least onelocal data system 140, eachlocal data system 140 being associated with one ofmachines 110.Local data system 140 may embody a single microprocessor or multiple microprocessors that include a means for receiving machine data fromsensors 120 and/or otherlocal data systems 140 and for communicating with other systems. Numerous commercially available microprocessors can be configured to perform the functions oflocal data system 140. It should be appreciated thatlocal data system 140 could readily embody a general machine or engine microprocessor capable of gathering machine data.Local data system 140 may include all the components required to run an application such as, for example, a memory, a secondary storage device, and a processor, such as a central processing unit or any other means known. Various other known circuits may be associated withlocal data system 140, including power source circuitry (not shown) and other appropriate circuitry. - In
FIG. 2 , eachmachine 110 infleet 100 includes at least onelocal data system 140, andlocal data system 140 may be associated with (e.g., located within)machine 110.Local data system 140 may be configured to receive machine data from the associatedmachine 110. For example,local data system 140 may receive machine data from one or more ofsensors 120 associated with thesame machine 110. Machine data may include any information related to the operation or the condition ofmachine 110, including machine conditions measured and/or reported bysensors 120. - According to some embodiments,
local data system 140 may continually overwrite machine data received fromsensors 120 with new machine data. Some embodiments oflocal data system 140 may be configured to store the most recent machine data, such as, for example, data from a predefined period of time such as the last thirty seconds of operation. By overwriting outdated data to store recent machine data, the memory oflocal data system 140 can be smaller, as it will only store a predefined maximum amount of data from eachsensor 120. - In some embodiments,
local data system 140 may be configured to stop storing and/or overwriting data fromsensors 120 in response to a triggering event. This prevents overwriting data that may be useful in diagnosing the cause of the triggering events. According to some embodiments,local data system 140 may collect event-specific data in response to a triggering event. For example, oncelocal data system 140 detects a triggering event,local data system 140 may be configured to continue to collect data fromsensors 120 for a predefined period of time, such as for ten seconds after the triggering event is detected. Data collected after the triggering event that is detected may be called “event-specific data.” According to some embodiments, event-specific data may be collected at a faster rate thansensors 120 collect data prior to identifying a triggering condition. For example,sensors 120 may collect event-specific data at 5 millisecond intervals. -
Local data system 140 may be capable of detecting anevent affecting machine 110 based on machine data thatlocal data system 140 receives related to its associatedmachine 110. Once the occurrence of an event is detected,local data system 140 may determine which portions of the collected machine data are relevant data based on the event. For example, the relevant data portion may depend upon the nature of the event. According to some embodiments, if the triggering event is an overheating condition, the relevant data portion may include temperature data thatlocal data system 140 has received from one or more ofsensors 120. Alternatively or additionally, the relevant data portion may depend on the timing of the triggering event. For example, if the event occurs within a predefined time of machine startup, the relevant data portion may include data related to the start up, including data related to subsystems that had been operating at the time of the triggering event and data related to the operability of those subsystems.Local data system 140 may identify the relevant data by thesensor 120 from which the data originates. -
Data collection system 115 may also include acentral data system 150.Central data system 150 may be associated with (e.g., located on) one of the plurality ofmachines 110. Alternatively,central data system 150 may be located remotely with respect tomachines 110.Central data system 150 may embody a single microprocessor or multiple microprocessors that include a means for receiving relevant data from and/or for sending instructions tolocal data systems 140.Central data system 150 may be configured to receive machine data from at least one of the plurality oflocal data systems 140.Central data system 150 may also be configured to analyze and/or process the machine data to determine the cause of or solution to the triggering event. Numerous commercially available microprocessors can be configured to perform the functions ofcentral data system 150. It should be appreciated thatcentral data system 150 could readily embody a general machine or engine microprocessor capable of gathering machine data.Central data system 150 may include all the components required to run an application such as, for example, a memory, a secondary storage device, and a processor, such as a central processing unit or any other means known. Various other known circuits may be associated withcentral data system 150, including power source circuitry (not shown) and other appropriate circuitry. -
