Patent · US11397422B2 · B2 · US
System, method, and apparatus for changing a sensed parameter group for a mixer or agitator
- (11) Publication number
- US11397422B2
- (21) Application number
- 16/698,747
- (22) Filing date
- 2019-11-27
- (30) Priority date
- 2016-05-09
- (43) Publication date
- 2022-07-26
- (45) Date of grant
- 2022-07-26
- (51) IPC
- H04L 29/08; G05B 13/02; G05B 19/418; G05B 23/02; G06K 9/62; G06N 20/00; G06N 3/00; G06N 3/02; G06N 3/04; G06N 3/08; G06N 5/04; G06N 7/00; H04B 17/309; H04B 17/318; H04L 1/00; H04L 1/18; G01M 13/028; G01M 13/045; G06Q 10/04; G06Q 10/06; G06Q 30/02; G06Q 30/06; H03M 1/12; H04B 17/23; H04B 17/345; H04L 67/1097; H04L 67/12; H04W 4/38; H04W 4/70
- (52) CPC
- G05B Control or regulating systems in general; functional elements of such systems; monitoring or testing arrangements for such systems or elements: 19/4185, 13/028, 19/042, 19/4183, 19/4184, 19/41845, 19/41865, 19/41875, 2219/32287, 2219/35001, 2219/37337, 2219/37351, 2219/37434, 2219/37537, 2219/40115, 2219/45004, 2219/45129, 23/02, 23/0208, 23/0221, 23/0229, 23/024, 23/0264, 23/0283, 23/0286, 23/0289, 23/0291, 23/0294, 23/0297
- B62D Motor vehicles; trailers: 15/0215, 5/0463
- F01D Non-positive displacement machines or engines, e.g. steam turbines: 21/003, 21/12, 21/14
- G01M Testing static or dynamic balance of machines or structures; testing of structures or apparatus, not otherwise provided for: 13/028, 13/04, 13/045
- G06F Electric digital data processing: 16/2477, 17/18, 18/21, 18/217, 18/2178, 18/25, 3/0608, 3/0619, 3/0635, 3/067
- G06K Graphical data reading; presentation of data; record carriers; handling record carriers: 9/6263, 9/6288
- G06N Computing arrangements based on specific computational models: 20/00, 3/006, 3/02, 3/044, 3/0445, 3/045, 3/0454, 3/047, 3/0472, 3/0499, 3/084, 3/088, 3/126, 5/046, 7/005, 7/01
- G06Q Information and communication technology [ICT] specially adapted for administrative, commercial, financial, managerial or supervisory purposes; systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not otherwise provided for: 10/04, 10/0639, 30/02, 30/0278, 30/06, 50/00
- G06V Image or video recognition or understanding: 10/7784, 10/82
- G16Z Information and communication technology [ICT] specially adapted for specific application fields, not otherwise provided for: 99/00
- H02M Apparatus for conversion between AC and AC, between AC and DC, or between DC and DC, and for use with mains or similar power supply systems; conversion of DC or AC input power into surge output power; control or regulation thereof: 1/12
- H03M Coding; decoding; code conversion in general: 1/12
- H04B Transmission: 17/23, 17/26, 17/29, 17/309, 17/318, 17/345, 17/40
- H04L Transmission of digital information, e.g. telegraphic communication: 1/0002, 1/0009, 1/0041, 1/18, 1/1874, 5/0064, 67/1097, 67/12, 67/306
- H04W Wireless communication networks: 4/38, 4/70
- Y02P Climate change mitigation technologies in the production or processing of goods: 80/10, 90/02, 90/80
- Y04S Systems integrating technologies related to power network operation, communication or information technologies for improving the electrical power generation, transmission, distribution, management or usage, i.e. smart grids: 50/00, 50/12
- Y10S Technical subjects covered by former uspc cross-reference art collections [xracs] and digests: 707/99939
- (73) Assignee
- Strong Force IoT Portfolio 2016 LLC
- (72) Inventors
- Charles Howard Cella; Gerald William Duffy, JR.; Jeffrey P. McGuckin; Mehul Desai
- (54) Title
- System, method, and apparatus for changing a sensed parameter group for a mixer or agitator
- (57) Abstract
Systems and methods for changing a sensed parameter group include a data collector communicatively coupled to a plurality of input sensors, each of the plurality of input sensors operatively coupled to a one of a mixer or an agitator, wherein the one of the mixer or the agitator comprises a component of an industrial environment; a controller, comprising: a data acquisition circuit structured to interpret a plurality of detection values corresponding to a sensed parameter group, wherein the sensed parameter group comprises at least a portion of the plurality of input sensors; a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of detection values; and a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value.
