System, method, and apparatus for changing a sensed parameter group for a mixer or agitator
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.
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.