Central data system 150 may be configured to receive the relevant data portion from at least one of the plurality oflocal data systems 140. Oncecentral data system 150 has received the relevant data portion, this data may be analyzed and/or processed (e.g., by central data system 150) to determine the cause or solution of the triggering event. According to some embodiments,central data system 150 may receive the relevant data portion from allmachines 110 infleet 100 at the same time. Alternatively,central data system 150 may receive the relevant data portion from the affectedmachine 110 and then request the relevant data portion fromother machines 110. The order of data receipt may be configured by the user according to the particular application. -
Local data system 140 may be configured to transmit the relevant data portion and the event-specific data tocentral data system 150. According to some embodiments,local data system 140 may transmit event-specific data tocentral data system 150 automatically. Alternatively,local data system 140 may transmit the event-specific data tocentral data system 150 in response to a request fromcentral data system 150. In a similar manner,local data system 140 may transmit the relevant data portion tocentral data system 150 based on the triggering event. Alternatively,central data system 150 may identify the relevant data portion and communicate this tolocal data system 140. For example,central data system 150 may identify the relevant data portion by the sensor(s) 120 from which the data originated. - For example,
central data system 150 may be further configured to receive a first signal from one or more oflocal data systems 140 indicative of the nature of the event. In response,central data system 150 may send a second signal tolocal data system 140 identifying the event-specific data and requestinglocal data system 140 to begin collecting the event-specific data to transmit tocentral data system 150. The second signal may also identify the relevant data portion, such thatlocal data system 140 is able to identify and collect the relevant data portion forcentral data system 150. -
Central data system 150 may share information amongdifferent machines 110. For example,central data system 150 may be configured to identify a plurality ofmachines 110 that share one or more characteristics withother machines 110. According to some embodiments, the plurality ofmachines 110 sharing one or more characteristics may make up a portion or all ofmachine fleet 100. In response to a triggering event in onemachine 110 of aparticular machine fleet 100,central data system 150 may be configured to communicate the identity of the relevant data portion to otherlocal data systems 140 inmachine fleet 100.Central data system 150 may receive the relevant data portion from each of thelocal data systems 140 inmachine fleet 100. - According to some embodiments, when a triggering event occurs on one
machine 110,local data system 140 not associated with that machine may be configured to collect event-specific data. For example,central data system 150 may be configured to send a signal to alllocal data systems 140 in aparticular machine fleet 100 indicative of the occurrence of an event on at least onemachine 110 ofmachine fleet 100. Likewise,central data system 150 may request event-specific data from allmachines 110 in response to an event associated with onemachine 110. - According to some embodiments, once
local data system 140 has determined that a second event has occurred, it may be configured to collect event-specific data and transmit this data tocentral data system 150.Central data system 150 may be configured to receive the event-specific data and the relevant data portion from eachmachine 110 inmachine fleet 100.Central data system 150 may analyze event-specific data from allmachines 110 inmachine fleet 100 to determine any anomalies particular tomachine 110 on which the event occurred to narrow down the cause of or solution to the event. -
FIG. 3 is a flowchart of an exemplary method for collecting data from at least onemachine 110. For example, atstep 200, the method may include detecting an event associated with anaffected machine 110 of the at least onemachine 110. Once the event has been detected,step 210 may include determining a relevant data portion of machine data based on the event. According to some embodiments,local data system 140 may determine the relevant data portion. Alternatively or additionally,central data system 150 may send a first signal tolocal data system 140 of the affected machine identifying the relevant data portion.Central data system 150 may also send the first signal toother machines 110 identified as having a common characteristic with theaffected machine 110. - According to some embodiments,
central data system 150 may command affectedmachine 110 to collect event-specific data atstep 220. According to some embodiments,central data system 150 may communicate this command to thelocal data system 140 associated with theaffected machine 110.Central data system 150 may also request event-specific data fromother machines 110 identified as having a common characteristic with theaffected machine 110. Atstep 230,central data system 150 may receive the event-specific data and the relevant data portion. Optionally,central data system 150 may receive similar data fromother machines 110 inmachine fleet 100. - The disclosed systems and methods may provide a robust solution for maintenance and diagnostics of a fleet of machines. As a result of event-based data collection, the disclosed systems and methods may provide a more refined solution to data collection, decreasing the amount of unnecessary data transmission.