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Claims (28)
- A system, comprising: a data collector communicatively coupled to a plurality of input sensors, each of the plurality of input sensors operatively coupled to one of a mixer or an agitator, wherein the one of the mixer or the agitator comprises a component of an industrial environment; and a controller, comprising: a data acquisition circuit structured to interpret a plurality of detection values corresponding to a sensed parameter group, wherein the sensed parameter group comprises at least a portion of the plurality of input sensors; a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of detection values, wherein the recognized pattern value is a pattern recognized by the pattern recognition circuit; and a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value, wherein the pattern recognition circuit is further structured to determine a sensor effectiveness value in response to the recognized pattern value, and wherein the sensor learning circuit is further structured to update the sensed parameter group in response to the sensor effectiveness value, wherein the sensor learning circuit is further structured to update the sensed parameter group by at least one of: adding one of the plurality of input sensors to the sensed parameter group, or replacing one of the plurality of input sensors of the sensed parameter group with a distinct one of the plurality of input sensors.
- The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by adding the one of the plurality of input sensors to the sensed parameter group.
- The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by replacing the one of the plurality of input sensors of the sensed parameter group with the distinct one of the plurality of input sensors.
- The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by changing a setting of one of the plurality of input sensors of the sensed parameter group.
- The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by adjusting a resolution of the one of the plurality of input sensors.
- The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by adjusting a sensor range of the one of the plurality of input sensors.
- The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by adjusting a sensor scaling value of the one of the plurality of input sensors.
- The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by changing a sampling frequency of the one of the plurality of input sensors.
- The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by changing a sampling rate of the data collector with regard to at least one of the plurality of input sensors.
- The system of claim 1, wherein the pattern is recognized by a neural network of the pattern recognition circuit.
- The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining an effectiveness of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.
- The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a sensitivity of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.
- The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive confidence of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.
- The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive delay time of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.
- The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive accuracy of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.
- The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive precision of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.
- The system of claim 1, wherein the pattern recognition circuit determines the recognized pattern value based on combined data from a fused pairing of sensors including a vibration sensor and an electric or magnetic field sensor.
- A method, comprising: detecting a plurality of detection values corresponding to a sensed parameter group using at least a portion of a plurality of input sensors operatively coupled to one of a mixer or an agitator, the sensed parameter group comprising the at least the portion of the plurality of input sensors, wherein the one of the mixer or the agitator comprises a component of an industrial environment; interpreting the plurality of detection values corresponding to the sensed parameter group; determining a recognized pattern value in response to the plurality of detection values, wherein the determining the recognized pattern value comprises using a neural network to recognize a pattern in the plurality of detection values; updating the sensed parameter group in response to the recognized pattern value; determining a sensor effectiveness value in response to the recognized pattern value; and further updating the sensed parameter group in response to the sensor effectiveness value, wherein updating the sensed parameter group comprises at least one of: adding one of the plurality of input sensors to the sensed parameter group, or replacing one of the plurality of input sensors of the sensed parameter group with a distinct one of the plurality of input sensors.
- The method of claim 18, wherein updating the sensed parameter group further comprises changing a setting of one of the plurality of input sensors of the sensed parameter group.
- The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises adjusting a resolution of the one of the plurality of input sensors.
- The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises adjusting a sensor range of the one of the plurality of input sensors.
- The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises adjusting a sensor scaling value of the one of the plurality of input sensors.
- The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises changing a sampling frequency of the one of the plurality of input sensors.
- The method of claim 18, further comprising determining the sensor effectiveness value by determining an effectiveness of the sensed parameter group to determine a value of interest of the one of the mixer or the agitator.
- The method of claim 18, further comprising determining the sensor effectiveness value by determining a predictive delay time of the sensed parameter group to determining a value of interest of the one of the mixer or the agitator.
- The method of claim 18, wherein the recognized pattern value is determined based on combined data from a fused pairing of sensors including a vibration sensor and an electric or magnetic field sensor.
