Method and system for adjusting an operating parameter on a production line
Systems, methods and apparatus for data collection in an industrial environment are disclosed. A system according to one embodiment can include a plurality of input sensors operatively coupled to a production line, the plurality of sensors communicatively coupled to a data collector having a controller, the controller including: a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data, a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn output data patterns indicative of a state of the production line, and a response circuit structured to adjust an operating parameter of a component of the production line based on one of a mismatch or a match of the output data pattern and the state of the production line.
1. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors operatively coupled to a production line, the plurality of input sensors communicatively coupled to a data collector having a controller, wherein the plurality of input sensors are configured to collect sensor data from the production line;
the controller comprising:
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of input sensors from which to process output data;
a pattern recognition circuit structured to recognize at least one output data pattern and select a signature from a plurality of signatures associated with output data patterns;
a machine learning data analysis circuit comprising a neural network structured to receive the output data from the at least one of the plurality of input sensors and wherein the neural network is trained with the signatures associated with the output data patterns provided by the pattern recognition circuit to detect a state of the production line and the trained neural network predicts an anticipated state of the production line based on the signature which corresponds to an operational state of a component within the production line; and
a response circuit structured to adjust an operating parameter of the component of the production line based on a predictive accuracy of the plurality of input sensors for the predicted anticipated state of the production line.
2. The system of claim 1 , wherein the operating parameter comprises a task of the component of the production line.
3. The system of claim 1 , wherein the operating parameter is adjusted to implement at least one of: an increase in fuel efficiency; a reduction in wear; an increase of production output; an increase of an operating life of the component of the industrial environment; avoidance of a fault condition; or a reduction of a load on the component of the production line.
4. The system of claim 1 , wherein the operating parameter comprises a future component design for the component of the production line.
5. The system of claim 1 , wherein the state of the production line corresponds to at least one of an outcome, or an anticipated outcome, relating to a product of the production line.
6. The system of claim 1 , wherein the machine learning data analysis circuit is further structured to learn the received output data patterns by being seeded with a model.
7. The system of claim 1 , wherein the neural network of the machine learning data analysis circuit is further trained with the output data patterns indicative of a progress towards at least one of a goal or an alignment with a guideline.
8. The system of claim 1 , wherein the data collection band circuit is further structured to adjust the at least one collection parameter based on one or more of the received output data patterns.
9. The system of claim 1 , wherein the at least one collection parameter is at least one of a bandwidth parameter, a multiplexing configuration, a timing parameter, a frequency range, or a granularity of collection of sensor data.
10. A monitoring apparatus, comprising:
a plurality of input sensors operatively coupled to a production line, the plurality of input sensors communicatively coupled to a data collector having a controller;
the controller comprising:
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of input sensors from which to process output data;
a pattern recognition circuit structured to recognize at least one output data pattern and select a signature from a plurality of signatures associated with output data patterns;
a machine learning data analysis circuit structured to receive the output data from the at least one of the plurality of input sensors, wherein the machine learning data analysis circuit is trained with the signatures associated with the output data patterns provided by the pattern recognition circuit to detect a state of the production line and the trained machine learning data analysis circuit predicts an anticipated state of the production line based on the signature which corresponds to an operational state of a component within the production line; and
a response circuit structured to adjust an operating parameter of the component of the production line based on a predictive accuracy of the predicted anticipated state of the production line;
wherein the data collection band circuit is further structured to adjust the at least one collection parameter based on one or more of the output data.
11. The apparatus of claim 10 , wherein the operating parameter comprises a task of the component of the production line.
12. The apparatus of claim 10 , wherein the operating parameter is adjusted to implement at least one of: an increase in fuel efficiency; a reduction in wear; an increase of production output; an increase of an operating life of the component of the production line; an avoidance of a fault condition; or a reduction of a load on the component of the production line.
13. The apparatus of claim 10 , wherein the operating parameter comprises a future component design for the component of the production line.
14. The apparatus of claim 10 , wherein the state corresponds to at least one of an outcome, or an anticipated outcome, relating to a product of the production line.
15. The apparatus of claim 10 , wherein the machine learning data analysis circuit is further structured to learn the received output data patterns by being seeded with a model.
16. The apparatus of claim 10 , wherein the machine learning data analysis circuit is further trained with the output data patterns indicative of a progress towards at least one of a goal or an alignment with a guideline.
17. A method for data collection in an industrial environment, comprising:
collecting sensor data from a plurality of input sensors operatively coupled to a production line, the plurality of input sensors communicatively coupled to a data collector;
determining at least one collection parameter for at least one of the plurality of input sensors from which to process output data;
receiving the output data from the at least one of the plurality of input sensors:
performing a pattern recognition operation on the output data to recognize at least one output data pattern and select a signature from a plurality of signatures associated with output data patterns;
performing a machine learning operation to train the a neural network with the signatures associated with the output data patterns provided by the pattern recognition operation to detect one or more states of a component of the production line;
predicting an anticipated state of the production line using the trained neural network based on the signature which corresponds to an operational state of the component within the production line; and
adjusting an operating parameter of the component of the production line in response to a predictive accuracy of the predicted anticipated state of the component.
18. The method of claim 17 , wherein the adjusting the operating parameter comprises implementing at least one of: increasing a fuel efficiency; reducing wear of the component; increasing a production output of the production line; increasing an operating life of the component; avoiding a fault condition; or reducing a load on the component.
19. The method of claim 17 , wherein the adjusting the operating parameter comprises scheduling a maintenance for the component.
20. The method of claim 17 , wherein the adjusting the operating parameter comprises ordering at least one of a new component or a replacement component.
21. The method of claim 17 , wherein the adjusting the operating parameter comprises providing a future component design for the production line.
22. The method of claim 17 , wherein the training the neural network further comprises training with an output data pattern indicative of a progress toward at least one of: a goal, or an alignment with a guideline.