- The presently disclosed systems and methods may have several advantages. For example, the disclosed data collection methods may more accurately diagnose a problem by collecting data related to the particular event. This solution may allow for customized data collection in response to a particular fault or triggering event, which helps collect more data that is likely to provide the key to correcting the identified fault.
- Furthermore, the disclosed systems and methods may provide a more efficient solution for data communication, which may be particularly useful when time spent transmitting or processing data may result in additional downtime. For machines that are unable to operate until a fault is corrected, this downtime may be costly and prevent operators from meeting deadlines. While more sensors are incorporated into complex machines to monitor their operation, the key issue may no longer be collecting enough information to identify the problem. Rather, the key to efficient repair of machine fleets may be identifying what portions, if any, of the gathered operational data are useful in solving a particular problem.
- Additionally, by gathering relevant data from all machines in the fleet, rather than just those suffering from a fault, the systems and methods may facilitate identifying a cause, rather than just a solution, of particular faults. For example, collecting event-specific and relevant machine data from all machines in a fleet may help an engineer determine which values are particular to the broken machine and which characteristics are within a normal range for that machine. Furthermore, sharing known problems among a fleet can help identify likely problems of a particular machine within the fleet before that machine data is analyzed.
- It will be apparent to those skilled in the art that various modifications and variations can be made to the data collection systems and associated methods for operating the same. Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the present disclosure. It is intended that the specification and examples be considered as exemplary only, with a true scope of the present disclosure being indicated by the following claims and their equivalents.
Claims (8)
1-8. (canceled)
9. A processor-implemented method for collecting data from at least one machine, the method comprising:
detecting via a first processor an event associated with an affected machine of the at least one machine;
determining a relevant data portion of machine data based on the event;
commanding the affected machine to collect event-specific data; and
receiving via a second processor the event-specific data and the relevant data portion from the affected machine.
10. The method of claim 9 , further including sending a first signal to the affected machine identifying the relevant data portion.
11. The method of claim 10 , further including:
identifying a plurality of machines that share a characteristic with the at least one machine;
sending the first signal to each of the plurality of machines identifying the relevant data portion; and
receiving the relevant data portions from each of the plurality of machines.
12. The method of claim 11 , further including:
commanding each of the plurality of machines to collect the event-specific data; and
receiving the event-specific data from each of the plurality of machines.
13. The method of claim 11 , wherein the shared characteristic includes at least one of a machine type, a machine model, a working condition, a geographic location, and an environmental condition.
14. The method of claim 9 , wherein the event includes a system fault of the affected machine.
15-20. (canceled)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/464,342 US20140359068A1 (en) | 2012-05-08 | 2014-08-20 | Trigger-based data collection system |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/466,592 US8850000B2 (en) | 2012-05-08 | 2012-05-08 | Trigger-based data collection system |
US14/464,342 US20140359068A1 (en) | 2012-05-08 | 2014-08-20 | Trigger-based data collection system |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/466,592 Division US8850000B2 (en) | 2012-05-08 | 2012-05-08 | Trigger-based data collection system |
Publications (1)
Publication Number | Publication Date |
---|---|
US20140359068A1 true US20140359068A1 (en) | 2014-12-04 |