- A system, comprising: a data collector communicatively coupled to a plurality of input sensors, each of the plurality of input sensors operatively coupled to one of a mixer or an agitator, wherein the one of the mixer or the agitator comprises a component of an industrial environment; and a controller, comprising: a data acquisition circuit structured to interpret a plurality of detection values corresponding to a sensed parameter group, wherein the sensed parameter group comprises at least a portion of the plurality of input sensors; a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of detection values, wherein the recognized pattern value is a pattern recognized by a neural network of the pattern recognition circuit; and a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value, wherein the pattern recognition circuit is further structured to determine a sensor effectiveness value in response to the recognized pattern value, wherein the sensor learning circuit is further structured to update the sensed parameter group in response to the sensor effectiveness value, and wherein the sensor learning circuit is further structured to update the sensed parameter group by at least one of: adding one of the plurality of input sensors to the sensed parameter group, or replacing one of the plurality of input sensors of the sensed parameter group with a distinct one of the plurality of input sensors.
- The system of claim 27, wherein the pattern recognition circuit determines the recognized pattern value based on combined data from a fused pairing of sensors including a vibration sensor and an electric or magnetic field sensor.
Description
The present disclosure relates to methods and systems for data collection in industrial environments, as well as methods and systems for leveraging collected data for monitoring, remote control, autonomous action, and other activities in industrial environments.
Heavy industrial environments, such as environments for large scale manufacturing (such as manufacturing of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Historically, data has been collected in heavy industrial environments by human beings using dedicated data collectors, often recording batches of specific sensor data on media, such as tape or a hard drive, for later analysis. Batches of data have historically been returned to a central office for analysis, such as undertaking signal processing or other analysis on the data collected by various sensors, after which analysis can be used as a basis for diagnosing problems in an environment and/or suggesting ways to improve operations.
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Record as JSON
{
"publication_number": "US11397422B2",
"country": "US",
"kind": "B2",
"title": "System, method, and apparatus for changing a sensed parameter group for a mixer or agitator",
"abstract": "Systems and methods for changing a sensed parameter group include a data collector communicatively coupled to a plurality of input sensors, each of the plurality of input sensors operatively coupled to a one of a mixer or an agitator, wherein the one of the mixer or the agitator comprises a component of an industrial environment; a controller, comprising: a data acquisition circuit structured to interpret a plurality of detection values corresponding to a sensed parameter group, wherein the sensed parameter group comprises at least a portion of the plurality of input sensors; a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of detection values; and a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value.",
"claims": [
"1. A system, comprising: a data collector communicatively coupled to a plurality of input sensors, each of the plurality of input sensors operatively coupled to one of a mixer or an agitator, wherein the one of the mixer or the agitator comprises a component of an industrial environment; and a controller, comprising: a data acquisition circuit structured to interpret a plurality of detection values corresponding to a sensed parameter group, wherein the sensed parameter group comprises at least a portion of the plurality of input sensors; a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of detection values, wherein the recognized pattern value is a pattern recognized by the pattern recognition circuit; and a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value, wherein the pattern recognition circuit is further structured to determine a sensor effectiveness value in response to the recognized pattern value, and wherein the sensor learning circuit is further structured to update the sensed parameter group in response to the sensor effectiveness value, wherein the sensor learning circuit is further structured to update the sensed parameter group by at least one of: adding one of the plurality of input sensors to the sensed parameter group, or replacing one of the plurality of input sensors of the sensed parameter group with a distinct one of the plurality of input sensors.",
"2. The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by adding the one of the plurality of input sensors to the sensed parameter group.",
"3. The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by replacing the one of the plurality of input sensors of the sensed parameter group with the distinct one of the plurality of input sensors.",
"4. The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by changing a setting of one of the plurality of input sensors of the sensed parameter group.",
"5. The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by adjusting a resolution of the one of the plurality of input sensors.",
"6. The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by adjusting a sensor range of the one of the plurality of input sensors.",
"7. The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by adjusting a sensor scaling value of the one of the plurality of input sensors.",
"8. The system of claim 4, wherein the sensor learning circuit is further structured to change the setting of the one of the plurality of input sensors by changing a sampling frequency of the one of the plurality of input sensors.",
"9. The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by changing a sampling rate of the data collector with regard to at least one of the plurality of input sensors.",
"10. The system of claim 1, wherein the pattern is recognized by a neural network of the pattern recognition circuit.",
"11. The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining an effectiveness of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.",
"12. The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a sensitivity of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.",