Family
ID=49549531
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/466,592 Active 2032-08-16 US8850000B2 (en) | 2012-05-08 | 2012-05-08 | Trigger-based data collection system |
US14/464,342 Abandoned US20140359068A1 (en) | 2012-05-08 | 2014-08-20 | Trigger-based data collection system |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/466,592 Active 2032-08-16 US8850000B2 (en) | 2012-05-08 | 2012-05-08 | Trigger-based data collection system |
Country Status (1)
Country | Link |
---|---|
US (2) | US8850000B2 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20170269566A1 (en) * | 2016-03-17 | 2017-09-21 | Fanuc Corporation | Operation management method for machine tool |
Families Citing this family (44)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8850000B2 (en) * | 2012-05-08 | 2014-09-30 | Electro-Motive Diesel, Inc. | Trigger-based data collection system |
US10176032B2 (en) | 2014-12-01 | 2019-01-08 | Uptake Technologies, Inc. | Subsystem health score |
US10176279B2 (en) | 2015-06-05 | 2019-01-08 | Uptake Technologies, Inc. | Dynamic execution of predictive models and workflows |
US10254751B2 (en) | 2015-06-05 | 2019-04-09 | Uptake Technologies, Inc. | Local analytics at an asset |
US10579750B2 (en) | 2015-06-05 | 2020-03-03 | Uptake Technologies, Inc. | Dynamic execution of predictive models |
US10878385B2 (en) | 2015-06-19 | 2020-12-29 | Uptake Technologies, Inc. | Computer system and method for distributing execution of a predictive model |
US9868430B2 (en) * | 2015-08-06 | 2018-01-16 | Progress Rail Services Corporation | Communication network having locomotive expansion module |
US20170039646A1 (en) * | 2015-08-06 | 2017-02-09 | Gal Lachovitz | System for monitoring investments |
JP6420912B2 (en) * | 2015-08-12 | 2018-11-07 | 京セラ株式会社 | Management server, management method and management system |
US10291732B2 (en) | 2015-09-17 | 2019-05-14 | Uptake Technologies, Inc. | Computer systems and methods for sharing asset-related information between data platforms over a network |
US20170161386A1 (en) * | 2015-12-02 | 2017-06-08 | International Business Machines Corporation | Adaptive product questionnaire |
US10623294B2 (en) | 2015-12-07 | 2020-04-14 | Uptake Technologies, Inc. | Local analytics device |
US11295217B2 (en) | 2016-01-14 | 2022-04-05 | Uptake Technologies, Inc. | Localized temporal model forecasting |
US10510006B2 (en) | 2016-03-09 | 2019-12-17 | Uptake Technologies, Inc. | Handling of predictive models based on asset location |
US10796235B2 (en) | 2016-03-25 | 2020-10-06 | Uptake Technologies, Inc. | Computer systems and methods for providing a visualization of asset event and signal data |
MX2018011802A (en) | 2016-04-08 | 2019-01-24 | New York Air Brake Llc | Train handling rules compliance system. |
US10333775B2 (en) | 2016-06-03 | 2019-06-25 | Uptake Technologies, Inc. | Facilitating the provisioning of a local analytics device |
CN109661626B (en) * | 2016-07-07 | 2022-05-31 | Ats自动化加工***公司 | System and method for diagnosing an automation system |
US10210037B2 (en) | 2016-08-25 | 2019-02-19 | Uptake Technologies, Inc. | Interface tool for asset fault analysis |
US10474932B2 (en) | 2016-09-01 | 2019-11-12 | Uptake Technologies, Inc. | Detection of anomalies in multivariate data |
US10228925B2 (en) | 2016-12-19 | 2019-03-12 | Uptake Technologies, Inc. | Systems, devices, and methods for deploying one or more artifacts to a deployment environment |
US10579961B2 (en) | 2017-01-26 | 2020-03-03 | Uptake Technologies, Inc. | Method and system of identifying environment features for use in analyzing asset operation |
ES2881485T3 (en) * | 2017-03-10 | 2021-11-29 | Knorr Bremse Systeme | Method for recording and synchronizing diagnostic-related events |
US10671039B2 (en) | 2017-05-03 | 2020-06-02 | Uptake Technologies, Inc. | Computer system and method for predicting an abnormal event at a wind turbine in a cluster |
US10255526B2 (en) | 2017-06-09 | 2019-04-09 | Uptake Technologies, Inc. | Computer system and method for classifying temporal patterns of change in images of an area |
US11232371B2 (en) | 2017-10-19 | 2022-01-25 | Uptake Technologies, Inc. | Computer system and method for detecting anomalies in multivariate data |
US10552246B1 (en) | 2017-10-24 | 2020-02-04 | Uptake Technologies, Inc. | Computer system and method for handling non-communicative assets |