"13. The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive confidence of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.",
"14. The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive delay time of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.",
"15. The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive accuracy of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.",
"16. The system of claim 1, wherein the pattern recognition circuit is further structured to determine the sensor effectiveness value by determining a predictive precision of the sensed parameter group in determining a value of interest of the one of the mixer or the agitator.",
"17. The system of claim 1, wherein the pattern recognition circuit determines the recognized pattern value based on combined data from a fused pairing of sensors including a vibration sensor and an electric or magnetic field sensor.",
"18. A method, comprising: detecting a plurality of detection values corresponding to a sensed parameter group using at least a portion of a plurality of input sensors operatively coupled to one of a mixer or an agitator, the sensed parameter group comprising the at least the portion of the plurality of input sensors, wherein the one of the mixer or the agitator comprises a component of an industrial environment; interpreting the plurality of detection values corresponding to the sensed parameter group; determining a recognized pattern value in response to the plurality of detection values, wherein the determining the recognized pattern value comprises using a neural network to recognize a pattern in the plurality of detection values; updating the sensed parameter group in response to the recognized pattern value; determining a sensor effectiveness value in response to the recognized pattern value; and further updating the sensed parameter group in response to the sensor effectiveness value, wherein updating the sensed parameter group comprises at least one of: adding one of the plurality of input sensors to the sensed parameter group, or replacing one of the plurality of input sensors of the sensed parameter group with a distinct one of the plurality of input sensors.",
"19. The method of claim 18, wherein updating the sensed parameter group further comprises changing a setting of one of the plurality of input sensors of the sensed parameter group.",
"20. The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises adjusting a resolution of the one of the plurality of input sensors.",
"21. The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises adjusting a sensor range of the one of the plurality of input sensors.",
"22. The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises adjusting a sensor scaling value of the one of the plurality of input sensors.",
"23. The method of claim 19, wherein changing the setting of the one of the plurality of input sensors comprises changing a sampling frequency of the one of the plurality of input sensors.",
"24. The method of claim 18, further comprising determining the sensor effectiveness value by determining an effectiveness of the sensed parameter group to determine a value of interest of the one of the mixer or the agitator.",
"25. The method of claim 18, further comprising determining the sensor effectiveness value by determining a predictive delay time of the sensed parameter group to determining a value of interest of the one of the mixer or the agitator.",
"26. The method of claim 18, wherein the recognized pattern value is determined based on combined data from a fused pairing of sensors including a vibration sensor and an electric or magnetic field sensor.",
"27. A system, comprising: a data collector communicatively coupled to a plurality of input sensors, each of the plurality of input sensors operatively coupled to one of a mixer or an agitator, wherein the one of the mixer or the agitator comprises a component of an industrial environment; and a controller, comprising: a data acquisition circuit structured to interpret a plurality of detection values corresponding to a sensed parameter group, wherein the sensed parameter group comprises at least a portion of the plurality of input sensors; a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of detection values, wherein the recognized pattern value is a pattern recognized by a neural network of the pattern recognition circuit; and a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value, wherein the pattern recognition circuit is further structured to determine a sensor effectiveness value in response to the recognized pattern value, wherein the sensor learning circuit is further structured to update the sensed parameter group in response to the sensor effectiveness value, and wherein the sensor learning circuit is further structured to update the sensed parameter group by at least one of: adding one of the plurality of input sensors to the sensed parameter group, or replacing one of the plurality of input sensors of the sensed parameter group with a distinct one of the plurality of input sensors.",
"28. The system of claim 27, wherein the pattern recognition circuit determines the recognized pattern value based on combined data from a fused pairing of sensors including a vibration sensor and an electric or magnetic field sensor."
],
"description_excerpt": "The present disclosure relates to methods and systems for data collection in industrial environments, as well as methods and systems for leveraging collected data for monitoring, remote control, autonomous action, and other activities in industrial environments.\n\nHeavy industrial environments, such as environments for large scale manufacturing (such as manufacturing of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Historically, data has been collected in heavy industrial environments by human beings using dedicated data collectors, often recording batches of specific sensor data on media, such as tape or a hard drive, for later analysis. Batches of data have historically been returned to a central office for analysis, such as undertaking signal processing or other analysis on the data collected by various sensors, after which analysis can be used as a basis for diagnosing problems in an environment and/or suggesting ways to improve operations.",
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"assignees": [
"Strong Force IoT Portfolio 2016 LLC"
],
"inventors": [
"Charles Howard Cella",
"Gerald William Duffy, JR.",
"Jeffrey P. McGuckin",
"Mehul Desai"
],
"filing_date": "2019-11-27",
"publication_date": "2022-07-26",
"grant_date": "2022-07-26",
"priority_date": "2016-05-09",
"application_number": "US-201916698747-A",
"family_id": "63669288",
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