US10379982B2 (en) | 2017-10-31 | 2019-08-13 | Uptake Technologies, Inc. | Computer system and method for performing a virtual load test |
US10635519B1 (en) | 2017-11-30 | 2020-04-28 | Uptake Technologies, Inc. | Systems and methods for detecting and remedying software anomalies |
US10815966B1 (en) | 2018-02-01 | 2020-10-27 | Uptake Technologies, Inc. | Computer system and method for determining an orientation of a wind turbine nacelle |
US10554518B1 (en) | 2018-03-02 | 2020-02-04 | Uptake Technologies, Inc. | Computer system and method for evaluating health of nodes in a manufacturing network |
US10169135B1 (en) | 2018-03-02 | 2019-01-01 | Uptake Technologies, Inc. | Computer system and method of detecting manufacturing network anomalies |
US10635095B2 (en) | 2018-04-24 | 2020-04-28 | Uptake Technologies, Inc. | Computer system and method for creating a supervised failure model |
US10860599B2 (en) | 2018-06-11 | 2020-12-08 | Uptake Technologies, Inc. | Tool for creating and deploying configurable pipelines |
US10579932B1 (en) | 2018-07-10 | 2020-03-03 | Uptake Technologies, Inc. | Computer system and method for creating and deploying an anomaly detection model based on streaming data |
US11119472B2 (en) | 2018-09-28 | 2021-09-14 | Uptake Technologies, Inc. | Computer system and method for evaluating an event prediction model |
US11181894B2 (en) | 2018-10-15 | 2021-11-23 | Uptake Technologies, Inc. | Computer system and method of defining a set of anomaly thresholds for an anomaly detection model |
US11480934B2 (en) | 2019-01-24 | 2022-10-25 | Uptake Technologies, Inc. | Computer system and method for creating an event prediction model |
US11030067B2 (en) | 2019-01-29 | 2021-06-08 | Uptake Technologies, Inc. | Computer system and method for presenting asset insights at a graphical user interface |
US11797550B2 (en) | 2019-01-30 | 2023-10-24 | Uptake Technologies, Inc. | Data science platform |
DE102019108278A1 (en) * | 2019-03-29 | 2020-10-01 | Liebherr-Components Biberach Gmbh | Device for determining the current state and / or the remaining service life of a construction machine |
US11208986B2 (en) | 2019-06-27 | 2021-12-28 | Uptake Technologies, Inc. | Computer system and method for detecting irregular yaw activity at a wind turbine |
US10975841B2 (en) | 2019-08-02 | 2021-04-13 | Uptake Technologies, Inc. | Computer system and method for detecting rotor imbalance at a wind turbine |
US11892830B2 (en) | 2020-12-16 | 2024-02-06 | Uptake Technologies, Inc. | Risk assessment at power substations |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6487478B1 (en) * | 1999-10-28 | 2002-11-26 | General Electric Company | On-board monitor for railroad locomotive |
US6778893B2 (en) * | 2000-09-14 | 2004-08-17 | Komatsu Ltd. | Control system for construction machines |
US6993675B2 (en) * | 2002-07-31 | 2006-01-31 | General Electric Company | Method and system for monitoring problem resolution of a machine |
US7043566B1 (en) * | 2000-10-11 | 2006-05-09 | Microsoft Corporation | Entity event logging |
US20070179747A1 (en) * | 2006-02-01 | 2007-08-02 | General Electric Company | Systems and methods for scheduling machines for inspection |
US20080051955A1 (en) * | 2006-08-25 | 2008-02-28 | General Motors Corporation | Method for conducting vehicle-related survey |
US20090106571A1 (en) * | 2007-10-21 | 2009-04-23 | Anthony Low | Systems and Methods to Adaptively Load Balance User Sessions to Reduce Energy Consumption |
US20110035094A1 (en) * | 2009-08-04 | 2011-02-10 | Telecordia Technologies Inc. | System and method for automatic fault detection of a machine |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5719771A (en) | 1993-02-24 | 1998-02-17 | Amsc Subsidiary Corporation | System for mapping occurrences of conditions in a transport route |
US5570284A (en) | 1994-12-05 | 1996-10-29 | Westinghouse Air Brake Company | Method and apparatus for remote control of a locomotive throttle controller |
US7096096B2 (en) | 2003-07-02 | 2006-08-22 | Quantum Engineering Inc. | Method and system for automatically locating end of train devices |
US7330117B2 (en) | 2004-08-25 | 2008-02-12 | Caterpillar Inc. | Systems and methods for radio frequency trigger |
US20070120736A1 (en) | 2005-11-29 | 2007-05-31 | General Electric Company | Method and system for discrete location triggering for enhanced asset management and tracking |
US9367967B2 (en) | 2009-09-29 | 2016-06-14 | GM Global Technology Operations LLC | Systems and methods for odometer monitoring |
US8850000B2 (en) * | 2012-05-08 | 2014-09-30 | Electro-Motive Diesel, Inc. | Trigger-based data collection system |
-
2012
- 2012-05-08 US US13/466,592 patent/US8850000B2/en active Active
-
2014
- 2014-08-20 US US14/464,342 patent/US20140359068A1/en not_active Abandoned
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6487478B1 (en) * | 1999-10-28 | 2002-11-26 | General Electric Company | On-board monitor for railroad locomotive |
US6778893B2 (en) * | 2000-09-14 | 2004-08-17 | Komatsu Ltd. | Control system for construction machines |
US7043566B1 (en) * | 2000-10-11 | 2006-05-09 | Microsoft Corporation | Entity event logging |
US6993675B2 (en) * | 2002-07-31 | 2006-01-31 | General Electric Company | Method and system for monitoring problem resolution of a machine |
US20070179747A1 (en) * | 2006-02-01 | 2007-08-02 | General Electric Company | Systems and methods for scheduling machines for inspection |
US20080051955A1 (en) * | 2006-08-25 | 2008-02-28 | General Motors Corporation | Method for conducting vehicle-related survey |
US20090106571A1 (en) * | 2007-10-21 | 2009-04-23 | Anthony Low | Systems and Methods to Adaptively Load Balance User Sessions to Reduce Energy Consumption |
US20110035094A1 (en) * | 2009-08-04 | 2011-02-10 | Telecordia Technologies Inc. | System and method for automatic fault detection of a machine |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20170269566A1 (en) * | 2016-03-17 | 2017-09-21 | Fanuc Corporation | Operation management method for machine tool |
Also Published As
Publication number | Publication date |
---|---|
US8850000B2 (en) | 2014-09-30 |
US20130304896A1 (en) | 2013-11-14 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8850000B2 (en) | Trigger-based data collection system | |
CN106406273B (en) | Determination of the cause of a fault in a vehicle | |
US10665040B2 (en) | Method and apparatus for remote vehicle diagnosis | |
CN106527403A (en) | Vehicle intelligent diagnostic method and device | |
KR102031116B1 (en) | Remote self-diagnostic feedback system and method of electric car charger | |
CN104058336B (en) | Front-handling mobile crane and control method thereof and system | |
CN111024416B (en) | Fault diagnosis method and system for train traction system | |
EP3126243B1 (en) | System and method for improved drive system diagnostics | |
US8805639B1 (en) | Idle detection for improving fuel consumption efficiency in a vehicle | |
JP4661380B2 (en) | Failure diagnosis device, failure diagnosis system, failure diagnosis method, and in-vehicle device | |
JP2007058344A (en) | Vehicle diagnosis system, vehicle information transmission apparatus and vehicle information transmission method | |
WO2012157603A1 (en) | Shovel, monitoring device therefor, and shovel output device | |
US20070021895A1 (en) | System and method for monitoring the status of a work machine | |
CN201657031U (en) | Vehicle running information acquisition system based on CAN bus | |
CN104972867B (en) | Manual air conditioning system and its method for diagnosing faults with fault diagnosis functions | |
US20080291014A1 (en) | System and method for remote diagnosis and repair of a plant malfunction with software agents | |
CN105988461A (en) | Internet-based automobile remote network software refreshing and diagnostic system | |
US20170002549A1 (en) | Abnormality diagnostic system for work system of construction machinery and method using the same | |
CN110015601B (en) | Remote control system and method for analyzing elevator fault reason | |
KR102111010B1 (en) | Agricultural machine failure diagnosis system based OBD terminal | |
CN116686018A (en) | System for detecting a condition of a vehicle component | |
CN201288780Y (en) | Hydraulic braking system safety operation on-line monitoring device | |
CN109885039A (en) | A kind of fault remote/automatic diagnosis method of the anti-Fatigue equipment based on slave | |
KR102284620B1 (en) | Industrial integrated measurement and monitoring system | |
KR20210046399A (en) | Self-Diagnostic Method of EV Charging Station Using Feedback |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |